Rapport d'activité Guix-HPC 2018–2019

Guix-HPC is a collaborative effort to bring reproducible software deployment to scientific workflows and high-performance computing (HPC). Guix-HPC builds upon the GNU Guix software deployment tool and aims to make it a better tool for HPC practitioners and scientists concerned with reproducible research.This report highlights key achievements of Guix-HPC between our previous report a year ago and today, February 2020. This year was marked by a major milestone: the release in May 2019 of GNU Guix 1.0, seven years and more than 40,000 commits after its inception

Guix-HPC Activity Report 2017–2018

Guix-HPC is a collaborative effort to bring reproducible software deployment to scientific workflows and high-performance computing (HPC). Guix-HPC builds upon the GNU Guix software deployment tool and aims to make it a better tool for HPC practitioners and scientists concerned with reproducible research.Guix-HPC was launched in September 2017 as a joint software development project involving three research institutes: Inria, the Max Delbrück Center for Molecular Medicine (MDC), and the Utrecht Bioinformatics Center (UBC). GNU Guix for HPC and reproducible science has received contributions from additional individuals and organizations, including Cray, Inc. and Tourbillion Technology.This report highlights key achievements of Guix-HPC between its launch date in September 2017 and today, February 2019

Rapport d’activité Guix-HPC 2019–2020

Guix-HPC is a collaborative effort to bring reproducible software deployment to scientific workflows and high-performance computing (HPC). Guix-HPC builds upon the GNU Guix software deployment tool and aims to make it a better tool for HPC practitioners and scientists concerned with reproducible research. This report highlights key achievements of Guix-HPC between our previous report a year ago and today, February 2021.This report highlights developments on GNU Guix proper, but also downstream on Guix-Jupyter, the Guix Workflow Language, as well as tools for end-to-end reproducible research article authoring pipelines

Evaluating the performance of tools used to call minority variants from whole genome short-read data.

Background: High-throughput whole genome sequencing facilitates investigation of minority virus sub-populations from virus positive samples. Minority variants are useful in understanding within and between host diversity, population dynamics and can potentially assist in elucidating person-person transmission pathways. Several minority variant callers have been developed to describe low frequency sub-populations from whole genome sequence data. These callers differ based on bioinformatics and statistical methods used to discriminate sequencing errors from low-frequency variants. Methods: We evaluated the diagnostic performance and concordance between published minority variant callers used in identifying minority variants from whole-genome sequence data from virus samples. We used the ART-Illumina read simulation tool to generate three artificial short-read datasets of varying coverage and error profiles from an RSV reference genome. The datasets were spiked with nucleotide variants at predetermined positions and frequencies. Variants were called using FreeBayes, LoFreq, Vardict, and VarScan2. The variant callers' agreement in identifying known variants was quantified using two measures; concordance accuracy and the inter-caller concordance. Results: The variant callers reported differences in identifying minority variants from the datasets. Concordance accuracy and inter-caller concordance were positively correlated with sample coverage. FreeBayes identified the majority of variants although it was characterised by variable sensitivity and precision in addition to a high false positive rate relative to the other minority variant callers and which varied with sample coverage. LoFreq was the most conservative caller. Conclusions: We conducted a performance and concordance evaluation of four minority variant calling tools used to identify and quantify low frequency variants. Inconsistency in the quality of sequenced samples impacts on sensitivity and accuracy of minority variant callers. Our study suggests that combining at least three tools when identifying minority variants is useful in filtering errors when calling low frequency variants

Genetic Contribution to Initial and Progressive Alcohol Intake Among Recombinant Inbred Strains of Mice

We profiled individual differences in alcohol consumption upon initial exposure and during 5 weeks of voluntary alcohol intake in female mice from 39 BXD recombinant inbred strains and parents using the drinking in the dark (DID) method. In this paradigm, a single bottle of 20% (v/v) alcohol was presented as the sole liquid source for 2 or 4 h starting 3 h into the dark cycle. For 3 consecutive days mice had access to alcohol for 2 h followed by a 4th day of 4 h access and 3 intervening days where alcohol was not offered. We followed this regime for 5 weeks. For most strains, 2 or 4 h alcohol intake increased over the 5-week period, with some strains demonstrating greatly increased intake. There was considerable and heritable genetic variation in alcohol consumption upon initial early and sustained weekly exposure. Two different mapping algorithms were used to identify QTLs associated with alcohol intake and only QTLs detected by both methods were considered further. Multiple suggestive QTLs for alcohol intake on chromosomes (Chrs) 2, 6, and 12 were identified for the first 4 h exposure. Suggestive QTLs for sustained intake during later weeks were identified on Chrs 4 and 8. Thirty high priority candidate genes, including Entpd2, Per3, and Fto were nominated for early and sustained alcohol intake QTLs. In addition, a suggestive QTL on Chr 15 was detected for change in 2 h alcohol intake over the duration of the study and Adcy8 was identified as a strong candidate gene. Bioinformatic analyses revealed that early and sustained alcohol intake is likely driven by genes and pathways involved in signaling, and/or immune and metabolic function, while a combination of epigenetic factors related to alcohol experience and genetic factors likely drives progressive alcohol intake.Peer Reviewe

R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations.

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely used R/qtl software package to include multiparent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes, including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2, which is free and open source software written in the R and C++ programming languages, comes with a test framework

Guix-HPC Activity Report 2020-2021: Reproducible software deployment for high-performance computing

Guix-HPC is a collaborative effort to bring reproducible software deployment to scientific workflows and high-performance computing (HPC). Guix-HPC builds upon the GNU Guix software deployment tool and aims to make it a better tool for HPC practitioners and scientists concerned with reproducible research. This report highlights key achievements of Guix-HPC between our previous report a year ago and today, February 2022. This report highlights developments on GNU Guix proper, but also downstream on Guix-Jupyter, the Guix Workflow Language, upstream with Software Heritage integration, as well as experience reports on end-to-end reproducible research article authoring pipelines

Community-driven development for computational biology at Sprints, Hackathons and Codefests

Background: Computational biology comprises a wide range of technologies and approaches. Multiple technologies can be combined to create more powerful workflows if the individuals contributing the data or providing tools for its interpretation can find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approaches. Traditionally, scientific conferences feature talks presenting novel technologies or insights, followed up by informal discussions during coffee breaks. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one group typically visits the other. However, this does not scale well when the number of technologies or research groups is large. Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals with common interests to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies, and BoFs are generally too short to allow time for participants to program together. Results: This report summarises our experience with computational biology Codefests, Hackathons and Sprints, which are interactive developer meetings. They are structured to reduce the limitations of traditional scientific meetings described above by strengthening the interaction among peers and letting the participants determine the schedule and topics. These meetings are commonly run as loosely scheduled "unconferences" (self-organized identification of participants and topics for meetings) over at least two days, with early introductory talks to welcome and organize contributors, followed by intensive collaborative coding sessions. We summarise some prominent achievements of those meetings and describe differences in how these are organised, how their audience is addressed, and their outreach to their respective communities. Conclusions: Hackathons, Codefests and Sprints share a stimulating atmosphere that encourages participants to jointly brainstorm and tackle problems of shared interest in a self-driven proactive environment, as well as providing an opportunity for new participants to get involved in collaborative projects

Mapping Determinants of Gene Expression Plasticity by Genetical Genomics in C. elegans

Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene expression variations between genetically different individuals have been mapped to the causal regulatory regions, termed expression quantitative trait loci. Whether the environment-induced plastic response of gene expression also shows heritable difference has not yet been studied. Here we show that differential expression induced by temperatures of 16 °C and 24 °C has a strong genetic component in Caenorhabditis elegans recombinant inbred strains derived from a cross between strains CB4856 (Hawaii) and N2 (Bristol). No less than 59% of 308 trans-acting genes showed a significant eQTL-by-environment interaction, here termed plasticity quantitative trait loci. In contrast, only 8% of an estimated 188 cis-acting genes showed such interaction. This indicates that heritable differences in plastic responses of gene expression are largely regulated in trans. This regulation is spread over many different regulators. However, for one group of trans-genes we found prominent evidence for a common master regulator: a transband of 66 coregulated genes appeared at 24 °C. Our results suggest widespread genetic variation of differential expression responses to environmental impacts and demonstrate the potential of genetical genomics for mapping the molecular determinants of phenotypic plasticity