BOL: Related items

SeqMonk:A tool to visualise and analyse high throughput mapped sequence data

Jit — Tue, 11 Sep 2018 04:39:38 -0500

SeqMonk is a program to enable the visualisation and analysis of mapped sequence data. It was written for use with mapped next generation sequence data but can in theory be used for any dataset which can be expressed as a series of genomic positions. It's main features are:

Import of mapped data from mapped data (BAM/SAM/bowtie etc)
Creation of data groups for visualisation and analysis
Visualisation of mapped regions against an annotated genome.
Flexible quantitation of the mapped data to allow comparisons between data sets
Statistical analysis of data to find regions of interest
Creation of reports containing data and genome annotation

Address of the bookmark: http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/

ClipCrop: a tool for detecting structural variations with single-base resolution using soft-clipping information

Rahul Nayak — Thu, 04 Oct 2018 16:39:28 -0500

This is a tool for detecting structural variations using soft-clipping information From SAM files.

https://github.com/shinout/clipcrop

Address of the bookmark: https://github.com/shinout/clipcrop

Hawkeye: an interactive visual analytics tool for genome assemblies

Abhimanyu Singh — Tue, 01 Jan 2019 11:56:17 -0600

Genome sequencing remains an inexact science, and genome sequences can contain significant errors if they are not carefully examined. Hawkeye is our new visual analytics tool for genome assemblies, designed to aid in identifying and correcting assembly errors. Users can analyze all levels of an assembly along with summary statistics and assembly metrics, and are guided by a ranking component towards likely mis-assemblies. Hawkeye is freely available and released as part of the open source AMOS project http://amos.sourceforge.net/hawkeye.

https://genomebiology.biomedcentral.com/articles/10.1186/gb-2007-8-3-r34

Address of the bookmark: http://amos.sourceforge.net/wiki/index.php?title=Hawkeye

Patterns: a modeling tool dedicated to biological network modeling

Abhimanyu Singh — Fri, 26 Jul 2019 01:11:59 -0500

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.

Address of the bookmark: https://github.com/fbertran/Patterns

Chromonomer: a tool set for repairing and enhancing assembled genomes through integration of genetic maps and conserved synteny

Jit — Mon, 17 Feb 2020 05:38:46 -0600

Chromonomer is a program designed to integrate a genome assembly with a genetic map. Chromonomer tries very hard to identify and remove markers that are out of order in the genetic map, when considered against their local assembly order; and to identify scaffolds that have been incorrectly assembled according to the genetic map, and split those scaffolds.

Address of the bookmark: http://catchenlab.life.illinois.edu/chromonomer/

CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs)

Jit — Mon, 18 May 2020 10:53:32 -0500

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies. The core algorithm of both programs involves gene calling, mapping of predicted ORFs against the nr protein database, and voting-based classification of the entire contig / MAG based on classification of the individual ORFs. CAT and BAT can be run from intermediate steps if files are formated appropriately (see Usage).

Address of the bookmark: https://github.com/dutilh/CAT

mixtureS: a novel tool for bacterial strain reconstruction from reads

BioStar — Fri, 21 Aug 2020 08:23:19 -0500

mixtureS that can de novo identify bacterial strains from shotgun reads of a clonal or metagenomic sample, without prior knowledge about the strains and their variations. Tested on 243 simulated datasets and 195 experimental datasets, mixtureS reliably identified the strains, their numbers and their abundance. Compared with three tools, mixtureS showed better performance in almost all simulated datasets and the vast majority of experimental datasets.

Availability

The source code and tool mixtureS is available at http://www.cs.ucf.edu/˜xiaoman/mixtureS/.

Address of the bookmark: http://www.cs.ucf.edu/~xiaoman/mixtureS/

vsFilt: A tool to improve virtual screening by structural filtration of docking poses

Neel — Wed, 25 Nov 2020 02:39:16 -0600

The vsFilt is the first open application for post-docking structural filtration, available as a web-server. The new tool is easy to use and configure to detect a wide range of interaction types that are known to be involved in molecular recognition, including hydrogen and halogen bonds, ionic interactions, hydrophobic contacts, π-stacking, and cation-π interactions. The web-server can process large libraries of up to 150’000 docked ligand poses. The results are web-based and can be operated on-line using the built-in HTML5 interactive analysis tools, or can be downloaded for a local use. The vsFilt is freely available on-line, no login required.

Address of the bookmark: https://biokinet.belozersky.msu.ru/vsfilt

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