<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/41691?offset=410 https://bioinformaticsonline.com/researchlabs/view/4212/eivind-hovigs-lab Tue, 03 Sep 2013 19:06:29 -0500 <![CDATA[Eivind Hovig's Lab]]> Bioinformatics relevant research topics are:

genomic scale studies
endogenous mechanisms of mutations, germ line and somatic
computational aspects of immunology in cancer
signalling networks
three-dimensional organization of information in the nucleus
gene silencing
metastatic cross-talk
kinase signaling
personalized medicine
detection of biomarkers in cancer
historical DNA variation

From : http://www.ous-research.no/hovig/

Group address:
Eivind Hovig, The Norwegian Radium Hospital, Montebello, 0310 Oslo,Norway
Email: ehovig@radium.uio.no

]]> https://bioinformaticsonline.com/bookmarks/view/5350/introduction-of-epigenomics Sun, 06 Oct 2013 04:59:30 -0500 https://bioinformaticsonline.com/bookmarks/view/5350/introduction-of-epigenomics <![CDATA[Introduction of Epigenomics]]>
  • What is the epigenome?
  • What does the epigenome do?
  • What makes up the epigenome?
  • Is the epigenome inherited?
  • What is imprinting?
  • Can the epigenome change?
  • What makes the epigenome change?
  • How do changes in the epigenome contribute to cancer?
  • How are researchers exploring the epigenome?

    • Address of the bookmark: http://www.genome.gov/27532724

    ]]>     Rahul Agarwal     https://bioinformaticsonline.com/news/view/6302/a-allele-of-slc24a5-gene-is-found-to-be-responsible-for-variation-in-skin-color-of-south-east-asians-and-europeans Tue, 12 Nov 2013 21:02:27 -0600 https://bioinformaticsonline.com/news/view/6302/a-allele-of-slc24a5-gene-is-found-to-be-responsible-for-variation-in-skin-color-of-south-east-asians-and-europeans <![CDATA[A-allele of SLC24A5 gene is found to be responsible for variation in skin color of South-East Asians and Europeans]]> Key finding:

    1. rs1426654 SNP of SLC24A5 gene is decider of skin pigmentation variation in South Asia
    2. rs1426654-A allele is widely spread throughout the Indian subcontinent 
    3. Skin pigmentation is also account by the combination of processes like selection and demographic history of populations affected by their language and origin
    4. Sign of positive selection in Europeans, Middle East, Pakistan, Central Asia and North India but not in South India
    5. In European , A-allele is almost reached to fixation

    Paper:

    http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003912

    ]]>     Rahul Agarwal     https://bioinformaticsonline.com/news/view/10379/your-stressdepression-came-from-ancestor Sun, 04 May 2014 18:46:20 -0500 https://bioinformaticsonline.com/news/view/10379/your-stressdepression-came-from-ancestor <![CDATA[Your stress/depression came from ancestor]]> "A study published in Nature Neuroscience finds that stress in early life alters the production of small RNAs, called microRNAs, in the sperm of mice. The mice show depressive behaviours that persist in their progeny."

    Source:

    http://www.nature.com/news/sperm-rna-carries-marks-of-trauma-1.15049

    ]]>     Rahul Agarwal     https://bioinformaticsonline.com/bookmarks/view/33826/geneprof-analysis-of-high-throughput-sequencing-experiment Wed, 05 Jul 2017 16:47:05 -0500 https://bioinformaticsonline.com/bookmarks/view/33826/geneprof-analysis-of-high-throughput-sequencing-experiment <![CDATA[GeneProf: analysis of high-throughput sequencing experiment]]> GeneProf is a web-based, graphical software suite that allows users to analyse data produced using high-throughput sequencing platforms (RNA-seq and ChIP-seq; "Next-Generation Sequencing" or NGS): Next-gen analysis for next-gen data!

    Some of GeneProf's highlights include:

    • Easy-to-use web-based interface:Access your data at any time from any computer with a working internet connection -- no need to install software! (cp. Section 'System Requirements').
    • Analysis wizards make your life easy:Step-by-step workflows make it easy to analyse high-throughput data within a minimum of hands-on time. (cp. SubConcept 'Analysis Wizards').
    • Versatile modules:Advanced users and data analysis experts benefit from GeneProf's broad range of analysis modules, which can be combined freely into sophisticated workflows (cp. Concept 'Workflows').
    • Integrated Analysis:Analysis of ChIP-seq and RNA-seq data in one place, plus support for the integration of other external data (e.g. from microarrays).
    • Comprehensive Resource:GeneProf provides a comprehensive resource of fully analyzed next-generation sequencing data. Experimental results can be easily accessed and compared and the analysis procedures employed to produce the data are fully transparent (cp. Tutorial 'Examining Public Next-Gen Data..').
    • Extensibility:Algorithm developers and computer programmers can develop their own modules and extend GeneProf. Existing software can be easily wrapped in the workflow framework (cp. Section 'Module Development: Adding new..') and data from GeneProf may be used externally (cp. Section 'Web API: Retrieving Data from ..').

     

    GeneProf is academic software developed at the Centre for Regenerative Medicine / Institute for Stem Cell Research, University of Edinburgh and has benefited from funding by the Medical Research Council and the EU Framework 7 Project "EuroSyStem".

    Address of the bookmark: https://www.geneprof.org/GeneProf/index.jsp

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/34748/airvf-a-filtering-toolbox-for-precise-variant-calling-in-ion-torrent-sequencing Fri, 22 Dec 2017 00:31:06 -0600 https://bioinformaticsonline.com/bookmarks/view/34748/airvf-a-filtering-toolbox-for-precise-variant-calling-in-ion-torrent-sequencing <![CDATA[AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing]]> AIRVF that works on flowgram, raw and mapped reads and called variants to reduce artifact-driven false variant calls. Tests on sequencing data of standard reference material showed up to ∼98% reduction of false variants when combined to conventional public pipelines and ∼48% to the in-house commercial solution, with a minimal loss of sensitivity

    The program with a detailed manual is available at https://sourceforge.net/projects/airvf/

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

    ]]>     BioStar     https://bioinformaticsonline.com/bookmarks/view/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application Wed, 23 May 2018 06:54:32 -0500 https://bioinformaticsonline.com/bookmarks/view/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application <![CDATA[BlasR Mapping single molecule sequencing reads using Basic Local Alignment with Successive Refinement (BLASR): Theory and Application,]]> BLASR (Basic Local Alignment with Successive Refinement) for mapping Single Molecule Sequencing (SMS) reads that are thousands to tens of thousands of bases long with divergence between the read and genome dominated by insertion and deletion error.

    Here is how I use the blasr to align PacBio reads to the contigs (target.fasta). The “target.fasta.sa” is the suffix array from “target.fasta” generated by sawriter.

    blasr query.fa ./target.fasta -sa ./target.fasta.sa -bestn 40 -maxScore -500 -m 4 -nproc 24 -out target.m4 -maxLCPLength 15

    the output format option “-m 4″ generate the alignment coordinate. Not fully documented, but I can explain that to you. 

    I use a 24 cores / 48G ram server for the alignment. It took about 2 to 3 hours aligning 3G PacBio Reads to 10^6 sequences of short read contigs with a mean 3.5kbp length.

    Address of the bookmark: http://bix.ucsd.edu/projects/blasr/

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/37457/nanofilt-filtering-and-trimming-of-long-read-sequencing-data Mon, 30 Jul 2018 12:01:52 -0500 https://bioinformaticsonline.com/bookmarks/view/37457/nanofilt-filtering-and-trimming-of-long-read-sequencing-data <![CDATA[nanofilt: Filtering and trimming of long read sequencing data]]> Filtering on quality and/or read length, and optional trimming after passing filters.
    Reads from stdin, writes to stdout.

    Intended to be used:

    • directly after fastq extraction
    • prior to mapping
    • in a stream between extraction and mapping

    https://github.com/wdecoster/nanofilt

    Address of the bookmark: https://github.com/wdecoster/nanofilt

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/37527/nanopack-visualizing-and-processing-long-read-sequencing-data Fri, 10 Aug 2018 18:41:34 -0500 https://bioinformaticsonline.com/bookmarks/view/37527/nanopack-visualizing-and-processing-long-read-sequencing-data <![CDATA[NanoPack: visualizing and processing long-read sequencing data]]> The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.

     https://academic.oup.com/bioinformatics/article/34/15/2666/4934939

    Address of the bookmark: https://github.com/wdecoster/nanoQC

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic Tue, 13 Nov 2018 07:25:38 -0600 https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic <![CDATA[sim3C: Read-pair simulation of 3C-based sequencing methodologies (HiC, Meta3C, DNase-HiC)]]> Required python modules

    • biopython
    • intervaltree
    • numpy
    • scipy
    • tqdm
    • PyYAML

    Address of the bookmark: https://github.com/cerebis/sim3C

    ]]>     Jit