<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/26426?offset=30 https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis Tue, 26 Apr 2016 03:48:23 -0500 https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis <![CDATA[HOMER: Software for motif discovery and next-gen sequencing analysis]]> This tutorial covers topics independently of HOMER, and represents knowledge which is important to know before diving head first into more advanced analysis tools such as HOMER.

  1. Setting up your computing environment
  2. Retrieving and storing sequencing files (your own data or from public sources)
  3. Checking sequence quality, trimming, general sequence manipulation
  4. Mapping reads to a reference genome
  5. Manipulating SAM/BAM alignment files
  6. Visualizing data in a genome browser


RNA-Seq

  1. De novo transcript discovery and differential analysis with Cufflinks
  2. Differential expression analysis with R/Bioconductor
  3. Clustering of large expression datasets (microarray or RNA-Seq)


Microarray

  1. Basic analysis of Affymetrix Gene Expression Arrays using R/Bioconductor

General Tips for Data Analysis

  1. Excel workarounds, adding gene annotation, X-Y plots tips, etc.

Address of the bookmark: http://homer.salk.edu/homer/basicTutorial/

]]> Neel https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph Thu, 28 Apr 2016 11:16:51 -0500 https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph <![CDATA[GATB : Genome Analysis Toolbox with de-Bruijn graph]]> 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/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/27225/painless-package-development-for-r Tue, 03 May 2016 05:31:06 -0500 https://bioinformaticsonline.com/bookmarks/view/27225/painless-package-development-for-r <![CDATA[Painless package development for R]]> 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/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/27238/slurm Wed, 04 May 2016 05:13:21 -0500 https://bioinformaticsonline.com/bookmarks/view/27238/slurm <![CDATA[SLURM]]> 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

]]> Jit https://bioinformaticsonline.com/bookmarks/view/29103/genome-strip Tue, 06 Sep 2016 03:58:19 -0500 https://bioinformaticsonline.com/bookmarks/view/29103/genome-strip <![CDATA[Genome STRiP]]> 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/

]]> Neel https://bioinformaticsonline.com/bookmarks/view/29018/crossmap Mon, 05 Sep 2016 04:07:38 -0500 https://bioinformaticsonline.com/bookmarks/view/29018/crossmap <![CDATA[CrossMap]]>
  • 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/

    ]]>     Abhimanyu Singh     https://bioinformaticsonline.com/bookmarks/view/28807/organellargenomedraw Tue, 16 Aug 2016 08:13:13 -0500 https://bioinformaticsonline.com/bookmarks/view/28807/organellargenomedraw <![CDATA[OrganellarGenomeDRAW]]> 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

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/28844/teannot Thu, 18 Aug 2016 10:02:03 -0500 https://bioinformaticsonline.com/bookmarks/view/28844/teannot <![CDATA[TEannot]]> 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

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/28870/genemania Mon, 22 Aug 2016 09:55:16 -0500 https://bioinformaticsonline.com/bookmarks/view/28870/genemania <![CDATA[GeneMANIA]]> 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/

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/28903/genevalidator-identify-problems-with-predicted-genes Fri, 26 Aug 2016 06:00:03 -0500 https://bioinformaticsonline.com/bookmarks/view/28903/genevalidator-identify-problems-with-predicted-genes <![CDATA[GeneValidator - Identify problems with predicted genes]]> 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

    ]]>     Poonam Mahapatra