github.com - ShadowCaster implements an evolutionary model to calculate Bayesian likelihoods for each ‘alien genes’ with an unusual sequence composition according to the host genome background to detect HGT events in...
github.com - The GenomeQC web application is implemented in R/Shiny version 1.5.9 and Python 3.6 and is freely available at https://genomeqc.maizegdb.org/ under the GPL license. All source code and a containerized version of the GenomeQC pipeline is...
sourceforge.net - AccNET is a Perl application that presents a new way to study the accessory genome of a given set of organisms. Using the proteomes of these organisms, AccNET create a bipartite network compatible with common network analysis platforms. AccNET...
142.150.188.236 - NAViGaTOR – Network Analysis, Visualization, & Graphing TORonto is a software system for scaleable visualizing and analyzing networks.
The current version, NAViGaTOR 3, increases modularity, improves scaleability, extends input/output...
github.com - This tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. Jasmine...
github.com - It is designed to work with patterned data. Famous examples of problems related to patterned data are:
recovering signals in networks after a stimulation (cascade network reverse engineering),
analysing periodic signals.
github.com - jumboDB tool for fast de Bruijn graph construction from long sequences (reads or genomes) with very low error rate. JumboDB is not a genome assembler by itself but rather a subroutine that translates a set of reads into compressed de Bruijn...
DESeq2 is a powerful and widely-used R package that identifies differentially expressed genes (DEGs) from RNA-seq data. Whether you're comparing treated vs untreated samples, disease vs healthy conditions, or wild-type vs mutant strains, DESeq2...
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