README.md

In yilixu/vigilante: Visualizing Integrated Genomics in Linked and Nested Translational Elaboration & Knowing Numbers in Genomic Hypothesis Through Statistics

vigilante & knights

Visualizing Integrated Genomics in Linked and Nested Translational Elaboration & Knowing Numbers in Genomic Hypothesis Through Statistics

The goal of vigilante & knights is to facilitate data analysis and visualization in high-throughput next-generation sequencing (HT-NGS) researches by empowering researchers to perform complex, cutting-edge informatics-based analyses on their whole genome/exosome sequencing (WGS/WES) and RNA-seq data in an interactive open-source environment with statistical modules built to assess biological events. Also, vigilante & knights works in a highly self-contained pipeline style, and is designed to be user-friendly even for non-informatics researchers who don't have much experience in programming.

Installation

Currently vigilante & knights is under reconfiguration for the official R package release, and the development version (phased release) of vigilante & knights can be installed from GitHub with:

devtools::install_github("yilixu/vigilante", ref = "main")

In addition, due to the requirement of CRAN that general packages should not exceed 5MB, the supplemental workbook or reference datasets (sword) required by vigilante & knights have been extracted and put in the separate package vigilante.knights.sword. Please check vigilante.knights.sword for more information.

The development version of vigilante.knights.sword can be installed following the in-house prompts, or manually from GitHub with:

devtools::install_github("yilixu/vigilante.knights.sword", ref = "main")

A Quick Glance at vigilante & knights

• The below diagram shows the input data file formats accepted by vigilante & knights and a few output figure examples created by vigilante & knights as part of the module-based analysis results:

References:

• Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.
• Krämer A, Green J, Pollard J Jr, Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014 Feb 15;30(4):523-30.
• Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017 Nov 15;18(1):220.
• Díaz-Gay, M., Vila-Casadesús, M., Franch-Expósito, S. et al. Mutational Signatures in Cancer (MuSiCa): a web application to implement mutational signatures analysis in cancer samples. BMC Bioinformatics 19, 224 (2018).

yilixu/vigilante documentation built on June 4, 2021, 5:07 a.m.