scholarly journals glactools: a command-line toolset for the management of genotype likelihoods and allele counts

2017 ◽  
Author(s):  
Gabriel Renaud
Keyword(s):  
Population Genomics ◽  
Allele Frequencies ◽  
Supplementary Information ◽  
Intermediate Representation ◽  
Command Line ◽  
Summary Statistics ◽  
Biologically Relevant ◽  
Individual Genotype ◽  
File Formats ◽  
Intermediate Format

AbstractMotivationResearch projects involving population genomics routinely need to store genotyping information, population allele frequencies, combine files from different samples, query the data and export it to various formats. This is often done using bespoke in-house scripts which cannot be easily adapted to new projects and seldom constitute reproducible workflows.ResultsWe introduce glactools, a set of command-line utilities which can import data from genotypes or population-wide allele frequencies into an intermediate representation, compute various operations on it and export the data to several file formats used by population genetics software. This intermediate format can take 2 forms, one to store per-individual genotype likelihoods and a second for allele counts from one or more individuals. glactools allows users to perform operations such as intersecting datasets, merging individuals into populations, creating subsets, perform queries (e.g. return sites where a given population does not share an allele with a second one) and compute summary statistics to answer biologically relevant questions.Availabilityglactools is freely available for use under the GPL. It requires a C++ compiler and the htslib library. (https://grenaud.github.io/glactools/)[email protected] informationSupplementary methods and results are available at Bioinformatics online.

scholarly journals DamageProfiler: Fast damage pattern calculation for ancient DNA

2021 ◽  
Author(s):  
Judith Neukamm ◽  
Alexander Peltzer ◽  
Kay Nieselt
Keyword(s):  
Ancient Dna ◽  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Central Importance ◽  
Command Line Interface ◽  
Analysis Pipeline ◽  
File Formats ◽  
Programming Knowledge ◽  
User Friendly

Abstract Motivation In ancient DNA research, the authentication of ancient samples based on specific features remains a crucial step in data analysis. Because of this central importance, researchers lacking deeper programming knowledge should be able to run a basic damage authentication analysis. Such software should be user-friendly and easy to integrate into an analysis pipeline. Results DamageProfiler is a Java based, stand-alone software to determine damage patterns in ancient DNA. The results are provided in various file formats and plots for further processing. DamageProfiler has an intuitive graphical as well as command line interface that allows the tool to be easily embedded into an analysis pipeline. Availability All of the source code is freely available on GitHub (https://github.com/Integrative-Transcriptomics/DamageProfiler). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals E-MAGMA: an eQTL-informed method to identify risk genes using genome-wide association study summary statistics

2021 ◽  
Author(s):  
Zachary F Gerring ◽  
Angela Mina-Vargas ◽  
Eric R Gamazon ◽  
Eske M Derks
Keyword(s):  
Genome Wide Association ◽  
Chromosome 1 ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Tissue Specific ◽  
Complex Disorders ◽  
Risk Variants ◽  
Genome Wide ◽  
Causal Genes

Abstract Motivation Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with human traits and diseases, but the exact causal genes are largely unknown. Common genetic risk variants are enriched in non-protein-coding regions of the genome and often affect gene expression (expression quantitative trait loci, eQTL) in a tissue-specific manner. To address this challenge, we developed a methodological framework, E-MAGMA, which converts genome-wide association summary statistics into gene-level statistics by assigning risk variants to their putative genes based on tissue-specific eQTL information. Results We compared E-MAGMA to three eQTL informed gene-based approaches using simulated phenotype data. Phenotypes were simulated based on eQTL reference data using GCTA for all genes with at least one eQTL at chromosome 1. We performed 10 simulations per gene. The eQTL-h2 (i.e., the proportion of variation explained by the eQTLs) was set at 1%, 2%, and 5%. We found E-MAGMA outperforms other gene-based approaches across a range of simulated parameters (e.g. the number of identified causal genes). When applied to genome-wide association summary statistics for five neuropsychiatric disorders, E-MAGMA identified more putative candidate causal genes compared to other eQTL-based approaches. By integrating tissue-specific eQTL information, these results show E-MAGMA will help to identify novel candidate causal genes from genome-wide association summary statistics and thereby improve the understanding of the biological basis of complex disorders. Availability A tutorial and input files are made available in a github repository: https://github.com/eskederks/eMAGMA-tutorial. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals COVID-Align: Accurate online alignment of hCoV-19 genomes using a profile HMM

2020 ◽  
Author(s):  
Frédéric Lemoine ◽  
Luc Blassel ◽  
Jakub Voznica ◽  
Olivier Gascuel
Keyword(s):  
Daily Basis ◽  
Supplementary Information ◽  
Summary Statistics ◽  
Evolutionary Novelty ◽  
Bioinformatics Analyses ◽  
Link Type ◽  
Sequencing Quality ◽  
User Friendly ◽  
Profile Hmm ◽  
New Mutations

AbstractMotivationThe first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1,000, and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data.ResultshCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of ∼2,500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1,000 genomes requires less than 20mn on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels).Availabilityhttps://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/[email protected], [email protected] informationSupplementary information is available at Bioinformatics online.

scholarly journals Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data

2020 ◽  
Vol 36 (10) ◽  
pp. 3263-3265 ◽  
Author(s):  
Lucas Czech ◽  
Pierre Barbera ◽  
Alexandros Stamatakis
Keyword(s):  
Phylogenetic Trees ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Computationally Efficient ◽  
Data Types ◽  
Low Level ◽  
Phylogenetic Placement ◽  
Command Line Tool ◽  
High Level

Abstract Summary We present genesis, a library for working with phylogenetic data, and gappa, an accompanying command-line tool for conducting typical analyses on such data. The tools target phylogenetic trees and phylogenetic placements, sequences, taxonomies and other relevant data types, offer high-level simplicity as well as low-level customizability, and are computationally efficient, well-tested and field-proven. Availability and implementation Both genesis and gappa are written in modern C++11, and are freely available under GPLv3 at http://github.com/lczech/genesis and http://github.com/lczech/gappa. Supplementary information Supplementary data are available at Bioinformatics online.

Spliceogen: an integrative, scalable tool for the discovery of splice-altering variants

2019 ◽  
Vol 35 (21) ◽  
pp. 4405-4407 ◽  
Author(s):  
Steven Monger ◽  
Michael Troup ◽  
Eddie Ip ◽  
Sally L Dunwoodie ◽  
Eleni Giannoulatou
Keyword(s):  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
In Silico Prediction ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Prediction Tools ◽  
Motif Prediction ◽  
Command Line Tool ◽  
Genome Scale

Abstract Motivation In silico prediction tools are essential for identifying variants which create or disrupt cis-splicing motifs. However, there are limited options for genome-scale discovery of splice-altering variants. Results We have developed Spliceogen, a highly scalable pipeline integrating predictions from some of the individually best performing models for splice motif prediction: MaxEntScan, GeneSplicer, ESRseq and Branchpointer. Availability and implementation Spliceogen is available as a command line tool which accepts VCF/BED inputs and handles both single nucleotide variants (SNVs) and indels (https://github.com/VCCRI/Spliceogen). SNV databases with prediction scores are also available, covering all possible SNVs at all genomic positions within all Gencode-annotated multi-exon transcripts. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BioKEEN: a library for learning and evaluating biological knowledge graph embeddings

2019 ◽  
Vol 35 (18) ◽  
pp. 3538-3540 ◽  
Author(s):  
Mehdi Ali ◽  
Charles Tapley Hoyt ◽  
Daniel Domingo-Fernández ◽  
Jens Lehmann ◽  
Hajira Jabeen
Keyword(s):  
Supplementary Information ◽  
Knowledge Graph ◽  
Biological Knowledge ◽  
Command Line ◽  
Graph Embeddings ◽  
Command Line Interface ◽  
Software Ecosystem ◽  
Mapping Resource ◽  
Significant Attention

Abstract Summary Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies. Availability and implementation BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals aCLImatise: automated generation of tool definitions for bioinformatics workflows

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.

scholarly journals COVID-Align: Accurate online alignment of hCoV-19 genomes using a profile HMM

2020 ◽  
Author(s):  
Frédéric Lemoine ◽  
Luc Blassel ◽  
Jakub Voznica ◽  
Olivier Gascuel
Keyword(s):  
Daily Basis ◽  
Multiple Alignment ◽  
Supplementary Information ◽  
Summary Statistics ◽  
Evolutionary Novelty ◽  
Bioinformatics Analyses ◽  
Sequencing Quality ◽  
User Friendly ◽  
Profile Hmm ◽  
New Mutations

Abstract Motivation The first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1,000, and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data. Results hCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of ∼2,500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1,000 genomes requires ∼50mn on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels). Availability https://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/covid-align Supplementary information Supplementary information is available at Bioinformatics online.

scholarly journals Visualization of circular RNAs and their internal splicing events from transcriptomic data

2020 ◽  
Vol 36 (9) ◽  
pp. 2934-2935 ◽  
Author(s):  
Yi Zheng ◽  
Fangqing Zhao
Keyword(s):  
Supplementary Information ◽  
Circular Rnas ◽  
Visualization Tool ◽  
Command Line ◽  
Supplementary Data ◽  
Transcriptomic Data ◽  
Command Line Tool ◽  
Transcriptome Comparison ◽  
Multiple Samples ◽  
Splicing Patterns

Abstract Summary Circular RNAs (circRNAs) are proved to have unique compositions and splicing events distinct from canonical mRNAs. However, there is no visualization tool designed for the exploration of complex splicing patterns in circRNA transcriptomes. Here, we present CIRI-vis, a Java command-line tool for quantifying and visualizing circRNAs by integrating the alignments and junctions of circular transcripts. CIRI-vis can be applied to visualize the internal structure and isoform abundance of circRNAs and perform circRNA transcriptome comparison across multiple samples. Availability and implementation https://sourceforge.net/projects/ciri/files/CIRI-vis. Supplementary information Supplementary data are available at Bioinformatics online.

Gene-based association tests using GWAS summary statistics

2019 ◽  
Vol 35 (19) ◽  
pp. 3701-3708 ◽  
Author(s):  
Gulnara R Svishcheva ◽  
Nadezhda M Belonogova ◽  
Irina V Zorkoltseva ◽  
Anatoly V Kirichenko ◽  
Tatiana I Axenovich
Keyword(s):  
Association Analysis ◽  
Association Studies ◽  
R Package ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
New Material ◽  
Artery Disease ◽  
The Many ◽  
Functional Linear Regression

Abstract Motivation A huge number of genome-wide association studies (GWAS) summary statistics freely available in databases provide a new material for gene-based association analysis aimed at identifying rare genetic variants. Only a few of the many popular gene-based methods developed for individual genotype and phenotype data are adapted for the practical use of the GWAS summary statistics as input. Results We analytically prove and numerically illustrate that all popular powerful methods developed for gene-based association analysis of individual phenotype and genotype data can be modified to utilize GWAS summary statistics. We have modified and implemented all of the popular methods, including burden and kernel machine-based tests, multiple and functional linear regression, principal components analysis and others, in the R package sumFREGAT. Using real summary statistics for coronary artery disease, we show that the new package is able to detect genes not found by the existing packages. Availability and implementation The R package sumFREGAT is freely and publicly available at: https://CRAN.R-project.org/package=sumFREGAT. Supplementary information Supplementary data are available at Bioinformatics online.

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