BOL: Related items

pyGenomeTracks: Standalone program and library to plot beautiful genome browser tracks

Neel — Fri, 09 Nov 2018 12:34:23 -0600

pyGenomeTracks aims to produce high-quality genome browser tracks that are highly customizable. Currently, it is possible to plot:

bigwig
bed (many options)
bedgraph
links (represented as arcs)
Hi-C matrices (if HiCExplorer is installed)

Address of the bookmark: https://github.com/deeptools/pyGenomeTracks

DISTRUCT: a program for the graphical display of population structure

Abhimanyu Singh — Mon, 25 Mar 2019 03:33:44 -0500

distruct is a program that can be used to graphically display results produced by the genetic clustering program structure or by other similar programs. The figures produced by distructdisplay individual membership coefficients in the same form as used in "Genetic structure of human populations" Science 298: 2381-2385 (2002). Various options enable the user to control left-to-right printing order of populations, bottom-to-top printing order of clusers, colors, and other graphical details. [Example]

[Download software package (includes the manual)] (you will be directed first to a registration page and we would very much appreciate if you register)
[Download manual]
[Download software note from Molecular Ecology Notes 4: 137-138 (2004)]

To use the UNIX versions, unzip and untar the files in an appropriate directory using

gunzip filename.tar.gz; tar xvf filename.tar

where "filename.tar.gz" is the downloaded file. Winzip will unzip the Windows version. Run the program by typing

./distruct

in UNIX or

distruct

from a Dos prompt in Windows. It will produce a figure using the data that are represented in the Central/South Asia K=5 plot in Science 298: 2381-2385 (2002).

Please send comments or problems with distruct to Noah Rosenberg.

October 15, 2014 — Users of Distruct may also find CLUMPP and CLUMPAK of interest.

Address of the bookmark: https://rosenberglab.stanford.edu/distruct.html

chromosight: Computer vision based program for pattern recognition in chromosome (Hi-C) contact maps

Jit — Mon, 23 Mar 2020 06:20:04 -0500

Python package to detect chromatin loops (and other patterns) in Hi-C contact maps.

Stable version with pip:

pip3 install --user chromosight

Stable version with conda:

conda install -c bioconda -c conda-forge chromosight

or, if you want to get the latest development version:

pip3 install --user -e git+https://github.com/koszullab/chromosight.git@master#egg=chromosight

Address of the bookmark: https://github.com/koszullab/Chromosight

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/

LoRDEC: a hybrid error correction program for long, PacBio reads

Jit — Mon, 10 Apr 2017 04:16:09 -0500

LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.

Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.
After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.

Why is LoRDEC different?

It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.
It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.

Address of the bookmark: http://www.atgc-montpellier.fr/lordec/

FGENESH - Program for predicting multiple genes in genomic DNA sequences

BioStar — Thu, 20 Dec 2018 11:55:08 -0600

FGENESH is the fastest (50-100 times faster than GenScan) and most accurate gene finder available - see the figure and the table below. In recent rice genome sequencing projects, it was cited "the most successful (gene finding) program (Yu et al. (2002) Science 296:79) and was used to produce 87% of all high-evidence predicted genes (Goff et al. (2002) Science 296:79).

Address of the bookmark: http://www.softberry.com/berry.phtml?topic=fgenesh&group=help&subgroup=gfind

Tbl2asn: a command-line program that automates the creation of sequence records for submission to GenBank

Poonam Mahapatra — Mon, 29 May 2017 07:37:08 -0500

Tbl2asn is a command-line program that automates the creation of sequence records for submission to GenBank. It uses many of the same functions as Sequin but is driven generally by data files. Tbl2asn generates .sqn files for submission to GenBank. Additional manual editing is not required before submission.

Tbl2asn is available by anonymous FTP. Copy the right version for your platform, then uncompress the file, rename it to "tbl2asn", and set the permissions, as necessary for the platform.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/genbank/tbl2asn2/

MCMCTREE: a phylogenetic program for Bayesian estimation of species divergence times

Poonam Mahapatra — Sat, 02 Jun 2018 07:40:06 -0500

MCMCTREE is a phylogenetic program for Bayesian estimation of species divergence times using soft fossil constraints under various molecular clock models. This is part of the PAML package. In this tutorial I will analyze an easy example modified from dataset of Inoue et al. (2010). Here we conduct a commonly used time estimation method, "Approximate Likelihood Method", for the datasets including more than 10 species.

Address of the bookmark: http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html

kallisto: a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data

Jit — Mon, 07 Jan 2019 10:35:14 -0600

kallisto is a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data, kallisto can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and kallisto is therefore not only fast, but also as accurate as existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks kallisto significantly outperforms existing tools. kallisto is described in detail in:

Nicolas L Bray, Harold Pimentel, Páll Melsted and Lior Pachter, Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, 525–527 (2016), doi:10.1038/nbt.3519

Address of the bookmark: https://pachterlab.github.io/kallisto/about

Cactus: a reference-free whole-genome multiple alignment program

Jit — Mon, 12 Aug 2019 07:52:33 -0500

Cactus is a reference-free whole-genome multiple alignment program. The principal algorithms are described here: https://doi.org/10.1101/gr.123356.111

Cactus uses substantial resources. For primate-sized genomes (3 gigabases each), you should expect Cactus to use approximately 120 CPU-days of compute per genome, with about 120 GB of RAM used at peak. The requirements scale roughly quadratically, so aligning two 1-megabase bacterial genomes takes only 1.5 CPU-hours and 14 GB RAM.

Address of the bookmark: https://github.com/ComparativeGenomicsToolkit/cactus