CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts.

This package provides Python bindings for spoa, a C++ implementation of the partial order alignment (POA) algorithm (as described in 10.1093/bioinformatics/18.3.452) which is used to generate consensus sequences

The khmer software is a set of command-line tools for working with DNA shotgun sequencing data from genomes, transcriptomes, metagenomes and single cells. Khmer can make de novo assemblies faster, and sometimes better. Khmer can also identify and fix problems with shotgun data.

Arriba is a command-line tool for the detection of gene fusions from RNA-Seq data. It was developed for the use in a clinical research setting. Therefore, short runtimes and high sensitivity were important design criteria. It is based on the fast STAR aligner and the post-alignment runtime is typically just around two minutes. In contrast to many other fusion detection tools which build on STAR, Arriba does not require to reduce the alignIntronMax parameter of STAR to detect small deletions.

This package provides data for the book "Computational Genomics with R".

This is an R package for pre-processing of flow and mass cytometry data. This package includes panel editing or renaming for FCS files, bead-based normalization and debarcoding.

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).

This is a set of R functions that allows you to generate precise figures. This tool will create clean markdown reports about what you just discovered.

The package is ideal for analyzing RNA structure and chemical probing data.

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.

gkm-SVM, a sequence-based method for predicting regulatory DNA elements, is a useful tool for studying gene regulatory mechanisms. LS-GKM is an effort to improve the method. It offers much better scalability and provides further advanced gapped k-mer based kernel functions. As a result, LS-GKM achieves considerably higher accuracy than the original gkm-SVM.

MyGene.Info provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. Mygene is a Python wrapper to access MyGene.Info services.

The SRA Toolkit from NCBI is a collection of tools and libraries for reading of sequencing files from the Sequence Read Archive (SRA) database and writing files into the .sra format.

The goal of anpan is to consolidate statistical methods for strain analysis. This includes automated filtering of metagenomic functional profiles, testing genetic elements for association with outcomes, phylogenetic association testing, and pathway-level random effects models.

This package provides a toolkit for measuring and comparing ATAC-seq results. It was written to make it easier to spot differences that might be caused by ATAC-seq library prep or sequencing. The main program, ataqv, examines aligned reads and reports some basic metrics.

This is an R package to query and expand DisGeNET data, and to visualize the results within R framework. The disgenet2r package is designed to retrieve data from DisGeNET v6.0 (Jan, 2019).

This is a package providing efficient operations for single cell ATAC-seq fragments and RNA counts matrices. It is interoperable with standard file formats, and introduces efficient bit-packed formats that allow large storage savings and increased read speeds.

This package provides basic routines for estimation of gene-specific transcriptional derivatives and visualization of the resulting velocity patterns.

PiGX ChIPseq is an analysis pipeline for preprocessing, peak calling and reporting for ChIP sequencing experiments. It is easy to use and produces high quality reports. The inputs are reads files from the sequencing experiment, and a configuration file which describes the experiment. In addition to quality control of the experiment, the pipeline enables to set up multiple peak calling analysis and allows the generation of a UCSC track hub in an easily configurable manner.

This is package for including transposable elements in differential enrichment analysis of sequencing datasets. TEtranscripts and TEcount take RNA-seq (and similar data) and annotates reads to both genes and transposable elements. TEtranscripts then performs differential analysis using DESeq2. Note that TEtranscripts and TEcount rely on specially curated GTF files, which are not included due to their size.

Azimuth utilizes an annotated reference dataset. It automates the processing, analysis, and interpretation. This applies specifically to new single-cell RNA-seq or ATAC-seq experiments. Azimuth leverages a reference-based mapping pipeline that inputs accounts matrix and performs normalization, visualization, cell annotation, and differential expression.

SortMeRNA is a biological sequence analysis tool for filtering, mapping and OTU picking of NGS reads. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering rRNA from metatranscriptomic data.

Circe is a Python package for inferring co-accessibility networks from single-cell ATAC-seq data, using skggm for the graphical lasso and python-scanpy for data processing.

This package provides a new batch effect correction method based on Projection to Latent Structures Discriminant Analysis named “PLSDA-batch” to correct data prior to any downstream analysis. PLSDA-batch estimates latent components related to treatment and batch effects to remove batch variation. The method is multivariate, non-parametric and performs dimension reduction. Combined with centered log ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far.