tugHall: a simulator of cancer-cell evolution based on the hallmarks of cancer and tumor-related genes

Abstract Summary The flood of recent cancer genomic data requires a coherent model that can sort out the findings to systematically explain clonal evolution and the resultant intra-tumor heterogeneity (ITH). Here, we present a new mathematical model designed to computationally simulate the evolution of cancer cells. The model connects the well-known hallmarks of cancer with the specific mutational states of tumor-related genes. The cell behavior phenotypes are stochastically determined, and the hallmarks probabilistically interfere with the phenotypic probabilities. In turn, the hallmark variables depend on the mutational states of tumor-related genes. Thus, our software can deepen our understanding of cancer-cell evolution and generation of ITH. Availability and implementation The open-source code is available in the repository https://github.com/nagornovys/Cancer_cell_evolution. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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KEC: unique sequence search by K-mer exclusion

Bioinformatics ◽

10.1093/bioinformatics/btab196 ◽

2021 ◽

Author(s):

Pavel Beran ◽

Dagmar Stehlíková ◽

Stephen P Cohen ◽

Vladislav Čurn

Keyword(s):

Amino Acid ◽

Nucleic Acid ◽

Source Code ◽

Unique Sequence ◽

Supplementary Information ◽

Supplementary Data ◽

Laptop Computers ◽

Sequence Search ◽

Target Sequences ◽

Cross Reference

Abstract Summary Searching for amino acid or nucleic acid sequences unique to one organism may be challenging depending on size of the available datasets. K-mer elimination by cross-reference (KEC) allows users to quickly and easily find unique sequences by providing target and non-target sequences. Due to its speed, it can be used for datasets of genomic size and can be run on desktop or laptop computers with modest specifications. Availability and implementation KEC is freely available for non-commercial purposes. Source code and executable binary files compiled for Linux, Mac and Windows can be downloaded from https://github.com/berybox/KEC. Supplementary information Supplementary data are available at Bioinformatics online.

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BioCommons: a robust java library for RNA structural bioinformatics

Bioinformatics ◽

10.1093/bioinformatics/btab069 ◽

2021 ◽

Author(s):

Tomasz Zok

Keyword(s):

Source Code ◽

Structural Bioinformatics ◽

Supplementary Information ◽

Supplementary Data ◽

Bioinformatic Tools ◽

Data Formats ◽

Central Repository ◽

Diverse Data ◽

2D And 3D ◽

Java Library

Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

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PINSPlus: a tool for tumor subtype discovery in integrated genomic data

Bioinformatics ◽

10.1093/bioinformatics/bty1049 ◽

2018 ◽

Vol 35 (16) ◽

pp. 2843-2846 ◽

Cited By ~ 15

Author(s):

Hung Nguyen ◽

Sangam Shrestha ◽

Sorin Draghici ◽

Tin Nguyen

Keyword(s):

Personal Computer ◽

Genomic Data ◽

Supplementary Information ◽

Omics Data ◽

Tumor Subtype ◽

Supplementary Data ◽

Significant Survival ◽

Survival Differences

Abstract Summary Since cancer is a heterogeneous disease, tumor subtyping is crucial for improved treatment and prognosis. We have developed a subtype discovery tool, called PINSPlus, that is: (i) robust against noise and unstable quantitative assays, (ii) able to integrate multiple types of omics data in a single analysis and (iii) dramatically superior to established approaches in identifying known subtypes and novel subgroups with significant survival differences. Our validation on 12,158 samples from 44 datasets shows that PINSPlus vastly outperforms other approaches. The software is easy-to-use and can partition hundreds of patients in a few minutes on a personal computer. Availability and implementation The package is available at https://cran.r-project.org/package=PINSPlus. Data and R script used in this manuscript are available at https://bioinformatics.cse.unr.edu/software/PINSPlus/. Supplementary information Supplementary data are available at Bioinformatics online.

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SVIM-asm: Structural variant detection from haploid and diploid genome assemblies

10.1101/2020.10.27.356907 ◽

2020 ◽

Author(s):

David Heller ◽

Martin Vingron

Keyword(s):

Genetic Information ◽

Source Code ◽

Supplementary Information ◽

Supplementary Data ◽

Diploid Genome ◽

Insertions And Deletions ◽

Structural Variant ◽

Sequencing Technologies ◽

Variant Detection ◽

Genome Assemblies

AbstractMotivationWith the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes.ResultsWe introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores for the detection of insertions and deletions on two recently published assemblies of the HG002 individual.Availability and ImplementationSVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/[email protected] informationSupplementary data are available online.

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ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions

Bioinformatics ◽

10.1093/bioinformatics/btz431 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4754-4756 ◽

Cited By ~ 29

Author(s):

Egor Dolzhenko ◽

Viraj Deshpande ◽

Felix Schlesinger ◽

Peter Krusche ◽

Roman Petrovski ◽

...

Keyword(s):

Tandem Repeat ◽

Broad Class ◽

Source Code ◽

Computational Method ◽

Supplementary Information ◽

Dna Repeats ◽

Supplementary Data ◽

Sequence Graph ◽

Version 2.0 ◽

Short Tandem

Abstract Summary We describe a novel computational method for genotyping repeats using sequence graphs. This method addresses the long-standing need to accurately genotype medically important loci containing repeats adjacent to other variants or imperfect DNA repeats such as polyalanine repeats. Here we introduce a new version of our repeat genotyping software, ExpansionHunter, that uses this method to perform targeted genotyping of a broad class of such loci. Availability and implementation ExpansionHunter is implemented in C++ and is available under the Apache License Version 2.0. The source code, documentation, and Linux/macOS binaries are available at https://github.com/Illumina/ExpansionHunter/. Supplementary information Supplementary data are available at Bioinformatics online.

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LIONS: analysis suite for detecting and quantifying transposable element initiated transcription from RNA-seq

Bioinformatics ◽

10.1093/bioinformatics/btz130 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3839-3841 ◽

Cited By ~ 6

Author(s):

Artem Babaian ◽

I Richard Thompson ◽

Jake Lever ◽

Liane Gagnier ◽

Mohammad M Karimi ◽

...

Keyword(s):

Transposable Elements ◽

Transposable Element ◽

Test Data ◽

Source Code ◽

Supplementary Information ◽

Transcriptional Networks ◽

Supplementary Data ◽

Rna Seq ◽

Transcriptional Initiation ◽

Instruction Manual

Abstract Summary Transposable elements (TEs) influence the evolution of novel transcriptional networks yet the specific and meaningful interpretation of how TE-derived transcriptional initiation contributes to the transcriptome has been marred by computational and methodological deficiencies. We developed LIONS for the analysis of RNA-seq data to specifically detect and quantify TE-initiated transcripts. Availability and implementation Source code, container, test data and instruction manual are freely available at www.github.com/ababaian/LIONS. Supplementary information Supplementary data are available at Bioinformatics online.

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OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

Bioinformatics ◽

10.1093/bioinformatics/btaa274 ◽

2020 ◽

Vol 36 (13) ◽

pp. 4097-4098 ◽

Cited By ~ 3

Author(s):

Anna Breit ◽

Simon Ott ◽

Asan Agibetov ◽

Matthias Samwald

Keyword(s):

Link Prediction ◽

Large Scale ◽

Source Code ◽

Machine Learning Algorithms ◽

Knowledge Networks ◽

Supplementary Information ◽

Supplementary Data ◽

Biomedical Knowledge ◽

High Quality ◽

Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

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Capybara: equivalence ClAss enumeration of coPhylogenY event-BAsed ReconciliAtions

Bioinformatics ◽

10.1093/bioinformatics/btaa498 ◽

2020 ◽

Vol 36 (14) ◽

pp. 4197-4199

Author(s):

Yishu Wang ◽

Arnaud Mary ◽

Marie-France Sagot ◽

Blerina Sinaimeri

Keyword(s):

Source Code ◽

Supplementary Information ◽

Optimal Solutions ◽

Supplementary Data ◽

Huge Number ◽

Tree Reconciliation ◽

Class Enumeration ◽

Suboptimal Solutions ◽

The Many ◽

Event Based

Abstract Motivation Phylogenetic tree reconciliation is the method of choice in analyzing host-symbiont systems. Despite the many reconciliation tools that have been proposed in the literature, two main issues remain unresolved: (i) listing suboptimal solutions (i.e. whose score is ‘close’ to the optimal ones) and (ii) listing only solutions that are biologically different ‘enough’. The first issue arises because the optimal solutions are not always the ones biologically most significant; providing many suboptimal solutions as alternatives for the optimal ones is thus very useful. The second one is related to the difficulty to analyze an often huge number of optimal solutions. In this article, we propose Capybara that addresses both of these problems in an efficient way. Furthermore, it includes a tool for visualizing the solutions that significantly helps the user in the process of analyzing the results. Availability and implementation The source code, documentation and binaries for all platforms are freely available at https://capybara-doc.readthedocs.io/. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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aCLImatise: automated generation of tool definitions for bioinformatics workflows

Bioinformatics ◽

10.1093/bioinformatics/btaa1033 ◽

2020 ◽

Author(s):

Michael Milton ◽

Natalie Thorne

Keyword(s):

Source Code ◽

Supplementary Information ◽

Command Line ◽

Supplementary Data ◽

Automated Generation ◽

Base Camp ◽

Python Package ◽

Bioinformatics Workflow ◽

Bioinformatics Workflows

Abstract Summary aCLImatise is a utility for automatically generating tool definitions compatible with bioinformatics workflow languages, by parsing command-line help output. aCLImatise also has an associated database called the aCLImatise Base Camp, which provides thousands of pre-computed tool definitions. Availability and implementation The latest aCLImatise source code is available within a GitHub organisation, under the GPL-3.0 license: https://github.com/aCLImatise. In particular, documentation for the aCLImatise Python package is available at https://aclimatise.github.io/CliHelpParser/, and the aCLImatise Base Camp is available at https://aclimatise.github.io/BaseCamp/. Supplementary information Supplementary data are available at Bioinformatics online.

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INDRA-IPM: interactive pathway modeling using natural language with automated assembly

Bioinformatics ◽

10.1093/bioinformatics/btz289 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4501-4503 ◽

Cited By ~ 9

Author(s):

Petar V Todorov ◽

Benjamin M Gyori ◽

John A Bachman ◽

Peter K Sorger

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Web Service ◽

Language Processing ◽

Source Code ◽

Supplementary Information ◽

Expression Data ◽

Supplementary Data ◽

Automated Assembly ◽

Web Based

Abstract Summary INDRA-IPM (Interactive Pathway Map) is a web-based pathway map modeling tool that combines natural language processing with automated model assembly and visualization. INDRA-IPM contextualizes models with expression data and exports them to standard formats. Availability and implementation INDRA-IPM is available at: http://pathwaymap.indra.bio. Source code is available at http://github.com/sorgerlab/indra_pathway_map. The underlying web service API is available at http://api.indra.bio:8000. Supplementary information Supplementary data are available at Bioinformatics online.

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