BOL: Related items

GATB : Genome Analysis Toolbox with de-Bruijn graph

Jit — Thu, 28 Apr 2016 11:16:51 -0500

The Genome Analysis Toolbox with de-Bruijn graph (GATB) provides a set of highly efficient algorithms to analyse NGS data sets. These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (e.g. metagenomes).

More at https://gatb.inria.fr/

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

Painless package development for R

Abhi — Tue, 03 May 2016 05:31:06 -0500

Devtools makes package development a breeze: it works with R’s existing conventions for code structure, adding efficient tools to support the cycle of package development. With devtools, developing a package becomes so easy that it will be your default layout whenever you’re writing a significant amount of code.

Before you get started be sure to check out:

Address of the bookmark: https://www.rstudio.com/products/rpackages/devtools/

SLURM

Jit — Wed, 04 May 2016 05:13:21 -0500

SLURM workload manager software, a free open-source workload manager designed specifically to satisfy the demanding needs of high performance computing.

This page is a HOWTO guide for setting up a SLURM installation, currently focused on a CentOS 7 Linux OS. Please send feedback to Ole.H.Nielsen /at/ fysik.dtu.dk.

See the SLURM homepage (also https://computing.llnl.gov/linux/slurm/).

Address of the bookmark: https://wiki.fysik.dtu.dk/niflheim/SLURM

Genome STRiP

Neel — Tue, 06 Sep 2016 03:58:19 -0500

Genome STRiP (Genome STRucture In Populations) is a suite of tools for discovering and genotyping structural variations using sequencing data. The methods are designed to detect shared variation using data from multiple individuals.

Genome STRiP looks both across and within a set of sequenced genomes to detect variation. The methods are adaptive and support heterogeneous data sets, including variations in sequencing depth, read lengths and mixtures of paired and single-end reads. A minimum of 20 to 30 genomes are required to get acceptable results, but the method gains power across genomes and processing more genomes provide better results.

To run discovery or genotyping on a single sequenced genome or a small set of genomes, you need to call your data against a background population, such as a set of genomes from the 1000 Genomes Project. The background population does not need to be matched to the target individuals.

Address of the bookmark: http://software.broadinstitute.org/software/genomestrip/

CrossMap

Abhimanyu Singh — Mon, 05 Sep 2016 04:07:38 -0500

CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) <> hg19 (GRCh37), Mouse mm9 (MGSCv37) <> mm10 (GRCm38)).
It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.
CrossMap is designed to liftover genome coordinates between assemblies. It’s not a program for aligning sequences to reference genome.
We do not recommend using CrossMap to convert genome coordinates between species.

Address of the bookmark: http://crossmap.sourceforge.net/

OrganellarGenomeDRAW

Jit — Tue, 16 Aug 2016 08:13:13 -0500

OrganellarGenomeDRAW is dedicated to convert genetic information stored in GenBank entries to graphical maps. The input text file has to be in GenBank flat file format, whereas the output format can be chosen among several formats. The application is especially optimized and adapted for the creation of high-quality, detailed circular maps of organellar genomes like the plastid genome (plastome) or the mitochondrial genome (chondriome). Nevertheless, you can upload any GenBank entry. The workflow is devided into three steps.

More at http://ogdraw.mpimp-golm.mpg.de/cgi-bin/ogdraw.pl

Address of the bookmark: http://ogdraw.mpimp-golm.mpg.de/index.shtml

TEannot

Jit — Thu, 18 Aug 2016 10:02:03 -0500

We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the project name is "DmelChr4".

Address of the bookmark: https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto

GeneMANIA

Jit — Mon, 22 Aug 2016 09:55:16 -0500

Faster, more accurate algorithms function prediction "GeneMANIA (Multiple Association Network Integration Algorithm)" have however been developed in recent years and are publicly available on the web, indicating the future direction of function prediction.

Address of the bookmark: http://genemania.org/

GeneValidator - Identify problems with predicted genes

Poonam Mahapatra — Fri, 26 Aug 2016 06:00:03 -0500

GeneValidator helps in identifing problems with gene predictions and provide useful information extracted from analysing orthologs in BLAST databases. The results produced can be used by biocurators and researchers who need accurate gene predictions.

If you would like to use GeneValidator on a few sequences, see our online GeneValidator Web App -http://genevalidator.sbcs.qmul.ac.uk.

If you use GeneValidator in your work, please cite us as follows:

Dragan M‡, Moghul MI‡, Priyam A, Bustos C & Wurm Y. 2016. GeneValidator: identify problems with protein-coding gene predictions. Bioinformatics, doi: 10.1093/bioinformatics/btw015.

Address of the bookmark: https://github.com/wurmlab/genevalidator

Redundans

Jit — Thu, 01 Sep 2016 08:28:11 -0500

Redundans pipeline assists an assembly of heterozygous genomes.
Program takes as input assembled contigs, paired-end and/or mate pairs sequencing libraries and returns scaffolded homozygous genome assembly, that should be less fragmented and with total size smaller than the input contigs. In addition, Redundans will automatically close the gaps resulting from genome assembly or scaffolding more details.

The pipeline consists of three steps/modules:

redundancy reduction: detection and selectively removal of redundant contigs from an initial de novo assembly
scaffolding: joining of genome fragments using paired-end and/or mate-pairs reads
gap closing

Redundans is:

fast & lightweight, multi-core support and memory-optimised, so it can be run even on the laptop for small-to-medium size genomes
flexible toward many sequencing technologies (Illumina, 454 or Sanger) and library types (paired-end, mate pairs, fosmids)
modular: every step can be ommited or replaced by another tools

Address of the bookmark: https://github.com/Gabaldonlab/redundans