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diytranscriptomics.com - A semester-long course covering best practices for the analysis of high-throughput sequencing data from gene expression (RNA-seq) studies, with a primary focus on empowering students to be independent in the use of lightweight and open-source...

A major breakthrough (replaced microarrays) in the late 00’s and has been widely used since Measures the average expression level for each gene across a large population of input cells Useful for comparative transcriptomics,...

github.com - Delta is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes. Delta takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically...

http://mgcv.cmbi.ru.nl/ - MGcV is an interactive web-based visalization tool tailored to facilitate small scale genome analysis. To start using MGcV: Supply your genes/genomic segments/phylogenetic tree of interest in the input-box by selecting the type of identifier...

github.com - KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. The following tools are currently available in KAT: hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in...

github.com - DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation...

github.com - Juicebox is visualization software for Hi-C data. This distribution includes the source code for Juicebox, Juicer Tools, and Assembly Tools. Download Juicebox here, or use Juicebox on the web. Detailed documentation is...

github.com - wgd is a easy to use command-line tool for KS distribution construction named wgd. The wgd suite provides commonly used KS and colinearity analysis workflows together with tools for modeling and visualization, rendering these...

master.bioconductor.org - Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were quantified to the reference transcripts, and prepare gene-level count...

the sequenced reads can be mapped to the organism’s genes to assess how differently the genes are expressed under the experimental circumstances as opposed to the control scenario. This is known as differential expression (DE) analysis