The Filtlong package is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.

PRANK is a probabilistic multiple sequence alignment program for DNA, codon and amino-acid sequences. It is based on a novel algorithm that treats insertions correctly and avoids over-estimation of the number of deletion events. In addition, PRANK borrows ideas from maximum likelihood methods used in phylogenetics and correctly takes into account the evolutionary distances between sequences. Lastly, PRANK allows for defining a potential structure for sequences to be aligned and then, simultaneously with the alignment, predicts the locations of structural units in the sequences.

This package provides a Python environment for phylogenetic tree exploration.

python-cwlformat is a specification and a reference implementation for a very opinionated CWL code formatter. It outputs CWL in a standardized YAML format.

This package provides helper functions to detect cross-hybridization on Illumina DNAm arrays.

PyEGA3 is a tool for viewing and downloading files from authorized EGA datasets. It uses the EGA data API and has several key features:

• Files are transferred over secure https connections and received unencrypted, so no need for decryption after download.

• Downloads resume from where they left off in the event that the connection is interrupted.

• Supports file segmenting and parallelized download of segments, improving overall performance.

• After download completes, file integrity is verified using checksums.

• Implements the GA4GH-compliant htsget protocol for download of genomic ranges for data files with accompanying index files.

RSeQC provides a number of modules that can comprehensively evaluate high throughput sequence data, especially RNA-seq data. Some basic modules inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, etc.

MuSiC is a deconvolution method that utilizes cross-subject scRNA-seq to estimate cell type proportions in bulk RNA-seq data.

GEMMA provides a standard linear mixed model resolver with application in GWAS.

CENTIPEDE applies a hierarchical Bayesian mixture model to infer regions of the genome that are bound by particular transcription factors. It starts by identifying a set of candidate binding sites, and then aims to classify the sites according to whether each site is bound or not bound by a transcription factor. CENTIPEDE is an unsupervised learning algorithm that discriminates between two different types of motif instances using as much relevant information as possible.

NGS is a domain-specific API for accessing reads, alignments and pileups produced from Next Generation Sequencing. The API itself is independent from any particular back-end implementation, and supports use of multiple back-ends simultaneously.

WhatsHap is software for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but works also well with short reads.

Picard is a set of Java command line tools for manipulating high-throughput sequencing (HTS) data and formats. Picard is implemented using the HTSJDK Java library to support accessing file formats that are commonly used for high-throughput sequencing data such as SAM, BAM, CRAM and VCF.

Picard is a set of Java command line tools for manipulating high-throughput sequencing (HTS) data and formats. Picard is implemented using the HTSJDK Java library to support accessing file formats that are commonly used for high-throughput sequencing data such as SAM, BAM, CRAM and VCF.

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.

Several studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. CRISPR has also been used to reconstruct lineage trees by inserting random mutations. The tbsp package implements an alternative method to detect significant, cell type specific sequence mutations from scRNA-Seq data.

CellBender is a software package for eliminating technical artifacts from high-throughput single-cell RNA sequencing (scRNA-seq) data.

This package provides a lightweight interface to Stan <https://mc-stan.org>. The CmdStanR interface is an alternative to RStan that calls the command line interface for compilation and running algorithms instead of interfacing with C++ via Rcpp'.

This is a package that lets you process UMI-4C data from scratch to produce nice plots.

Screed parses FASTA and FASTQ files and generates databases. Values such as sequence name, sequence description, sequence quality and the sequence itself can be retrieved from these databases.

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

This is a Python package for the interactive visualization of bulk RNA-seq data. It provides a range of plotting functions and interactive tools to explore and analyze bulk RNA-seq data.

PAIRADISE is a method for detecting allele-specific alternative splicing (ASAS) from RNA-seq data. Unlike conventional approaches that detect ASAS events one sample at a time, PAIRADISE aggregates ASAS signals across multiple individuals in a population. By treating the two alleles of an individual as paired, and multiple individuals sharing a heterozygous SNP as replicates, PAIRADISE formulates ASAS detection as a statistical problem for identifying differential alternative splicing from RNA-seq data with paired replicates.

This package offers a quick and straight-forward way to explore and perform basic analysis of single cell sequencing data coming from droplet sequencing. It has been particularly tailored for Drop-seq.