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GFF3sort: A Perl Script to sort gff3 files and produce suitable results for tabix tools

icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis.

The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data.

Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis.

Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) <doi: 10.1038/nprot.2013.150>. Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data.

Provides a framework for allelic specific expression investigation using RNA-seq data.

Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation.

Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues.

Prediction of mRNA subcellular localization using deep recurrent neural networks | RNATracker is a deep learning approach to learn mRNA subcellular localization patterns and to infer its outcome. It operates on the cDNA of the longest isoformic protein-coding transcript of a gene with or without its corresponding secondary structure annnotations. The learning targets are fractions/percentage of the transcripts being localized to a fixed set of subcellular compartments of interest

The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method.

This package is used for the analysis of long-range chromatin interactions from 3C-seq assay.

VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions.

thromboSeq is a bioinformatics tool designed for the analysis of thrombosis-related sequencing data, providing functionalities for variant calling, annotation, and functional interpretation. It streamlines the processing of high-throughput sequencing data to identify genetic variants associated with thrombotic disorders.

Telseq is a tool for estimating telomere length from whole genome sequence data.

AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output.

Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments.

Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation.

GA4GHshiny package provides an easy way to interact with data servers based on Global Alliance for Genomics and Health (GA4GH) genomics API through a Shiny application. It also integrates with Beacon Network.

Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng).

rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks.

This package provides a web interface to compute transcriptional regulatory modules with rTRM.

This software is meant to be used for classification of images of cell-based assays for neuronal surface autoantibody detection or similar techniques. It takes imaging files as input and creates a composite score from these, that for example can be used to classify samples as negative or positive for a certain antibody-specificity. The reason for its name is that I during its creation have thought about the individual picture as an archielago where we with different filters control the water level as well as ground characteristica, thereby finding islands of interest.

This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis.