Gene expression Tools

It unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network.

Tool for single-species active module discovery.

Automated system that provides dynamic genome annotations for custom input files that contain transcriptomic regions, which can be peak regions detected by CLIP-Seq analysis (that detect protein-RNA interactions), RNA modifications (alias the epitranscriptome), CAGE-tag locations, or any other collection of target regions at the level of the transcriptome. It is designed as a reporting tool for the functional analysis of RNA-binding sites detected by high-throughput experiments.

This package implements the remove unwanted variation (RUV) methods for the normalization of RNA-Seq read counts between samples.

Scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.

A tool for transcript expression quantification from RNA-seq data

Targetfinder.org provides a web based resource that allows users to find genes that have a similar expression to a query gene signature.

Trinity is a transcriptome assembler which relies on three different tools, inchworm an assembler, chrysalis which pools contigs and butterfly which amongst others compacts a graph resulting from butterfly with reads.

Comprehensive annotation suite designed for automatic functional annotation of transcriptomes, particularly de novo assembled transcriptomes, from model or non-model organisms.

A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.

BAMscale is a one-step tool for either 1) quantifying and normalizing the coverage of peaks or 2) generated scaled BigWig files for easy visualization of commonly used DNA-seq capture based methods.

Streaming tool for quantifying the abundances of a set of target sequences from sampled subsequences. Example applications include transcript-level RNA-Seq quantification, allele-specific/haplotype expression analysis (from RNA-Seq), transcription factor binding quantification in ChIP-Seq, and analysis of metagenomic data. It can be used to resolve ambiguous mappings in other high-throughput sequencing based applications.

A program for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment.

Regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments.

A preprocessing pipeline for single cell RNA-seq data that starts from the fastq files and produces a gene count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols.

A statistical model and software application for RNA-seq differential expression analysis.