Life Sciences Data
and Data-Centric Research
Sarah Butcher

s.butcher@imperial.ac.uk
www.imperial.ac.uk/bioinfsupport

The World of Data Technology producing complex (interdisciplinary) data at exponential
rates— data deluge
 Data are a resource BUT size and complexity are still overwhelming
scientists’ current practices to extract useful information
 Exploiting this resource requires better tools, practices and newsolutions
 Need to combine scientific expertise, computational knowledge andstatistical skills to solve critical problems and make new discoveries Requires new initiatives, institutional commitment, people-power andtechnology

Data-Centric Science – It’s All About the Data

“Hypotheses are not only tested through directed datacollection and analysis but also generated by combining andmining the pool of data already available “
Goble and Roure (2009) from The Fourth Paradigm: Data-Intensive Scientific
Discovery Edited by Hey, Tansley and Tolle).

But In order to do this – data have to be discoverable and reuseableSummary - Questions
 Overview of work
 How did you start working with methodology side?
 Collaborative work with methodology side – sharedbenefits New research themes from your collaborative work
and write technical papers?
 How do you educate/train pi-shaped scientists?

Data as a Resource –The Rothamsted Park Grass Experiment Oldest continuing experiment on permanent
grassland in the world – started 1856
 Investigate ways of improving hay yield byusing inorganic fertilisers and organic manure
Picture from Rothamsted e-RA
 Measured species diversity and soil function
also interactions with meteorological conditions
 Park Grass results are increasingly important to ecologists,
environmentalists and soil scientists
 Being used in ways never imagined by the original scientists
 Possible as DATA and SAMPLES were kept, WE KNOW WHERE
THEY ARE and samples can be re-analysed to provide missingdata

A Brief History Of Genome Sequencing
1977 first complete genome phage Ф-X174 (5,375bp)
1980 ~56 DNA gene sequences in public domain, ~180 by 1983
1995 first complete bacterial genome Haemophilus influenzae1996 first complete eukaryotic genome Saccharomyces cerevisiae
1998 first multicellular eukaryote genome
Caenorhabditis elegans - (97Mb)

2001 Draft human genome publishedover 11 million records in EMBL2015 1939 completed eukaryotes, 31611
prokaryotes
6Bio-data Characteristics – The Basics
 Lack of structure, rapid growth but not (very) huge volume,
high heterogeneity
 Multiple file formats, widely differing sizes, acquisition rates
 Considerable manual data collection
 Multiple format changes over data lifetime including
production of (evolving) exchange formats
 Huge range of analysis methods, algorithms andsoftware in use with wide ranging computational profiles
 Association with multiple metadata standards andontologies, some of which are still evolving
 Increasing reference or link to patient data with associatedsecurity requirements

Data Diversity And Volume?
Transcriptome

Genomes

Other -omes
Bio-Imaging

Improved understandingof complex biological system
Challenges in primary analyses (smaller)
AND in meaningful integration (huge)

Proteome

Large-scale field studies

Metabolomics

Clinical data,
Sample-related data

Variantanalyses

Proteininteractions

Adding Complexity – Formats, Standards, Repositories

 One raw data type BUT many file formats -may be humanreadable, require specific software, proprietary or open source Over 1552 different public databases, most limited by data
domain, origin or both (NAR online Molecular Biology Database Collection) 30+ minimum reporting guidelines for bio/ biomedical data but
few cross experimental types= fragmentation, confusion for non-domain specialists

The Systems Biology Lesson – Integration Takes Effort
Building

Bridging skills:
Understanding ofProgramming
Data types
Some methodologies
Necessary software

Data integration
ModelsFull cycle systems
biology?

Biological dataBiologists

ModelsNumerical scientists

Interdisciplinary
traininginterpreter

The Bioinformatics Support Service – What We Do
We support all stages in the data lifecycle - experimental design,
data and metadata capture, primary and later stage analyses,
data management, visualisation, sharing and publication
Large-scale genomics & Next Generation Sequencing Analyses
Tools for multiplatform data and metadata management
Bespoke clinical and biological databases, tissue-banking
Software and script development, data visualisation, mobile apps
Full grant-based collaboration across disciplines
Brokering, skills sharing, advocacy
New ways of high throughput working – e.g. cloud, workflows
Teaching, Workshops and One-to-One tutorials

Variety of skill-sets cover wet-lab bio, statistics, computer science

The Publication Complication
 Public bio-database formats lead to data fragmentation
 May cross-reference datasets across databases (good) Each has its own format and metadata requirements
 Quality assurance can be variable
 Data submission may be a requirement for journal
publication (good) Large datasets can take weeks to prepare/validate andgenerate 100’s of thousands of lines of XML, TB of data
 Automation complicated by regular changes to uploaders Where to put the other associated data – that may not belinked to a publication?

Example - Bridging the Gaps In One Domain– Bio-imaging

Confocal image
analysis feature detection••••

Sample tracking for image analysis
specialists
Bespoke automated analysis systems
for biologists
Maintaining OMERO OME databasefor Photonics researchers
MRI scan management solution forresearch groups

Example - Encouraging Electronic Data Capture- Mobile applications For Data Input

LabBook http://labbook.cc

customisable geo-tagged
data capture in the field
automated remote
database storage

Secure backup, sharing, search, version
control via website
Handwritten notes, annotation
Supports photos, videos, file attachments,
voice memos, barcode scanningPractical Improvements For
Increasingly Large Scale Data

What can we learn fromCollaborators:
High Energy Physics
Astronomy
Photonics
Chemistry
Mathematics
Computer Science
GenomeThreader
in the MapReduce
framework

The iTAG Annotation Pipeline
NGS
sequencing

Eugene:integration
andgene prediction

CDS
Proteinpredictions

Overlap >= 70%
E-value < 1< 60% gaps

Species tree

2012

Gene tree
S. Rombauts, iTAG

Grass Roots Challenges
 Integrative approaches repeatedly show that complete
metadata are vital for optimal data reuse BUT
 Metadata capture still a complex time-consuming task
 Data fragmentation across multiple sites still a major barrier
to uptake (can’t find it… can’t use it…)
 Practical aspects – cost of storage & curation, sheer volumeof datasets Difficulty of obtaining consistent funding for fundamentals- maintaining core infrastructure, software, databases
 Staff – shortage of truly inter-disciplinary infrastructure &knowledge providers, career progression

The Blumeria Story
Spanu et al

Pietro Spanu
Maike BothA Collaboration Story Cereal powdery mildews
 Obligate biotrophs of Wheat, Barley Fungal Haustoria fill the living plant cells and siphon off food Also may deliver the Effectors that turn off the Plant ‘immune’
responseHow a wet-lab went multi –omic by collaboration

Changes in technology:
Genome sequencing
became cost-effective
The genome produced
surprises
Wide team of Collaborators
coalesced - still working
together

Spanu et al DOI:10.1126/science.11 94573

Needed input from manyother organisms, other datasets,other methodologies to get thebigger pictureComplex Heterogeneous Data Blumeria Genome - 5 different sequencing technologies
required complex hybrid assemblies
 Annotation - automated pipeline AND extensive collaborative
manual annotation across multiple countries
 Comparative analyses using data from 3 other species’genomes Integration across multiple data types:
 RNA-seq data
 Mass spec proteomics data
 NMR data
 Protein structural prediction AND AND AND……

 AND - originating lab had no informatics expertiseSurprise no 1: Powdery mildew genome ~4 x larger than expected

Gene dno 4: surprising
low gene density

Surprise no 2: practically all
primary metabolic pathways
are conserved

Surprise no 3:
Powdery mildews have biggenomes with few genes

Number ofgenes
in average
fungal genome(~12,000)

Number ofgenes
CerealPowdery
Mildew
Genomes(~6500)0(Spanu et al, 2010
Wicker et al, 2013)

100

200

300

400

Gene/Mb

500

600700Surprise no 5: a hugesuperfamily of effectorlike genes (CSEPs)>7% of total genes
RNA-Seq shows:• vast majority of these are expressedat high levels• the majority is more highlyexpressed in the haustoria
Proteomics shows:• These proteins are someof the dominant proteinsin haustoria

Pedersen et al. (2012)
doi:10.1186/1471-2164-13-694

The End of the Beginning – Enabling New Investigations
A whole new theme of investigation - effectors:
 Host-Induced Gene Silencing to look at effects on pathogenicity
 Expression profiling during infection
 Transient expression in plants to study effect on susceptibility tosome pathogens
 Structure prediction for RNAse-like (“RALPH”) candidate effectors(PHYRE and INFOLD) Solved structure for some candidates RNA binding demonstration - Nucleic Acids
induce NMR shift
Ribonuclease T1 (1RNT)
CSEP0090
 Ongoing studies on binding function
Superposition

Pedersen et al (2012)
CSEP0093

CSEP0347

The role of intestinal microbiota
in non-alcoholic fatty liverdisease (NAFLD)
Clinicaldata
135 variables(n = 753)

Proteomic
data(serum)(n = 88)

Transcriptomic
data (liver biopsy)(n = 88, 17800
genes)

Phylogenomic data
(16S rRNA gene,faeces) (n = 110)
Metagenomic
data (faeces)(n = 73)
L. Hoyles

Not originally planned

Metabolomic dataUrine (NMR)(n = 413, 30000 datapoints)
Serum (NMR)(n = 421, 30000 datapoints)
Serum lipidomics (MS,+ve and –ve mode)(n = 426, ≤5500 datapoints)

Metagenome Pipeline

Quality Trim
(FastX)( Karlsson et al,. 2012 )Human Filter(bowtie vs Humanb37)

TaxonomicalAnalysis(bowtie vs ref. genomes)

de-novo Assembly(Velvet)

Gene Prediction(Metagenemark)Non-redundantgene catalog

Functional
Annotation(cd-hit)(Usearch vs KEGG)

 New Data Types
Determine Methodologies changeAbundance(bowtievs gene catalog) Need to continually re-evaluate Hard to do this unless you are in the field Practical computational constraints
Abundance-basedclustering(MCL)Metagenome Pipeline

ICL 2015

Quality Analysis(FastQC)

viralparasites
plantsFilter(bwa)

Human

TaxonomicalAnalysis(MetaPhlAn)

de-novo Assembly(IDBA-UD)

Gene Prediction(Metagenemark)

Non-redundantgene catalog

Presence in IGC
Catalogue(cd-hit)(Usearch vs KEGG)

Every step re-evaluatedDetermine
Functional annotation extendedAbundance(bowtie vs gene catalog)
Now expecting continual dataset input
Faster turnaround requires larger compute

J. Abbott

Abundance-basedclustering(MCL)

Functional
Annotation(Usearch vs KEGG)

Functional
Annotation(Interproscan)

Better Instrumentation,
Higher Throughput, More Integration
NMR

UPLC-MS

SPECTROSCOPIC
PLATFORM

Advancement & application ofmetabolic profiling methods &technologies

BIOINFORMATICS
AND MODELLINGANALYTICALDATASPECTRAIMAGE RECONSTRUCTION

CLASSIFICATION& PREDICTION

EXPERT SYSTEMSSTATISTICAL
SPECTROSCOPYOMICS INTEGRATION- THE INTERACTOME• Undertake and develop state-ofthe-art (mass spectrometric andNMR spectroscopic) analyses formetabolic finger-printing ofbiofluids• Combine metabolic analyses withother clinical, lifestyle and –omics
datasets• A national resource and research
capacity, enabling researchers toderive clinically-relevant insights to
identify bio-markers or profiles• Develop new methods andtechnologies

UK MED-BIO:

Aggregation, Integration, Visualisation andAnalysis of Large, Complex Data

 Example of newly funded multi-disciplinary initiatives
 1 of 6 national projects to improve infrastructure for medicalinformatics
 Multiple partner Institutions, multiple areas: Imperial (population studies, GWAS, Metabolomics, dataintegration)
 Institute of Cancer Research (cancer informatics)
 European Bioinformatics Institute (Metabolights database) Centre for the Improvement of Population Health through Ehealth Research (e-health records)
 MRC Clinical Sciences Centre (data integration, statistics)
 MRC Human Nutrition Research (phospho-proteomics)

 Multiple Industrial partners

MED-BIO – Complex Large Data
Largest primary data volume producer ismetabolomics
Also:• NGS (exomes, genomes, targetted)• Proteomics (mass spec)• Transcriptomics and methylation-based• Gut metagenomics and metatranscriptomics• Genome wide association studies
Need to support primary data analyses
AND Integration and intelligent data-miningof large, heterogeneous, high dimensional
datasets (from all of above)
Also secure integration with patient data

The Expososome

More Practical Challenges
 1-off Capital funding to buy the big compute, big storage needed BUT future needs are emergent – need flexibility and scaleability

 Little funding provision for staff to build and maintain (andhelp/support) the complex software/data infrastructures
 Requires additional resources - or a bottleneck develops

 Funded mid-career Fellowships encourage innovation BUT
 They also need integrative support

 Data and metadata management will be vital BUT not ‘trendy’ or easily fundable and require domain-specificknowledge – automate as much as possible

Scaling
 Support primary data analyses as well as later integrationand mining Heterogeneous job profiles: standard cluster compute (3280
additional cores), cache-coherent memory (640 cores, 8 TBRAM), large memory nodes (40 cores, 1-2TB RAM each) Centralised active tiered storage – 800TB GPFS, 2 PB object
store, 2 PB tape – duplicated across 2 sites
 Video wall, touch overlay, 3D projection capability forvisualisation
 Centrally-managed software, scheduling, metadata capture BUSINESS MODEL for growth, sustainability

Training and Skills – The π Scientist
A Recent survey of vulnerable skills and capabilities for UK
Research Councils (BBSRC, MRC) identified: Lack of inter-disciplinary skills at postgraduate and postdoc.
level, and need for depth as well as breadth of knowledge
 Data analytics especially bioinformatics vulnerable – but also
general large scale data analysis skills – interpretation, storage,programming Maths, statistics and computational biology lacking at thepostgrad and postdoc level – so recruiting difficult, not just inUK
 Quality and provision of operational and support roles an issue
 Bioinformatics now on Home Office’s Shortage Occupation list

Over 30 Bioinformatics and Systems Biology ModellingGroups Across The College

Formal Training - MSc Bioinformatics and TheoreticalSystems Biology
 Aim - Train both numerical and biological undergraduatesin bioinformatics and theoretical systems biology so they
can progress to research posts in world leading academic,governmental and commercial centres
 Annual intake c. 15 students- always both numericaland biological Over 75% progress to PhDs in best institutions (Imperial,UCL, Cambridge, Oxford, ETH, EMBL)
 In last BBSRC funding round, this MSc was ranked top
from all biological science proposals
http://www.imperial.ac.uk/study/pg/courses/life-sciences/bioinformatics/

MSc in Bioinformatics and Theoretical Systems Biology- a 12 month course
 1st three months formal training Fundamentals of biology
 Statistics and mathematical modelling Bioinformatics and theoretical systems biology
 Computer programming (Python, Java, MySQL)

 Project 1 – group database Project 2 – data analysis and web design Project 3 – research topic (sometimes published) Over 30 groups provide research topics from many Imperialdepartments including clinical groups

PhD Training Next Generation Computational Biologists
 Across departments, faculties and campuses With about 30 theoretical groups over 100 PhD students
currently being trained
 Research supported by £25M grants
 Some purely theoretical, others mixed wet / dry Industrial partnership studentships – e.g. CASE BUT training, mentoring required for all stages – and notso easy to support or fund