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The heterogeneous stock (HS) is an outbred rat population derived from eight inbred rat strains. HS rats are ideally suited for genome wide association studies; however, only a few genotyping microarrays have ever been designed for rats and none of them are currently in production. To address the need for an efficient and cost effective method of genotyping HS rats, we have adapted genotype-by-sequencing (GBS) to obtain genotype information at large numbers of single nucleotide polymorphisms (SNPs). In this paper, we have outlined the laboratory and computational steps we took to optimize double digest genotype-by-sequencing (ddGBS) for use in rats. We also evaluate multiple existing computational tools and explain the workflow we have used to call and impute over 3.7 million SNPs. We also compared various rat genetic maps, which are necessary for imputation, including a recently developed map specific to the HS. Using our approach, we obtained concordance rates of 99% with data obtained using data from a genotyping array. The principles and computational pipeline that we describe could easily be adapted for use in other species for which reliable reference genome sets are available.

MutantHuntWGS is a user-friendly pipeline for analyzing Saccharomyces cerevisiae whole-genome sequencing data. It uses available open-source programs to: (1) perform sequence alignments for paired and single-end reads, (2) call variants, and (3) predict variant effect and severity. MutantHuntWGS outputs a shortlist of variants while also enabling access to all intermediate files. To demonstrate its utility, we use MutantHuntWGS to assess multiple published datasets; in all cases, it detects the same causal variants reported in the literature. To encourage broad adoption and promote reproducibility, we distribute a containerized version of the MutantHuntWGS pipeline that allows users to install and analyze data with only two commands. The MutantHuntWGS software and documentation can be downloaded free of charge from https://github.com/mae92/MutantHuntWGS.

With recent advances in sequencing technologies, the scientific community has begun to probe the potential genetic bases behind complex phenotypes in humans and model organisms. In many cases, the genomes of genetically distinct strains of model organisms, such as the mouse (Mus musculus), have not been fully sequenced. Here, we report on a tool designed to use single-nucleotide polymorphism (SNP) and insertion-deletion (indel) data to predict gene, mRNA, and protein sequences for up to 36 genetically distinct mouse strains. By automated querying of freely accessible databases through a graphical interface, the software requires no data and little computational experience. As a proof of concept, we predicted the gene and amino acid sequence of the aryl hydrocarbon receptor (Ahr) for all inbred mouse strains of which variant data were currently available through Mouse Genome Project. Predicted sequences were compared with fully sequenced genomes to show that the tool is effective in predicting gene and protein sequences.

CRISPR-Cas9 is a powerful genome editing technology in which a single guide RNA (sgRNA) confers target site specificity to achieve Cas9-mediated genome editing. Numerous sgRNA design tools have been developed based on reference genomes for humans and model organisms. However, existing resources are not optimal as genetic mutations or single nucleotide polymorphisms (SNPs) within the targeting region affect the efficiency of CRISPR-based approaches by interfering with guide-target complementarity. To facilitate identification of sgRNAs (1) in non-reference genomes, (2) across varying genetic backgrounds, or (3) for specific targeting of SNP-containing alleles, for example, disease relevant mutations, we developed a web tool, SNP-CRISPR (https://www.flyrnai.org/tools/snp_crispr/). SNP-CRISPR can be used to design sgRNAs based on public variant data sets or user-identified variants. In addition, the tool computes efficiency and specificity scores for sgRNA designs targeting both the variant and the reference. Moreover, SNP-CRISPR provides the option to upload multiple SNPs and target single or multiple nearby base changes simultaneously with a single sgRNA design. Given these capabilities, SNP-CRISPR has a wide range of potential research applications in model systems and for design of sgRNAs for disease-associated variant correction.

Present-day and ancient population genomic studies from different study organisms have rapidly become accessible to diverse research groups worldwide. Unfortunately, as datasets and analyses become more complex, researchers with less computational experience often miss their chance to analyze their own data. We introduce FrAnTK, a user-friendly toolkit for computation and visualization of allele frequency-based statistics in ancient and present-day genome variation datasets. We provide fast, memory-efficient tools that allow the user to go from sequencing data to complex exploratory analyses and visual representations with minimal data manipulation. Its simple usage and low computational requirements make FrAnTK ideal for users that are less familiar with computer programming carrying out large-scale population studies.

Identification and retrieval of genes of interest from genomic data are an essential step for many bioinformatic applications. We present orthofisher, a command-line tool for automated identification and retrieval of genes with high sequence similarity to a query profile Hidden Markov Model sequence alignment across a set of proteomes. Performance assessment of orthofisher revealed high accuracy and precision during single-copy orthologous gene identification. orthofisher may be useful for assessing gene annotation quality, identifying single-copy orthologous genes for phylogenomic analyses, estimating gene copy number, and other evolutionary analyses that rely on identification and retrieval of homologous genes from genomic data. orthofisher comes complete with comprehensive documentation (https://jlsteenwyk.com/orthofisher/), is freely available under the MIT license, and is available for download from GitHub (https://github.com/JLSteenwyk/orthofisher), PyPi (https://pypi.org/project/orthofisher/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/orthofisher).

Motivation: Bacterial genomes are composed by a core and an accessory genome. The first composed of housekeeping and essential genes, while the second is composed, in its majority, of mobile genetic elements, including transposable elements (TEs). Insertion sequences (ISs), the smallest TEs, have an important role in genome evolution, and contribute to bacterial genome plasticity and adaptability. ISs can spread in a genome, presenting different locations in nearly related strains, and producing phenotypic variations. Few tools are available which can identify differentially located ISs (DLIS) on assembled genomes. Results: We developed ISCompare to profile IS mobilization events in related bacterial strains using complete or draft genome assemblies. ISCompare was validated using artificial genomes with simulated random IS insertions and real sequences, achieving the same or better results than other available tools, with the advantage that ISCompare can analyse multiple ISs at the same time and outputs a list of candidate DLIS. We think that ISCompare provides an easy and straightforward approach to look for differentially located ISs on bacterial genomes., Instituto de Biotecnología y Biología Molecular

Microsatellites are repetitive DNA sequences usually found in non-coding regions of the genome. Their quantification and analysis have applications in fields from population genetics to evolutionary biology. As genome assemblies become commonplace, the need for software that can facilitate analyses has never been greater. In particular, R packages that can analyze genomic data are particularly important since this is one of the most popular software environments for biologists. We created an R package, micRocounter, to quantify microsatellites. We have optimized our package for speed, accessibility, and portability, making the automated analysis of large genomic data sets feasible. Computationally intensive algorithms were built in C++ to increase speed. Tests using benchmark datasets show a 200-fold improvement in speed over existing software. A moderately sized genome of 500 Mb can be processed in under 50 sec. Results are output as an object in R increasing accessibility and flexibility for practitioners.

The dna is the fundamental basis of genetic information, just as bits are for computers. Whenever computers are used to represent genetic data, the computational encoding must be efficient to allow the representation of processes driving the inheritance and variability. This is especially important across simulations in view of the increasing complexity and dimensions brought by genomics. This paper introduces a new binary representation of genetic information. Algorithms as bitwise operations that mimic the inheritance of a wide range of polymorphisms are also presented. Different kinds and mixtures of polymorphisms are discussed and exemplified. Proposed algorithms and data structures were implemented in C++ programming language and is available to end users in the R package “isqg” which is available at the R repository (cran). Supplementary data are available online.

Copy number variants (CNV) are associated with phenotypic variation in several species. However, properly detecting changes in copy numbers of sequences remains a difficult problem, especially in lower quality or lower coverage next-generation sequencing data. Here, inspired by recent applications of machine learning in genomics, we describe a method to detect duplications and deletions in short-read sequencing data. In low coverage data, machine learning appears to be more powerful in the detection of CNVs than the gold-standard methods of coverage estimation alone, and of equal power in high coverage data. We also demonstrate how replicating training sets allows a more precise detection of CNVs, even identifying novel CNVs in two genomes previously surveyed thoroughly for CNVs using long read data.

Poly(A)-tail targeted RNAseq approaches, such as 3′READS, PAS-Seq and Poly(A)-ClickSeq, are becoming popular alternatives to random-primed RNAseq to focus sequencing reads just to the 3′ ends of polyadenylated RNAs to identify poly(A)-sites and characterize changes in their usage. Additionally, we and others have demonstrated that these approaches perform similarly to other RNAseq strategies for differential gene expression analysis, while saving on the volume of sequencing data required and providing a simpler library synthesis strategy. Here, we present DPAC (Differential Poly(A)-Clustering); a streamlined pipeline for the preprocessing of poly(A)-tail targeted RNAseq data, mapping of poly(A)-sites, poly(A)-site clustering and annotation, and determination of differential poly(A)-cluster usage using DESeq2. Changes in poly(A)-cluster usage is simultaneously used to report differential gene expression, differential terminal exon usage and alternative polyadenylation (APA).

The PacBio sequencing is a powerful approach to study the DNA or RNA sequences in a longer scope. It is especially useful in exploring the complex structural variants generated by random integration or multiple rearrangement of internal or external sequences. However, there is still no tool designed to uncover their structural organization in the host genome. Here, we present a tool, TSD, for complex structural variant discovery using PacBio targeted sequencing data. It allows researchers to identify and visualize the genomic structures of targeted sequences by unlimited splitting, alignment and assembly of long PacBio reads. Application to the sequencing data derived from an HBV integrated human cell line(PLC/PRF/5) indicated that TSD could recover the full profile of HBV integration events, especially for the regions with the complex human-HBV genome integrations and multiple HBV rearrangements. Compared to other long read analysis tools, TSD showed a better performance for detecting complex genomic structural variants. TSD is publicly available at: https://github.com/menggf/tsd

Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by GS to perform predictions of candidate genotypes that were not phenotyped. Because there is no universally better model for prediction and models for each type of response variable are needed (continuous, binary, ordinal, count, etc.), an active area of research aims to develop statistical models for the prediction of univariate and multivariate traits in GS. However, most of the models developed so far are for univariate and continuous (Gaussian) traits. Therefore, to overcome the lack of multivariate statistical models for genome-based prediction by improving the original version of the BMTME, we propose an improved Bayesian multi-trait and multi-environment (BMTME) R package for analyzing breeding data with multiple traits and multiple environments. We also introduce Bayesian multi-output regressor stacking (BMORS) functions that are considerably efficient in terms of computational resources. The package allows parameter estimation and evaluates the prediction performance of multi-trait and multi-environment data in a reliable, efficient and user-friendly way. We illustrate the use of the BMTME with real toy datasets to show all the facilities that the software offers the user. However, for large datasets, the BME() and BMTME() functions of the BMTME R package are very intense in terms of computing time; on the other hand, less intensive computing is required with BMORS functions BMORS() and BMORS_Env() that are also included in the BMTME package.

Low or uneven read depth is a common limitation of genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq), resulting in high missing data rates, heterozygotes miscalled as homozygotes, and uncertainty of allele copy number in heterozygous polyploids. Bayesian genotype calling can mitigate these issues, but previously has only been implemented in software that requires a reference genome or uses priors that may be inappropriate for the population. Here we present several novel Bayesian algorithms that estimate genotype posterior probabilities, all of which are implemented in a new R package, polyRAD. Appropriate priors can be specified for mapping populations, populations in Hardy-Weinberg equilibrium, or structured populations, and in each case can be informed by genotypes at linked markers. The polyRAD software imports read depth from several existing pipelines, and outputs continuous or discrete numerical genotypes suitable for analyses such as genome-wide association and genomic prediction.

This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait’s realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation. MeSCoT performs a detailed simulation of genes’ regulatory interactions for variable genomic architectures and generates complete set of transcriptional and translational data together with simulated quantitative trait values. Such data provide opportunities to study, for example, verification of novel statistical methods aiming to integrate intermediate phenotypes together with final phenotype in quantitative genetic analyses or to investigate novel approaches for exploiting gene-by-gene and gene-by-environment interactions.

Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.

In microbiome research, metagenomic sequencing generates enormous amounts of data. These data are typically classified into taxa for taxonomy analysis, or into genes for functional analysis. However, a joint analysis where the reads are classified into taxa-specific genes is often overlooked. To enable the analysis of this biologically meaningful feature, we developed a novel bioinformatic toolkit, MetaPrism, which can analyze sequence reads for a set of joint taxa/gene analyses to: 1) classify sequence reads and estimate the abundances for taxa-specific genes; 2) tabularize and visualize taxa-specific gene abundances; 3) compare the abundances between groups; and 4) build prediction models for clinical outcome. We illustrated these functions using a published microbiome metagenomics dataset from patients treated with immune checkpoint inhibitor therapy and showed the joint features can serve as potential biomarkers to predict therapeutic responses. MetaPrism is a toolkit for joint taxa and gene analysis. It offers biological insights on the taxa-specific genes on top of the taxa-alone or gene-alone analysis. MetaPrism is open-source software and freely available at https://github.com/jiwoongbio/MetaPrism. The example script to reproduce the manuscript is also provided in the above code repository.

Allelic imbalance (AI) occurs when alleles in a diploid individual are differentially expressed and indicates cis acting regulatory variation. What is the distribution of allelic effects in a natural population? Are all alleles the same? Are all alleles distinct? The approach described applies to any technology generating allele-specific sequence counts, for example for chromatin accessibility and can be applied generally including to comparisons between tissues or environments for the same genotype. Tests of allelic effect are generally performed by crossing individuals and comparing expression between alleles directly in the F1. However, a crossing scheme that compares alleles pairwise is a prohibitive cost for more than a handful of alleles as the number of crosses is at least (n2-n)/2 where n is the number of alleles. We show here that a testcross design followed by a hypothesis test of AI between testcrosses can be used to infer differences between nontester alleles, allowing n alleles to be compared with n crosses. Using a mouse data set where both testcrosses and direct comparisons have been performed, we show that the predicted differences between nontester alleles are validated at levels of over 90% when a parent-of-origin effect is present and of 60%−80% overall. Power considerations for a testcross, are similar to those in a reciprocal cross. In all applications, the testing for AI involves several complex bioinformatics steps. BayesASE is a complete bioinformatics pipeline that incorporates state-of-the-art error reduction techniques and a flexible Bayesian approach to estimating AI and formally comparing levels of AI between conditions. The modular structure of BayesASE has been packaged in Galaxy, made available in Nextflow and as a collection of scripts for the SLURM workload manager on github (https://github.com/McIntyre-Lab/BayesASE).

Neurospora crassa has been an important model organism for molecular biology and genetics for over 60 years. Neurospora crassa has a complex life cycle, with over 28 distinct cell types and is capable of transcriptional responses to many environmental conditions including nutrient availability, temperature, and light. To quantify variation in N. crassa gene expression, we analyzed public expression data from 97 conditions and calculated the Shannon Entropy value for Neurospora’s approximately 11,000 genes. Entropy values can be used to estimate the variability in expression for a single gene over a range of conditions and be used to classify individual genes as constitutive or condition-specific. Shannon entropy has previously been used measure the degree of tissue specificity of multicellular plant or animal genes. We use this metric here to measure variable gene expression in a microbe and provide this information as a resource for the N. crassa research community. Finally, we demonstrate the utility of this approach by using entropy values to identify genes with constitutive expression across a wide range of conditions and to identify genes that are activated exclusively during sexual development.

Advances in genomics techniques are expanding the range of nematode species that are amenable to transgenesis. Due to divergent codon usage biases across species, codon optimization is often a critical step for the successful expression of exogenous transgenes in nematodes. Platforms for generating DNA sequences codon optimized for the free-living model nematode Caenorhabditis elegans are broadly available. However, until now such tools did not exist for non-Caenorhabditis nematodes. We therefore developed the Wild Worm Codon Adapter, a tool for rapid transgene codon optimization for expression in non-Caenorhabditis nematodes. The app includes built-in optimization for parasitic nematodes in the Strongyloides, Nippostrongylus and Brugia genera as well as the predatory nematode Pristionchus pacificus. The app also supports custom optimization for any species using user-provided optimization rules. In addition, the app supports automated insertion of synthetic or native introns, as well as the analysis of codon bias in transgene and native sequences. Here, we describe this web-based tool and demonstrate how it may be used to analyze genome-wide codon bias in Strongyloides species.

Soil-transmitted gastrointestinal parasitic nematodes infect approximately 1 billion people worldwide, predominantly in low-resource communities. Skin-penetrating gastrointestinal nematodes in the genus Strongyloides are emerging as model systems for mechanistic studies of soil-transmitted helminths due to the growing availability of functional genomics tools for these species. To facilitate future genomics studies of Strongyloides species, we have designed a web-based application, the Strongyloides RNA-seq Browser, that provides an open source, user-friendly portal for accessing and analyzing Strongyloides genomic expression data. Specifically, the Strongyloides RNA-seq Browser takes advantage of alignment-free read mapping tools and R-based transcriptomics tools to re-analyze publicly available RNA sequencing datasets from four Strongyloides species: Strongyloides stercoralis, Strongyloides ratti, Strongyloides papillosus, and Strongyloides venezuelensis. This application permits on-demand exploration and quantification of gene expression across life stages without requiring previous coding experience. Here, we describe this interactive application and demonstrate how it may be used by nematode researchers to conduct a standard set of bioinformatics queries.

Phenotypes of sessile organisms, such as plants, rely not only on their own genotype but also on the genotypes of neighboring individuals. Previously, we incorporated such neighbor effects into a single-marker regression using the Ising model of ferromagnetism. However, little is known about how to incorporate neighbor effects in quantitative trait locus (QTL) mapping. In this study, we propose a new method for interval QTL mapping of neighbor effects, named “Neighbor QTL”. The algorithm of neighbor QTL involves the following: (i) obtaining conditional self-genotype probabilities with recombination fraction between flanking markers, (ii) calculating neighbor genotypic identity using the self-genotype probabilities, and (iii) estimating additive and dominance deviation for neighbor effects. Our simulation using F2 and backcross lines showed that the power to detect neighbor effects increased as the effective range became smaller. The neighbor QTL was applied to insect herbivory on Col × Kas recombinant inbred lines ofArabidopsis thaliana. Consistent with previous evidence, the pilot experiment detected a self QTL effect on the herbivory atGLABRA1locus. We also observed a weak QTL on chromosome 4 regarding neighbor effects on the herbivory. The neighbor QTL method is available as an R package (https://cran.r-project.org/package=rNeighborQTL), providing a novel tool to investigate neighbor effects in QTL studies.

Advances in genome engineering and high throughput imaging technologies have enabled genome-scale screens of single cells for a variety of phenotypes, including subcellular morphology and protein localization. We constructed TheCellVision.org, a freely available and web-accessible image visualization and data browsing tool that serves as a central repository for fluorescence microscopy images and associated quantitative data produced by high-content screening experiments. Currently, TheCellVision.org hosts 575,590 images and associated analysis results from two published high-content screening (HCS) projects focused on the budding yeast Saccharomyces cerevisiae. TheCellVision.org allows users to access, visualize and explore fluorescence microscopy images, and to search, compare, and extract data related to subcellular compartment morphology, protein abundance, and localization. Each dataset can be queried independently or as part of a search across multiple datasets using the advanced search option. The website also hosts computational tools associated with the available datasets, which can be applied to other projects and cell systems, a feature we demonstrate using published images of mammalian cells. Providing access to HCS data through websites such as TheCellVision.org enables new discovery and independent re-analyses of imaging data.

Post-translational modification (PTM) serves as a regulatory mechanism for protein function, influencing their stability, interactions, activity and localization, and is critical in many signaling pathways. The best characterized PTM is phosphorylation, whereby a phosphate is added to an acceptor residue, most commonly serine, threonine and tyrosine in metazoans. As proteins are often phosphorylated at multiple sites, identifying those sites that are important for function is a challenging problem. Considering that any given phosphorylation site might be non-functional, prioritizing evolutionarily conserved phosphosites provides a general strategy to identify the putative functional sites. To facilitate the identification of conserved phosphosites, we generated a large-scale phosphoproteomics dataset from Drosophila embryos collected from six closely-related species. We built iProteinDB (https://www.flyrnai.org/tools/iproteindb/), a resource integrating these data with other high-throughput PTM datasets, including vertebrates, and manually curated information for Drosophila. At iProteinDB, scientists can view the PTM landscape for any Drosophila protein and identify predicted functional phosphosites based on a comparative analysis of data from closely-related Drosophila species. Further, iProteinDB enables comparison of PTM data from Drosophila to that of orthologous proteins from other model organisms, including human, mouse, rat, Xenopus tropicalis, Danio rerio, and Caenorhabditis elegans., G3: Genes, Genomes, Genetics, 9 (1), ISSN:2160-1836

One of the most commonly used tools to compare protein or DNA sequences against databases is BLAST. We introduce a web tool that allows the performance of BLAST-searches of protein/DNA sequences in whole-genome sequenced bacteria/archaea, and displays a large amount of BLAST-results simultaneously. The circular bacterial replicons are projected as horizontal lines with fixed length of 360, representing the degrees of a circle. A coordinate system is created with length of the replicon along the x-axis and the number of replicon used on the y-axis. When a query sequence matches with a gene/protein of a particular replicon, the BLAST-results are depicted as an “x,y” position in a specially adapted plot. This tool allows the visualization of the results from the whole data to a particular gene/protein in real time with low computational resources.

Genomic analysis in Juglans (walnuts) is expected to transform the breeding and agricultural production of both nuts and lumber. To that end, we report here the determination of reference sequences for six additional relatives of Juglans regia: Juglans sigillata (also from section Dioscaryon), Juglans nigra, Juglans microcarpa, Juglans hindsii (from section Rhysocaryon), Juglans cathayensis (from section Cardiocaryon), and the closely related Pterocarya stenoptera. While these are ‘draft’ genomes, ranging in size between 640Mbp and 990Mbp, their contiguities and accuracies can support powerful annotations of genomic variation that are often the foundation of new avenues of research and breeding. We annotated nucleotide divergence and synteny by creating complete pairwise alignments of each reference genome to the remaining six. In addition, we have re-sequenced a sample of accessions from four Juglans species (including regia). The variation discovered in these surveys comprises a critical resource for experimentation and breeding, as well as a solid complementary annotation. To demonstrate the potential of these resources the structural and sequence variation in and around the polyphenol oxidase loci, PPO1 and PPO2 were investigated. As reported for other seed crops variation in this gene is implicated in the domestication of walnuts. The apparently Juglandaceae specific PPO1 duplicate shows accelerated divergence and an excess of amino acid replacement on the lineage leading to accessions of the domesticated nut crop species, Juglans regia and sigillata.

Two major stumbling blocks exist in high-throughput sequencing (HTS) data analysis. The first is the sheer file size, typically in gigabytes when uncompressed, causing problems in storage, transmission, and analysis. However, these files do not need to be so large, and can be reduced without loss of information. Each HTS file, either in compressed .SRA or plain text .fastq format, contains numerous identical reads stored as separate entries. For example, among 44,603,541 forward reads in the SRR4011234.sra file (from a Bacillus subtilis transcriptomic study) deposited at NCBI’s SRA database, one read has 497,027 identical copies. Instead of storing them as separate entries, one can and should store them as a single entry with the SeqID_NumCopy format (which I dub as FASTA+ format). The second is the proper allocation of reads that map equally well to paralogous genes. I illustrate in detail a new method for such allocation. I have developed ARSDA software that implement these new approaches. A number of HTS files for model species are in the process of being processed and deposited at http://coevol.rdc.uottawa.ca to demonstrate that this approach not only saves a huge amount of storage space and transmission bandwidth, but also dramatically reduces time in downstream data analysis. Instead of matching the 497,027 identical reads separately against the B. subtilis genome, one only needs to match it once. ARSDA includes functions to take advantage of HTS data in the new sequence format for downstream data analysis such as gene expression characterization. I contrasted gene expression results between ARSDA and Cufflinks so readers can better appreciate the strength of ARSDA. ARSDA is freely available for Windows, Linux. and Macintosh computers at http://dambe.bio.uottawa.ca/ARSDA/ARSDA.aspx.

The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myriad of specialized software for its analysis. Each software module typically targets a specific step within the analysis pipeline, making it necessary to join several of them to get a single cohesive workflow. Multiple software programs automating this procedure have been proposed, but often lack modularity, transparency or flexibility. We present ARMOR, which performs an end-to-end RNA-seq data analysis, from raw read files, via quality checks, alignment and quantification, to differential expression testing, geneset analysis and browser-based exploration of the data. ARMOR is implemented using the Snakemake workflow management system and leverages conda environments; Bioconductor objects are generated to facilitate downstream analysis, ensuring seamless integration with many R packages. The workflow is easily implemented by cloning the GitHub repository, replacing the supplied input and reference files and editing a configuration file. Although we have selected the tools currently included in ARMOR, the setup is modular and alternative tools can be easily integrated.

We created a suite of packages to enable analysis of extremely large genomic data sets (potentially millions of individuals and millions of molecular markers) within the R environment. The package offers: a matrix-like interface for .bed files (PLINK’s binary format for genotype data), a novel class of linked arrays that allows linking data stored in multiple files to form a single array accessible from the R computing environment, methods for parallel computing capabilities that can carry out computations on very large data sets without loading the entire data into memory and a basic set of methods for statistical genetic analyses. The package is accessible through CRAN and GitHub. In this note, we describe the classes and methods implemented in each of the packages that make the suite and illustrate the use of the packages using data from the UK Biobank.

Time-course high-throughput assays of gene expression and enhancer usage in zebrafish provide a valuable characterization of the dynamic mechanisms governing gene regulatory programs during CNS axon regeneration. To facilitate the exploration and functional interpretation of a set of fully-processed data on regeneration-associated temporal transcription networks, we have created an interactive web application called Regeneration Rosetta. Using either built-in or user-provided lists of genes in one of dozens of supported organisms, our web application facilitates the (1) visualization of clustered temporal expression trends; (2) identification of proximal and distal regions of accessible chromatin to expedite downstream motif analysis; and (3) description of enriched functional gene ontology categories. By enabling a straightforward interrogation of these rich data without extensive bioinformatic expertise, Regeneration Rosetta is broadly useful for both a deep investigation of time-dependent regulation during regeneration in zebrafish and hypothesis generation in other organisms.

Genomic Selection (GS) is the procedure whereby molecular information is used to predict complex phenotypes and it is standard in many animal and plant breeding schemes. However, only a small number of studies have been reported in horticultural crops, and in polyploid species in particular. In this paper, we have developed a versatile forward simulation tool, called polyploid Sequence Based Virtual Breeding (pSBVB), to evaluate GS strategies in polyploids; pSBVB is an efficient gene dropping software that can simulate any number of complex phenotypes, allowing a very flexible modeling of phenotypes suited to polyploids. As input, it takes genotype data from the founder population, which can vary from single nucleotide polymorphisms (SNP) chips up to sequence, a list of causal variants for every trait and their heritabilities, and the pedigree. Recombination rates between homeologous chromosomes can be specified, so that both allo- and autopolyploid species can be considered. The program outputs phenotype and genotype data for all individuals in the pedigree. Optionally, it can produce several genomic relationship matrices that consider exact or approximate genotype values. pSBVB can therefore be used to evaluate GS strategies in polyploid species (say varying SNP density, genetic architecture or population size, among other factors), or to optimize experimental designs for association studies. We illustrate pSBVB with SNP data from tetraploid potato and partial sequence data from octoploid strawberry, and we show that GS is a promising breeding strategy for polyploid species but that the actual advantage critically depends on the underlying genetic architecture. Source code, examples and a complete manual are freely available in GitHub https://github.com/lauzingaretti/pSBVB., MLZ is recipient of a FPI grant to achieve the PhD research from Ministry of Economy and Science (MINECO, Spain), associated with ‘Centro de Excelencia Severo Ochoa 2016-2019’ award SEV-2015-0533 to CRAG. Work funded by MINECO grant RTA2013-00010-00-00 to AM and AGL2016-78709-R to MPE.

Multi-parental recombinant inbred populations, such as the Collaborative Cross (CC) mouse genetic reference population, are increasingly being used for analysis of quantitative trait loci (QTL). However specialized analytic software for these complex populations is typically built in R that works only on command-line, which limits the utility of these powerful resources for many users. To overcome analytic limitations, we developed gQTL, a web accessible, simple graphical user interface application based on the DOQTL platform in R to perform QTL mapping using data from CC mice.

Existing methods for QTL mapping in experimental crosses assume that the amount of residual variation is constant across all individuals. Many common situations violate this assumption. For example, female mice often have more variable phenotypes than males, some experimenters make more precise measurements than others, and specific genetic loci may influence the extent of variation between individuals. In all such cases, the heterogeneous variance modeling approach demonstrated here provides higher power, better protection against false positives, and allows for detection of QTL that influence phenotype variance, termed vQTL. The R package vqtl makes it easy for geneticists to apply the hetero-geneous variance model, control family-wide error rate (FWER), and visualize and interpret their results. Because this package is interoperable with the popular R/qtl package and uses many of the same data structures and input patterns, it will be easy for geneticists to analyze the results of their experimental crosses with vqtl, possibly discovering new QTL. Here, we demonstrate typical usage.