BOL: Related items

CGView.js is a Circular Genome Viewing tool

LEGE — Wed, 27 Mar 2024 11:16:24 -0500

CGView.js is a Circular Genome Viewing tool for visualizing and interacting with small genomes. This software is an adaptation of the Java program CGView.

CGView.js is the genome viewer of Proksee, an expert system for genome assembly, annotation and visualization.

Features

Circular and linear views of genomes
Capable of drawing genomes up to 10 Mbp with 1000's of features and 100's contigs
Smooth zooming down to the sequence level
Easily generate features and plots directly form the sequence (e.g. ORFs, GC-content and GC-Skew)
Save high resolution PNG maps up to 8000x8000px
Fully documented API for interacting with CGView.js maps

Address of the bookmark: https://js.cgview.ca/

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

progressiveCactus

Abhimanyu Singh — Thu, 18 May 2017 05:29:29 -0500

Progressive Cactus is a whole-genome alignment package.

Distribution package for the Prgressive Cactus multiple genome aligner. Dependencies are linked as submodules

https://github.com/glennhickey/progressiveCactus

Address of the bookmark: https://github.com/glennhickey/progressiveCactus

AccessSyRI: finding genomic rearrangements and local sequence differences from whole-genome assemblies

Jit — Sat, 01 Feb 2020 13:38:49 -0600

AccessSyRI: finding genomic rearrangements andlocal sequence differences from whole-genome assemblies

SyRI, a pairwise whole-genome comparison tool for chromosome-level assemblies. SyRI starts by finding rearranged regions and then searches for differences in the sequences, which are distinguished for residing in syntenic or rearranged regions. This distinction is important as rearranged regions are inherited differently compared to syntenic regions.

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1911-0

Address of the bookmark: https://github.com/schneebergerlab/syri

doubletrouble: identify duplicated genes from whole-genome protein sequences and classify

LEGE — Tue, 05 Mar 2024 00:23:49 -0600

doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks.

Address of the bookmark: https://bioconductor.org/packages/release/bioc/html/doubletrouble.html

RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data

Jit — Mon, 27 Nov 2017 08:25:33 -0600

RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.

This work has been accepted for publication:

MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to Nucleic Acids Research April 4, 2012.

If you have any questions or bug reports, please let us know at .

Address of the bookmark: http://kiwi.cs.dal.ca/Software/RITA

VariantBam: Filtering and profiling of next-generational sequencing data using region-specific rules

Rahul Nayak — Thu, 04 Oct 2018 16:30:44 -0500

VariantBam is a tool to extract/count specific sets of sequencing reads from next-generational sequencing files. To save money, disk space and I/O, one may not want to store an entire BAM on disk. In many cases, it would be more efficient to store only those read-pairs or reads who intersect some region around the variant locations. Alternatively, if your scientific question is focused on only one aspect of the data (e.g. breakpoints), many reads can be removed without losing the information relevant to the problem.

Address of the bookmark: https://github.com/broadinstitute/VariantBam

EXCAVATOR: detecting copy number variants from whole-exome sequencing data

Radha Agarkar — Fri, 04 Jan 2019 10:10:48 -0600

EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.

EXCAVATOR is a novel software package for the detection of copy number variants (CNVs) from whole-exome sequencing data.
EXCAVATOR has been published on Genome Biology (http://genomebiology.com/2013/14/10/R120/abstract).

Address of the bookmark: https://sourceforge.net/projects/excavatortool/

nQuire: A statistical framework for ploidy estimation using NGS short-read data

Jit — Thu, 31 Jan 2019 05:12:19 -0600

nQuire implements a set of commands to estimate ploidy level of individuals from species, where recent polyploidization occurred and intraspecific ploidy variation is observed. Specifically, nQuire uses next-generation sequencing data to distinguish between diploids, triploids and tetraploids, on the basis of frequency distributions at variant sites where only two bases are segregating.

For more background see also the publication at BMC Bioinformatics.

https://github.com/clwgg/nQuire

Address of the bookmark: https://github.com/clwgg/nQuire

Trelliscope: flexibly visualize large, complex data in great detail from within the R statistical programming environment.

Jit — Tue, 21 Jan 2020 04:22:49 -0600

Trelliscope provides a way to flexibly visualize large, complex data in great detail from within the R statistical programming environment. Trelliscope is a component in the DeltaRho environment.

For those familiar with Trellis Display, faceting in ggplot, or the notion of small multiples, Trelliscope provides a scalable way to break a set of data into pieces, apply a plot method to each piece, and then arrange those plots in a grid and interactively sort, filter, and query panels of the display based on metrics of interest. With Trelliscope, we are able to create multipanel displays on data with a very large number of subsets and view them in an interactive and meaningful way.

Address of the bookmark: http://deltarho.org/docs-trelliscope/#introduction