majiq.biociphers.org -
Ability to detect, quantify, and visualize complex and de-novo splicing variations from RNASeq.
MAJIQ’s accuracy compares favorably to other algorithms.
MAJIQ 2 is *way* faster, more memory and I/O efficient
New visualization (VOILA...
github.com - Trinity, developed at the Broad Institute and the Hebrew University of Jerusalem, represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules:...
ncbi.github.io - Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of...
genome.ucsc.edu - In Encode, regulatory elements investigated via DNA hypersensitivity assays, assays of DNA methylation, and chromatin immunoprecipitation (ChIP) of proteins that interact with DNA, including modified histones and transcription factors, followed...
Live Webinar on RNA-Seq Data Analysis
Abstract: Strand NGS supports an extensive workflow for the analysis and visualization of RNA-Seq data. The workflow includes Transcriptome / Genome alignment, Differential expression analysis with Statistical...
ccb.jhu.edu - StringTie is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length...
Strand NGS is a biologist friendly NGS analysis tool that allows biologists to analyze their data using a very intuitive workflow for the analysis and visualization of RNA-Seq data. This webinar will give an overview of the workflow which includes...
github.com - Automatic Filtering, Trimming, Error Removing and Quality Control for fastq dataAfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good...
github.com - Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out...