BOL: Related items

Bio-Graphics-2.37

Jit — Sun, 25 Jun 2017 17:52:21 -0500

BioPerl modules Bio::Graphics + Bio::DB:GFF and example scripts. It can draw some of the (but not all) feature types GBrowse can draw. This script should contain everything you can probably make use of (e.g. transcripts, segments, etc.) and you can try to find a good way of visualization by experimenting with its options.

http://search.cpan.org/~lds/Bio-Graphics-2.37/

Address of the bookmark: http://search.cpan.org/~lds/Bio-Graphics-2.37/

Installing Perl environment on Linux

biogeek — Tue, 26 Dec 2017 21:21:50 -0600

By using plenv, you can easily install and switch among different version of Perl. This will be installed under your home directory in~/.plenv.

Install latest Perl (with supporting multithreading) and CPANMinus.

 $ cd
 $ git clone git://github.com/tokuhirom/plenv.git ~/.plenv
 $ git clone git://github.com/tokuhirom/Perl-Build.git ~/.plenv/plugins/perl-build/
 $ echo 'export PATH="$HOME/.plenv/bin:$PATH"' >> ~/.bashrc
 $ echo 'eval "$(plenv init -)"' >> ~/.bashrc
 $ source ~/.bashrc
 $ plenv install 5.18.1 -Dusethreads
 $ plenv rehash
 $ plenv global 5.18.1
 $ plenv install-cpanm

git is a distributed revision control and source code management software which can help you to download files from GitHub server.
echo means "print".
>> means adding the output into the end of the file, while > means adding the output by overwriting the whole file. Please use> with additional cares.
In Linux system, there are two types of outputs when you execute a command. One is called standard output (or sometimes STDOUT for short), and the other is a standard error (STDERR). 1> is for STDOUT only, 2> is for STDERR only, and &>means for both. In default > is the same to 1>.
exec is execution.
Remember to install Perl in supporting multithreading (with option -Dusethreads), which is important for many NGS analysis packages (e.g. Trinity). In this setting, you can use multiple CPU for Perl software.
Install the CPAN (Comprehensive Perl Archive Network) manager software, CPANMinus, by install-cpanm.

You can use plenv global and plenv local to change the different version of Perl to fulfil different needs of your Perl software.

For example, if the specific version of Perl is not compatible with your script, you can switch to the different version by:

 $ plenv local

It is similar to set the local version of your script language when you use pyenv and rbenv as the following.

Put the following path into ~/.bashrc file.

export PERL5LIB="$HOME/.plenv/build/perl-5.18.1/lib"

Install BioPerl and PerlIO::gzip

CPANMinus is a very good Perl module manager, use cpanm to install BioPerl can save you a lot of time. Here are some useful modules:

$ cpanm Bio::Perl
$ cpanm Bio::SearchIO
$ cpanm PerlIO::gzip

For more information, please visit: https://github.com/tokuhirom/plenv

RestrictionDigest: A powerful Perl module for simulating genomic restriction digests

Neel — Tue, 12 Jun 2018 13:17:13 -0500

RestrictionDigest can simulate the reference genome digestion and generate comprehensive information of the simulation. It can simulate single-enzyme digestion, double-enzyme digestion and size selection process. It can also analyze multiple genomes at one run and generates concise comparison of enzyme(s) performance across the genomes. For more information, please see the academic paper published online (http://www.sciencedirect.com/science/article/pii/S071734581630001X).

Address of the bookmark: https://github.com/JINPENG-WANG/RestrictionDigest

Benchmarking Perl Module !

Rahul Nayak — Sat, 25 Aug 2018 11:40:42 -0500

The benchmark module is a great tool to know the time the code takes to run. The output is usually in terms of CPU time. This module provides us with a way to optimize our code. With the advent of petascale computing and other multicore processor it is becoming a neccesity to know about the CPU time taken by our perl program.

This is the simple way to use the module

Example1:
use Benchmark;
$first_time = Benchmark->new;
our code……
$second_time = Benchmark->new;
$final_difference = timediff($first_time,$second_time);
print “the code took, timestr($final_difference),”\n”;

that was a very simple way to know the time diff , we can use it to know the time taken by some part of the code in the program.

More sophisticated way:
use Benchmark;
sub first {
my(arguments) = @_;
}
timethese(100, { first => ‘first_sub(arguments)’});
The first argument to timethese is 100 (evaluate 100 times).

Hope this very small tutorial with Benchmark will help people get started.

Machine learning in Perl

Jit — Sun, 16 Feb 2020 15:32:03 -0600

this is a fourth blog post in the Machine learning in Perl series, focusing on the AI::MXNet, a Perl interface to Apache MXNet, a modern and powerful machine learning library.

If you're interested in refreshing your memory or just new to the series, please check previous entries over here: 1 2 3

https://metacpan.org/pod/AI::MXNet

Address of the bookmark: http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html

Download mutliple fasta file from NCBI in one GO!!

Rahul Agarwal — Wed, 21 Aug 2013 08:13:30 -0500

if you have less time, then use three ways mentioned in bookmark link to extract/download all fasta sequences in single click given that you already have a list of GIs or accession IDs .

Alternatively, use one liner perl script:

perl -ne 'if(/^>(\S+)/){$c=$i{$1}}$c?print:chomp;$i{$_}=1 if @ARGV' GIs.txt >sequence.fasta

where GIs.txt contains a list of GIs or accession IDs.

(from :http://edwards.sdsu.edu/labsite/index.php/robert?start=5)

Address of the bookmark: http://edwards.sdsu.edu/labsite/index.php/robert/380-ncbi-sequence-or-fasta-batch-download-using-entrez

Awesome perl frameworks, libraries and software - PART 5

Neel — Fri, 07 Jul 2017 04:12:47 -0500

robelix/sub2srt - subtitle converter
reyjrar/graphite-scripts - A Collections of Scripts for Working with Graphite
regilero/check_nginx_status - Nagios check for nginx status report
omniti-labs/resmon - resmon
motemen/App-htmlcat - redirect stdin to web browser
moose/Moo - Minimalist Object Orientation (with Moose compatibility)
miyagawa/fastpass - Tiny, XS free, standalone and preforking FastCGI daemon for PSGI
miyagawa/Filesys-Notify-Simple - Simple and dumb file system watcher
mhop/fhem-mirror - Branch 'master' is a read-only-mirror of svn://svn.code.sf.net/p/fhem/code which is updated once a day. On branch 'enocean' I am going to add some Enocean-Devices
lopnor/Plack-App-DAV - simple DAV server for Plack
kazuho/url_compress - a static PPM-based URL compressor / decompressor
jnthn/6model - Just a place that I'm keeping some meta-model prototyping; anything that matters will make it to another repo (e.g. nqp-rx one or Rakudo one) at some point.
jasonhancock/nagios-puppetdb - Nagios plugins and pnp4nagios templates related to Puppetlab's PuppetDB project.
goccy/p5-Compiler-Parser - Create Abstract Syntax Tree for Perl5
cgutteridge/Grinder - Create RDF data from spreadsheets or CSV
c9s/Plack-Middleware-OAuth - Plack Middleware for OAuth1 and OAuth2
bzip2-cuda/bzip2-cuda - Parallel implementation of bzip2 using cuda
alanstevens/ChocoPackages - Chocolatey Nuget Packages
SoylentNews/slashcode - The slashcode repository for SoylentNews. The initial code base was uploaded as it appeared on Sourceforge as of the last commit in September 2009
Miserlou/XSS-Harvest - XSS Weaponization

getopts.pl file

Jit — Fri, 15 Jun 2018 04:43:03 -0500

SSPACE_longread complain for getopts.pl file.

To resolve this, download and have in SSPACED-Longreads folder.

Cheers :)

Genomics for Bioinformatician

Jitendra Narayan — Sat, 20 Jul 2013 07:03:00 -0500

Genomics is the study of the genomes of organisms. The field includes intensive efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping efforts. The field also includes studies of intragenomic phenomena such as heterosis, epistasis, pleiotropy and other interactions between loci and alleles within the genome. In contrast, the investigation of the roles and functions of single genes is a primary focus of molecular biology or genetics and is a common topic of modern medical and biological research. Research of single genes does not fall into the definition of genomics unless the aim of this genetic, pathway, and functional information analysis is to elucidate its effect on, place in, and response to the entire genome's networks.

Genomics was established by Fred Sanger when he first sequenced the complete genomes of a virus and a mitochondrion. His group established techniques of sequencing, genome mapping, data storage, and bioinformatic analyses in the 1970-1980s. A major branch of genomics is still concerned with sequencing the genomes of various organisms, but the knowledge of full genomes has created the possibility for the field of functional genomics, mainly concerned with patterns of gene expression during various conditions. The most important tools here are microarrays and bioinformatics. Study of the full set of proteins in a cell type or tissue, and the changes during various conditions, is called proteomics. A related concept is materiomics, which is defined as the study of the material properties of biological materials (e.g. hierarchical protein structures and materials, mineralized biological tissues, etc.) and their effect on the macroscopic function and failure in their biological context, linking processes, structure and properties at multiple scales through a materials science approach. The actual term 'genomics' is thought to have been coined by Dr. Tom Roderick, a geneticist at the Jackson Laboratory (Bar Harbor, ME) over beer at a meeting held in Maryland on the mapping of the human genome in 1986.

The outcome of almost two years of intense discussions with literally hundreds of scientists and members of the public, has three major areas of focus: Genomics to Biology, Genomics to Health, and Genomics to Society.

Genomics to Biology:
The human genome sequence provides foundational information that now will allow development of a comprehensive catalog of all of the genome's components, determination of the function of all human genes, and deciphering of how genes and proteins work together in pathways and networks.

Genomics to Health:
Completion of the human genome sequence offers a unique opportunity to understand the role of genetic factors in health and disease, and to apply that understanding rapidly to prevention, diagnosis, and treatment. This opportunity will be realized through such genomics-based approaches as identification of genes and pathways and determining how they interact with environmental factors in health and disease, more precise prediction of disease susceptibility and drug response, early detection of illness, and development of entirely new therapeutic approaches.

Genomics to Society:
Just as the HGP has spawned new areas of research in basic biology and in health, it has created new opportunities in exploring the ethical, legal, and social implications (ELSI) of such work. These include defining policy options regarding the use of genomic information in both medical and non-medical settings and analysis of the impact of genomics on such concepts as race, ethnicity, kinship, individual and group identity, health, disease, and "normality" for traits and behaviors.

This vision for the future of genomics is not just about the NHGRI. It encompasses the whole field of genomics, including the work of all the other Institutes and Centers at the NIH and of a number of other federal agencies. All of the NIH Institutes are already taking full advantage of the sequence and will apply its data to the better understanding of both rare and common diseases, almost all of which have a genetic component. A recent example of the way that the HGP and the knowledge and new technologies it has spawned are already facilitating science is the extremely rapid sequencing by groups in Canada and at the Centers for Disease Control and Prevention (CDC) in Atlanta of the genome of the virus that causes Severe Acute Respiratory Syndrome (SARS). The sequencing of the SARS virus genome provides insight into this new and deadly disease at a speed never before possible in science. In turn, this should lead to the rapid development of diagnostic tests and, in time, vaccines and effective treatments.

Links for the addition material available on Net

Genomes and genomics:

Bioinformatics and Genomics:

Structural genomics tutorial:

Comparative Genomics Tutorial:

GENOME TUTORIAL:

Tools and resources for identifying protein families, domains and motifs

Bioinformatics Tools
Tips, Tutorials, and Terminology for Using Selected Resources in Genome Database Guide:

A Web-Based Comparative Genomics Tutorial for Investigating Microbial Genomes:

Free Online Tutorials Teach Anyone How to Use Genome Databases:

Circos to create concise, explanatory, unique and print-ready visualizations of your data:

Genomics and Comparative Genomics Learning Module:

Computational Challenges in Comparative Genomics

A Tutorial:

A Comparative Genomics Resource for Grains:

PLAZA: A Comparative Genomics Resource to Study Gene and Genome Evolution in Plants:

VISTA :

Software for Genomics

Artemis Artemis is a free genome viewer and annotation tool that allows visualization of sequence features and the results of analyses within the context of the sequence, and its six-frame translation.
Chromas It will display and prints chromatogram files from ABI automated DNA sequencers, and Staden SCF files which the analysis programs for ALF, Li-Cor and Visible Genetics OpenGene sequencers can create.
Glimmer A system for finding genes in microbial DNA, especially the genomes of bacteria and archaea.Glimmer (Gene Locator and Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DN
Glimmer HMM A fast and accurate gene finder based on a GHMM architecture, developed specifically for eukaryotes. It incorporates splice site models adapted from the GeneSplicer program and uses interpolated Markov models for evaluating the coding regions.
Glimmer M A gene finder derived from Glimmer, but developed specifically for eukaryotes. It is based on a dynamic programming algorithm that considers all combinations of possible exons for inclusion in a gene model and chooses the best of these combinations. The d
MUMmer MUMmer is a system for rapidly aligning entire genomes, whether in complete or draft form.
pDRAW pDRAW32 is being developed as a free time hobby project. It is far from finished, but as it has reached a point where it could be helpful for many labs, it is now available to the scientific community.
Sequin Sequin is a stand-alone software tool developed by the NCBI for submitting and updating entries to the GenBank, EMBL, or DDBJ sequence databases. It is capable of handling simple submissions that contain a single short mRNA sequence, and complex submissio
Staden The Staden Package consists of a series of tools for DNA sequence preparation (pregap4), assembly (gap4), editing (gap4) and DNA/protein sequence analysis (spin).

For more software @ http://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools

R and Bioconductor Tutorial

Jitendra Narayan — Fri, 23 Aug 2013 08:23:59 -0500

This tutorial is intended to introduce users quickly to the basics of R, focusing on a few common tasks that biologists need to perform some basic analysis: load a table, plot some graphs, and perform some basic statistics. More extensive tutorials can be found on the project website and via bioconductor (not covered here).

You can add more tutorial links in comments if found new pages.

Address of the bookmark: http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual