• January 20th, 2016 - New! Bpipe 0.9.9 released!
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Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

Calculate how much sequencing you need to hit a target depth of coverage (or vice versa).

Instructions: set the read length/configuration and genome size, then select what you want to calculate.

Written by Stephen Turner, based on the Lander-Waterman formula, inspired by a similar calculator written by James Hadfield. Coverage is calculated as C=LN/G and reads as N=CG/L where C = Coverage (X),L = Read length (bp), G = Haploid genome size (bp), and N = Number of reads. Source code on GitHub.

Address of the bookmark: http://apps.bioconnector.virginia.edu/covcalc/

Tool at https://github.com/kbseah/genome-bin-tools

Address of the bookmark: http://journal.frontiersin.org/article/10.3389/fmicb.2015.01451/full

BBSplit is a tool that bins reads by mapping to multiple references simultaneously, using BBMap. The reads go to the bin of the reference they map to best. There are also disambiguation options, such that reads that map to multiple references can be binned with all of them, none of them, one of them, or put in a special "ambiguous" file for each of them. Paired reads will always be kept together.

For example, if you had a library of something that was contaminated with e.coli and salmonella, you could do this:

bbsplit.sh in=reads.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu=clean.fq int=t

This will produce 3 output files:
out_ecoli.fq (ecoli reads)
out_salmonella.fq (salmonella reads)
clean.fq (unmapped reads)

In this case, "int=t" means that the input file is paired and interleaved. For single-end reads you would leave that out. For paired reads in 2 files, you would do this:
bbsplit.sh in1=reads1.fq in2=reads2.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu1=clean1.fq outu2=clean2.fq

BBSplit is available here:
https://sourceforge.net/projects/bbmap/

The sensitivity can be raised to be equivalent to BBMap with these flags: "minratio=0.56 minhits=1 maxindel=16000"

https://www.hindawi.com/journals/ijpg/2011/373875/

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

Address of the bookmark: https://rpubs.com/Devashish13/PCAGenomics

FIGENO is a
  FIGure
    GENerator
for GENOmics

With figeno, you can plot various types of sequencing data along genomic coordinates. Video overview: https://www.youtube.com/watch?v=h1cBeXoSYTA.

Address of the bookmark: https://github.com/CompEpigen/figeno

Address of the bookmark: https://github.com/pnnl/fqc

Copy & Paste this line into your terminal:

\curl -L https://install.perlbrew.pl | bash

Or, if your system does not have curl but something else:

# Linux
\wget -O - https://install.perlbrew.pl | bash

# FreeBSD
\fetch -o- https://install.perlbrew.pl | sh

If you prefer to install with cpan, there are two steps:

sudo cpan App::perlbrew
perlbrew init

If it is installed with cpan, the perlbrew executable should be installed as /usr/bin/perlbrew or /usr/local/bin/perlbrew. For all users who want to use perlbrew, a prior perlbrew init needs to be executed.

The default perlbrew root directory is ~/perl5/perlbrew, which can be changed by setting PERLBREW_ROOTenvironment variable before the installation and initialization. For more advanced installation process, please read the perlbrew document.

Address of the bookmark: https://perlbrew.pl/

Relying on unique software architecture, PLAST takes full advantage of recent multi-core personal computers without requiring any additional hardware devices.

PLAST stands for Parallel Local Sequence Alignment Search Tool and is was published in BMC Bioinformatics.

PLAST is a general purpose sequence comparison tool providing the following benefits:

• PLAST is a high-performance sequence comparison tool designed to compare two sets of sequences (query vs. reference),
• Reduces the processing time of sequences comparisons while providing highest quality results,
• Contains a fully integrated data filtering engine capable of selecting relevant hits with user-defined criteria (E-Value, identity, coverage, alignment length, etc.),
• Does not require any additional hardware, since it is a software solution. It is easy to install, cost-effective, takes full advantage of multi-core processors and uses a small RAM footprint,
• Ready to be used on desktop computer, cluster, cloud as well as within distributed system running Hadoop.

https://plast.inria.fr/

Address of the bookmark: https://plast.inria.fr/