BOL: Related items

Quick tour of Genetic Algorithms !

Jit — Thu, 17 Jan 2019 03:42:48 -0600

The R package GA provides a collection of general purpose functions for optimization using genetic algorithms. The package includes a flexible set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand.

https://cran.r-project.org/web/packages/GA/vignettes/GA.html

Address of the bookmark: https://cran.r-project.org/web/packages/GA/vignettes/GA.html

GPOPSIM: a simulation tool for whole-genome genetic data

Jit — Wed, 17 Jan 2018 03:47:46 -0600

GPOPSIM is a simulation tool for pedigree, phenotypes, and genomic data, with a variety of population and genome structures and trait genetic architectures. It provides flexible parameter settings for a wide discipline of users, especially can simulate multiple genetically correlated traits with desired genetic parameters and underlying genetic architectures.

Address of the bookmark: https://github.com/SCAU-AnimalGenetics/GPOPSIM

Flanker

Rahul Nayak — Sat, 27 Feb 2021 22:04:53 -0600

Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of mobile genetic elements (MGEs) without prior knowledge of their structure. Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence.

Address of the bookmark: https://github.com/wtmatlock/flanker

Shinyheatmap

Jit — Fri, 21 Oct 2016 05:12:11 -0500

Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap.

More at http://biorxiv.org/content/early/2016/09/21/076463

Address of the bookmark: http://shinyheatmap.com/

JBrowse: Embeddable genome browser built completely with JavaScript and HTML5

Jit — Fri, 29 Jun 2018 09:19:56 -0500

JBrowse is a fast, embeddable genome browser built completely with JavaScript and HTML5, with optional run-once data formatting tools written in Perl. Headline Features: Fast, smooth scrolling and zooming. Explore your genome with unparalleled speed. Scales easily to multi-gigabase genomes and deep-coverage sequencing. Quickly open and view data files on your computer without uploading them to any server. Supports GFF3, BED, FASTA, Wiggle, BigWig, BAM, VCF (with either .tbi or .idx index), REST, and more. BAM, BigBed, BigWig, and VCF data are displayed directly from chunks of the compressed binary files, no conversion needed. Includes an optional “faceted” track selector (see demo) suitable for large installations with thousands of tracks. Very light server resource requirements. In fact, JBrowse has no back-end server code, just tools for formatting data files to be read directly over HTTP. Serve huge datasets from a single low-cost cloud instance. Can run as a stand-alone app on OSX and Windows using the Electron platform Highly extensible plugin architecture, with a large plugin registry of existing examples here https://gmod.github.io/jbrowse-registry https://jbrowse.org/

Address of the bookmark: https://github.com/GMOD/jbrowse

Regular Expression Cheat Sheet

Jitendra Narayan — Tue, 09 Jul 2013 17:38:42 -0500

The Regular Expression are the sole of Perl language, and for bioinformatician it is just a magical stick to resolve gingatic string data. We did not find any good and user friendly regular expression cheat sheet, hence write our own cheat sheet. The Regular Expressions Cheat Sheet, a quick reference guide for regular expressions, including symbols, ranges, grouping, assertions and some sample patterns to get you started.

Perl Module Installation

Jitendra Narayan — Fri, 12 Jul 2013 11:19:41 -0500

Nice step wide information on perl module installation.

Address of the bookmark: http://bioinformaticsonline.com/blog/view/710/how-to-install-perl-modules-manually-using-cpan-command-and-other-quick-ways

Perl Poem: Parse it in both Perl and English!

Rahul Agarwal — Mon, 12 Aug 2013 19:00:55 -0500

Larry's, er, corpus has fortunately been overshadowed by that of the reigning Perl Poet, Sharon Hopkins. She has written quite a few Perl poems, as well as a paper on Perl poetry that she presented at the Usenix Winter 1992 Technical Conference, entitled "Camels and Needles: Computer Poetry Meets the Perl Programming Language". (The paper is available as misc/poetry.ps on CPAN.) Besides being the most prolific Perl poet, Sharon is also the most widely published, having had the following poem published in both the Economist and the Guardian:

#!/usr/bin/perl

APPEAL:

listen (please, please);

open yourself, wide;
join (you, me),
connect (us,together),

tell me.

do something if distressed;

@dawn, dance;
@evening, sing;
read (books,$poems,stories) until peaceful;
study if able;

write me if-you-please;

sort your feelings, reset goals, seek (friends, family, anyone);

do*not*die (like this)
if sin abounds;

keys (hidden), open (locks, doors), tell secrets;
do not, I-beg-you, close them, yet.

accept (yourself, changes),
bind (grief, despair);

require truth, goodness if-you-will, each moment;

select (always), length(of-days)

# listen (a perl poem)
# Sharon Hopkins
# rev. June 19, 1995

Bioinformatics 101 - Running BLAST

Tue, 03 Sep 2013 14:59:50 -0500

How to format the database for BLAST, run the command, view the output file, and use BioPerl and Perl to parse the output. By David Francis, Ohio State University. Delivered live at the Tomato Disease Workshop 2010. For more information, please visit http://www.extension.org/pages/32521/bioinformatics-101-video.

Bioinformatics Scripts

Jit — Thu, 22 Jan 2015 22:29:39 -0600

Some of the useful bioinformatics scripts.

For example ... contig-stats.pl is a Perl script that will automatically describe features of a sequence assembly.

http://milkweedgenome.org/?q=scripts

Address of the bookmark: http://milkweedgenome.org/?q=scripts