Software tools implemented and developed by the consortium for computational analysis
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- HiCDCPlus — sourceThe package HiCDCPlus provides methods to determine significant and differential chromatin interactions by use of a negative binomial generalized linear model, as well as implementations for TopDom to call topologically associating domains (TADs), and Juicer eigenvector to find the A/B compartments. This vignette explains the use of the package and demonstrates typical workflows on HiC and HiChIP data.
- sQTLseekeR — sourcesQTLseekeR is a package to detect splicing QTLs (sQTLs), which are variants associated with change in the splicing pattern of a gene. In sQTLSeeker, splicing patterns are modeled by the relative expression of the transcripts of a gene. The most recent version of sQTLseekeR can be employed to detect genetic variant associated to any multivariate phenotypeSoftware type: variant annotation
- ggsashimi — sourcea command-line tool for the visualization of splicing events across multiple samples. Given a specified genomic region, ggsashimi creates sashimi plots for individual RNA-seq experiments as well as aggregated plots for groups of experiments. It uses popular bioinformatics file formats, it is annotation-independent, and allows the visualization of splicing events even for large genomic regions by scaling down the genomic segments between splice sites. It is implemented in python, and internally generates R code for plotting.Software type: visualization
- apricot — sourceapricot implements submodular optimization for the purpose of summarizing massive data sets into minimally redundant subsets that are still representative of the original data. These subsets are useful for both visualizing the modalities in the data and for training accurate machine learning models with just a fraction of the examples and compute.
- scPOST — sourceSimulation of single-cell datasets for power analyses that estimate power to detect cell state frequency shifts between conditions (e.g. an expansion of a cell state in disease vs. healthy), as described in our manuscript “Maximizing statistical power to detect clinically associated cell states with scPOST”.Software type: other
- cdr3-QTL — sourceWe tested associations between HLA genotypes and TCR-CDR3 amino acid compositions. We treated the amino acid composition of CDR3 as a quantitative trait, and tested its association with HLA genotype; we call this CDR3 quantitative trait loci analysis (cdr3-QTL), as described in our manuscript “HLA autoimmune risk alleles restrict the hypervariable region of T cell receptors”.Software type: other
- Imperio — sourceThis software includes (i) DeepBoost, a gradient boosting method for constructing boosted deep learning annotations by integrating deep learning allelic-effect annotations with fine-mapped SNPs; (ii) tools to combine these deep learning annotations with SNP-to-gene (S2G) linking strategies and relevant gene sets, and (iii) Imperio, a method for integrating deep learning annotations with S2G strategies to predict gene expression in whole blood and construct allelic-effect annotations based on changes in predicted expression. Applications of these 3 approaches to blood-related traits are described in our manuscript “Integrative approaches to improve the informativeness of deep learning models for human complex diseases”.Software type: other
- GSSG — sourceGSSG consists of tools to generate enhancer-driven and master-regulator gene scores in blood, and combine these gene scores with distal and proximal SNP-to-gene (S2G) linking strategies to construct SNP annotations for blood-related traits, as described in our manuscript “Unique contribution of enhancer-driven and master-regulator genes to autoimmune disease revealed using functionally informed SNIP-to-gene linking strategies”.Software type: other
- rtracklayer — sourceExtensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport.
- Genomic Alignments — sourceProvides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments.
- snow — sourceThe snow package provides support for simple parallel computing on a network of workstations using R. A master R process calls makeCluster to start a cluster of worker processes; the master process then uses functions such as clusterCall and clusterApply to execute R code on the worker processes and collect and return the results on the master. This framework supports many forms of "embarrassingly parallel" computations.Software type: other
- Genomedata — sourceEfficiently stores multiple tracks of numeric data anchored to a genome. The format allows fast random access to hundreds of gigabytes of data, while retaining a small disk space footprint. Utilities have also been developed to load data into this format. A reference implementation in Python and C components is available under the GNU General Public License.
- Segway — sourceUses a machine learning method to analyze multiple tracks of functional genomics data, searching for recurring patterns. The software automatically partitions the genome into non-overlapping segments and assigns each segment a label. The resulting annotation provides a human-interpretable summary of the functional landscape of the genome, yielding hypotheses about novel instances or classes of functional elements.Software type: genome segmentation
- Segtools — sourceA Python package that analyzes genomic segmentations. The software efficiently calculates a variety of summary statistics and produces corresponding publication quality visualizations. The overall goal of Segtools is to provide a bird's-eye view of complex genomic data sets, allowing researchers to easily generate and confirm hypotheses.Software type: genome segmentation