pwc-archive/papers-with-abstracts huggingface dataset:
Computational tools for the multiscale analysis of Hi-C data in bacterial chromosomes
Authors: Nelle Varoquaux, Virginia S. Lioy, Frédéric Boccard, Ivan Junier
Date: 2020-10-04 00:00:00

Just as in eukaryotes, high-throughput chromosome conformation capture (Hi-C) data have revealed nested organizations of bacterial chromosomes into overlapping interaction domains. In this chapter, we present a multiscale analysis framework aiming at capturing and quantifying these properties. These include both standard tools (e.g. contact laws) and novel ones such as an index that allows identifying loci involved in domain formation independently of the structuring scale at play. Our objective is two-fold. On the one hand, we aim at providing a full, understandable Python/Jupyter-based code which can be used by both computer scientists as well as biologists with no advanced computational background. On the other hand, we discuss statistical issues inherent to Hi-C data analysis, focusing more particularly on how to properly assess the statistical significance of results. As a pedagogical example, we analyze data produced in {\it Pseudomonas aeruginosa}, a model pathogenetic bacterium. All files (codes and input data) can be found on a github repository. We have also embedded the files into a Binder package so that the full analysis can be run on any machine through internet.
MOSGA: Modular Open-Source Genome Annotator
Authors: Roman Martin, Thomas Hackl, Georges Hattab, Matthias G. Fischer, Dominik Heider
Date: 2020-09-08 00:00:00

The generation of high-quality assemblies, even for large eukaryotic genomes, has become a routine task for many biologists thanks to recent advances in sequencing technologies. However, the annotation of these assemblies - a crucial step towards unlocking the biology of the organism of interest - has remained a complex challenge that often requires advanced bioinformatics expertise. Here we present MOSGA, a genome annotation framework for eukaryotic genomes with a user-friendly web-interface that generates and integrates annotations from various tools. The aggregated results can be analyzed with a fully integrated genome browser and are provided in a format ready for submission to NCBI. MOSGA is built on a portable, customizable, and easily extendible Snakemake backend, and thus, can be tailored to a wide range of users and projects. We provide MOSGA as a publicly free available web service at https://mosga.mathematik.uni-marburg.de and as a docker container at registry.gitlab.com/mosga/mosga:latest. Source code can be found at https://gitlab.com/mosga/mosga
Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data
Authors: Natanael Spisak, Aleksandra M. Walczak, Thierry Mora
Date: 2020-07-23 00:00:00

Somatic hypermutations of immunoglobulin (Ig) genes occuring during affinity maturation drive B-cell receptors' ability to evolve strong binding to their antigenic targets. The landscape of these mutations is highly heterogeneous, with certain regions of the Ig gene being preferentially targeted. However, a rigorous quantification of this bias has been difficult because of phylogenetic correlations between sequences and the interference of selective forces. Here, we present an approach that corrects for these issues, and use it to learn a model of hypermutation preferences from a recently published large IgH repertoire dataset. The obtained model predicts mutation profiles accurately and in a reproducible way, including in the previously uncharacterized Complementarity Determining Region 3, revealing that both the sequence context of the mutation and its absolute position along the gene are important. In addition, we show that hypermutations occurring concomittantly along B-cell lineages tend to co-localize, suggesting a possible mechanism for accelerating affinity maturation.
Applied Category Theory for Genomics -- An Initiative
Authors: Yanying Wu
Date: 2020-09-06 00:00:00

The ultimate secret of all lives on earth is hidden in their genomes -- a totality of DNA sequences. We currently know the whole genome sequence of many organisms, while our understanding of the genome architecture on a systematic level remains rudimentary. Applied category theory opens a promising way to integrate the humongous amount of heterogeneous informations in genomics, to advance our knowledge regarding genome organization, and to provide us with a deep and holistic view of our own genomes. In this work we explain why applied category theory carries such a hope, and we move on to show how it could actually do so, albeit in baby steps. The manuscript intends to be readable to both mathematicians and biologists, therefore no prior knowledge is required from either side.
Metagenomics for clinical diagnostics: technologies and informatics
Authors: Caitlin Loeffler, Keylie M. Gibson, Lana Martin, Liz Chang, Jeremy Rotman, Ian V. Toma, Christopher E. Mason, Eleazar Eskin, Joseph P. Zackular, Keith A. Crandall, David Koslicki, Serghei Mangul
Date: 2019-11-26 00:00:00

The human-associated microbiome is closely tied to human health and is of substantial clinical interest. Metagenomics-based tools are emerging for clinical diagnostics, tracking the spread of diseases, and surveillance of potential pathogens. In some cases, these tools are overcoming limitations of traditional clinical approaches. Metagenomics has limitations barring the tools from clinical validation. Once these hurdles are overcome, clinical metagenomics will inform doctors of the best, targeted treatment for their patients and provide early detection of disease. Here we present an overview of metagenomics methods with a discussion of computational challenges and limitations.
Revisiting the Neutral Dynamics Derived Limiting Guanine-Cytosine Content Using the Human De Novo Point Mutation Data
Authors: Wentian Li, Yannis Almirantis, Astero Provata
Date: 2020-08-04 00:00:00

We revisit the topic of human genome guanine-cytosine content under neutral evolution. For this study, the de novo mutation data within human is used to estimate mutational rate instead of using base substitution data between related species. We then define a new measure of mutation bias which separate the de novo mutation counts from the background guanine-cytosine content itself, making comparison between different datasets easier. We derive a new formula for calculating limiting guanine-cytosine content by separating CpG-involved mutational events as an independent variable. Using the formula when CpG-involved mutations are considered, the guanine-cytosine content drops less severely in the limit of neutral dynamics. We provide evidence, under certain assumptions, that an isochore-like structure might remain as a limiting configuration of the neutral mutational dynamics.
Genomics-guided drawing of malignant regulatory signatures revealed a pivotal role of human stem cell-associated retroviral sequences (SCARS) and functionally-active hESC enhancers
Authors: Gennadi Glinsky
Date: 2020-08-03 00:00:00
Tasks: Diagnostic, Management

From patients and physicians perspectives, the clinical definition of a tumor malignant phenotype could be restricted to the early diagnosis of sub-types of malignancies with the increased risk of existing therapy failure and high likelihood of death from cancer. It is the viewpoint from which the understanding of malignant regulatory signatures is considered in this contribution. Analyses from this perspective of experimental and clinical observations revealed the pivotal role of human stem cell-associated retroviral sequences (SCARS) in the origin and pathophysiology of clinically-lethal malignancies. SCARS represent evolutionary- and biologically-related family of genomic regulatory sequences, the principal physiological function of which is to create and maintain the stemness phenotype during human preimplantation embryogenesis. SCARS expression must be silenced during cellular differentiation and SCARS activity remains silent in most terminally-differentiated human cells performing specialized functions in the human body. De-repression and sustained activation of SCARS result in differentiation-defective phenotypes, tissue- and organ-specific clinical manifestations of which are diagnosed as pathological conditions defined by a consensus of pathomorphological, molecular, and genetic examinations as the malignant growth. Contemporary evidence are presented that high-fidelity molecular signals of continuing activities of SCARS in association with genomic regulatory networks of thousands functionally-active enhancers triggering engagements of down-stream genetic loci may serve as both reliable diagnostic tools and druggable molecular targets readily amenable for diagnosis and efficient therapeutic management of clinically-lethal malignancies.
Technology dictates algorithms: Recent developments in read alignment
Authors: Mohammed Alser, Jeremy Rotman, Kodi Taraszka, Huwenbo Shi, Pelin Icer Baykal, Harry Taegyun Yang, Victor Xue, Sergey Knyazev, Benjamin D. Singer, Brunilda Balliu, David Koslicki, Pavel Skums, Alex Zelikovsky, Can Alkan, Onur Mutlu, Serghei Mangul
Date: 2020-02-28 00:00:00

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to todays diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies.
PepSIRF: a flexible and comprehensive tool for the analysis of data from highly-multiplexed DNA-barcoded peptide assays
Authors: Zane W. Fink, Vidal Martinez, John Altin, Jason T. Ladner
Date: 2020-07-09 00:00:00

By coupling peptides with DNA tags (i.e., 'barcodes'), it is now possible to harness high-throughput sequencing (HTS) technologies to enable highly multiplexed peptide-based assays, which have a variety of potential applications including broad characterization of the epitopes recognized by antibodies. While the processing of HTS data, in general, is already well supported, there are very few software tools that have been developed for working with data generated in these highly-multiplexed peptide assays. In order to fill this gap, we present PepSIRF (Peptide-based Serological Immune Response Framework), which is a flexible and comprehensive software package designed specifically for the analysis of HTS data from highly-multiplexed peptide-based assays.
Approximate Search for Known Gene Clusters in New Genomes Using PQ-Trees
Authors: G. R. Zimerman, D. Svetlitsky, M. Zehavi, M. Ziv-Ukelson
Date: 2020-07-07 00:00:00

We define a new problem in comparative genomics, denoted PQ-Tree Search, that takes as input a PQ-tree $T$ representing the known gene orders of a gene cluster of interest, a gene-to-gene substitution scoring function $h$, integer parameters $d_T$ and $d_S$, and a new genome $S$. The objective is to identify in $S$ approximate new instances of the gene cluster that could vary from the known gene orders by genome rearrangements that are constrained by $T$, by gene substitutions that are governed by $h$, and by gene deletions and insertions that are bounded from above by $d_T$ and $d_S$, respectively. We prove that the PQ-Tree Search problem is NP-hard and propose a parameterized algorithm that solves the optimization variant of PQ-Tree Search in $O^*(2^{\gamma})$ time, where $\gamma$ is the maximum degree of a node in $T$ and $O^*$ is used to hide factors polynomial in the input size. The algorithm is implemented as a search tool, denoted PQFinder, and applied to search for instances of chromosomal gene clusters in plasmids, within a dataset of 1,487 prokaryotic genomes. We report on 29 chromosomal gene clusters that are rearranged in plasmids, where the rearrangements are guided by the corresponding PQ-tree. One of these results, coding for a heavy metal efflux pump, is further analysed to exemplify how PQFinder can be harnessed to reveal interesting new structural variants of known gene clusters. The code for the tool as well as all the data needed to reconstruct the results are publicly available on GitHub (github.com/GaliaZim/PQFinder).
Computational methods for cancer driver discovery: A survey
Authors: Vu Viet Hoang Pham, Lin Liu, Cameron Bracken, Gregory Goodall, Jiuyong Li, Thuc Duy Le
Date: 2020-07-02 00:00:00
Tasks: Driver Identification, Survey

Motivation: Uncovering the genomic causes of cancer, known as cancer driver genes, is a fundamental task in biomedical research. Cancer driver genes drive the development and progression of cancer, thus identifying cancer driver genes and their regulatory mechanism is crucial to the design of cancer treatment and intervention. Many computational methods, which take the advantages of computer science and data science, have been developed to utilise multiple types of genomic data to reveal cancer drivers and their regulatory mechanism behind cancer development and progression. Due to the complexity of the mechanistic insight of cancer genes in driving cancer and the fast development of the field, it is necessary to have a comprehensive review about the current computational methods for discovering different types of cancer drivers. Results: We survey computational methods for identifying cancer drivers from genomic data. We categorise the methods into three groups, methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. We also conduct a case study to compare the performance of the current methods. We further analyse the advantages and limitations of the current methods, and discuss the challenges and future directions of the topic. In addition, we investigate the resources for discovering and validating cancer drivers in order to provide a one-stop reference of the tools to facilitate cancer driver discovery. The ultimate goal of the paper is to help those interested in the topic to establish a solid background to carry out further research in the field.
A Public Website for the Automated Assessment and Validation of SARS-CoV-2 Diagnostic PCR Assays
Authors: Po-E Li, Adán Myers y Gutiérrez, Karen Davenport, Mark Flynn, Bin Hu, Chien-Chi Lo, Elais Player Jackson, Migun Shakya, Yan Xu, Jason Gans, Patrick S. G. Chain
Date: 2020-06-08 00:00:00
Tasks: Diagnostic

Summary: Polymerase chain reaction-based assays are the current gold standard for detecting and diagnosing SARS-CoV-2. However, as SARS-CoV-2 mutates, we need to constantly assess whether existing PCR-based assays will continue to detect all known viral strains. To enable the continuous monitoring of SARS-CoV-2 assays, we have developed a web-based assay validation algorithm that checks existing PCR-based assays against the ever-expanding genome databases for SARS-CoV-2 using both thermodynamic and edit-distance metrics. The assay screening results are displayed as a heatmap, showing the number of mismatches between each detection and each SARS-CoV-2 genome sequence. Using a mismatch threshold to define detection failure, assay performance is summarized with the true positive rate (recall) to simplify assay comparisons. Availability: https://covid19.edgebioinformatics.org/#/assayValidation. Contact: Jason Gans (jgans@lanl.gov) and Patrick Chain (pchain@lanl.gov)
Mutations on COVID-19 diagnostic targets
Authors: Rui Wang, Yuta Hozumi, Changchuan Yin, Guo-Wei Wei
Date: 2020-05-05 00:00:00
Tasks: Diagnostic

Effective, sensitive, and reliable diagnostic reagents are of paramount importance for combating the ongoing coronavirus disease 2019 (COVID-19) pandemic at a time there is no preventive vaccine nor specific drug available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It would be an absolute tragedy if currently used diagnostic reagents are undermined in any manner. Based on the genotyping of 7818 SARS-CoV-2 genome samples collected up to May 1, 2020, we reveal that essentially all of the current COVID-19 diagnostic targets have had mutations. We further show that SARS-CoV-2 has the most devastating mutations on the targets of various nucleocapsid (N) gene primers and probes, which have been unfortunately used by countries around the world to diagnose COVID-19. Our findings explain what has seriously gone wrong with a specific diagnostic reagent made in China. To understand whether SARS-CoV-2 genes have mutated unevenly, we have computed the mutation ratio and mutation $h$-index of all SARS-CoV genes, indicating that the N gene is the most non-conservative gene in the SARS-CoV-2 genome. Our findings enable researchers to target the most conservative SARS-CoV-2 genes and proteins for the design and development of COVID-19 diagnostic reagents, preventive vaccines, and therapeutic medicines.
Computational Drug Repositioning and Elucidation of Mechanism of Action of Compounds against SARS-CoV-2
Authors: Francesco Napolitano, Gennaro Gambardella, Diego Carrella, Xin Gao, Diego di Bernardo
Date: 2020-04-16 00:00:00

The COVID-19 crisis called for rapid reaction from all the fields of biomedical research. Traditional drug development involves time consuming pipelines that conflict with the urgence of identifying effective therapies during a health and economic emergency. Drug repositioning, that is the discovery of new clinical applications for drugs already approved for different therapeutic contexts, could provide an effective shortcut to bring COVID-19 treatments to the bedside in a timely manner. Moreover, computational approaches can help accelerate the process even further. Here we present the application of computational drug repositioning tools based on transcriptomics data to identify drugs that are potentially able to counteract SARS-CoV-2 infection, and also to provide insights on their mode of action. We believe that mucolytics and HDAC inhibitors warrant further investigation. In addition, we found that the DNA Mismatch repair pathway is strongly modulated by drugs with experimental in vitro activity against SARS-CoV-2 infection. Both full results and methods are publicly available.
Computational modelling in single-cell cancer genomics: methods and future directions
Authors: Allen W Zhang, Kieran R Campbell
Date: 2020-05-04 00:00:00

Single-cell technologies have revolutionized biomedical research by enabling scalable measurement of the genome, transcriptome, and proteome of multiple systems at single-cell resolution. Now widely applied to cancer models, these assays offer new insights into tumour heterogeneity, which underlies cancer initiation, progression, and relapse. However, the large quantities of high-dimensional, noisy data produced by single-cell assays can complicate data analysis, obscuring biological signals with technical artefacts. In this review article, we outline the major challenges in analyzing single-cell cancer genomics data and survey the current computational tools available to tackle these. We further outline unsolved problems that we consider major opportunities for future methods development to help interpret the vast quantities of data being generated.
Phylogenetic Study of 2019-nCoV by Using Alignment Free Method (Evolutionary Bifurcation of Novel Coronavirus Mutants)
Authors: Yang Gao, Tao Li, Liaofu Luo
Date: 2020-03-03 00:00:00

The phylogenetic tree of SARS-CoV-2 (nCov-19) viruses is reconstructed according to the similarity of genome sequences. The tree topology of Betacoronavirus is remarkably consistent with biologist's systematics. Because the tree construction contains enough information about virus mutants, it is suitable to study the evolutionary relationship between novel coronavirus mutants transmitted among humans. The emergences of 14 kinds of main mutants are studied and these strains can be classified as eight bifurcations of the phylogenetic tree. It is found that there exist three types of virus mutations, namely, the mutation among sub-branches of the same branch, the off-root mutation and the root-oriented mutation between large branches of the tree. From the point of the relation between viral mutation and host selection we found that individuals with low immunity provide a special environment for the positive natural selection of virus evolution. It gives a mechanism to explain why large mutations between two distant branches generally occur in the nCov-19 phylogenetic tree. The finding is helpful to formulate strategies to control the spread of COVID-19.
A comparative analysis for SARS-CoV-2
Authors: Goksel Misirli
Date: 2020-04-08 00:00:00

COVID-19 has affected the world tremendously. It is critical that biological experiments and clinical designs are informed by computational approaches for time- and cost-effective solutions. Comparative analyses particularly can play a key role to reveal structural changes in proteins due to mutations, which can lead to behavioural changes, such as the increased binding of the SARS-CoV-2 surface glycoprotein to human ACE2 receptors. The aim of this report is to provide an easy to follow tutorial for biologists and others without delving into different bioinformatics tools. More complex analyses such as the use of large-scale computational methods can then be utilised. Starting with a SARS-CoV-2 genome sequence, the report shows visualising DNA sequence features, deriving amino acid sequences, and aligning different genomes to analyse mutations and differences. The report provides further insights into how the SARS-CoV-2 surface glycoprotein mutated for higher binding affinity to human ACE2 receptors, compared to the SARS-CoV protein, by integrating existing 3D protein models.
PolyLinkR: A linkage-sensitive gene set enrichment R package
Authors: Raymond Tobler, Angad Johar, Christian Huber, Yassine Souilmi
Date: 2020-04-07 00:00:00
Tasks: Clustering

We introduce PolyLinkR, an R package for gene set enrichment analysis that implements a novel null-model that accounts for linkage disequilibrium between genes belonging to the same gene set - a potential cause of false positives that is often not controlled for in similar tools. Our benchmarks show that PolyLinkR has improved performance compared to two similar tools, achieving comparable power to detect enriched gene sets while producing less than one falsely detected gene set on average, even at high genetic clustering levels and nominal false discovery rates of 20%.