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ABACBS/COMBINE/Phylomania 2021 Conference Program

Organising Committee
Chair: Michael Charleston (UTas)
Jimmy Breen (UAdelaide)
Aaron Darling (UTS)
Melanie Smith (UAdelaide)
Jarny Choi (UMelb)
Denis Bienroth (MCRI)

A.J. Sethi (ANU)
Patricia Sullivan (CCIA, UNSW)
Sachintha Wijegunasekara (DeakinU)
Mirana Ramialison (ARMI/MCRI)
Ignatius Pang (CMRI)
Nicola Armstrong (CurtinU)

Program Committee
Mirana Ramialison (Coordinator, ARMI/MCRI)

Fatemeh Vafaee (UNSW)

Emily Wong (Co-coordinator, VCCRI)Jean Yang (USyd)

A.J. Sethi (COMBINE)

Jimmy Breen (UAdelaide)

Alicia Oshlack (Peter Mac)John Ormerod (USyd)

Allan McRae (IMB)

Joshua Ho (HKU)David Powell (Monash U)

Lan Nguyen (Monash U)

Eduardo Eyras (ANU)

Mike Charleston (UTas)

Eleni Giannoulatou (VCCRI)

Pengyi Yang (USyd)

Ellis Patrick (USyd)

Sara Ballouz (Garvan)

Hub Coordinators
Adelaide: Michael Roach (Flinders U)
Brisbane: Ariane Mora, Apoorva Prabhu, Ebony
Watson, Gabriel Foley and Sam Davis (UQ) andAnita Sathyanarayanan (QUT)
Hobart: Barbara Holland (UTas)

Melbourne: Fernando Rossello (UMelb)
Perth: Phillip Bayer (UWA)
Sydney: Mathieu Fourment (UTS)Our Joint ABACBS/COMBINE/Phylomania 2021 Conference isproudly supported byAttendee information
Each ABACBS, COMBINE & Phylomania conference sessions will be chaired remotely on Zoom by separate
locations around the country. Invited speakers and talks selected through abstract submissions will present via
Zoom, or at a local hub site if available (Adelaide, Brisbane, Perth and Hobart). Additionally, attendees will
have the opportunity to interact with each other via our conference interaction platform Gather.Town(https://www.gather.town/) and look at posters in each ABACBS/COMBINE/Phylomania room. Sponsors will
also have the opportunity to interact with delegates interactively.
The information for the Zoom links (video conferencing) and Gather.town links (social interaction and posters
presentation) for the ABACBS conference will be available in the following URL(https://conference2021.abacbs.org/abacbs/conf). Please authenticate with the ORCID that you have provided
during registration and enter your ORCID password. You will be able to see the Zoom and Gather.Town links
once you’ve logged in.
Poster/Talk Presentation Upload
For poster presentations, we will be uploading slides onto the Gather.town platform. Use only PNG image
format as Gather.Town only supports PNG format currently. Images do need a min of 600px*1000px and max
3MB in size. Please upload the file using this CloudStor link
(https://cloudstor.aarnet.edu.au/plus/s/NBSTiSm2ui9lmYn). Please setup your file name in the following format:<Surname>_<First Name>_poster_<a random number between 1 to 10>_<a random alphabet letter>.PNG
If you want to replace a file just upload a file with the same file name again and the new file will be kept.
Please upload your slides before 5pm Friday 19th November

For short/lightning talk presentations, please send your slides to the session chairs by 5pm Friday 19th
November:● COMBINE○ Sachintha Wijegunasekara (sachinthajw@gmail.com)○ AJ Sethi (aditya.sethi@anu.edu.au)● ABACBS○ Fernando Rossollo (fjrossello@gmail.com)○ Philipp Bayer (philipp.bayer@uwa.edu.au)○ Mathieu Fourment (mathieu.fourment@uts.edu.au)○ Michael Roach (beardymcjohnface@gmail.com)○ Ariane Mora (ariane.n.mora@gmail.com)● Phylomania○ Venta Terauds (venta.terauds@utas.edu.au)

Social Networking
ABACBS Slack Channel: #conference2021_public https://abacbs.slack.com/archives/C02MKCJ45FY Thereis also a channel for each session of the conference. They are pinned in the main Slack channel or search forthe channel using the string “conf2021_”. We encourage users to continue discussions on Slack after thesession is over.
Twitter Hashtags: #ABACBS2021 as the official twitter hashtag. #COMBINE21 and #Phylomania can be usedalongside the main one for the respective conferences.

In-person conference hubs
For states that are able to host events (Western Australia, South Australia, Queensland and Tasmania) we will
be holding in-person conference hubs.
Hub City

Location

Additional information

Adelaide

Room G030 University of Adelaide Adelaide
Health and Medical School (AHMS), 4 North
Terrace, Adelaide (City West)

The Room G030 lecture theatre is on thebottom floor of AHMS.
Monday and Wednesday will be catered with
our local speaker Kat Pillman. Adelaide nodedinner and in-person social networking eventat the West Oak Hotel on Wednesday (24thNov)

BrisbaneMonday (COMBINE): Molecular

Wednesday will be catered since we’ll bebiosciences building: Learning theatre
76-228, the University of Queensland, St
Lucia, QLD
Tuesday-Thursday (ABACBS): Chemistrybuilding, 68-415, the University of
Queensland, St Lucia, QLD

having a live talk from Dr Ben Woodcroft, ourinvited local speaker.

PerthSeminar room 1.18
M Block QE-II (UWA)

Directions: Go in the main entrance of M
Block (QE2), up one flight of stairs and on
your left is an old, brown corridor (no swipe
needed). 1.18 is the first glass door on yourleft. See blue star below. Lunch will be
provided on Monday and Tuesday.

HobartSocial Sciences Video Conferencing Room
Sandy Bay Campus

Directions: From the central concourse of the
Sandy Bay campus, head south up the hill;
the Social Science building (5 on the map
below) is to the left of Lazenby’s Cafe. Follow
the signs, the room numbers, or your nose toroom 210.

Dinner Thursday night at Botanica,
Salamanca Place
Our in-person hubs are proudly supported by:Conference Program

COMBINE Student Symposium (Monday 22nd November)Time (AEDT)(Melb/Syd/Canb/
Hob)

COMBINE Student Symposium

10:30

Symposium Start and Welcome

10:50
11:05
11:20
11:35
11:50
12:05

12:20

12:40
1:10

1:45

Session 1: Genomics(Session Chair: Akari Komori)
Abstract Talk: Swapnil Tichkule
Genomic landscape of diversification, selective sweeps, and demographic history of an anthroponotic parasite
Abstract Talk: Anushi Shah
An automated meta-caller to detect de novo mutations from whole genome trio data using cloud computing
technology
Abstract Talk: Nehleh-Fatemeh Kargarfard
PhiloBacteria: A new tool to infer phylogenetic trees from recombinant bacterial genomes
Abstract Talk: Hugh Cottingham
Developing a novel CRISPR-Cas9 based method for characterisation of bacterial pathogens using Oxford
Nanopore sequencing
Abstract Talk: Ly Trong Nhan
AliSim: Phylogenetic Sequence Simulator in the Genomic Era
Abstract Talk: Katherine Caley
Is the Human genome in mutation equilibrium?
Lightning Talks:● Chelsea Matthews● Amarinder S. Thind● Holly A. Withers● Urwah Nawaz
BreakDiscussion session (Zoom)
Keynote Address
Prof. Alicia Oshlack
Peter Maccallum Cancer Center, Melbourne, Australia
Session 2: Transcriptomics(Session Chair: Yunwei Zhang)
Abstract Talk: Wenjun Liu
RNA-seq regulatory network inference revealed an association between transcription factor SETX andneurogenerative pathways under prolonged autophagy induction

2:00

Abstract Talk: Angelita Liang
A universal naming system for human alternative splicing

2:15

Abstract Talk: Adrian (Abbas) Salavaty
Identification, classification, and prioritization of most influential players in normal biological processes anddiseases

2:30

Abstract Talk: Yue Cao
scFeatures: automatic feature generation for single-cell and spatial data

2:45

Abstract Talk: Andy Tran
Using multimodal single cell data to predict regulatory gene relationships and to build a computational direct cell
reprogramming model

3:00

Break
Discussion session (Zoom)

3:15

Session 3: Translational bioinformatics(Session Chair: Fred Jaya)
Abstract Talk: Anh Phuong Le (Phuong)​Integrating genomic location and sequence contexts of point mutations to improve classifying cancer tissue oforigin

3:20
3:35

Abstract Talk: Gulrez Chahal
CaraVaN: Prioritising Cardiac Variants in the Non-coding genome using boosting algorithm

3:50

Abstract Talk: Keeley OGrady
Genomic epidemiology of Clostridioides difficile in Australia, 2013-2018

4:05

Abstract Talk: Himal Shrestha
Landscape genetics to determine factors for ongoing transmission of onchocerciasis in the transition region of
Ghana

4:20

Abstract Talk: Rachel Bowen-James
disTIL: a turnkey approach to profile the immune landscape in cancer

4:35

Abstract Talk: Alejandro Casar
Investigating how transcriptional plasticity drives drug resistance in cancer by combining computationalmodelling with single cell genomics

4:50

Lightning Talks:● Mikhail Dia● Boris Ka Leong Wong
● Sanghyun Lee● Ann Rann Wong
● Holly Martin
BreakDiscussion session (Zoom)

17:05

17:15

18:15

Careers Panel(Chairs: Akarai Komori and Alice)● Sarah Beecroft● Thom Quinn● Sonika Tyagi● Etsuko Uno
Prizes and symposium close

ABACBS Day 1 (Tuesday 23rd November)Time (AEDT)

Session 1: Transcriptomics (Melbourne)(Session Chair: Fernando Rossello)International Invited Speaker
Roser Vento-Tormo
Wellcome Sanger Institute, UK
Mapping tissues in vivo and in vitro

8:30-9:05

10:50

ABACBS Conference opening

11:00

International Invited Speaker
Smita Krishnaswamy
Yale School of Medicine, USA

11:35

12:10
12:25
12:40

12:55

National Invited Speaker
Matthew Lewsey
La Trobe University
Gene regulatory dynamics of plant embryos from bulk tissue to single-cell resolution​Abstract Talk: Patricia Sullivan
Breaking the black box: Dissecting deep neural network models to reveal splicing motifs
Abstract Talk: Jeffrey Pullin
Benchmarking methods for selecting marker genes in single cell RNA sequencing data
Abstract Talk: Yingxin Lin
Large scale single-cell multi-sample multi-condition data integration using scMerge2Lightning Talks:● Natalie Charitakis, Benchmarking methods for the identification of spatially variable genes in spatialtranscriptomics datasets.
● Andy Tran, Using multimodal single cell data to predict regulatory gene relationships and to build acomputational direct cell reprogramming model● Alejandro Casar, Investigating how transcriptional plasticity drives drug resistance in cancer by
combining computational modelling with single cell genomics● David Humphreys, Overlapping transcripts within gene models can influence bioinformatic analysis.

13:10
Time (AEDT)
13:30

14:05

14:20
14:35

14:50

15:25

Break
Poster session (GatherTown)
Session 2: Genomics (Perth)(Session Chair: Philipp Bayer)
International Invited Speaker
Isobel Parkin
Agri-Food, Canada
Towards fully assembled plant genomes and the promise of pan-genomes​Abstract Talk: Ulf Schmitz
Multi-omics data analysis identifies epigenetic regulators of alternative splicing
Abstract Talk: Isobel J BeasleyPredicting the cross-population portability of human expression quantitative trait loci (eQTLs)
Abstract Talk: Dan Andrews
Most disease phenotypes are actually very subtle: Deep learning with a massive, mouse, mutant cellular
dataset to detect sub-clinical phenotypic variation
National Invited Speaker
Parwinder Kaur
University of Western Australia
Breaking Barriers: From conventional to next generation genetics
Lightning Talks:● Aidan P. Tay, INSIDER: alignment-free detection of foreign DNA sequences● Yunwei Zhang, HISP: cohort heterogeneity identification and risk factors detection for survival
prediction● Jiru Han, Population-level genome-wide STR typing in Plasmodium species reveals higher resolution
population structure and genetic diversity relative to SNP typing● Hieu T. Nim, Finding needles in a haystack: identifying cardiac disease-causing genes fromcis-regulatory elements
15:40
Time (AEDT)
16:15

16:50

Break
Poster session (GatherTown)
Session 3: Metagenomics, Long Reads & Proteomics (Sydney)(Session Chair: Mathieu Fourment)International Invited Speaker
Rohan Williams
Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Singapore
Long (and short) read metagenome analysis of complex microbial communities: progress, problems andprospects​Abstract Talk: Rebecca C Poulos
A pan-cancer proteomic map of 949 human cell lines

17:05​Abstract Talk: Pablo Acera-Mateos
Detection of m6A and m5C RNA modifications at single-molecule resolution using Nanopore sequencing

17:20​Abstract Talk: Aaron Chuah
Genome-wide estimation of the change in protein stability due to missense mutation

17:35-18:30

Main Poster Session (GatherTown)

ABACBS Day 2 (Wednesday 24th November)Time (AEDT)

11:00

11:35

11:50
Time (AEDT)

Session 3: Metagenomics, Long Reads & Proteomics (Sydney) - Continued(Session Chair: Mathieu Fourment)International Invited Speaker
Joep de Ligt
ESR, New ZealandPutting single molecule sequencing to use for healthLightning Talks:● James M. Ferguson, InterARTIC: an interactive web application for whole-genome nanopore
sequencing analysis of SARS-CoV-2 and other viruses● Richard J Edwards, DepthSizer and DepthKopy: genome size and copy number prediction using
single-copy long-read depth profiles● Kaitao Lai, Shotgun microbial profiling reveals geographic disparities in aggressive prostate cancer● Hasindu Gamaarachchi, SLOW5: a new file format enables massive acceleration of nanopore
sequencing data analysis
Break
Poster session (GatherTown)
Session 4: Biomedical (Adelaide)(Session Chair: Michael Roach)

13:10

International Invited Speaker
Jill Moore
University of Massachusetts Chan Medical School, USA
The ENCODE Registry of candidate Cis-Regulatory Elements: a summary of curation and applications
National Invited Speaker
Katherine Pillman
Centre for Cancer Biology, University of South Australia
Uncovering the functions of splicing and transcription factors in Breast Cancer​Abstract Talk: Feng Yan
DNA Methylation in pre-leukemic stem cells in a T-cell acute lymphoblastic leukemia mouse model

13:25

Abstract Talk: Feargal Ryan
Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection

13:40

Abstract Talk: Bennet McComish
Using signatures of natural selection to focus the search for causal genetic variants in multiple sclerosis.

13:55

Lightning Talks:● Andreas Zankl, Ontoclick: a web browser extension to facilitate biomedical knowledge curation

12:00

12:35

●
●
●

Chelsea Mayoh, Utilising the transcriptome to functionally resolve molecular aberrations in precision
medicine
Andreas Halman, STRipy: a graphical application for detecting short tandem repeat expansions in all
known pathogenic loci
Letitia Sng, Novel Coronary Artery Disease Variants and Epistatic Interactions Identified with Machine
Learning Pipeline using VariantSpark and BitEpi
Break
Poster session (GatherTown)

14:10
Time (AEDT)

Session 5: Stats & Methods (Brisbane)(Session Chair: Ariane Mora)

15:40

National Invited SpeakerAnna Trigos
Peter MacCallum Cancer Centre
Single-cell and spatial transcriptomics to dissect modulators of radionuclide therapy response in prostatecancerNational Invited Speaker
Ben Woodcroft
Queensland University of Technology
Scalable community profiling of shotgun metagenomes​Abstract Talk: Nicolas P. Canete
spicyR: Spatial analysis of in situ cytometry data in R

15:55

Abstract Talk: Yuzhou Feng
SPIAT: Novel Computational and Statistical Tools for the Simulation and Analysis of Spatial Data

16:10

Abstract Talk: Sabrina Yan
Helium: Automatic variant prioritization for surfacing cancer drivers

16:25

Lightning Talks:● Dillon Hammill, CytoExploreR: Next-Generation Open-Source Software for Cytometry Data Analysis● Mikhail Gudkov, Quantifying negative selection on synonymous variants● Daniel Cameron, Single breakends: a new paradigm for structural variant calling● Angelita Liang, A universal naming system for human alternative splicing

16:40

Break

16:45

ABACBS Awards

14:30

15:05

ABACBS Day 3 / Phylomania Day 1 (Thursday 25th November)Time (AEDT)

11:00

11:20
11:35
11:50

12:05

ABACBS Session 6 / Phylomania Session 1: Phylogenetics, Evolution & BigData (Hobart)(Session Chair: Michael Charleston)
National Invited Speaker:
Barbara Holland
University of Tasmania
Some possibly bad ideas for detecting convergent selection​Abstract Talk: Zhaoxiang Cai
Integrating multi-omics data with biological knowledge by Transformer-based deep learning
Abstract Talk: Paola Cornejo-Páramo
Transcription Factor-binding motif composition of cis-regulatory regions in a sea sponge species suggest theexistence of some constraints in metazoan regulatory grammar
Nicholas Fountain-Jones
Hunting alters viral transmission and evolution in a large carnivore
Lightning Talks:● Tyrone Chen, Identifying the complete functional and regulatory signatures driving antimicrobial
resistance in sepsis.
● Ariane Mora, Integrated gene landscapes uncover multi-layered roles of repressive histone marks
during mouse CNS development● Nehleh-Fatemeh Kargarfard, PhiloBacteria: A new tool to infer phylogenetic trees from recombinantbacterial genomes● Jarny Choi, Creating interactive visualisation of gene expression data online using latest web

technologies
12:20

Best COMBINE talk

12:40-13:00

ABACBS Wrap Up
Break
ABACBS ends - but Phylomania continues!

Time (AEDT)

Phylomania Session 2: applications(Session Chair: Jeremy Sumner)

13:45

Folagbade Abitogun: Identification and Analysis of Novel Putative Drug Targets in Hypervirulent Klebsiellapneumoniae Proteomes
Abhinay Thakur: In silico based approach of FDA Approved Drugs for targeting against nsp10 (6W75) of
2019-nCoV (novel coronavirus)
Eike Steinig: Signatures of epidemic growth in the emergence of community-associated MRSA

14:00
14:15
14:30
14:45

Swapnil Tichkule : Genomic landscape of diversification, selective sweeps, and demographic history of ananthroponotic parasite
Amarinder Thind : Potential non-coding regulations in cSCC Metastasis cancer
Break

15:00
15:30

Joshua Stevenson: Rearrangement Events on Circular Genomes

15:45

Venta Terauds: Genome algebras in action
Jiahao Diao: A stochastic model of evolution to improve the prediction of independent or dependent gain andloss of genes
Jonathan Mitchell: Consistent inference of species networks from gene trees under the multispecies coalescentusing a generalized AIC

16:00
16:15
16:30

17:00-17:40
19:00

Break
International Invited Speaker:
Arndt von Haeseler
Sebastian Burgstaller-Mühlbacher
Center for Integrative Bioinformatics Vienna, Vienna University and Medical University, Vienna, Austria(Hobart) conference dinner - Botanica in Salamanca Place

Phylomania Day 2 (Friday 26th November)Time (AEDT)
09:00(Earlier start!)
09:15
09:30
09:45
10:00
10:15

Phylomania Session 3: methods comparing trees(Session Chair: Barbara Holland)Martin Smith: Improving consensus trees by detecting rogue taxa
Caitlin Cherryh: Does Filtering Recombinant Loci Improve the Estimation of Species Trees?
Ana Serra Silva: From islands of trees to clumps: can we generate informative clusters of partially overlapping
trees?
Sarah Alver: Improvement to GLASS/Maximum Tree Method of Species Tree Inference from Estimated Gene
Trees Using Measurement Error Modified Single Linkage Clustering
Lena Collienne: Distances between phylogenetic time trees
Gleb Zhelezov: Trying out a million genes to find the perfect pair with MTrip
Break

10:30
Time (AEDT)

Phylomania Session 4: statistical models(Session Chair: Venta Terauds)

11:00

Ben Kaehler: Phylogenetics if you stop ignoring non-stationary evolution

11:15

Cassius Manuel Perez: A test for phylogenetic saturation

Qin Liu: Is AIC An Appropriate Metric For Model Selection In Phylogenetics?

11:30

Samuel Davis: Inference of biochemical and biophysical parameters for ancestral proteins

11:45

Conrad Burden: Stationary Distributions of Neutral Multi-type Branching Diffusions

12:00

Break (Lunch in Hobart)

12:15
Time (AEDT)

Phylomania Session 5: statistics, now with geometry(Session Chair: Jimmy Breen)
Yao-ban Chan: The effect of copy number hemiplasy on gene family evolution

13:15

Albert Soewongsono: The Shape of Phylogenies Under Phase-Type Distributed Times to Speciation andExtinction

13:30
13:45

Matthew Macaulay: Hyperbolic Tree Embeddings for Bayesian Phylogenetic Inference

14:00

Ruriko Yoshida: Tree Topologies along a Tropical Line Segment

14:15

Ben Wilson: Learning phylogenetic trees as hyperbolic point configurations

14:30

Michael Charleston: Landscapes of split space part 2: the quest for utility.

14:45
15:00

Break

Time (AEDT)
15:30

Phylomania Session 6: Eclectica(Session Chair: Jonathan Mitchell!)Luke Cooper: Models of biomolecular network evolution

15:45

Andrew Francis: The perfect picnic project: Sandwiches, eggs, and trees.
Mareike Fischer: Phylogenetic Diversity Rankings in the Face of Extinctions: the Robustness of the Fair
Proportion Index
Phylomania ends
Brief announcements and slightly whimsical awards

16:00
16:15
16:16

Beer o’clock

Workshops
PATHWAY ANALYSIS OF MULTI-OMICS DATA USING THE REACTOME DATABASE (Thursday, 25
November)●●●Ignatius Pang (main contact) ipang@cmri.org.au
Nader Aryamanesh
Pablo Galaviz

Start time: AEDT 14:15
Run Time: 2 hours 45 mins
Overview
Transcriptomics and proteomics experiments often produce lists of genes or proteins that show statisticallysignificant differential expression when samples from different experimental conditions, genotypes, or tissue
types were compared. To investigate whether significantly differentially expressed genes or proteins wereoverrepresented in specific biological pathways and provide insights on their functions, curated databases thatmap genes and proteins to biological pathways were often used. One such curated database is the Reactome

(Jassal et al. 2020 NAR, 48(D1):2115-2125), which is a database of manually curated biological pathway maps
(e.g. metabolic, signalling and regulatory pathways). The Reactome analysis engine also enables quantitative-omics data as input, using tools such as CAMERA (Wu and Smyth 2012 Mol. Cell Proteomics, 19(12):e133)
and PADOG (Tarca et al. 2012 BMC Bioinformatics 3:136), to increase statistical power of identifying relevant
pathways as compared to overrepresentation analysis with gene lists. The interactive web interface enables
users to visually locate their protein of interest in the curated pathway map, while the Bioconductor/R interface
enables analysis to be automated for larger or more complex analyses. This workshop will include an
introduction to the Reactome database and hands-on tutorials, including:●●●Analysis of a publicly available proteomics dataset using the interactive web interface,
Comparison of proteomics and transcriptomics data using the ReactomeGSA Bioconductor/R package,andAnalysis of a publicly available single-cell expression atlas dataset using the ReactomeGSA (Griss etal. 2020 NAR, 48(D1):D498-D503) Bioconductor/R interface.

The workshop is tailored for researchers of all levels of experience. The workshop will include step-by-step
instructions on how to perform analysis on the web-based tool and also to run the R command lines on Google
Colab. Some programming experiences (e.g. R in particular) is advantageous but is not essential to fully
participate in this course.
The online environment for the participants to run the R command lines during the hands-on part of the tutorial
will be Google Colab’s R notebook (https://colab.to/r). Details will be delivered to people who have registeredby email about one week prior to the workshop.

VIRAL METAGENOMICS WITH HECATOMB (Friday, 26th November)
Organisers●●●●

Michael Roach (michael.roach@flinders.edu.au)
Rob Edwards
Scott Handley
Sarah Giles

Start time: AEDT 10:30-16:30
Run Time: 6 hours
Overview
One particular virus has been at the forefront of everyone's mind recently. However, the impact from thepandemic is a drop in the ocean compared to the impact that viruses (including bacteriophages) have on ourdaily lives. Viruses are a core component of every microbiome and they influence every environment including
human health and disease. While some are associated with disease states, most viruses are harmless or evenbeneficial. For instance, there is a renewed interest in the use of phages for combating the rise of antimicrobial
resistance.
Viral metagenomics is fraught with challenges. Viruses are both highly diverse and poorly represented inreference databases. Viruses share large portions of sequence homology with other domains of life. Viral
metagenomics studies are hence typically populated with false-positive hits while novel viruses fly under theradar. We've developed Hecatomb to address these issues and make accurate viral metagenomics accessible
to anyone.
This workshop with cover an overview of the challenges associated with viral metagenomics; methods andapproaches for viral enrichment and DNA extraction in the lab; a detailed overview of installing and running the
Hecatomb pipeline for different platforms and usage cases; and an extensive hands-on component forvisualizing and statistically interrogating your data. By the end of this workshop, you will have everything youneed to perform your own viral metagenomics analysis.
Running sheet (Adelaide time)
1hr 09:00 - 10:00 - OPTIONAL - Installation of required software (Michael Roach & Rob Edwards)
1hr 10:00 - 11:00 - Challenges associated with viral metagenomics (Scott Handley)
45min 11:00 - 11:45 - Viral enrichment and DNA extraction methods (Sarah Giles)
45min 11:45 - 12:30 - Running Hecatomb demonstration (Michael Roach or Rob Edwards)
1hr 12:30 - 13:30 - Lunch break
1.25hr 13:30 - 14:45 - Hands-on part 1: Loading files and filtering alignments (Michael Roach or Rob
Edwards)
1.25hr 14:45 - 16:00 - Hands-on part 2: Visualisation and statistical interrogation (Michael Roach or Rob
Edwards)
30min 16:00 - 16:30 - OPTIONAL - Hands-on extra help and troubleshooting (Michael Roach & Rob Edwards)

International Invited Speakers
Roser Vento-Tormo
Wellcome Sanger InstituteVento-Tormo’s research interest is to understand the influence of cellular
microenvironments on individual cellular identities and responses, in thecontext of immunity and development. Her team (https://ventolab.org/)
employs single-cell and spatial transcriptomics methods to deconstruct thecell signals in human organs and tissues, and utilise this information to
inform the reconstruction of novel in vitro models. Essential for this work, isthe novel computational tools her team develops to build cell–cellinteractions networks from transcriptomics data. Her training in genomics
and bioinformatics puts her in a unique position to lead multidisciplinary projects. In her predoctoral research, shestudied the interplay between cell signalling and epigenetic machinery key to regulating cellular fate decisions inmyeloid cells. She pursued her postdoctoral studies in the Teichmann laboratory as an EMBO / HFSP fellow, where she
developed CellPhoneDB.org, a computational tool to study cell-cell communication from single-cell transcriptomics
data, and use it to disentangle the complex communication between maternal and fetal cells in the uterine-placental
interface during early human pregnancy. Vento-Tormo work has been funded by many recognised international
agencies (H2020, MRC, CZI, Wellcome-LEAP), and she has recently obtained the Early Career Research Award fromthe Biochemistry Society (2021).

Smita Krishnaswarmy
Yale School of Medicine

Smita Krishnaswamy is an Associate professor in Genetics and Computer Science. Sheis affiliated with the applied math program, computational biology program, Yale Center
for Biomedical Data Science and Yale Cancer Center. Her lab works on the developmentof machine learning techniques to analyze high dimensional high throughput biomedical
data. Her focus is on unsupervised machine learning methods, specifically manifold
learning and deep learning techniques for detecting structure and patterns in data. She
has developed algorithms for non-linear dimensionality reduction and visualization,
learning data geometry, denoising, imputation, inference of multi-granular structure, andinference of feature networks from big data. Her group has applied these techniques tomany data types such as single cell RNA-sequencing, mass cytometry, electronic healthrecord, and connectomic data from a variety of systems. Specific application areas
include immunology, immunotherapy, cancer, neuroscience, developmental biology and health outcomes. Smita has a
Ph.D. in Computer Science and Engineering from the University of Michigan.

Isobel Parkin
University of Saskatchewan

Isobel Parkin has been a Research Scientist with Agriculture and Agri-Food
Canada (AAFC) since 1999 and an adjunct Professor at the University of
Saskatchewan since 2004. Her research interests include Brassica genomics,
comparative genome organisation, global gene expression analysis, and abiotic
stress responses. Together with Prof. Andrew Sharpe, she co-led the Canadian
Canola Genome Sequencing (CanSeq) industry consortium project that developed
a high quality genome sequence as part of the multinational Brassica Genome
Sequencing initiative. Isobel’s current research funding sources include the Global
Institute for Food Security (GIFS), Saskatchewan Agriculture Development Fund,
AAFC Crop Genomics Initiative, and canola producer groups.

Rohan Williams
Singapore Centre for Environmental Life Sciences (SCELSE)

Rohan Williams is an Australian computational biologist based at the Singapore Centre
for Environmental Life Sciences Engineering (SCELSE), a Research Centre of
Excellence co-hosted by the National University of Singapore and Nanyang
Technological University. At SCELSE he is Head of the Integrative Analysis Unit, which
combines research, service and training activity in bioinformatics and computational
biology. His research interests lie in metagenomics and multi-omics of microbiomes andcomplex microbial communities, mostly in the context of wastewater bioprocesssystems. Previously he was a research group leader at the Australian National University (2077-2011) and an NHMRCPeter Doherty Fellow at UNSW (2003-2007). He holds a PhD (UNSW) in Medicine and a BAppSc (UTS) in Physics.
Williams is cofounder of the ASEAN Industrial Wastewater Genomics Consortium, a new initiative targeting microbialcommunities underpinning the agri-industrial systems in South East Asia and a scientific confounder of BluMaiden
Biosciences Pte Ltd, a Singapore headquartered biotechnology platform company that is developing new small
molecule therapeutics derived from human microbiomes.

Jill Moore
University of Massachusetts, Amherst

Dr. Moore’s research focuses on the large scale integration of multi-omic
datasets to better understand gene regulation, particularly in the context ofhuman disease. She received her Bachelors of Science in Mathematics withminors in Biology and Chemistry as a member of the CommonwealthHonors College from the University of Massachusetts Amherst. She
received her PhD in 2017 from the University of Massachusetts Medical
School under Prof. Zhiping Weng where she was an active member of theEncyclopedia of DNA Elements (ENCODE) project. Since completing her
graduate training, Dr. Moore has continued working with the ENCODE
consortium as the Project Manager for the Data Analysis Center. In this role, she is very interested in improving ways tomake multi-omic data and computational analyses accessible to the wider biological community.

Arndt von Haeseler
Vienna University and Medical University, Vienna, Austria
Professor Arndt von Haeseler is the scientific director of the Max F. Perutz
Laboratories at the Vienna Biocenter and a professor of bioinformatics at
the University of Vienna and the Medical University of Vienna. Arndt
obtained his doctorate in mathematics from the University of Bielefeld in1988. He habilitated in 1994 at the Department of Zoology of the Universityof Munich, where he remained as a lecturer until 1998. From 1998 until
2001, von Haeseler was a group leader at the Max Planck Institute for
Evolutionary Anthropology in Leipzig. Subsequently, he was professor of
bioinformatics at the University of Düsseldorf and, in 2005, he joined the
Max F. Perutz Laboratories (MFPL), where he leads the Center for
Integrative Bioinformatics Vienna (CIBIV). At the University of Vienna, he is
the dean of the Center for Molecular Biology and at the Medical Universityof Vienna, he is the head of the Department for Medical Biochemistry.
Arndt's research focuses on developing computational methods for the reconstruction of phylogenetic trees. He
co-authored the phylogenetics software packages TREEFINDER, TREE-PUZZLE, and its successor, IQ-TREE. He sitson the editorial boards of Molecular Biology and Evolution and BMC Evolutionary Biology. In 2015, von Haeseler was
elected as a corresponding member of the mathematics and science class of the Austrian Academy of Sciences. Since
1999, he holds an honorary professorship in theoretical biology at the University of Leipzig.

National Invited Speakers
Matthew Lewsey
La Trobe University, Melbourne University

I am a hands-on lab biologist turned data-crunching genome scientist. My lab studies
how plants perceive the world around them and interact with their environments byregulation of their genomes. We apply this work with commercial partners who growa range of agricultural crops including cannabis, opium poppies, barley, oats andpeas. I completed my PhD in Molecular Virology at the University of Cambridge with
Prof John Carr. I next conducted postdoctoral research at the Salk Institute (La Jolla,USA) with Prof Joe Ecker and at the Centro Nacional de Biotecnología (Madrid,
Spain) with Prof Roberto Solano. My projects at that time focused on applying
functional genomics to understand plant hormone biology and small RNA signalling
between organs. This work was performed in both labs as part of my Marie Curie
Fellowship. I joined La Trobe University in April 2016 to further my interests ingenomics, systems biology and plant science. In 2017 I became Director of the La Trobe Genomics Platform, and in
2019 Co-Deputy Director of the ARC Research Hub for Medicinal Agriculture.

Parwinder KaurThe University of Western Australia
Assoc. Prof. Parwinder Kaur leads an innovative Translational Genomics
research program that aims to translate fundamental science into
ready-to-use solutions across the agricultural and medical sectors. Her
DNA Lab team enables research to span the spectrum of scientificactivities beyond the traditional ‘Lab-to-Landscape’ model, using new age
technologies such as CRISPR, single-cell and 3D genomics. With DNA
Zoo Australia she is on a mission to provide genomic empowerment tounique Australian biodiversity facilitating conservation efforts for the
threatened and endangered species She has made substantial
contributions to the field of biotechnology and was appointed as UWA “Be
Inspired” for Agricultural & Environmental Biotechnology in 2019. Her studies tracking genomic variation to breed low
methanogenic forages in Australia provided a new paradigm for reducing the environmental footprint of ruminants. She
has been honored by the prestigious “Science and Innovation Award” for Young People in Agriculture, Fisheries andForestry by the Australian Academy of Sciences in 2013. For DNA Zoo innovative work developments won theMicrosoft's AI for Earth award for 2019-20. She is an active mentor for gender equity and GirlsXTech international
ambassador working to close the gender gap in technology.

Katherine Pillman
Centre for Cancer Biology, University of South Australia

Dr Katherine Pillman is a bioinformatician and RAH Florey Fellow at theCentre for Cancer Biology in South Australia. She uses a variety oftranscriptomic methods and next-gen sequencing data types to dissect gene
regulation through analysis of microRNA biology and targeting, circular RNAs,
alternative splicing, epigenetic modifications, gene regulatory networks andexpression. She obtained her PhD in Molecular Biology from the University of
Adelaide in 2009 working on transcriptional regulation in barley plants. Her
postdoctoral research at Oregon State University involved RNA-seq analysisof stress-responsive gene regulatory networks in potato plants, which fuelled
her interest and transition into the field of Bioinformatics. In 2012, she returned
to Australia to take up her current role as Lead Bioinformatician in the Gene Regulatory Section at the Centre forCancer Biology, working in collaboration with Prof. Greg Goodall. Career highlights include a seminal paper in Cell
which identified the first protein known to control the formation of circular RNAs, and the subsequent discovery that thisprotein, Quaking, is a key emerging player in circular splicing and linear alternative splicing in cancer (published in
EMBO J). Other work has identified genome-wide microRNA regulatory networks controlling cell invasion (also in
EMBO J) and uncovered the cooperative role of microRNAs in cancer (published in Nucleic Acids Research and Cell
Systems). In addition to her fellowship, she is a Co-Investigator on NHMRC Ideas and MRFF grants which fund hercurrent work.

Anna Trigos
Peter MacCallum Cancer Centre
Dr. Anna Trigos is a postdoctoral researcher at the Peter
MacCallum Cancer Centre in the team of A/Prof. Shahneen
Sandhu in Prof. Rick Pearson’s lab. She was recently awarded
two NHMRC Ideas grants and a CASS Foundation grant toinvestigate prostate cancer evolution and heterogeneity anddevelop novel spatial analysis methods to study the tumour
immune microenvironment. In 2019 she was awarded the Joseph
Sambrook Prize for Research Excellence and the Peter Mac
Postgraduate Research Medal, and in 2020 she received the Lea
Medal Award recognising emerging female researchers.

Ben Woodcroft
School of Biomedical Sciences, Queensland University of Technology

Starting from a computational background at the University of Queensland, Ben’s
broad interest in biological systems was sparked by an undergraduate project inprotein structure with Dr. Nicholas Hamilton, then an honours project in Prof.
Bernie Degnan’s marine biology laboratory studying the genome structure of themost basal animals, sponges. Then he moved south to the University of Melbourne
for his PhD, under the guidance of A/Prof. Stuart Ralph and Prof. Terry Speed,
concentrating on the development of bioinformatic tools to understand the malaria
parasite’s complex cell biology. During his post-doctoral studies with Prof. Gene
Tyson at the Australian Centre for Ecogenomics, UQ, and continuing until thepresent time as microbial informatics team leader within the Centre for Microbiome
Research at QUT, he has studied the microbial world using informatic techniques.
Direct sequencing of DNA and RNA derived from natural microbial systems has great scientific and applied potential,
and the wealth of sequencing data bring many bioinformatic opportunities and challenges, including the recovery andannotation of genomes with strain-level specificity, exploring large public datasets and linking microbial communities
with their function. A primary study site he has been involved with is Stordalen Mire, in northern Sweden, a permafrost
thaw gradient home to complex microbial ecosystems. This system serves as a model for understanding how climatechange is affecting microbial communities in thawing permafrost, which are in turn generating the potent greenhousegas methane, exacerbating global warming. Primary contributions at this site involved the recovery of >1000 highquality genomes from the site, discovery of novel methanogenic lineages and linking specific community members to
the isotopic composition of released methane, with implications for global climate modeling.

Barbara Holland
University of Tasmania
Barbara completed a PhD in Mathematical Biology at Massey University in New
Zealand followed by postdoctoral studies at the Ruhr Universität Bochum(Germany) and in the Allan Wilson Centre for Molecular Ecology and Evolution(New Zealand). Prior to joining the University of Tasmania she worked as a
Mathematics lecturer and researcher at Massey University. Since beginning her
PhD she has enjoyed the challenge of working with biologists in trying totranslate the problems they face into the language of mathematics. Biology isawash with data since the advent of DNA sequencing technology and this has
opened up a range of very interesting research questions that require a
combination of skills from mathematics, biology and computer science.

ABACBS Accepted Abstract List
Abstract #6
Jarny Choi (The University of Melbourne)
Creating interactive visualisation of gene expression data online using latest web technologies
Stemformatics.org is an established online gene expression data portal containing hundreds of carefully curated andannotated datasets. The website has recently been completely re-worked using latest software, and contains a suite ofvisualisation tools which enable easy exploration of the data, as well as an API server which can be used to access thedata directly. A list of key features will be discussed, including the integrated data atlas where the user can project theirown data and classify their cell types against a reference. Also discussed will be the lessons learnt from building an online
data portal, including what resources may be required and which latest technologies may be most useful.

Abstract #12
Hasindu Gamaarachchi (Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW,
Australia)
SLOW5: a new file format enables massive acceleration of nanopore sequencing data analysis
Nanopore sequencing is an emerging genomic technology with great potential. However, the storage and analysis of
nanopore sequencing data have become major bottlenecks preventing more widespread adoption in research and clinicalgenomics. Here, we elucidate an inherent limitation in the file format used to store raw nanopore data, known as FAST5,
that prevents efficient analysis on high-performance computing (HPC) systems. To overcome this, we have developed
SLOW5, an alternative file format that permits efficient parallelisation and, thereby, acceleration of nanopore data
analysis. For example, we show that using SLOW5 format, instead of FAST5, reduces the time and cost of genome-wide
DNA methylation profiling by an order of magnitude on common HPC systems (up to >30X times), and delivers consistentimprovements on a wide range of different architectures. With a simple, accessible file structure and significant reductions
in size compared to FAST5 (~25% reduction for a typical human genome), SLOW5 format will deliver substantial benefits
to all areas of the nanopore community. Example result: With the maximum resource allocation available on Australia's
National Computing Infrastructure, genome-wide DNA methylation profiling on a single ~30X human genome sequencing
dataset runs for >14 days at a cost of >$500 when using FAST5 files. We were able to complete whole-genome
methylation profiling on a single 30X human dataset in just ~10.5 hours when using SLOW5 format as the input. SLOW5
format and all associated software are free and open source.
SLOW5 format specification documents: https://hasindu2008.github.io/slow5specs;
Slow5lib: https://hasindu2008.github.io/slow5lib;
Slow5tools:https://hasindu2008.github.io/slow5tools;
Pre-print: https://www.biorxiv.org/content/10.1101/2021.06.29.450255v1.

Abstract #13
Paola Cornejo-Paramo (Victor Chang Cardiac Research Institute)
TF binding motif composition of cis-regulatory regions in a sea sponge species suggest the existence of some constraints
in metazoan regulatory grammar
Chromatin accessibility plays a major role in regulating animal gene expression and therefore it is key to decipher animal
embryogenesis. Here we profile chromatin accessibility of six embryonic stages, along with a larval and adult stage in thesea sponge Amphimedon queenslandica, a member of the early branching phylum Porifera, in order to study theregulatory landscape of metazoan embryogenesis. On the grounds that most transcription factor (TF) families areconserved in eukaryotes, we examine the TF binding motif composition of Amphimedon cis-regulatory regions. We
leveraged the high performance of tree-based machine learning models and tree ensemble interpretation algorithms to
extract complex patterns and identify some TF binding motifs that are commonly enriched in Amphimedon cis-regulatoryregions. We further tested the machine learning models trained on Amphimedon on other species data and were able topredict cis-regulatory regions in other animal species but not in the close relative of animals, Capsaspora owczarzaki.
Overall, we find that animal cis-regulatory regions share some resemblance and constraints in motif composition besides
the extreme divergent life strategies adopted by different phyla.

Abstract #17
Aditya Sarkar (School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, North Campus,
Kamand, Mandi, HP 175005, India)
Comprehensive analysis of microblogging landscape in science and medicine
Twitter is one of the most popular microblogging and social networking services, where users can post, retweet, comment,
and engage in collaborative discussions. However, improper usage of Twitter can be detrimental to science and evenhave a negative impact on mental health. Thus, analysing tweets and Twitter data of various researchers will help us todeduce appropriate ways of using Twitter to advance in our research careers. Existing literature has analysed the activityof scientists on Twitter, such as studying the relationship between Twitter mentions and article citations, determining thebenefits of Twitter in the development and distribution of scientific knowledge, relevant metrics for prediction of highly citedarticles, and type of content that researchers tweet etc. For example, Eysenbach et al performed an analysis of tweets
and citations and how one can predict citations using tweets. Most of the existing literature analysed a limited number of
researchers, compromising the generalizability of derived results. In our study, we have taken a comprehensive andsystematic approach to analyse 4,257,739 scientists who published research papers on PubMed using data-driven
methods. We observed various parameters like number of followers, number of friends, citation count and K-index. There
was a low correlation between number of followers and number of friends and on an average scientists have 2111.45
followers and 554.6 friends. Citation count and number of followers are also having low correlation and researchers havean average citation count of 292.37. Average K-index of researchers is 15.56 and there is no significant relationshipbetween number of followers and K-index.

Abstract #18
Angelita Liang (UNSW Sydney)
A universal naming system for human alternative splicing
Our understanding of alternative RNA isoforms has progressed rapidly over the past decade. Advances in bioinformatic
tools to analyse isoform usage from short-read RNA-seq and the advent of long-read RNA-seq have led to the rapid
expansion of the RNA splicing field, with an ever-increasing number of annotated and novel alternative splicing events
characterized both structurally and functionally. Yet, there is a lack of standard nomenclature to describe these splicing
events. This issue is compounded by the fact that there is no stable system to refer to transcript isoforms by their HGNC
gene symbol and existing naming conventions are ineffective as text representations of alternative exon structures. We
propose a simple and compact shorthand notation to represent splicing events of any complexity without the aid of a
figure, hereby termed the "Extended Delta Notation". An amalgamation of existing naming conventions already familiar tobioinformatics and molecular biologists, our proposed nomenclature uses HGNC gene symbols to improve readability
whilst preserving the reproducibility of Ensembl, LRG and RefSeq reference transcripts. In addition, the notation conveys
all structural information whilst remaining concise. To assist in the understanding, interpretation and generation of the
Notation, we have an R Shiny web app currently in development, which will serve as a hub for the RNA splicing
community. In turn, the adoption of consistent naming conventions in literature will form the basis of more uniform
standards of reporting in the future, alleviating the reproducibility crisis.

Abstract #20
Patricia Sullivan (Children’s Cancer Institute, University of New South Wales, Randwick, Sydney, NSW, Australia)
Breaking the black box: Dissecting deep neural network models to reveal splicing motifs

SpliceAI is a state-of-the-art deep neural network that outperforms previous in silico approaches to model splicing.
However, deep neural networks are notoriously black-box, making it challenging to elucidate biological relevance from thelearned models. Explainable artificial intelligence (XAI) methods make it possible to interrogate a deep neural network's
behaviour and can potentially uncover the biological patterns learnt by SpliceAI. We use an XAI method called activation
maximisation (AM) by gradient ascent to explain individual nodes within the SpliceAI neural network. AM works byidentifying which aspects of the input contribute most to the activity of that node. Using this method, we produced 160
position weight matrices (PWMs) representing the 103nt input sequences that contribute most to the activity of each nodein SpliceAI's final CNN layer, and thus help characterise the hidden latent space of the model. Approximately one-third of
the PWMs were classified as a known splicing motif, most of which being variations of the canonical acceptor and donorsites. Many unclassified motifs appeared to demarcate exon/intron boundaries, prompting us to investigate the featuresthat influence SpliceAI's predictions. Using in silico perturbation experiments, we found that SpliceAI has implicitly learnt
the codon triplet code. Given a notable absence of non-canonical motifs, we used validated splice-altering mutations toconfirm that SpliceAI under-performs on non-canonical variants. Despite SpliceAI's state-of-the-art performance, theselimitations could considerably impact the results of critical scientific applications. Thus, we highlight the importance ofrigorously dissecting black-box neural networks as part of the model development and operationalisation workflow.

Abstract #21
Andy Tran (University of Sydney)
Using multimodal single cell data to predict regulatory gene relationships and to build a computational direct cell
reprogramming model
Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite
decades of research, discovering the transcription factors that promote direct cell reprogramming has largely beenaccomplished through trial and error, a time-consuming and costly method. A computational model for direct cell
reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time andresources. Current methods often cannot account for the heterogeneity observed in reprogramming, or they only makeshort-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel
computational model for direct cell reprogramming that leverages single cell multiomics data, enabling a more holistic
view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential ofeach transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate theeffect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect ofoverexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations.
Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate
model for direct cell reprogramming with significant potential to accelerate research in regenerative medicine.

Abstract #23
Rebecca C Poulos (ProCan, Children’s Medical Research Institute, Faculty of Medicine and Health, The University ofSydney, Westmead, NSW, Australia)
A pan-cancer proteomic map of 949 human cell lines
Proteomic data can reveal novel associations between genotype and phenotype, beyond what is apparent from genomics
or transcriptomics alone. However, a lack of large proteomic datasets across a range of cancer types has limited ourunderstanding of proteome network organisation and regulation. We produced a pan-cancer proteomic map derived from949 human cancer cell lines. The map encompasses more than 40 cancer types derived from over 28 distinct humantissues. The samples were processed with a clinically-relevant workflow involving rapid and minimally complex sample
preparation, quantifying 8,500 proteins. The raw proteomic data were acquired by data independent acquisition mass
spectrometry (DIA-MS) at ProCan in Australia. The processed data were analysed with a bespoke deep learning-based
pipeline (DeeProM) that integrates multi-omics, CRISPR-Cas9 gene essentiality and drug sensitivity information produced
at the Wellcome Sanger Institute. First, our findings reveal pervasive post-transcriptional modification and thousands ofputative protein biomarkers of cancer vulnerabilities. Second, DeeProM statistics show that a fraction of the proteome canconfer similar predictive power to the entire transcriptome. This has key implications for the clinical application of

proteomics in drug response prediction. Third, we demonstrate that a random proportion of the identified proteins canprovide robust predictions of cancer cell phenotypes, underpinning the concept of pervasive co-regulation of protein
networks. This pan-cancer cell line proteomic map is a comprehensive resource that expands our understanding ofcancer proteomes. These data reveal principles of cancer cell phenotypes, including genetic vulnerabilities and drug
sensitivities, that are important for developing novel targeted anticancer therapies.

Abstract #26
James M. Ferguson (Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW,
Australia)
InterARTIC: an interactive web application for whole-genome nanopore sequencing analysis of SARS-CoV-2 and othervirusesInterARTIC is an interactive web application for the analysis of viral whole-genome sequencing (WGS) data generated on
Oxford Nanopore Technologies (ONT) devices. A graphical interface enables users with no bioinformatics expertise to
analyse WGS experiments and reconstruct consensus genome sequences from individual isolates of viruses, such as
SARS-CoV-2. InterARTIC is intended to facilitate widespread adoption and standardisation of ONT sequencing for viral
surveillance and molecular epidemiology. We demonstrate the use of InterARTIC for the analysis of ONT viral WGS datafrom SARS-CoV-2 and Ebola virus, using a laptop computer or the internal computer on an ONT GridION sequencing
device. We showcase the intuitive graphical interface, workflow customisation capabilities and job-scheduling system thatfacilitate execution of small- and large-scale WGS projects on any common virus.

Abstract #27
Isobel J Beasley (Melbourne Integrative Genomics, The University of Melbourne, Royal Parade, 3010, Parkville, Victoria,
Australia)
Predicting the cross-population portability of human expression quantitative trait loci (eQTLs)
Individuals of European ancestry disproportionately dominate participation in human genetic studies, to the detriment ofscientific inquiry and the equitable translation of genomic research. One genomic study type, expression quantitative trait
locus (eQTL) mapping, has been a valuable tool for understanding the regulatory consequences of disease associatedgenetic variants. However, not all associations between genotype at given loci and variation in gene expression (eQTLs)
are shared across populations. Understanding the features of these eQTLs could enable a priori prediction of eQTLs in
understudied populations. Here we use summary statistics from three previous multi-population eQTL studies to classify
eQTLs as population-specific or shared between at least two populations (African American, European American,
Indonesian etc.). We train machine learning models to predict whether an eQTL is specific to its discovery population
using publicly available information on the evolutionary, functional, and expression features of these eQTLs. As per similar
applications, we find trained random forest classifiers perform better than other algorithms on a held out test set of eQTLs(auROC > 0.8). Features of gene conservation (e.g. LOEUF, phyloP), Gene Ontology, allele frequency and eQTL effectsize have the highest importance scores in our best performing models. This result suggests these features could be used
to understand gene regulatory trait differences across populations. Since current Eurocentric biases in genomic resources
are likely to persist for some time, our approach could be an important step toward a more equitable understanding ofgene regulation, and hence more equitable personalised medicine.

Abstract #36
Venta Terauds (University of Tasmania)
Genome algebras in action
We demonstrate some features of our recently introduced genome algebra framework by applying it to echinoderm
mitochondrial genome data. In particular, the framework facilitates a fine-grained consideration of rearrangement models.
We show how varying the underlying rearrangement model and the choice of genomic distance measure can affect thephylogeny that is produced.

Abstract #39
Kaitao Lai (Ancestry & Health Genomics Laboratory, Charles Perkins Centre, School of Medical Sciences, University ofSydney, New South Wales, Australia)
Shotgun microbial profiling reveals geographic disparities in aggressive prostate cancer
The significant geographic disparity has been identified in prostate cancer (PCa). Specifically, men from Sub-SaharanAfrica have the highest mortality rates, over double the global average. Additionally, rates for infection-associated cancers
within Sub-Saharan Africa are also over double global averages. While PCa risk is associated with inflammation, no
pathological agent has been identified. Our hypothesis, a microbial pathogen is contributing, at least in part, to adverse
PCa outcomes for men in Africa.We have investigated the prostate tissue microbiome from 176 men from Sub-SaharanAfrica (African) and Australia (European ancestry), with or without PCa, and a bias towards aggressive disease. In the
differential analysis, the relative abundances of different bacterial taxa have been found to be associated with the risk
level of PCa and population with sampled countries. The relative abundances of Klebsiella pneumoniae, Comamonas
testosterone and Moraxella osloensis, significantly increased with high-risk PCa, were associated with the risk ofhigh-grade PCa; while Akkermansia muciniphila and Anaerostipes hadrus increased with men with benign prostate, havebeen considered potential probiotic. Escherichia coli, identified from all patient groups, are crucial pathogens and may
cause significant morbidity and mortality of PCa5. The highest mortality rates in Sub-Saharan Africa may be associatedwith the Human mastadenovirus C, found in all Africa sampled groups (except the Australia group). Geographic disparity
is significant in bacteria diversity from prostate tissue for the PCa patients. The PCa tissue microbiota profile could be anovel method for identifying pathogens, the prediction of high-risk PCa and for developing the treatment of PCa.

Abstract #40
Ulf Schmitz (College of Public Health, Medical & Vet Sciences, James Cook University, Sydney, Australia)
Multi-omics data analysis identifies epigenetic regulators of alternative splicing
The phenomenon of widespread and dynamic intron retention (IR) programs in cells of vertebrate species has recently
gained increasing attention. It has been shown that IR is involved in a multitude of cell-physiological processes, whileaberrant IR profiles have been associated with numerous human diseases including several cancers. Despite consistentreports about intrinsic sequence features that predispose introns to being retained, conflicting findings about cell type or
condition-specific IR regulation by trans-regulatory and epigenetic mechanisms demand an unbiased and systematic
analysis of IR in a controlled experimental setting. We integrated matched mRNA sequencing (RNA-seq), whole genome
bisulfite sequencing (WGBS), nucleosome occupancy methylome sequencing (NOMe-Seq), and chromatin
immunoprecipitation sequencing (ChIP-seq) data from primary human myeloid and lymphoid cells. Using these
multi-omics data and machine learning we trained two complementary models to determine the role of epigenetic factors
in the regulation of IR in cells of the innate immune system. Our results suggest that intrinsic characteristics are key for
introns to evade splicing and that epigenetic marks can modulate IR levels. However, cell type-specific IR profiles arelargely caused by changes in chromatin accessibility, whereby predisposed introns in nucleosome free regions are more
likely to be retained. This study is the first to demonstrate the important role of nucleosome occupancy in IR regulation.
Our results have profound implications for the analysis of other forms of alternative splicing as well. Since an increasing
number of studies describe pathogenic alterations in splicing regulation and therapeutic approaches targeting aberrant
splicing, our findings will inform novel epigenetic therapy development.

Abstract #41
Jeffrey Pullin (University of Melbourne)
Benchmarking methods for selecting marker genes in single cell RNA sequencing data
Over the past ten years, the advent of single cell RNA sequencing (scRNA-seq) technology has allowed scientists tostudy the transcriptomes of individual cells in unprecedented detail. A crucial step in all scRNA-seq analysis is to identify

the cell types present in the sample. Currently, this step is often performed using so-called marker genes. First, cells are
partitioned into clusters using a clustering algorithm. Then, computational methods are used to select a small subset ofgenes which distinguish each cluster of cells: the marker genes. The data for these genes can be interpreted by abiologist to identify the cell type of each cluster. Today, there are over 40 methods for identifying marker genes, ranging
from simple statistical tests to recent methods developed explicitly for the task. However, no independent studies
comparing and scrutinizing the competing methods currently exist. In my talk I will describe the space of existing methods
for selecting marker genes, and then present the results of a comprehensive benchmarking of the methods. We compare
the performance of the methods on over 50 datasets simulated using the Splatter package with parameters estimatedfrom 8 publicly available datasets. Methods are also compared on the predictive performance of the marker genes they
select and their ability to recapitulate known marker genes across a range of real datasets. In addition, I will highlight
unexpected implementation details of the widely used methods implemented in the Scanpy and Seurat packages. I willconclude with recommendations for best practice selection of marker genes.

Abstract #42
Jiru Han (Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne,
VIC, Australia)
Population-level genome-wide STR typing in Plasmodium species reveals higher resolution population structure andgenetic diversity relative to SNP typing
Short tandem repeats (STRs) are highly informative genetic markers that have been used extensively in population
genetics to study the population structure and genetic diversity in Plasmodium species. In this study, we performed thefirst large-scale in-silico STR typing study using HipSTR (Willems et al. 2017) in more than 3,000 P. falciparum(MalariaGEN Plasmodium falciparum Community Project) and 174 P. vivax (Plasmodium vivax Genome Variation project
and P. vivax data from Hupalo et al. 2016) WGS samples sourced from global malaria hot-spots. We developed amultivariable logistic regression model for the measurement and prediction of the quality of STRs based on gold standard
SNP genotyping data, which provides a high fidelity set of STRs from across the whole genome (6,768 from P. falciparumand 3,496 from P. vivax). Additionally, we used this set of genome-wide high-quality STRs to study parasite population
genetics and compare them to genome-wide SNP genotyping data, revealing both high consistency with SNP based
signals, as well as identifying some signals unique to the STR marker data. We also explored the biological importance of
STR variation in different populations and identified STR loci that may be linked to antimalarial drug resistance. These
results demonstrate that the identification of highly informative STR markers from large population screening is a powerfulapproach to study the genetic diversity, population structures and genomic signatures of selection in P. falciparum and P.
vivax.

Abstract #45
Zhaoxiang Cai (Zhaoxiang Cai - ProCan, Children’s Medical Research Institute, Faculty of Medicine and Health, The
University of Sydney, Westmead, NSW, Australia)
Integrating multi-omics data with biological knowledge by Transformer-based deep learning
Omics data analysis, powered by machine learning, has significantly improved cancer diagnosis and prognosis. However,most machine learning methods consider each gene as an independent feature, failing to integrate
experimentally-acquired gene regulation and pathway information. The benefit of utilising this information increases in theera of multi-omics, because gene regulation is the key mechanism that links different omic layers together. Here, we
present an interpretable deep learning model, DeepPathNet, which uses cancer-specific pathway information for both
single and multi-omics data analysis. DeePathNet leverages the cutting-edge deep learning technique, Transformer,
which is derived from the field of natural language processing, to model complex interactions between pathways fromomics data. The computation of self-attention in the Transformer module allows DeePathNet to learn the encoding of
pathways to achieve superior predictive performance and interpretability. Techniques such as drop out layers are also
integrated into DeePathNet to maximise its generalisability for unseen data. Moreover, DeePathNet supports any numberof omics layers and can handle missing values. Using multiple evaluation metrics, we demonstrate that DeePathNetrobustly outperforms traditional methods for predicting drug response and cancer type on four publicly available datasets,namely COSMIC Cell Lines, Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE) and
Cancer Therapeutics Response Portal (CTRP). DeePathNet also provides reliable model interpretation, potentiallyenabling biomarker discoveries at the pathway level. Using the Transformer, DeePathNet is the first method that supports
multi-omics data analysis, integrates cancer pathway knowledge into modelling, and provides pathway-level model
explanation.

Abstract #47
Andreas Zankl (Children's Hospital Westmead, University of Sydney, Garvan Institute)
Ontoclick: a web browser extension to facilitate biomedical knowledge curation
The world's biomedical literature has achieved a scale that the human brain cannot possibly process. Computers could
assist with searching through this vast sea of knowledge, but this requires knowledge curation, converting unstructured
text into well-defined units of information, such as a set of Human Phenotype Ontology terms, that a computer canprocess. Knowledge curation can be a repetitive and laborious process but without knowledge curation, vast amounts ofknowledge in the biomedical literature will remain untapped. The paucity of user-friendly tools is one of the reasons for thelack of widespread adoption of good biomedical knowledge curation practices. Here we present Ontoclick, a web browser
extension that streamlines the process of annotating a text span with a relevant ontology term, a key component ofknowledge curation. We hope this tool will make biocuration more accessible to a wider audience of biomedical
researchers.

Abstract #53
Jiayue-Clara Jiang, Chenwen Hu, Sonia Shah (The University of Queensland)
Using transcriptomic and statistical genomic approaches to investigate anti-depressive effects of statins
Statins are a class of lipid-lowering medications commonly prescribed for treating cardiovascular diseases. Statins exert
their pharmacological effects via inhibition of the HMG-CoA reductase (HMGCR). Observational studies have reported
both therapeutic and adverse unintended effects of statins, including conflicting evidence on their association withdepression risk. The effect of statins on depression is an important consideration when treating the increasing prevalenceof comorbid individuals with both cardiovascular disease and depression.||In this study, we leveraged the power oftranscriptomics and genome-wide association studies (GWAS) to interrogate the anti-depressive effects of statins. Using
the LINCS L1000 data, we profiled transcriptomic perturbations induced by commonly prescribed medications, including
statins. The statin-induced transcriptomic signatures were found to exhibit similarity to the transcriptomic impacts ofantidepressants. Functional enrichment analysis of genes commonly perturbed by statins and antidepressants indicatedfunctional involvement in immune processes, which have been shown to play an important role in depression. ||To
determine if the observed perturbation of immune pathways was an on-target (mediated through HMGCR inhibition) or
off-target (mediated through unintended targets, specifically ITGAL and HDAC2) effect, we queried published large-scale
GWAS summary statistics to determine whether eQTLs of these genes were associated with immune-related traits, andconducted Mendelian randomisation analysis to determine the causal effect of each target gene on depression risk.
||Overall, we showed that while statin use was not associated with the risk of developing depression, transcriptomic andgenomic analyses revealed an association of statins with changes in immune response pathways, which were also
perturbed by antidepressants. We also observed an association between HMGCR and ITGAL eQTLs with haematologicalparameters, suggesting that both on-target and off-target effects could potentially contribute to modulating disease
symptoms.

Abstract #62
Sebastian Burgstaller-Muehlbacher (Center for Integrative Bioinformatics Vienna, Max Perutz Labs, University of Vienna
and Medical University of Vienna, Vienna, Austriaustria)
Model selection on empirical data using deep learning

Selecting the correct model of sequence evolution (mSE) for a multiple sequence alignment (MSA) constitutes the firststep of tree reconstruction. State of the art approaches for inferring nucleotide models mostly apply maximum likelihood(ML) methods. Here, we demonstrate that neural networks can infer the correct mSE including the shape parameter 𝛼 ofthe 𝛤-distribution. A Residual Neural Network (Resnet) was trained with the six most frequently used mSE (JC, K2P, F81,
HKY, TN93 and GTR), whereas a Long Short-Term Memory (LSTM) network with attention was trained to determine α.
Our results show that the CNN correctly identifies the mSE in a range of 51.94% to 100%, depending on the true mSE.
Thus, it is comparable to results of IQ-Tree. Similar accuracies were obtained for 𝛼. However, the trained networks need
substantially less computing time (up to a 60x speedup), depending on the size of the MSA. We demonstrate, for the firsttime, that neural networks can be used to identify the correct mSE as well as rate heterogeneity of an MSA. Furthermore,
we intend to generalize our approach so all mSE of relevance in phylogenetics can be inferred using neural networks,
conferring a substantial reduction in computational requirements to model selection.

Abstract #63
Yunwei Zhang (School of Mathematics and Statistics, The University of Sydney, Sydney, Australia)
HISP: cohort heterogeneity identification and risk factors detection for survival prediction
Cohort heterogeneity exists in many clinical and omics datasets. Discovering sub-cohort specific risk factors is crucial for
personalised medicine and providing accurate survival prediction. While numerous unsupervised clustering methods havebeen applied to identify potential sub-cohorts in the population data, the detection of the associated risk factors and thecorresponding survival modelling are conducted in separate steps. Therefore, cohort specific prediction is not guaranteed
to be superior than fitting the full model. To address this research gap, we design Heterogeneity Identification and Survival
Prediction (HISP), which is a hybrid workflow that uses survival model predictability to guide sub-cohort identification
using both unsupervised and supervised learning strategies. Two key innovative ideas in HISP involve (1) using model
predictability to guide ensemble methods selection; (2) using the first layer of the data (recipient features) for sub-cohort
identification while using all three layers of data (recipients, donors and compatibilities variables) in the prediction. Here
we show that by applying HISP to the Australian and New Zealand dialysis and transplant registry data (ANZDATA,
2008-2017), different recipient subgroups are identified with different sets of risk factors driving their survival outcomes toprovide accurate post-transplant graft survival prediction. HISP has the capacity and flexibility to be applied to various
data modalities such as proteomics, genomics and metabolomics to identify sub-cohorts in a risk population to offer
precise survival predictions.

Abstract #67
Aidan P. Tay (Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, NewSouth Wales, Sydney, Australia)
INSIDER: alignment-free detection of foreign DNA sequences
External DNA sequences can be inserted into an organism’s genome either through natural processes such as genetransfer, or through targeted genome engineering strategies. Being able to robustly identify such foreign DNA is a crucial
capability for health and biosecurity applications, such as anti-microbial resistance (AMR) detection or monitoring gene
drives. This capability does not exist for poorly characterised host genomes or with limited information about the
integrated sequence. To address this, we developed the INserted Sequence Information DEtectoR (INSIDER). INSIDER
analyses whole genome sequencing data and identifies segments of potentially foreign origin by their significant shift in
k-mer signatures. We demonstrate the power of INSIDER to separate integrated DNA sequences from normal genomic
sequences on a synthetic dataset simulating the insertion of a CRISPR-Cas gene drive into wild-type yeast. As aproof-of-concept, we use INSIDER to detect the exact AMR plasmid in whole genome sequencing data from a Citrobacter
freundii patient isolate. INSIDER streamlines the process of identifying integrated DNA in poorly characterised wild
species or when the insert is of unknown origin, thus enhancing the monitoring of emerging biosecurity threats.

Abstract #68

Chelsea Mayoh (Children’s Cancer Institute, Lowy Cancer Centre, UNSW Sydney, Kensington, New South Wales,
Australia)
Utilising the transcriptome to functionally resolve molecular aberrations in precision medicine
The ZERO childhood cancer program integrates whole genome sequencing (WGS) and transcriptome sequencing(RNA-seq) to provide an integrated multi-omics‚ precision medicine pipeline to improve treatment outcomes in high-risk
paediatric cancer. We have developed a comprehensive RNA-seq pipeline that integrates with WGS to accurately identify
molecular aberrations and infer their predicted function and pathogenicity. WGS identifies complex structural
rearrangements however can have difficulty resolving the expressed fusion product, its translation and associatedretained protein domains which can change diagnosis or treatment. RNA-seq was able to resolve these complex
structural rearrangements and identified pathogenic fusions that WGS was unable to identify. For example, a CIC-DUX4L
fusion unidentified from WGS and an EML4-ALK fusion where WGS identified a multi-hop complex structural
rearrangement. Mutation calling from RNA-seq identified 90% of the aberrations reported from WGS. Of the 10% not
identified, the gene is not expressed in 53% and 21% were either nonsense mediated decay or allele specific expression.
RNA influenced the clinical actionability of 55 (11%) tumour suppressor gene mutations identified as heterozygous in DNA
but homozygous in RNA, having a direct clinical impact. Furthermore, RNA identified 34 (7%) splice site mutations where
88% were aberrantly spliced in the RNA either increasing or confirming the pathogenicity of the variant. Here we will
present the added clinical utility that a comprehensive RNA-seq pipeline provides to a precision medicine program on 477
patients enrolled on ZERO. In addition, we explore the question can we use RNA-seq as a stand-alone platform inprecision medicine?

Abstract #77
Feargal Ryan (Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, SA 5001,
Australia)
Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection
Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious respiratory virus whichis the causative agent behind the Coronavirus Disease 2019 (COVID-19) pandemic. It is increasingly clear that recovered
individuals, even those who had mild COVID-19, can suffer from persistent symptoms for many months after infection,
which is popularly referred to as, “long COVID”. However despite the plethora of research on COVID-19 relatively little is
known about the molecular underpinnings of these long-term effects. Methods We have undertaken an integrated
analysis of immune responses in blood at a transcriptional, cellular, and serological level at 12-, 16-, and 24-weeks
post-infection (wpi) in 69 patients recovering from mild, moderate, severe, or critical COVID-19 in comparison to
uninfected controls. Results Antibody responses were largely stable up to 24wpi and correlated with disease severity.
Deep immunophenotyping revealed significant differences in multiple innate (NK cells, LD neutrophils, CXCR3+
monocytes) and adaptive immune populations (T helper, T follicular helper and regulatory T cells) in convalescent patientscompared to controls, which were most strongly evident at 12 and 16wpi. RNASeq revealed perturbations to gene
expression in COVID-19 convalescents until 6 months post-infection, a subset of which was associated with ongoing “long
COVID” related symptoms. Conclusion Variation in the rate of recovery from infection at a cellular and transcriptional level
may explain the persistence of symptoms associated with long COVID in some individuals.

Abstract #81
Alejandro Casar (The University of Melbourne, School of Mathematics and Statistics, Victoria, Australia)
Investigating how transcriptional plasticity drives drug resistance in cancer by combining computational modelling withsingle cell genomics
Transcriptional plasticity is a phenomenon where cells reversibly change molecular states by altering their gene
expression. Recent evidence suggests transcriptional plasticity is a key driver of the emergence of drug resistance.
We designed a computational model that simulates Acute Myeloid Leukemia (AML) as a stochastic process at a discrete
time resolution. It is based on the rates of DNA mutation, cell division, cell death and switching rates between sensitiveand resistant states. We model tumors with initial growth rates between 0.001% and 0.1%; with driver mutation rates

between 3.4*10^(-8) and 3.4*10^(-5); testing switching rates between 4.0*10^(-7) and 9.0*10^(-6) from sensitive toresistant, and 0.5 to 50000 times those values for the resistant to sensitive switching rate.Results so far suggest that low
rates of transcriptional plasticity have little effect on the growth and development of the cancer pre-treatment. However, as
plasticity increases, we can see the tumor develop differently. Significant correlation was observed between tumor fitness,representing the weighted average net growth rate of all the different subpopulations of cells within in the tumor, and thenumber of resistant cells, which will be explored further. We are following up to understand the interactions between eachindependent simulation parameter, transcriptional plasticity, and clinical outcomes.
To
understandhowtranscriptional plasticity drives drug resistance in cancer, different drug treatment regimens for AML will be simulated andmodel predictions validated in mouse models of AML using SPLINTR, a synthetic expressed barcoding strategy thatallows for cell lineage tracking.
Abstract #83

Tyrone Chen (School of Biological Sciences, Monash University, Clayton, VIC, 3800, Australia)
Identifying the complete functional and regulatory signatures driving antimicrobial resistance in sepsis.
Sepsis, a bloodstream infection, has a high mortality rate and is a common comorbidity in cancer. Rising antimicrobial
resistance (AMR) globally amplifies this medical condition. Recently, the rapid growth in genotyping and multi-omics
datasets [1] has provided an opportunity to investigate AMR at a higher resolution. We therefore investigated available
heterogeneous datasets to study organism-specific resistance mechanisms of AMR. A major obstacle to a unified analysis
of such diverse datasets are their distinct formats. To overcome this, we are refining a universal, annotation-lessframework based on our previous work which showed that it is viable to use genomic data as raw fastq sequences [2],k-mers or abundance matrices [3,4] to recover signals of interest such as unannotated biomarkers [1]. We performed an
integrative analysis on matched transcriptome, proteome and metabolome profiles of bacterial strains known to cause
sepsis. Next, we re-coded the genome using natural language processing techniques, allowing us to overlay regulatory
information on the functional omics layers. Using this technique, we recovered informative markers of pathogenicity.
Scaling this method will allow us to identify complete functional and regulatory signatures driving AMR in sepsis.
Furthermore, our approach is not restricted to a single biological system, and can be scaled out to study a different
disease-pathogen interaction. [References:] [1] Chen T and Tyagi S. GigaScience 9.6 (2020). [2] Chen T, et al. [version
1; not peer reviewed] F1000Research (2019) (poster). [3] Chen T et al. Briefings in Bioinformatics (2021). [4] Chen T etal. [version 1; awaiting peer review] F1000Research (2021).

Abstract #95
Sabrina Yan (Computational Biology, Children's Cancer Institute, Sydney, Australia)
Helium: Automatic variant prioritization for surfacing cancer drivers
Zero Childhood Cancer is a precision medicine program at the Children’s Cancer Institute that aims to improve clinicaloutcomes for cancer patients through identifying optimal, personalised treatments based on their tumours unique genetic
characteristics. This is done using bioinformatic tools that identify millions of genetic changes in the tumour. The major
challenge is in identifying the medically-relevant mutations that could lead to better drugs being recommended fortreatment. To address this challenge, the Computational Biology group has developed Helium, a cancer variant
prioritisation tool. Helium `floats` the most relevant variants to the top for cancer experts to manually curate and tag those
responsible for the patient’s disease. Helium scores variants based on combinations of expertly tagged annotations. Boththe combinations of annotations used and their relative weighted contribution to the score are completely customisableusing a single configuration file. This flexibility allows Helium to be a versatile tool with many potential use cases. It is
written as an npm cli tool and package, allowing anyone with command line experience to run this with any csv, tsv or json
input file. The tool has also been dockerized and wrapped in CWL for integrating into larger workflows. Helium can score
500 variants per second and is fast enough to incorporate as a live scoring tool directly into our variant curation web
portal, ZeroDash, or be used to pre-score variants in our database for easy retrieval. We hope that the configurability andflexibility of Helium makes it a useful variant ranking tool for many in the precision medicine world.

Abstract #98
Andreas Halman (Peter MacCallum Cancer Centre, Melbourne, VIC, Australia)
STRipy: a graphical application for detecting short tandem repeat expansions in all known pathogenic loci
There are a handful of tools available for genotyping short tandem repeats (STRs) from high-throughput sequencing data.
However, there are several limitations for application in a clinical setting. Firstly, many of these tools are command-line
based. Secondly, without configuration, they can genotype only half of the known disease-causing loci and finally
genotyping lengths are often limited to either read or fragment length of the data which is less than pathogenic cut-off forsome diseases. Moreover, repeat lengths in a general population are not well defined for many STR loci which
complicates the interpretation of results. To address these issues, we have created a new software called STRipy that has
an intuitive graphical interface which significantly simplifies STRs genotyping from human short read sequencing data,
making it accessible for everyone. STRipy is configured to target all known disease-causing STRs loci, including therarest and most recently discovered. We have incorporated an established tool, ExpansionHunter, as a part of thesoftware to perform accurate genotyping and created additional functionality to enable detection of long alleles.
Additionally, we have analysed over two and half thousand samples from various populations and acquired genotypes foreach pathogenic STR locus which will provide further insights, particularly for newly discovered loci, into the normal rangeof STR lengths. This, along with information sourced from the literature has been also used to curate a comprehensive
database of pathogenic STRs which is made freely accessible online at stripy.org.

Abstract #102
Daniel Cameron (Walter and Eliza Hall Institute of Medical Research)
Single breakends: a new paradigm for structural variant calling
The reliable detection of structural variants is critical to understanding the role genome architecture plays in health anddisease. A fundamental obstacle to high quality structural variant calling is the inability to call breakpoints involving low
mappability or non-reference sequence. Here, I show single breakend variant calling improves the accessibility of suchregions.Using the Hartwig Medical Foundation cohort of 3,782 deeply sequenced WGS tumour/normal samples, I
demonstrate the power of single breakend variant calling has in the detection and resolution of somatic genomic
rearrangements. I show that GRIDSS2, the only SV caller to report single breakends, achieves a somatic FNR lower thanpossible for any purely breakpoint-based caller. I demonstrate the ability to detect viral integration into centromeric andtelomeric sequences with VIRUSBreakend, a single-breakend based viral integration detection tool. I use LINX, a
single-breakend-aware genomic rearrangement classification and interpretation tool, to resolve complex
centromere-overlapping events. Finally, I posit a novel somatic centromeric rearrangement signature. Single breakends
fundamental change the nature of breakpoint calling and provide insights into the “dark” regions of the genome currently
considered inaccessible to short read sequencing.

Abstract #103
Emily Swan (Genome Informatics Laboratory, Department of Immunology, The John Curtin School of Medical Research,
The Australian National University, Canberra, Australia)
Most disease phenotypes are actually very subtle: Deep learning with a massive, mouse, mutant cellular dataset to detect
sub-clinical phenotypic variation
The effect size range of single mutations has been predominantly studied in very simple model systems that include
viruses and single-cell organisms. However, with the modern imperative to interpret personal genome sequences forclinical application, this doesn’t provide the kind of information needed to stratify variation to predict complex andwhole-organism phenotypes. Here, we present the analysis of a historical dataset of fluorescence-activated cell sorting(FACS) experiments collected from 40,000+ individual, inbred mice that harbour 20-60 randomly-induced missense
mutations per individual. With this dataset, we can observed the phenotypic variation caused by a population of missense
mutations, assayed at the cellular level with a consistent and sensitive analytical procedure. We have trained bothclassical machine learning (random forest) models and convolutional neural network models to predict whether theimmune cell population present in a given, single mouse belies the presence of a phenotypic mutant. We identify thatdifferent mutant genotypes give rise to a continuous scale of disruption of mouse immune cell subsets. We use our deeplearning model to sensitively identify subtle mutant phenotypes that are consistently overlooked by non-automated,
human observations of multi-dimensional FACS data. Most mutant genotypes produce little to no phenotypic effect, as
expected, yet the proportion of slightly phenotypic and sub-clinical variants is substantially larger than previously
appreciated. The magnitude of sub-clinical genetic variation we have identified has implications for our understanding of
the combinatorial nature of phenotypic alleles that contribute to complex disease in humans.

Abstract #105
Ariane Mora (School of Chemistry and Molecular Biosciences University of Queensland, St Lucia QLD 4072, Australia)
Integrated gene landscapes uncover multi-layered roles of repressive histone marks during mouse CNS development
A prominent aspect of most, if not all, central nervous systems (CNSs) is that anterior regions (brain) are larger thanposterior ones (spinal cord). Studies in Drosophila and mouse have revealed that the Polycomb Repressor Complex 2(PRC2), a protein complex responsible for applying key repressive histone modifications, acts by several mechanisms topromote anterior CNS expansion. However, it is unclear what the full spectrum of PRC2 action is during embryonic CNS
development and how PRC2 integrates with the epigenetic landscape. We removed PRC2 function from the developing
mouse CNS, by mutating the key gene Eed, and generated spatio-temporal transcriptomic data. To decode the role of
PRC2, we developed a method that incorporates standard statistical analyses with probabilistic deep learning to integrate
the transcriptomic response to PRC2 inactivation with epigenetic information from ENCODE. This multi-variate analysis
corroborates the central involvement of PRC2 in anterior CNS expansion, and reveals layered regulation via PRC2. Thesefindings uncover a differential logic for the role of PRC2 upon functionally distinct gene categories that drive CNS anterior
expansion. To support the analysis of emerging multi-modal datasets, we provide a novel bioinformatics package that
integrates transcriptomic and epigenetic datasets to identify regulatory underpinnings of heterogeneous biologicalprocesses.

Abstract #108
Bennet McComish (Menzies Institute for Medical Research, University of Tasmania)
Using signatures of natural selection to focus the search for causal genetic variants in multiple sclerosis.
Multiple sclerosis prevalence shows a heterogeneous geographical pattern, with higher prevalence in populations ofEuropean ancestry, as well as increasing with distance from the equator within those populations. This pattern has likely
been shaped by both natural selection and neutral genetic drift. Identifying genes that have undergone selection at MS
risk loci will improve our understanding of the causative mechanisms behind the disease. Population genomics can beused to identify functional variation that has been subject to natural selection at loci associated with MS risk. We carriedout genome-wide scans for natural selection using cross-population extended haplotype homozygosity in population
genomic data. MS-related selection was localised by targeting genes prioritised in the large genome-wide association
study carried out by the International Multiple Sclerosis Genetics Consortium. Strong signatures of natural selection in
European and Asian populations were identified in several MS risk genes. Further analysis of these genes is underway toidentify likely causes of selection and mechanisms by which they may contribute to MS risk. This approach allows us tonarrow down candidate genes and pinpoint a small number of top causal candidates and mechanisms. This may enable
more informed targeting of the molecular mechanisms behind the disease.

Abstract #110
Letitia Sng (Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Australia)
Novel Coronary Artery Disease Variants and Epistatic Interactions Identified with Machine Learning Pipeline using
VariantSpark and BitEpi

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Although behavioural risk factors are important,
there is a strong genetic component in CVD aetiology too. Genome-wide association studies have identified hundreds ofloci associated with CVD risk, but account for less than 50% of CVD heritability. Epistasis, the combinatorial effect ofmultiple genetic variants, may explain part of this missing heritability. Yet, the nature of epistasis analysis poses multiple
challenges for parametric statistical methods, such as computational demand and the high multiple testing burden.
VariantSpark is a cloud-based machine-learning platform that can identify complex interactions between millions of SNPs
from thousands of samples efficiently. We have applied VariantSpark to the UK Biobank dataset and have identified 47
significant SNPs associated with coronary artery disease (CAD). These SNPs map to known CAD genes including LPA,CDKN2B, and CELSR2 as well as novel genes including ARAP3 and FBN2. For comparison, we ran a logistic regression
model on the same dataset, resulting in significant SNPs that have all been associated to CAD. We then used our novel
epistasis platform, BitEpi, to search for interactions between these significant SNPs. We found that all the novel SNPs
identified by VariantSpark were involved in higher order epistatic interactions with known CAD SNPs. Further explorationof these interactions found that interactions were occurring between key CAD pathways. The incorporation of these
epistatic interactions into risk prediction models can improve the predictive ability of existing genetic risk scores which are
based on variants with additive effects only.

Abstract #120
Mikhail Gudkov (Victor Chang Cardiac Research Institute, Australia and St Vincent's Clinical School, UNSW Sydney,
Australia)
Quantifying negative selection on synonymous variants
Most disease sequencing studies tend to focus primarily on missense and potential loss-of-function variants at theexpense of other classes of mutations. In particular, synonymous genetic variants, that is, those single-nucleotide variants(SNVs) that do not alter the produced amino acid sequence, are routinely considered to be non deleterious. However, therole of these mutations is potentially more important than was previously thought. For instance, synonymous SNVs(sSNVs) may create nonoptimal codons, thus affecting the stability of the produced mRNA and the overall translational
efficiency. It has also been shown that optimality-reducing sSNVs undergo purifying selection, the extent of which,nonetheless, remains unknown. Here we quantify the intensity of the negative selection acting on all possible sSNVs
using the Mutability-Adjusted Proportion of Singletons (MAPS) metric. We found that optimality-reducing sSNVs are
subject to stronger selection than optimality-increasing ones, which was confirmed using conservation analysis.
Furthermore, we found that purifying selection affects sSNVs in an amino acid-dependent manner, with glutamine being
particularly intolerant to such mutations. Apart from the effect on mRNA stability, other key mediators of this selection
include the reduction in tRNA availability of the mutated codon and presence of amino acid homorepeats. Incidentally, we
also propose an improved version of MAPS for sSNVs. We believe that the results of this work will help to further
elucidate the role of synonymous mutations in health and disease and, in particular, will aid in variant prioritisation forfinding the true causes of genetic disorders.

Abstract #122
Hieu T. Nim (Australian Regenerative Medicine Institute and Systems Biology Institute Australia, Monash University,
Clayton, VIC, Australia)
Finding needles in a haystack: identifying cardiac disease-causing genes from cis-regulatory elements
BACKGROUND
Congenital heart diseases (CHD) are the major cause of death in newborns, but the genetic aetiology of thisdevelopmental disorder is not fully known. The conventional approach to identify the disease-causing genes focuses onscreening genes that display heart-specific expression during development. However, this approach would havediscounted genes that are expressed widely in other tissues but may play critical roles in heart development.
RESULTSWe report an efficient pipeline of genome-wide gene discovery based on the identification of a cardiac-specificcis-regulatory element signature that points to candidate genes involved in heart development. With this pipeline, we
retrieved 76% of the known cardiac developmental genes, and predicted 35 novel genes that previously have noconnectivity to heart development. Functional validation of these novel cardiac genes by RNAi-mediated knockdown of
the conserved orthologs in Drosophila cardiac tissue revealed that disrupting the activity of 71% of these genes led toadult mortality, and among these genes, RpL14, RpS24 and Rpn8 were associated with heart phenotypes.
CONCLUSIONS
Our pipeline has enabled the discovery of novel genes with roles in heart development. This workflow, which relies onscreening for non-coding cis-regulatory signatures, is amenable for identifying developmental genes for an organ without
constraining to genes that are expressed exclusively in the organ of interest.

Abstract #123
P Acera-Mateos (The John Curtin School of Medical Research, Australian National University, Canberra, Australia)
RNA methylation detection at single-molecule resolution uncovers isoform-specific modifications in neurodevelopmental
disorders
The expanding field of epitranscriptomics has been setting to rival the epigenome in the diversity of biological process
involvement, prominently linking biochemical modifications of the RNA to development and disease onset. However,current methods based on short-read high-throughput sequencing to detect these modifications are often complicated andpresent significant accuracy limitations. Here we describe CHEUI, a new computational approach to identify
N6-methyladenosine (m6A) and 5-methylcytidine (m5C) using signals from Nanopore direct RNA sequencing reads at
single-nucleotide and single-molecule resolution. CHEUI uses a two-stage neural network to accurately predict
methylation in individual reads and transcriptomic sites in a single condition, as well as differential methylation between
any two conditions. Using extensive benchmarking with Nanopore data derived from in vitro modified and non-modified
transcripts as well as cells with or without methylation enzymes, CHEUI showed higher accuracy than other existing
methods in the prediction of m6A and m5C sites and their stoichiometry levels, while maintaining a lower number of false
positives. We applied CHEUI to Nanopore RNA data derived from mouse embryonic pre-frontal cortex and uncovered
isoform-specific modifications that are established and change stoichiometry during neuronal development. In a mousemodel of autism spectrum disorder (ASD), we identified specific alterations in modifications that are associated withneuronal development, highlighting the biological potential of the isoform specificity of RNA modifications inneurodevelopmental disorders. CHEUI’s ability to detect RNA modifications with high accuracy and resolution can be
expanded to other modifications to unveil the full span of the epitranscriptome in normal and disease conditions.

Abstract #127
Natalie Charitakis (Murdoch's Children's Research Institute, Parkville, VIC & Department of Paediatrics, University of
Melbourne, Parkville, VIC)
Benchmarking methods for the identification of spatially variable genes in spatial transcriptomics datasets.
Spatially transcriptomics (ST) is a novel, disruptive technology set to push the boundaries in exploring gene regulatory
networks while providing both spatial and temporal resolution. It is expected to be exponentially adopted by thetranscriptomics community after being named Nature’s Method of the Year in 2020. Despite this, analysis packages for ST
datasets are still in their infancy, with a clear forerunner yet to emerge. A comprehensive review of the performance ofcommonly used packages on the same datasets is lacking and there is a need to determine their performance in correctly
labelling spatially variable genes (SVGs) in a tissue section. Various studies have established the ability of ST to uncovernovel genetic determinants in different biological tissues and conditions; therefore, ensuring the most accurate analysis isperformed is critical. To establish which of the current packages is most effective in identifying spatially variable genes(SVGs) within data generated using the same experimental protocol, I am creating a benchmarking process by testing a
combination of publicly available, simulated, and novel 10X Visium datasets generated from cardiac tissues. Preliminary
results demonstrate less than half of SVGs identified by different packages overlap. Upon incorporation of the mostappropriately matched single cell RNA-Seq data for accurate cell-type deconvolution, identification of the cell types
expression SVGs will be possible allowing for future experimental validation. This will offer a clear workflow to be
implemented in future ST experiments and better understanding of mechanisms underlying disease and tissue
development.

Abstract #128
Dillon Hammill (Division of Genome Sciences and Cancer, John Curtin School of Medical Research, Australian National
University, Canberra, ACT)
CytoExploreR: Next-Generation Open-Source Software for Cytometry Data Analysis
Cytometry continues to be the preferred technique employed in clinical and research settings to classify, characterise, andquantify heterogeneous suspensions of cells. Recent technological advancements in cytometry have resulted in an
unprecedented increase in the size and dimensionality of cytometry data sets. Unfortunately, this rapid increase data
complexity has not been adequately met with innovations in commercially available software platforms to efficiently
analyse these immense data sets. Consequently, many cytometry users have resorted to developing their own
computational tools, in the form of R packages, to provide the additional tools required to analyse their data. Despite thequality of these packages, adoption of these tools within the broader cytometry community is limited, due to a lack ofprerequisite coding knowledge, interactivity, and coherence between packages required for end-to-end analysis.
Accordingly, there is exists an urgent need to develop a robust unified framework for cytometry data analysis, that is
intuitive, interactive, efficient, extensible, and freely accessible. At ABACBS 2021, I am excited to announce the official
release of CytoExploreR, the next generation of open-source software for cytometry data analysis. During this condensed
presentation, we will endeavour to explore the plethora of computational tools implemented within the new CytoExploreR
framework for end-to-end cytometry data analysis.

Abstract #130
Feng Yan (Australian Centre for Blood Diseases, Central Clinical School, Monash University, Melbourne, VIC, Australia)
DNA Methylation in pre-leukemic stem cells in a T-cell acute lymphoblastic leukemia mouse model
The role of DNA methylation in the initiation and evolution of cancer remains poorly understood due to lack of studies ofthe purified early pre-malignant state. We have analysed three stages of leukemogenesis using a Lmo2 transgenic mousemodel of T-cell acute lymphoblastic leukemia (T-ALL). Purified pre-leukemic stem cells (pre-LSCs), LSCs, T-ALL andwild-type controls were profiled with RNA-seq and enhanced reduced representation bisulfite sequencing for DNA
methylation. Hierarchical clustering of DNA methylation showed the greatest change between pre-LSCs and LSCs.
Hypermethylation predominated in pre-LSCs and LSCs, with hypomethylation predominantly in T-ALL. In pre-LSCs,
differentially methylated regions (DMRs) occurred mostly in CpG open seas marked by H3K27Ac/H3K4me2. In contrast,
DMRs in LSCs were predominantly in CpG islands marked by H3K27me3/H3K4me3, similar to those reported in cancers
and ageing. Finally, in T-ALL new hypomethylated regions emerged in CpG open seas. To focus on the clonal evolution in
T-ALL, we calculated an epi-polymorphism score indicating sample heterogeneity. The epi-polymorphism increased in
pre-LSCs and LSCs but decreased in T-ALL indicating a positive selection after disease onset. Differentially heterogenous
epialleles (DHEs) did not overlap with DMRs in pre-LSCs, but half of DHEs in pre-LSCs overlapped with DMRs in LSCs
suggesting methylation pre-seeding in pre-LSCs. Interestingly, DHEs showed stronger correlation with gene expression
compared to DMRs. In conclusion, we have used mouse model of T-ALL to describe the DNA methylation at clonal level
and associated gene expression during leukemogenesis. This will provide new insights into the mechanism and role of
DNA methylation in cancer development.

Abstract #133
Yuzhou Feng (Peter MacCallum Cancer Centre)
SPIAT: Novel Computational and Statistical Tools for the Simulation and Analysis of Spatial Data
Platforms for spatial profiling are becoming more popular and are soon to become commonplace. Unfortunately, there is asignificant lag in the methods for spatial analysis, making it challenging to get the most out of the data. In the cancer
space, this limits our ability to understand the tumour microenvironment and develop quantitative biomarkers based on
spatial patterns. To address this, we have developed SPIAT, an R package with a suite of spatial analysis algorithms
based on spatial statistics, GIS and ecological principles, integrated with a novel simulator of spatial data. SPIAT
canquantify diversity, spatial distribution, spatial heterogeneity and cell-cell interactions of different cell types, allowing a deep,
orthogonal profiling of cell colocalization, attraction and repulsion. For the analysis of tumour samples, SPIAT allows theautomatic detection of tumour borders and the quantification of immune populations relative to structures, allowing us toquickly calculate spatial properties of the tumour microenvironment, independent of user input or manually set thresholds.
Our analyses in prostate cancer and melanoma have found a strong association between the novel spatial metrics in
SPIAT and key clinical features, including prognosis and disease progression. SPIAT is currently compatible withsingle-cell spatial data generated from platforms such as OPAL, CODEX and MIBI. SPIAT provides a rich resource toprofile spatial profiles, and derive novel patterns associated with clinical outcomes. This work will also allow the discoveryof new biomarkers and provide novel insights into therapeutic opportunities.

Abstract #139
Alice R. Whitehead (A.R.W.: Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria
3052, Australia)
Characterising expression of RXFP1 splice variants across human cell types and tissues
Introduction: Relaxin Family Peptide Receptor 1 (RXFP1) is a hotly pursued therapeutic target for various cardiovascularand fibrotic conditions, due to its vasodilatory and anti-fibrotic effects when activated by the peptide hormone H2 relaxin.
Therapeutic targeting of this G-protein coupled receptor has been hampered by a lack of knowledge of its complex
mechanisms of action, including the roles of its alternatively spliced isoforms. In the present study we assess theexpression profile of RXFP1 transcript isoforms across tissues and cell types using Oxford Nanopore sequencing.
Methods: Oxford Nanopore sequencing was conducted on cDNA amplicons from a range of human cell types and tissues
prepared using RXFP1-targeting oligonucleotides. Sequencing reads were aligned to the human genome (Hg38) using
minimap2, and a high confidence set of transcript isoforms was determined using FLAIR. Results: The expression andrelative abundance of RXFP1 splice variants varied across tissues and cell types. All samples were found to express
multiple transcript isoforms of RXFP1. The canonical RXFP1 isoform was the highest expressed isoform in most, but notall, cell types and tissues. Of the top 10 most highly expressed variants across all samples, 7 were novel transcripts.
Discussion: Characterising expression of RXFP1 splice variants marks a critical first step in leveraging the distinct RXFP1
isoforms in therapeutic targeting efforts. The novel variants will require functional characterisation to reveal thecontribution of splice variants to RXFP1 physiology in health and disease.

Abstract #144
Nicolas P. Canete (The Westmead Institute for Medical Research, University of Sydney, Westmead, NSW, Australia;
Sydney Medical School, University of Sydney, Sydney, NSW, Australia)
spicyR: Spatial analysis of in situ cytometry data in R
Recently, there have been advances in high parameter histological techniques such as imaging mass cytometry andmultiplexed ion beam imaging by time of flight, and spatial-based transcriptomic techniques such as High-Definition
Spatial Transcriptomics and sequential fluorescence in situ hybridisation. These imaging methodologies have allowed forthe identification of a variety of distinct cell types within an image, providing a comprehensive overview of the tissue
environment. This allows the complex cellular architecture and environment of diseased tissue to be explored.
While
spatial analysis techniques have revealed how cell-cell interactions are important within the disease pathology, there
remains a gap in exploring changes in these interactions within the disease process. Specifically, there are currently few
established methods for per

Abstract #146
Yingxin Lin (The University of Sydney)
Large scale single-cell multi-sample multi-condition data integration using scMerge2

Technological advances such as large-scale single-cell profiling such as single-cell RNA-seq (scRNA-seq), Cytometry by
Time-Of-Flight (CyTOF) and imaging mass cytometry have exploded in recent years and enabled unprecedented insight
into the identity and function of individual cells. The recent emergence of multi-condition and multi-sample single-cell large
cohort studies further allows researchers to investigate cells from the same subpopulation in multiple cell states. Effective
integration of multiple collections of multi-condition large cohort studies promises biological insights of cells under different
conditions that can not be uncovered with individual study. Here, we present scMerge2, a scalable algorithm that allows
data integration of large-scale multi-sample multi-condition single-cell studies. We have generalised scMerge2 to enable
the merging of millions of cells from single-cell studies generated by various single-cell technologies, including
scRNA-seq, CyTOF, and imaging mass cytometry. Leveraging pseudo-bulk to perform factor analysis of stably expressed
genes and pseudoreplicates, scMerge2 is able to integrate 200,000 cells and 10,000 genes within an hour using a single
core. Using a large COVID-19 data collection with 2,740,510 cells from 779 samples of 6 studies, we demonstrate that
scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals distinct cellstates of COVID-19 patients with varying degrees of severity. We further illustrate that the data integration of different
cohorts enables characterisation of cell-cell interaction for COVID-19 patients, which can be used as potential signatures
for discriminating between moderate and severe patients.

Abstract #148
David Humphreys (Victor Chang Cardiac Research Institute)Overlapping transcripts within gene models can influence bioinformatic analysis.
Many bioinformatic pipelines are dependent on existing gene models for quantification and/or annotation. Transcript
information of gene models are efficiently compiled within GTF formatted files which enables the reconstruction andinterpretation of complex gene structures. In compiling expression patterns of “high confidence heart developmental”
genes we came across one gene, GDF1, that had unexpected low signal in many compiled RNA-Seq databases. A closer
examination of GDF1 revealed that this gene belongs to a rare class of bicistronic transcripts that are difficult to describe
in GTF format as GDF1 exons are shared with the other cistron annotated as CERS1. As GDF1 and CERS1 have near
identical exon structure they are interpreted as being overlapping by many bioinformatic pipelines and are therefore often
ignored. We therefore re-analysed single cell datasets and have been able to correctly capture the expression patterns of
GDF1, which involves a switch from single cistron to bicistronic transcript isoform. For the first time we also identify the
key cell types responsible for this expression pattern. Furthermore we compiled a list of other overlapping transcripts thatexist within gene models and explore the implications for various bioinformatic pipelines. From this we identified that 10x
single cell RNA-Seq bioinformatic pipelines are susceptible for not quantifying reads that align to overlapping transcripts
regions and highlight how this could have important implications for downstream analysis.

Abstract #151
Aaron Chuah (Genome Informatics Laboratory, Department of Immunology and Infectious Disease, The John Curtin
School of Medical Research, The Australian National University, Canberra, Australian Capital Territory)
Genome-wide estimation of the change in protein stability due to missense mutation
It is widely accepted that decreased protein stability due to missense genetic variation is strongly associated with inherited
genetic disease. However, the interpretative power of instability effects of mutation on proteins has been limited by theneed for structural data covering the observed variation. Consequently, analysis of the influence of genetic variation onprotein stability is not routinely performed as part of clinical genomic workflows to detect pathogenic variation. The
availability of AlphaFold predicted structures for whole genomes of proteins, including that of humans, now allows
genome-wide assessment of the stability effects of genetic variation. Importantly, this now allows routine assessment of
personal genetic variation for missense variants that potentially have clinical interpretive value. Here, we present both a
web-tool and a standalone software package that allows prioritisation of genetic variation to identify just those variants
that lead to strong destabilisation effects in essential proteins. Our methodology innately down-weights variation is
non-essential and redundant proteins, as these genes inherently harbour more variation deleterious to protein function.
Our predictive metrics are trained on the presumed non-disease-associated variation present in the GnomAD dataset.
For each missense variant from an incident clinical genome, a test statistic is calculated that assesses the magnitude ofthe destabilisation effect with respect to all other non-disease associated variation identified in this protein in the GnomAD
dataset. Missense variants in a personal genome are prioritised such that variation that produces unusually large stability
effects in the encoded protein, given the population variation observed in that protein, are easily identified. The results ofthis methodology are assessed and contrasted between the HapMap and two clinical cohorts.

Abstract #154
Richard J Edwards (Evolution & Ecology Research Centre, School of Biotechnology and Biomolecular Sciences, UNSW
Sydney, Sydney, New South Wales, Australia)
DepthSizer and DepthKopy: genome size and copy number prediction using single-copy long-read depth profiles
A fundamental part of any genome project is establishing the genome size of the organism being sequenced. The gold
standard for genome size measurement is flow cytometry, but this is not available to all groups and can give surprisingly
variable results. Popular bioinformatic approaches predict genome size using kmer frequency profiles from high-accuracy(e.g. illumina or hifi) sequencing reads, or the mean depth of coverage reads mapped to an assembly. Both of theseapproaches can be adversely affected by repetitive regions of the genome. Mean sequencing depth is also highly reliant
on assembly completeness. Here, we present DepthSizer (https://github.com/slimsuite/depthsizer), which refines thisapproach by estimating sequencing depth based on single-copy complete BUSCO genes. DepthSizer works on theprinciple that genuine single-copy regions will tend towards the same, true, single-copy read depth. In contrast, assembly
errors, collapsed repeats within those genes, or incorrect BUSCO predictions, will give inconsistent read depth deviations.
The modal read depth across single-copy BUSCO genes, calculated from a depth density profile of these regions, should
therefore provide a good estimate of the true depth of coverage. The method is benchmarked on model organism dataand corrections for possible contamination, biases/inconsistencies in read mapping and/or raw read insertion/deletion
error profiles are discussed. We also present DepthKopy (https://github.com/slimsuite/depthkopy), which uses the same
read depth approach to estimate the copy number of assembly regions. This can be useful for identifying haplotigs, andcollapsed repeat regions.

Phylomania Abstracts(In presentation order)
Abstract # 49
Barbara Holland (University of Tasmania)
Some possibly bad ideas for detecting convergent selection

Given a phylogeny, a character matrix and a binary trait of interest that has been gained/lost multiple times
across the phylogeny, how might we go about detecting characters that are associated with the trait of interest?
In this talk I will survey three different ideas for approaching this problem.
Idea 1: Detect characters (or suites of characters) that are more compatible with each other (and the trait ofinterest) than expected conditional on their level of homoplasy on the phylogeny. (A recycling of an old ideafrom Holland, Spencer, Worthy, & Kennedy (2010).)
Idea 2: Seed a phylogenetic mixture model with components whose edge weights vary systematicallydepending on the ancestral character estimate of the binary trait. (With Rob Lanfear and Stephen Crotty.)
Idea 3: Model convergent selection explicitly using convergence-divergence networks. (Joint with Jonathan
Mitchell)

Abstract # 45
Zhaoxiang Cai, Rebecca C Poulos, Qing Zhong (The University of Melbourne)
Integrating multi-omics data with biological knowledge by Transformer-based deep learning
Omics data analysis, powered by machine learning, has significantly improved cancer diagnosis andprognosis. However, most machine learning methods consider each gene as an independent feature, failing tointegrate experimentally-acquired gene regulation and pathway information. The benefit of utilising thisinformation increases in the era of multi-omics, because gene regulation is the key mechanism that links
different omic layers together. Here, we present an interpretable deep learning model, DeepPathNet, whichuses cancer-specific pathway information for both single and multi-omics data analysis. DeePathNet leverages
the cutting-edge deep learning technique, Transformer, which is derived from the field of natural language
processing, to model complex interactions between pathways from omics data. The computation ofself-attention in the Transformer module allows DeePathNet to learn the encoding of pathways to achieve
superior predictive performance and interpretability. Techniques such as drop out layers are also integrated into
DeePathNet to maximise its generalisability for unseen data. Moreover, DeePathNet supports any number ofomics layers and can handle missing values. Using multiple evaluation metrics, we demonstrate that
DeePathNet robustly outperforms traditional methods for predicting drug response and cancer type on four
publicly available datasets, namely COSMIC Cell Lines, Genomics of Drug Sensitivity in Cancer (GDSC),
Cancer Cell Line Encyclopedia (CCLE) and Cancer Therapeutics Response Portal (CTRP). DeePathNet also
provides reliable model interpretation, potentially enabling biomarker discoveries at the pathway level. Using
the Transformer, DeePathNet is the first method that supports multi-omics data analysis, integrates cancer
pathway knowledge into modelling, and provides pathway-level model explanation.
Abstract # 13
Paola Cornejo-Páramo, Emily S Wong, Katherine Roper, Sandie M Degnan, Bernard M Degnan(Victor Chang Cardiac Research Institute, School of Biological Sciences, University of Queensland)
Transcription Factor-binding motif composition of cis-regulatory regions in a sea sponge species
suggest the existence of some constraints in metazoan regulatory grammar
Chromatin accessibility plays a major role in regulating animal gene expression and therefore it is key todecipher animal embryogenesis. Here we profile chromatin accessibility of six embryonic stages, along with alarval and adult stage in the sea sponge Amphimedon queenslandica, a member of the early branching phylum
Porifera, in order to study the regulatory landscape of metazoan embryogenesis. On the grounds that mosttranscription factor (TF) families are conserved in eukaryotes, we examine the TF binding motif composition ofAmphimedon cis-regulatory regions. We leveraged the high performance of tree-based machine learningmodels and tree ensemble interpretation algorithms to extract complex patterns and identify some TF binding
motifs that are commonly enriched in Amphimedon cis-regulatory regions. We further tested the machinelearning models trained on Amphimedon on other species data and were able to predict cis-regulatory regionsin other animal species but not in the close relative of animals, Capsaspora owczarzaki. Overall, we find thatanimal cis-regulatory regions share some resemblance and constraints in motif composition besides theextreme divergent life strategies adopted by different phyla.
Nicholas Fountain-Jones et al. (University of Tasmania)
Hunting alters viral transmission and evolution in a large carnivore
Hunting can fundamentally alter wildlife population dynamics, but the consequences of hunting on pathogentransmission and evolution remain poorly understood. Here we present a study that leverages a uniquelandscape-scale quasiexperiment coupled with pathogen transmission tracing, network simulation, andphylodynamics to provide insights into how hunting shapes feline immunodeficiency virus (FIV) dynamics inpuma (Puma concolor). We show that removing hunting pressure enhances the role of males in transmission,increases the viral population growth rate, and increases the role of evolutionary forces on the pathogen
compared to when hunting was reinstated. Changes in transmission observed with the removal of hunting
could be linked to short term social changes while the male puma population increased. These findings aresupported through comparison with a region with stable hunting management over the same time period. This
study shows that routine wildlife management can have impacts on pathogen transmission and evolution notpreviously considered.

Abstract # 43
Nehleh-Fatemeh Kargarfard, Aaron Darling, Mathieu Fourment (ithree Institute, University of Technology
Sydney)PhiloBacteria: A new tool to infer phylogenetic trees from recombinant bacterial genomes
One of the vital services to maintain public health is detecting and tracking outbreaks of infectious diseases.
Phylogenetic tools are one of the most efficient tools to analyze the source, evolutionary history of epidemic
outbreaks. According to the rapidly growing number of genomes, it's essential to develop new methods toapply to increasingly large datasets. On the other hand, as asexual organisms, Bacteria have a clonal
reproduction; however, their genomes can evolve by different mechanisms such as point mutation andrecombination. When recombination occurs in bacteria genomes, several nucleotides can change together,
contributing to considerable evolutionary leaps, for example, helping pathogens develop resistancemechanisms against antibiotics. Practically, it's not only swapping genetic information among organisms, butalso it's exchanging their evolutionary histories. Hence, the evolutionary analysis would be different andchallenging in the presence of recombination, and ignoring the effect of recombination can result in misleadingin phylogenetic. Furthermore, detecting the boundary of recombination events and reconstructing a global tree
to illustrate the underlying evolutionary pattern of biological sequences has never been a straightforward
problem in terms of accuracy and scalability. We introduce PhiloBacteria, a new tool that uses a hidden Markov
model to detect recombination and infer the phylogeny of related organisms simultaneously. Specifically, thealgorithm‚Äôs goal is to reconstruct the true clonal evolutionary history of the organisms while avoiding
misleading evolutionary signals originating from recombination events. The method aims at quickly andaccurately analyzing large datasets of closely related bacterial genomes. Using simulated data, we investigate
the accuracy of our algorithm and compare our results to other established recombination detection methods.
Abstract # 54
Folagbade Abitogun (University College Hospital Ibadan, Nigeria)
Identification and Analysis of Novel Putative Drug Targets in Hypervirulent Klebsiella pneumoniae
Proteomes

The last 5 years have seen increasing cases of infection with hypervirulent Klebsiella pneumoniae, a strain
defined by the presence of many biomarkers, with increasing resistance to extended-spectrum beta-lactams
and carbapenems. The rapid evolution of this pathogen has led to the continued failure of antibiotics to combat
it, calling for alternative therapies that will combat the pathogen and reduce antibiotic resistance which thisstudy aimed to achieve. A series of In silico analyses were employed to identify putative drug targets from theproteome of two strains of Klebsiella pneumoniae- HvKp TK421 and HvKp1. A combined total of 1528
Hypothetical proteins were extracted using a Perl script from the proteomes of both stains. Of these, 159
essential pathogen-specific proteins that are also not homologous to any known human protein were further
extracted. Sixty eight (68) of the essential proteins were cytoplasmic and therefore chosen as potential drug
targets, with the majority having catalytic activities. Sixty (60) of the potential drug targets showed nointeractions with any known drug and were considered ‘novel’. Further, 11 of the novel drug targets showed
considerable interactions with human host proteins with 9 of them showing favorable docking with compoundsof established antibacterial activities. This study identified and analyzed putative drug targets from theproteomes of two emerging hypervirulent Klebsiella pneumoniae strains which also share highly related andevolving proteins, thereby providing data which will prove indispensable for monitoring pathogen evolution anddrug development towards preventing antibiotic resistance.
Abstract # 74
Abhinay Thakur, Dikshita Bansode, Pragati Ghare, Shrutika Sakpal*
In-silico based approach of FDA Approved Drugs for targeting against nsp10 (6W75) of 2019-nCoV(novel coronavirus)
The 2019-nCoV has triggered a global public health emergency due to its rapid spread, resulting in a pandemic
situation. The nsp10 plays a crucial role in viral transcription by stimulating exoribonuclease of nsp14 (3'-5')and nsp16 (2'-O-methyltransferase) activities thereby playing an essential role in viral mRNAs cap methylation.
The current study involves the molecular-docking of the nsp10 (6W75) using PyRx for FDA-approved drugs
available for the treatment of SARS-1 and MERS, with the hypothesis that these drugs could be suggested forthe treatment of 2019-nCoV or not. The Phylogenetic evolutionary relationship between SARS-1, MERS, and
SARS-2 confirmed the validation. The docking process was carried out through results revealed that the top
five drugs with the highest binding affinity rate are also used for Hepatitis-C virus treatment, and the Molecular
Dynamic Simulation was carried out for the drug highest binding affinity rate Elbasvir, using GROMACS. The
results indicated that Elbasvir could be used as a potential target against the 2019-nCoV nsp10 protein.
Abstract # 55
Eike Steinig, Izzard Aglua, Sébastien Duchêne et al. (University of Melbourne, Sir Joseph Nombri
Memorial-Kundiawa General Hospital, Kundiawa, Simbu Province, Papua New Guinea, and many more)
Signatures of epidemic growth in the emergence of community-associated MRSA
Community-associated, methicillin-resistant Staphylococcus aureus (MRSA) lineages have emerged in
geographically distinct regions around the world during the past thirty years. However, the genomic andepidemiological drivers behind their emergence have remained unclear. Here, we applied consistentphylodynamic methods across multiple community-associated MRSA lineages to describe and contrast theirpatterns of emergence and dissemination, including novel isolates from neglected populations and regional
outbreaks in northern Australia, Papua New Guinea, and Pakistan. We observed surges in the effective
reproduction number (Re > 1) at the divergence of antibiotic resistant clades from America, Asia, Australia, andEurope, coinciding with their establishment in urban host populations. Our data suggest that the emergence ofcommunity-associated S. aureus lineages in the late 20th century was driven by a combination of antibiotic
resistance acquisition and changes in host epidemiology, leading to noticeable shifts in transmission dynamics
and epidemic growth of emerging lineages.
Abstract # 114

Swapnil Tichkule, Simone M. Cacciò, Guy Robinson, Rachel M. Chalmers, Samantha J. Emery-Corbin, Kevin
M. Tyler, Cock van Oosterhout, Aaron R. Jex (Population Health and Immunity, Walter and Eliza Hall Instituteof Medical Research; Department of Infectious Disease, Istituto Superiore di Sanità, Rome, Italy;
Cryptosporidium Reference Unit, Public Health Wales Microbiology, Singleton Hospital, Swansea, UK;;
Biomedical Research Centre and School of Environmental Sciences, University of East Anglia, Norwich, UK)
Genomic landscape of diversification, selective sweeps, and demographic history of an anthroponotic
parasite
Cryptosporidium is a significant public health problem and one of the primary causes of diarrhoea in humans,particularly in very young children living in low- and middle-income countries. The zoonotic Cryptosporidium
parvum and anthroponotic C. hominis species account for most cases globally, but the latter is predominant inlow- and middle-income countries. Here, we present a comprehensive whole genome study of C. hominis,
comprising 114 isolates from 16 countries in five continents. We detect two highly diverged lineages with adistinct biology and demography that have diverged circa 500 years ago. We consider these lineages as two
subspecies, and provisionally propose the names C. hominis hominis (clade 1) and C. hominis acquapotentis(clade 2). C. hominis hominis is mostly found in low-income countries in Africa and Asia, and it appears tohave recently undergone population contraction and selective sweep. In marked contrast, we reveal asignature of population expansion in C. hominis acquapotentis found in high-income countries, mainly inEurope, North America, and Oceania. Moreover, we detect genomic regions of introgression representing geneflow after a secondary contact between the subspecies from low- and high-income countries. Furthermore, weshow that this gene flow resulted into genomic island of high diversity and divergence, and we find thatdiversity at potential virulence genes is maintained by balancing selection, which suggests that they are
involved in a coevolutionary arms race.

Abstract # 109
Amarinder S. Thind, Bruce Ashford, Dario Strbenac, Ruta Gupta, Jonathan R Clark, N. Gopalakrishna Iyer,
Jenny Mitchellm, Jenny Lee, Simon A Mueller, Elahe Minaei, Jay Perry, Marie Ranson (School of Medicine,
University of Wollongong, Wollongong, NSW; Illawarra Health and Medical Research Institute, Wollongong;
Illawarra Shoalhaven Local Health District Wollongong; Sydney Medical School, The University of Sydney;
Royal Prince Alfred Hospital, Sydney, and many more).
Potential non-coding regulations in cSCC Metastasis cancer
There is limited published data exploring whole-genome sequencing (WGS) in metastatic cutaneous
squamous cell carcinoma (cSCC). We used WGS on matched tumour and blood DNA to detect somatic
variants from 25 patients with regional metastases of head and neck cSCC. Computational analyses at coding
and non-coding levels are performed utilising a combination of bioinformatic tools to interrogate their clinicalimpacts on metastasis across the cohort. miRNA binding locations in 3’UTR were significantly functionallyaltered in EVC (71%), PPP1R1A (71%) and LUM (24%). Recurrent variation was observed in the tumour
suppressing lncRNA LINC01003 in 68% of specimens. Recurrent copy number loss in tumour suppressor
genes KANSL1 and PTPRD and gain in CALR, CCND1 and FGF3 was observed. Single nucleotide variation
with the greatest functional impacts was most frequently observed in TP53, CDKN2A, ZNF750, OR51S1 andTET2. Metastatic cSCC is characterized by a highly mutated genome, most pronounced in the non-coding
region, with recurrent patterns of variation in key regulatory elements, and recurrent copy number and shortvariation in cancer-associated genes.

Abstract # 30
Joshua Stevenson, Venta Terauds, Jeremy Sumner (University of Tasmania)
Rearrangement Events on Circular Genomes
In the context of estimating genome rearrangement distances, genomes are often represented by signed
permutations which form a group under composition. Further, the symmetry of genomes and rearrangementevents can also be described algebraically. I will give an introduction to genome rearrangement modelling, andexplore some of the questions that naturally arise when viewing this problem from an algebraic perspective,
with the help of Python/SageMath for demonstrations.

Abstract #36
Venta Terauds (University of Tasmania)
Genome algebras in action
We demonstrate some features of our recently introduced genome algebra framework by applying it toechinoderm mitochondrial genome data. In particular, the framework facilitates a fine-grained consideration ofrearrangement models. We show how varying the underlying rearrangement model and the choice of genomic
distance measure can affect the phylogeny that is produced.
Abstract # 62
Sebastian Burgstaller-Muehlbacher, Stephen Crotty, Tamara Drucks, Heiko Schmidt, Arndt von Haeseler(Center for Integrative Bioinformatics Vienna, Max Perutz Labs, University of Vienna and Medical University of
Vienna; University of Adelaide)
Model selection on empirical data using deep learning
Selecting the correct model of sequence evolution (mSE) for a multiple sequence alignment (MSA) constitutes
the first step of tree reconstruction. State-of-the-art approaches for inferring nucleotide models mostly apply
maximum likelihood (ML) methods.
Here, we demonstrate that neural networks can infer the correct mSE including the shape parameter α of the𝛤-distribution. A Residual Neural Network (Resnet) was trained with the six most frequently used mSE (JC,K2P, F81, HKY, TN93 and GTR), whereas a Long Short-Term Memory (LSTM) network with attention was
trained to determine 𝛾.
Our results show that the CNN correctly identifies the mSE in a range of 51.94% to 100%, depending on thetrue mSE. Thus, it is comparable to results of IQ-Tree. Similar accuracies were obtained for 𝛾. However, thetrained networks need substantially less computing time (up to a 60x speedup), depending on the size of theMSA.
We demonstrate, for the first time, that neural networks can be used to identify the correct mSE as well as rate
heterogeneity of an MSA. Furthermore, we intend to generalize our approach so all mSE of relevance in
phylogenetics can be inferred using neural networks, conferring a substantial reduction in computational
requirements to model selection.
Abstract # 126
Jiahao Diao, Barbara Holland, Malgorzata O'Reilly (University of Tasmania)
A Stochastic model of evolution to improve the prediction of independent or dependent gain and lossof genes
We consider a model inferring functional gene links. The model can detect the instances of independent or
correlated gain and loss of pairs of genes from species genomes. We start at a simple case that thephylogenetic trees generated by the birth-death process, which has a pair of genes in the evolutionary process.
The presence ('1') and absence ('0') of these two genes are affected by two different rate classes. The possible
states of these two genes in a phylogeny are applying R package corHMM from (Boyko & Beaulieu, 2020). We
aim to obtain the best fit of rates in these two different rate classes and how the transition between two rateclasses would be more realistic to understand the gene gain and loss in the evolutionary process.
Jonathan Mitchell, Elizabeth Allman, John Rhodes (University of Tasmania, University of Alaska)
Consistent inference of species networks from gene trees under the multispecies coalescent using ageneralized AIC

We developed an algorithm that builds on the NANUQ method for statistically consistent inference of species
networks under the multispecies coalescent model of incomplete lineage sorting. NANUQ uses hypothesis
tests to determine whether quartets better fit a tree or a network, then combines all sets of quartets to infer an
n-taxon species network. We increased the scope of NANUQ, allowing quartets to also be analyzed using ageneralized AIC. This generalized AIC outperforms the AIC in models with parameter space boundaries or
singularities, particularly at or near those points. Such models are common in many real world applications,
including phylogenetics. Simple examples of models with boundaries or singularities are those with parametersthat are constrained to be positive or non-negative, such as evolutionary times or rates. Using NANUQ weanalyzed a dataset of xiphophorus fish with expected extensive reticulate evolution.

Abstract #33
Martin Smith (Durham University)
Improving consensus trees by detecting rogue taxa
“Rogue” taxa of uncertain phylogenetic position can confound attempts to summarise phylogenetic results. By
reducing resolution and support values in consensus trees, rogues potentially obscure strong evidence forrelationships between other taxa.
My new, information-theoretic measure of the congruence between a set of trees and their consensus allows
rogue taxa to be identified more effectively, thus producing reduced consensus trees that are better resolved,
more accurate, and more informative than those generated by existing methods.
Abstract # 76
Caitlin Cherryh, Minh Bui, Robert Lanfear (Research School of Biology and Research School of Computer
Science, Australian National University)
Does Filtering Recombinant Loci Improve the Estimation of Species Trees?
Genome-scale data with thousands of loci are now routinely used to estimate species trees. Bothconcatenation and coalescent methods can be used to estimate species trees, and both methods assume that
there is no recombination within loci. This assumption is largely ignored within empirical analyses, but
recombination is present within loci from many empirical datasets. In this work, we examine how removing locithat show evidence of recombination affects species tree estimation for both coalescent and concatenation
methods for four empirical datasets. We find in some datasets (like those from mammals) with limited
recombination, filtering loci has no effect on species tree topology. However, for other datasets (e.g., fromclosely related plants), filtering loci can have dramatic effects on the species tree.
Abstract #111
Ana Serra Silva, Mark Wilkinson (Natural History Museum, London, UK University of Bristol, UK)
From islands of trees to clumps: can we generate informative clusters of partially overlapping trees?
Post-processing of trees often focuses on (multi)sets of phylogenetic trees on the same leaf set, which can beeffectively summarised using a variety of consensus and clustering methods. One such approach is identifying
islands of trees, or the disconnected components of a graph where vertices correspond to trees and edges
connect sufficiently similar trees. However, this approach cannot be easily applied to (multi)sets of trees withpartially overlapping leaf sets, such as those generated for phylogenomic studies, due to the confounding
effects introduced by small trees (with small distances to potentially disparate larger trees) and by pairs of trees
with non-overlapping leaf sets. In an attempt to identify informative clusters of trees with partially overlapping
leaf sets, we define a new subsetting approach, "clumps of trees", based on the distance between any tree in a
set and the set's supertree. While clumps were developed with the goal of minimising the negative effects
introduced by small trees and avoiding comparisons of trees with non-overlapping leaf sets, they can beapplied to (multi)sets of trees with identical taxonomic sampling. Time permitting we will introduce the conceptof clumps, an analytical pipeline and an empirical example of clumping.

Abstract # 116
Sarah Alver, James Degnan (University of Mexico)
Improvement to GLASS/Maximum Tree Method of Species Tree Inference from Estimated Gene Trees
Using Measurement Error Modified Single Linkage Clustering
The Global LAteSt Split (GLASS)/Maximum Tree (MaxTree) method of species tree inference performs welland is statistically consistent when inferring the species tree from known gene trees. When using estimatedgene trees, the inferred tree is the maximum likelihood tree and is still a consistent estimator of the species
tree under certain conditions. The method is implemented in software such as STEM (Species Tree Estimation
using Maximum likelihood). Unfortunately, it has been shown to perform relatively poorly when the input isgene trees estimated from DNA sequences, and sufficient conditions for statistical consistency in this case canbe unrealistic. We propose a modification to the STEM tree; the modification is an application of the method ofclustering in general measurement error models described by Su, Reedy and Carroll in 2018. The proposed
method replaces the estimated pairwise coalescence times used by STEM with randomly generated
realizations from the estimated distribution of the true pairwise coalescence times. This distribution isestimated through measurement error modeling. As with STEM, the minimum of these realizations is takenover all loci for each pairwise distance. These minimums then form a distance matrix, and single linkage
clustering is performed to infer the species tree. Our simulation studies find that the new method outperforms
STEM in terms of Robinson-Foulds distance and branch score distance from the true species tree.

Abstract # 52
Lena Collienne, Alex Gavryushkin, David Bryant (University of Otago; University of Canterbury)
Distances between phylogenetic time trees
Phylogenetic time trees are evolutionary histories where evolutionary events, i.e. internal nodes, are timed. To
measure the similarity of phylogenetic trees, including time trees, tree rearrangement operations are often
used. These operations apply local changes to a tree, that are motivated by biological processes. The
distance between two trees is then defined to be the minimum number of tree rearrangement operations
needed to transform one tree into the other.
Most popular are the tree rearrangements Nearest Neighbour Interchange (NNI), Subtree Prune and Regraft(SPR), and Tree Bisection and Reconnection (TBR). The downside is however that the distance between
trees under any of these tree rearrangement operations (NNI, SPR, or TBR) is NP-hard to compute andtherefore not suitable to be used in practice. Other distance measures like the Robinson-Foulds distance canbe computed efficiently, but lack biological interpretability. Moreover, none of the above mentioned distancemeasures takes times of internal nodes of phylogenetic trees into account.
In this talk, we introduce tree rearrangement operations for ranked phylogenetic trees. They are called ranked
nearest neighbour interchange (RNNI) operations, as they are based on the classical Nearest Neighbour
Interchange (NNI) operations. We show that RNNI distances between ranked trees can be computed
efficiently, and provide a polynomial time algorithm to do so. Afterwards, we generalise our results to trees withinteger-valued branch lengths.

Gleb Zhelezov (University of New Mexico)
Trying out a million genes to find the perfect pair with MTrip
Consensus methods can be used for reconstructing a species tree from several gene trees which exhibit
incompatible topologies due to incomplete lineage sorting. Motivated by the fact that there are no anomalous
rooted gene trees with three taxa and no anomalous unrooted gene trees with four taxa in the multispecies
coalescent model, several contemporary methods form the gene tree consensus by finding the median treewith respect to the triplet or quartet distance — i.e., estimate the species tree as the tree which minimizes thesum of triplet or quartet distances to the input gene trees. These methods reformulate the solution to theconsensus problem as the solution to a recursively-solved dynamic programming problem. We present an
iterative, easily-parallelizable approach to finding the exact median triplet tree. By taking advantage of the factthat gene trees associated with the same species tree often have distinct bipartitions of identical taxa subsets,
this implementation finds the exact median tree of many gene trees faster than comparable methods, has
better scaling properties with respect to the number of gene trees, and has a smaller memory footprint.

Abstract # 112
Benjamin D Kaehler, Gavin A Huttley (University of New South Wales;Australian National University)
Phylogenetics if you stop ignoring non-stationary evolution
Probabilistic phylogenetic models almost ubiquitously ignore that molecular evolution is usually anon-stationary process, to their demonstrable detriment. Largely the problem of fitting non-stationary models
has been relegated to something that happens after a tree has been built. If, however, non-stationary models
are used to build trees, it is possible to fit rooted trees, which drastically changes the algorithmic landscape,and could resolve some phylogenetic controversies that look suspiciously like model misspecification
problems. A long-standing barrier to adoption is that fitting models with lots of parameters is challenging. We
overcome that problem with the scalpel of model selection and the hammer of GPU acceleration, which weshow to yield orders of magnitude improvement in model fitting throughput. We then dust off some historicallyrelegated rooted tree building algorithms, see how they perform in the harsh light of noisy data, and updatethem with the benefit of over a decade of further development in graphical algorithms.
Abstract # 107
Cassius Manuel Perez, Arndt von Haeseler
A test for phylogenetic saturation
In phylogenetic inference, an alignment is called saturated if the observed distances between sequences
substantially underestimate the evolutionary distance (branch length). Saturation is typically discussed in thecontext of pairwise sequence comparison. However, even for this simple instance, a quantitative theory ofsaturation is to the best of our knowledge missing.
Here, we introduce a statistical test for saturation that assumes a reversible model of evolution and aphylogenetic tree. Our test is straightforward for two sequences. Moreover, we show how to generalize the test
for arbitrary branches in a phylogenetic tree. Such tests provide an additional quality control of phylogenetic
branching patterns.
As an illustrative example, we show that artifacts like long branch attraction or long branch repulsion can bedetected using our test for saturation.
Abstract # 121
Qin Liu, Barbara Holland, Michael Charleston, Shane Richards
Is AIC An Appropriate Metric For Model Selection In Phylogenetics?
Partition models and mixture models are two common approaches to accommodate heterogeneity in genomic
sequencing data. Both models have been shown to provide a better fit to the data than the conventional
homogeneous models. Assessing the adequacy of these models is important since incorrect inferences can bemade if the mechanism modelled is incorrect. Akaike Information Criterion (AIC) is an estimator of
Kullback-Leibler divergence (KLD) and is a popular tool to select conventional homogeneous models in
phylogenetics. A couple of recent papers have questioned the effectiveness of AIC in phylogenetics, but there
has not been any investigation of mixture or partition models. We are interested in whether AIC is anappropriate tool to compare models, in particular, to compare a partition model and a mixture model inphylogenetics. In my talk, I am going to show that, under non-standard conditions, AIC underestimates the
expected KLD and always chooses partition models over mixture models. Non-standard conditions occur whenthe branch lengths and/or the sequence lengths are small. I am also going to show some of the preliminary
results from the research that we have done on evaluating AIC between a partition and a mixture model.
Samuel Davis, Gabriel Foley & Mikael Boden (The University of Queensland)Inference of biochemical and biophysical parameters for ancestral proteins
Ancestral reconstruction of proteins is a powerful approach with potential benefits for several fields. A numberof available tools reliably infer the sequences of ancestral proteins, with broad evidence indicating therobustness of inferred sequences in laboratory resurrections. This ability greatly increases the available
sequence space in the selection of candidates for protein engineering. Despite this ability, the cost associatedwith synthesising and characterising ancestral variants is significant when performed for many potential
candidates. Improving the means by which ancestors are selected for synthesis would ultimately reduce theoverall cost of this process. Existing methods for inferring ancestral states are largely focused on species-leveltraits and rely on continuous-state stochastic processes to model evolution. Such models may be inappropriate
for the evolution of biochemical and biophysical parameters, which are capable of dramatic change resulting
from small numbers of mutations. We have designed an application for ancestral inference ofcontinuous-valued parameters which address these constraints. Our approach utilises latent, discrete states at
each node in the tree which evolve via Markov chains along branches. Real property values are generated
from Gaussian features corresponding to each of these latent states, the parameters of which can be learned
via expectation maximisation. Marginal inference at a given node takes the form of a weighted sum ofGaussian feature densities, with weights corresponding to the posterior probability distribution of the latentstate. We tested the performance of the application on both real and simulated datasets, with it out-performing
existing methods under certain circumstances.

Abstract #89
Conrad J.Burden, Robert C. Griffiths
Stationary Distributions of Neutral Multi-type Branching Diffusions
The stationary asymptotic properties of the diffusion limit of branching processes in which individuals within the
population can mutate between different allele types is considered. For the critical and subcritical processes
the interesting limits are those of quasi-stationary distributions conditioned on non-extinction. Limiting
distributions for supercritical and critical processes are found to collapse onto rays aligned with stationary
eigenvectors of the mutation rate matrix, in agreement with known results for discrete multi-type branchingprocesses. For the sub-critical process the quasi-stationary distribution is obtained to first order in the overallmutation rate, which is assumed to be small. The sampling distribution over allele types for a sample of given
finite size is found to agree to first order in mutation rates with the analogous sampling distribution for a
Wright-Fisher diffusion with constant population size.

Abstract #Qiuyi Li, Celine Scornavacca, Yao-ban Chan
The effect of copy number hemiplasy on gene family evolution
The evolution of gene families is complex, involving evolutionary events such as gene duplication, horizontal
gene transfer, and gene loss (DTL), and other processes such as incomplete lineage sorting (ILS). Because ofthis, topological differences often exist between the trees of genes and the species which contain them. A
number of models have been developed recently to explain these discrepancies, the most realistic of whichattempt to consider both DTL and ILS. When unified in a single model, the interaction between ILS and DTL
can cause polymorphism in gene copy number; we refer to this as copy number hemiplasy (CNH).

We study the effect of CNH on gene family evolution, by comparing two such models: MLMSC (MultiLocus
MultiSpecies Coalescent), which models CNH, and DLCoal (Duplication, Loss, and Coalescence), which doesnot. We generate comparable gene trees under both models, showing significant differences in various
summary statistics; most importantly, CNH reduces the number of gene copies greatly. If this is not taken into
account, the traditional method of estimating duplication rates (by counting the number of gene copies)
becomes inaccurate. We also use these trees for species tree inference with the summary methods ASTRAL
and ASTRAL-Pro, demonstrating that their accuracy, based on simulations calibrated on real data, may havebeen overestimated.
Abstract # 82
Albert Ch. Soewongsono, Barbara R. Holland, Małgorzata M. O'Reilly (University of Tasmania)
The Shape of Phylogenies Under Phase-Type Distributed Times to Speciation and Extinction
Phylogenetic trees are widely used to understand the evolutionary history of organisms. Tree shapes provide
information about macroevolutionary processes. However, existing macroevolutionary models are unreliable
for inferring the true processes underlying empirical trees. Here, we propose a flexible and biologicallyplausible macroevolutionary model for phylogenetic trees where times to speciation or extinction events are
drawn from a Coxian phase-type (PH) distribution. First, we show that different choices of parameters in ourmodel lead to a range of tree balances as measured by Aldous' 𝛽 statistic. In particular, we demonstrate that it
is possible to find parameters that correspond well to empirical tree balance. Next, we provide a naturalextension of the 𝛽 statistic to sets of trees. This extension produces less biased estimates of 𝛽 compared tousing the median 𝛽 values from individual trees. Furthermore, we derive a likelihood expression for the
probability of observing any tree with branch lengths under a model with speciation but no extinction. Finally,
we illustrate the application of our model by performing both absolute and relative goodness-of-fit tests for twolarge empirical phylogenies (squamates and angiosperms) that compare models with Coxian PH distributed
times to speciation with models that assume exponential or Weibull distributed waiting times. In our numericalanalysis, we found that, in most cases, models assuming a Coxian PH distribution provided the best fit. In
addition, this model allows us to fit hazard rate for speciation and we found evidence that speciation rates hadchanged through time in some clades of the squamate phylogeny.
Abstract # 25
Matthew Macaulay, Mathieu Fourment, Aaron Darling (The University of Technology Sydney)
Hyperbolic Tree Embeddings for Bayesian Phylogenetic Inference
Hyperbolic embeddings have successfully provided low dimensional representations of complex tree-like data.
For phylogenetics, tree embeddings could offer a way to overcome the super-exponential number of tree
combinations. However, such approaches are missing from Bayesian phylogenetics. In this talk, I will discussembedding trees in hyperbolic space to perform Bayesian analysis.
We begin with a Markov Chain Monte Carlo (MCMC) to compare the quality of embeddings to state-of-art
methods. We find that hyperbolic space offers low dimensional embeddings for MCMC and that proposals
between different trees - considering both continuous parameters and discrete topologies - can be given bystraightforward continuous distributions with fast sample methods.
Then we investigate the possibility of embedding variational distributions to make use of speed-up from
gradient-based optimisation. Variational inference in the embedding space is a challenging problem, primarily
due to the non-differentiability of the phylogenetic likelihood and prior as tree topologies change. We examine
the extent that variational approximations capture salient features of the posterior distribution and discusspossibilities to refine these approximations. The prospect of hyperbolic embeddings for Bayesian phylogenetic
inference is promising. It naturally envelops both discrete and continuous tree parameters of the posterior in a
unified manner.

Abstract # 34
Ruriko Yoshida, Shelby Cox
Tree Topologies along a Tropical Line Segment
Tropical geometry with the max-plus algebra has been applied to statistical learning models over tree spaces
because geometry with the tropical metric over tree spaces has some nice properties such as convexity interms of the tropical metric. One of the challenges in applications of tropical geometry to tree spaces is thedifficulty interpreting outcomes of statistical models with the tropical metric. This talk focuses on combinatorics
of tree topologies along a tropical line segment, an intrinsic geodesic with the tropical metric, between two
phylogenetic trees over the tree space and we show some properties of a tropical line segment between twotrees. Specifically we show that a probability of a tropical line segment of two randomly chosen trees going
through the origin (the star tree) is zero if the number of leave is greater than four, and we also show that if two
given trees differ only one nearest neighbor interchange (NNI) move, then the tree topology of a tree in thetropical line segment between them is the same tree topology of one of these given two trees with possible
zero branch lengths.
Abstract # 136
Benjamin Wilson (Lateral)
Learning phylogenetic trees as hyperbolic point configurations
We propose a novel method for the inference of phylogenetic trees that utilises point configurations on
hyperbolic space as its optimisation landscape. Each taxon corresponds to a point of the point configuration,
while the evolutionary distance between taxa is represented by the geodesic distance between theircorresponding points. The point configuration is iteratively modified to increase an objective function that
additively combines pairwise log-likelihood terms. After convergence, the final tree is derived from theinter-point distances using a standard distance-based method. The objective function, which is shown to mimic
the log-likelihood on tree space, is a differentiable function on a Riemannian manifold. Thus gradient-based
optimisation techniques can be applied, avoiding the need for combinatorial rearrangements of tree topology.

Abstract # 12
Hasindu Gamaarachchi, Hiruna Samarakoon, Sasha P. Jenner, James M. Ferguson, Timothy G. Amos, Jillian
M. Hammond, Hassaan Saadat, Martin A. Smith, Sri Parameswaran, Ira W. Deveson (Kinghorn Centre forClinical Genomics, Garvan Institute of Medical Research, Sydney; University of New South Wales, Universityof Montreal)
SLOW5: a new file format enables massive acceleration of nanopore sequencing data analysis
Nanopore sequencing is an emerging genomic technology with great potential. However, the storage andanalysis of nanopore sequencing data have become major bottlenecks preventing more widespread adoption
in research and clinical genomics. Here, we elucidate an inherent limitation in the file format used to store raw
nanopore data — known as FAST5 — that prevents efficient analysis on high-performance computing (HPC)
systems. To overcome this, we have developed SLOW5, an alternative file format that permits efficientparallelisation and, thereby, acceleration of nanopore data analysis. For example, we show that using SLOW5
format, instead of FAST5, reduces the time and cost of genome-wide DNA methylation profiling by an order ofmagnitude on common HPC systems (up to >30X times), and delivers consistent improvements on a wide
range of different architectures. With a simple, accessible file structure and significant reductions in size
compared to FAST5 (~25% reduction for a typical human genome), SLOW5 format will deliver substantial
benefits to all areas of the nanopore community.
Example result: With the maximum resource allocation available on Australia's National Computing
Infrastructure, genome-wide DNA methylation profiling on a single ~30X human genome sequencing dataset
runs for >14 days at a cost of >$500 when using FAST5 files. We were able to complete whole-genomemethylation profiling on a single 30X human dataset in just ~10.5 hours when using SLOW5 format as the
input.
SLOW5 format and all associated software are free and open source.
SLOW5 format specification documents: https://hasindu2008.github.io/slow5specs;
Slow5lib: https://hasindu2008.github.io/slow5lib;
Slow5tools: https://hasindu2008.github.io/slow5tools;
Pre-print: https://www.biorxiv.org/content/10.1101/2021.06.29.450255v1.

Abstract # 8
Michael Charleston (University of Tasmania)
Landscapes of split space part 2: the quest for utility.
The idea of attacking the problem of phylogenetic inference by divide- (or split-) and-conquer is appealing. Treespace grows super-exponentially with the number n of taxa, as O(2n n!), so breaking a problem in half, solving
that and then rejoining the solved pieces could present a massive time saving. Non-trivial bipartitions of taxa
are often referred to as splits and there are a number of potential score functions by which we could judge
whether a split is “good”: e.g., parsimony-based, cluster-based, or based on (sub)flattenings. The number ofsplits is still large (around 2n) so we need a heuristic search to hunt for good ones. We consider the
combinatorial landscape of splits as vertices (nodes) in a graph, weighted by the score function, and connectedby edges representing adjacencies between splits. Searching for a good split will be easier if the landscape issmooth, and useful if locally optimal splits are mostly correct.
This talk is an update from my talk at Phylomania 2020 and provides some progress in my ongoing quest forsome use of this approach.
Abstract # 147
Luke Cooper, Barbara Holland, Michael Charleston (University of Tasmania)
Models of biomolecular network evolution
The various types of biomolecular networks such as gene regulatory networks and protein interaction
networks, have the potential to provide a plethora of useful information about extant, and ancestral, organisms.
Given a pair of networks from two or more extant species that share a common ancestor, it is useful to know
which nodes are the 'same', or are homologous, between the networks. With this knowledge, we can gain anunderstanding of interactions (edges) in the networks that are conserved, and are different, across the two
networks.
Algorithms which are guaranteed to obtain 'the best' alignment are NP-hard, so we work with efficientheuristics. To measure the quality of an alignment produced by such an algorithm, we have to use abiologically informed measure of correctness. We propose such a measure that compares the alignment ofeach node to an “ideal” alignment that minimises distances between two nodes in a “gene duplication forest”
where the leaves represent the nodes in the extant networks. We simulate the gene duplication history of
networks via biologically motivated random graph models, and use them to compare the ability of network
algorithms to produce a biologically accurate alignment using topological information alone with our new
measure.
Mareike Fischer, Andrew Francis, Kristine Wicke (Greifswald University, University of Western Sydney)
Phylogenetic Diversity Rankings in the Face of Extinctions: the Robustness of the Fair Proportion
Index
Planning for the protection of species often involves difficult choices about which species to prioritize, given
constrained resources. One way of prioritizing species is to consider their “evolutionary distinctiveness”, i.e.
their relative evolutionary isolation on a phylogenetic tree. Several evolutionary isolation metrics or
phylogenetic diversity indices have been introduced in the literature, among them the so-called Fair Proportionindex (also known as the “evolutionary distinctiveness” score). This index apportions the total diversity of a tree
among all leaves, thereby providing a simple prioritization criterion for conservation. Here, we focus on theprioritization order obtained from the Fair Proportion index and analyze the effects of species extinction on thisranking. More precisely, we analyze the extent to which the ranking order may change when some species go
extinct and the Fair Proportion index is re-computed for the remaining taxa.
In my talk, I show that for each phylogenetic tree, there are edge lengths such that the extinction of one leaf
per cherry completely reverses the ranking. Moreover, I show that even if only the lowest ranked species goesextinct, the ranking order may drastically change.
Abstract # 92
Andrew Francis, Peter Jarvis
The perfect picnic project: Sandwiches, eggs, and trees
Persi Diaconis and Susan Holmes showed in 1998 that binary phylogenetic trees on n leaves can be encodedby matchings on a set of (2n-2) elements, that is, a partition of the set into pairs. In this talk we show how thismatching can be extended to a correspondence with unbalanced Brauer diagrams (and, more generally,
partition diagrams) that preserve elements of the structure of the tree. Such diagrams have an associatednatural action of the symmetric group, and through Green's relations of semigroup theory, we can construct“eggbox diagrams” displaying equivalence class structures. Another semigroup construction, the sandwich
product, allows us to define a product on the set of trees, which is given relative to a chosen tree. With this, we
can consider various related semigroup substructures, such as the regular subsemigroup, or idempotents, inthe context of phylogenetic trees.