BOL

Genome browsers are useful not only for showing final results but also for improving analysis protocols, testing data quality, and generating result drafts. Its integration in analysis pipelines allows the optimization of parameters, which...

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...

The genome assemblers generally take a file of short sequence reads and a file of quality-value as the input. Since the quality-value file for the high throughput short reads is usually highly memory-intensive, only a few assemblers, best suited for...

evirusbioinfc.notion.site - Bioinformatics tools play a crucial role in studying viruses, enabling researchers to analyze their genetic makeup, structure, function, and evolution. Here are some commonly used bioinformatics tools for virus...

Fungal research is a rapidly growing field with vast implications for medicine, agriculture, and industry. For bioinformaticians, the availability of specialized resources—databases, tools, and community platforms—opens doors to innovative...

Workflow Managers

www.bioconductor.org - This package is intended to provide tools for the quality assessment of data produced by Oxford Nanopore’s MinION sequencer. It includes a functions to generate a number plots for examining the statistics that we think will be useful for this...

github.com - HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for...

Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and...

To decide which strategy should be our “preferred” genome assembly approach based on data rather than my gut-feeling about the “best assembly” I decided to do some testing with a known “true” reference E Coli K12 MG1655