BOL: Related items

vt: a variant tool set that discovers short variants from Next Generation Sequencing data.

Jit — Tue, 28 Jan 2020 03:44:43 -0600

vt is a variant tool set that discovers short variants from Next Generation Sequencing data.

https://genome.sph.umich.edu/wiki/Vt

Address of the bookmark: https://genome.sph.umich.edu/wiki/Vt

Filtlong: quality filtering tool for long reads

Radha Agarkar — Wed, 13 May 2020 10:23:55 -0500

Filtlong is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.

Filtlong builds into a stand-alone executable:

git clone https://github.com/rrwick/Filtlong.git
cd Filtlong
make -j
bin/filtlong -h

Address of the bookmark: https://github.com/rrwick/Filtlong

truvari: Structural variant comparison tool for VCFs

Jit — Tue, 30 Jun 2020 21:30:44 -0500

Structural variant comparison tool for VCFs

Given benchmark and comparsion sets of SVs, calculate the recall, precision, and f-measure.

Spiral Genetics

Motivation

Address of the bookmark: https://github.com/spiralgenetics/truvari

Clustergrammer is a web-based tool for visualizing high-dimensional data as an interactive and shareable hierarchically clustered heatmap

BioStar — Sun, 23 Aug 2020 19:30:17 -0500

Clustergrammer is a web-based tool for visualizing high-dimensional data (e.g. a matrix) as an interactive and shareable hierarchically clustered heatmap. Clustergrammer's front end (Clustergrammer-JS) is built using D3.js and its back-end (Clustergrammer-PY) is built using Python. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several biology-specific features (e.g. enrichment analysis, see Biology-Specific Features) to facilitate the exploration of gene-level biological data.

Address of the bookmark: https://github.com/MaayanLab/clustergrammer

JumboDB: tool for de Bruijn graph construction

BioStar — Tue, 17 Aug 2021 13:33:46 -0500

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 graph.

More at https://github.com/AntonBankevich/jumboDB

Address of the bookmark: https://github.com/AntonBankevich/jumboDB

bacLIFE: an automated genome mining tool for identification of lifestyle associated genes

BioStar — Fri, 15 Mar 2024 04:59:14 -0500

bacLIFE is a streamlined computational workflow that annotates bacterial genomes and performs large-scale comparative genomics to predict bacterial lifestyles and to pinpoint candidate genes, denominated lifestyle-associated genes (LAGs), and biosynthetic gene clusters associated with each lifestyle detected. This whole process is divided into different modules:

Clustering module Predicts, clusters and annotates the genes of every input genome
Lifestyle prediction Employs a machine learning model to forecast bacterial lifestyle or other specified metadata
Analitical module (Shiny app) Results from the previous modules are embedded in a user-friendly interface for comprehensive and interactive comparative genomics.

You can find the complete wiki here [https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki]

Address of the bookmark: https://github.com/Carrion-lab/bacLIFE

NGenomeSyn: an easy-to-use and flexible tool for publication-ready visualization of syntenic relationships across multiple genomes

LEGE — Tue, 10 Sep 2024 04:54:55 -0500

NGenomeSyn: an easy-to-use and flexible tool for publication-ready visualization of syntenic relationships across multiple genomes

NGenomeSyn [multiple (N) Genome Synteny], for publication-ready visualization of syntenic relationships of the whole genome or local region and genomic features (e.g. repeats, structural variations, genes) across multiple genomes with a high customization. NGenomeSyn provides an easy way for its users to visualize a large amount of data with a rich layout by simply adjusting options for moving, scaling, and rotation of target genomes. Moreover, NGenomeSyn could be applied on the visualization of relationships on non-genomic data with similar input formats.

https://academic.oup.com/bioinformatics/article/39/3/btad121/7072460

Address of the bookmark: https://github.com/hewm2008/NGenomeSyn

Comparison of Short Read De Novo Alignment Algorithms

Rahul Agarwal — Wed, 21 Aug 2013 07:56:01 -0500

Excellent article to introduce different sequencing methods along with tools for de novo assembly of sequencing reads and their relevant references.

Title: Comparison of Short Read De Novo Alignment Algorithms

Author: Nikhil Gopal

Address of the bookmark: http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf

coursera genome assembly tutorial

Jit — Sat, 25 Nov 2017 08:57:25 -0600

Solutions to Coursera Genome Sequencing (Bioinformatics II)

Address of the bookmark: https://github.com/iansealy/coursera-assembly

3d-dna: 3D de novo assembly (3D DNA) pipeline

Jit — Thu, 28 Dec 2017 10:09:37 -0600

This code is designed to enable anyone to reproduce the Hs2-HiC and the AaegL4 genomes reported in: Dudchenko et al., De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science, 2017.

Unless otherwise noted, all terminology below is consistent with this paper, and all references to figures and tables in this readme refer to this paper. Specifically, some of the terminology used below is outlined in Figure S2. The assembly procedure is described in detail in the Supporting Online Materials, specifically in the section labelled “Pipeline description”.

In addition, the pipeline uses tools and methods from Juicer (Durand & Shamim et al., Cell Systems, 2016) and Juicebox (Durand & Robinson et al., Cell Systems, 2016), as well as additional dependencies noted below.

Feel free to post your questions and comments at: http://www.aidenlab.org/forum.html

http://aidenlab.org/documentation.html

Address of the bookmark: https://github.com/theaidenlab/3d-dna