SeqGL is a group lasso based algorithm to extract transcription factor sequence signals from ChIP, DNase and ATAC-seq profiles. This package presents a method which uses group lasso to discriminate between bound and non bound genomic regions to accurately identify transcription factors bound at the specific regions.

This library contains the genomics components of the Bio++ phylogenetics library. It is part of the Bio++ project.

dRep is a Python program for rapidly comparing large numbers of genomes. dRep can also "de-replicate" a genome set by identifying groups of highly similar genomes and choosing the best representative genome for each genome set.

gffcompare is a tool that can:

1. compare and evaluate the accuracy of RNA-Seq transcript assemblers (Cufflinks, Stringtie);

2. collapse (merge) duplicate transcripts from multiple GTF/GFF3 files (e.g. resulted from assembly of different samples);

3. classify transcripts from one or multiple GTF/GFF3 files as they relate to reference transcripts provided in a annotation file (also in GTF/GFF3 format).

A tandem repeat in DNA is two or more adjacent, approximate copies of a pattern of nucleotides. Tandem Repeats Finder is a program to locate and display tandem repeats in DNA sequences. In order to use the program, the user submits a sequence in FASTA format. The output consists of two files: a repeat table file and an alignment file. Submitted sequences may be of arbitrary length. Repeats with pattern size in the range from 1 to 2000 bases are detected.

GSEApy is a Python/Rust implementation for GSEA and wrapper for Enrichr. GSEApy can be used for RNA-seq, ChIP-seq, Microarray data. It can be used for convenient GO enrichment and to produce publication quality figures in Python.

Bamnostic is a pure Python Binary Alignment Map (BAM) file parser and random access tool.

BioJava is a project dedicated to providing a Java framework for processing biological data. It provides analytical and statistical routines, parsers for common file formats, reference implementations of popular algorithms, and allows the manipulation of sequences and 3D structures. The goal of the biojava project is to facilitate rapid application development for bioinformatics.

This package provides the core libraries.

This package is analyzing TCR and BCR sequences using unselected RNA sequencing data, profiled from fluid and solid tissues, including tumors. TRUST4 performs de novo assembly on V, J, C genes including the hypervariable CDR3 and reports consensus contigs of BCR/TCR sequences. TRUST4 then realigns the contigs to IMGT reference gene sequences to identify the corresponding gene and CDR3 details. TRUST4 supports both single-end and paired-end bulk or single-cell sequencing data with any read length.

Pairtools is a simple and fast command-line framework to process sequencing data from a Hi-C experiment. Process pair-end sequence alignments and perform the following operations:

• detect ligation junctions (a.k.a. Hi-C pairs) in aligned paired-end sequences of Hi-C DNA molecules

• sort .pairs files for downstream analyses

• detect, tag and remove PCR/optical duplicates

• generate extensive statistics of Hi-C datasets

• select Hi-C pairs given flexibly defined criteria

• restore .sam alignments from Hi-C pairs.

This package facilitates the analysis of single-cell RNA-seq UMI matrices. It does this by computing partitions of a cell similarity graph into small homogeneous groups of cells, which are defined as metacells (MCs). The derived MCs are then used for building different representations of the data, allowing matrix or 2D graph visualization forming a basis for analysis of cell types, subtypes, transcriptional gradients,cell-cycle variation, gene modules and their regulatory models and more.

This package computes informative enrichment and quality measures for ChIP-seq/DNase-seq/FAIRE-seq/MNase-seq data. It can also be used to obtain robust estimates of the predominant fragment length or characteristic tag shift values in these assays.

BioPerl is the product of a community effort to produce Perl code which is useful in biology. Examples include Sequence objects, Alignment objects and database searching objects. These objects not only do what they are advertised to do in the documentation, but they also interact - Alignment objects are made from the Sequence objects, Sequence objects have access to Annotation and SeqFeature objects and databases, Blast objects can be converted to Alignment objects, and so on. This means that the objects provide a coordinated and extensible framework to do computational biology.

VCFtools is a program package designed for working with VCF files, such as those generated by the 1000 Genomes Project. The aim of VCFtools is to provide easily accessible methods for working with complex genetic variation data in the form of VCF files.

Bio::Kseq provides ruby bindings to the kseq.h FASTA and FASTQ parsing code. It provides a fast iterator over sequences and their quality scores.

This R tool infers, visualizes, and analyzes cell-cell communication networks. It supports scRNA-seq and spatially resolved transcriptomics data.

TSIS is used for detecting transcript isoform switches in time-series data. Transcript isoform switches occur when a pair of alternatively spliced isoforms reverse the order of their relative expression levels. TSIS characterizes the transcript switch by defining the isoform switch time-points for any pair of transcript isoforms within a gene. In addition, this tool describes the switch using five different features or metrics. Also it filters the results with user’s specifications and visualizes the results using different plots for the user to examine further details of the switches.

SAIGE is a package for efficiently controlling for case-control imbalance and sample relatedness in single-variant assoc tests (SAIGE) and controlling for sample relatedness in region-based assoc tests in large cohorts and biobanks (SAIGE-GENE+).

bustools is a program for manipulating BUS files for single cell RNA-Seq datasets. It can be used to error correct barcodes, collapse UMIs, produce gene count or transcript compatibility count matrices, and is useful for many other tasks.

BWA-Meth works for single-end reads and for paired-end reads from the directional protocol (most common). It uses the method employed by methylcoder and Bismark of in silico conversion of all C's to T's in both reference and reads. It recovers the original read (needed to tabulate methylation) by attaching it as a comment which BWA appends as a tag to the read. It performs favorably to existing aligners gauged by number of on and off-target reads for a capture method that targets CpG-rich region.

BEDOPS is a suite of tools to address common questions raised in genomic studies---mostly with regard to overlap and proximity relationships between data sets. It aims to be scalable and flexible, facilitating the efficient and accurate analysis and management of large-scale genomic data.

BEDOPS provides tools that perform highly efficient and scalable Boolean and other set operations, statistical calculations, archiving, conversion and other management of genomic data of arbitrary scale. Tasks can be easily split by chromosome for distributing whole-genome analyses across a computational cluster.

Mosaicatcher counts Strand-seq reads and classifies strand states of each chromosome in each cell using a Hidden Markov Model.

MAFFT offers a range of multiple alignment methods for nucleotide and protein sequences. For instance, it offers L-INS-i (accurate; for alignment of <~200 sequences) and FFT-NS-2 (fast; for alignment of <~30,000 sequences).

RSEM is a software package for estimating gene and isoform expression levels from RNA-Seq data. The RSEM package provides a user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation. In addition, it provides posterior mean and 95% credibility interval estimates for expression levels. For visualization, it can generate BAM and Wiggle files in both transcript-coordinate and genomic-coordinate.