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Gene Set Clustering based on Functional annotation

GeneSCF serves as command line tool for clustering the list of genes given by the users based on functional annotation (Gene Ontology, KEGG, REACTOME and NCG 4.0). It requires gene list in the form of Entrez Gene ID (UIDs) or Official gene symbols as a input. GeneSCF supports more organisms from V1.1. Examples to download database as simple text file using GeneSCF "prepare_database" module, 1) https://www.biostars.org/p/197414/#197416 , 2) https://www.biostars.org/p/191532/#191540

The advantage of using GeneSCF over other enrichment tools is that, it performs enrichment analysis in real-time (v1.1 and above) by accessing source databases. With command-line versions of tools, as you know you can run multiple gene list simultaneously.

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Requirement:

GeneSCF only works on Linux system, it has been successfully tested on Ubuntu, Mint and Cent OS. Other distributions of Linux might work as well.

Documentation:

http://genescf.kandurilab.org/documentation.php

Report issues on Biostars or GitHub Project page

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More at http://www.vicbioinformatics.com/software.vague.shtml

Address of the bookmark: http://www.vicbioinformatics.com/software.vague.shtml

• exteremely fast (17 CPU hours for whole Human x Mouse genome (with 40 nodes: 35 wall minutes), or 8 mammals in 21 CPU hours (42 wall minutes))
• scalable (Arbitrarily parallelizable across multiple nodes using MPI)
• memory efficient. (Even a single node with 16GB of ram can handle over 1Gbp of sequence)
• unlimited by pattern length or selection
• repeat tolerant

Address of the bookmark: http://murasaki.dna.bio.keio.ac.jp/wiki/index.php?Murasaki

With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widley accepted and used to store variants detected. The Cancer Genome Atlas Project has seqenced over 30 different cancers with sample size of each cancer type being over 200. The resulting data consisting of genetic variants is stored in the form of Mutation Annotation Format. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner either from TCGA sources or any in-house studies as long as the data is in MAF format. Maftools can also handle ICGC Simple Somatic Mutation format.

maftools is on bioRxiv

Please cite the below if you find this tool useful for you.

Mayakonda, A. and H.P. Koeffler, Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. bioRxiv, 2016. doi: http://dx.doi.org/10.1101/052662

Address of the bookmark: https://github.com/PoisonAlien/maftools

Address of the bookmark: http://ritchielab.psu.edu/software/phenogram-downloads

https://en.wikipedia.org/wiki/Sankey_diagram

Address of the bookmark: https://christophergandrud.github.io/networkD3/#sankey

• bigwig
• bed (many options)
• bedgraph
• links (represented as arcs)
• Hi-C matrices (if HiCExplorer is installed)

Address of the bookmark: https://github.com/deeptools/pyGenomeTracks

To install the stable version: pip3 install --user tinycov

To install the development version:

git clone https://github.com/cmdoret/tinycov.git
cd tinycov
pip install .

Address of the bookmark: https://github.com/cmdoret/tinycov

https://wlcb.oit.uci.edu/modules/index.html

Address of the bookmark: https://wlcb.oit.uci.edu/NG-Circos

Address of the bookmark: https://github.com/olarerin/metaPlotR