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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/19631?offset=140</link>
	<atom:link href="https://bioinformaticsonline.com/related/19631?offset=140" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/915/researcher-in-computer-sciencebiology</guid>
  <pubDate>Mon, 15 Jul 2013 18:38:40 -0500</pubDate>
  <link></link>
  <title><![CDATA[Researcher in computer science/biology]]></title>
  <description><![CDATA[
<p>Researcher in Computer Science at the Computational Biology Unit - temporary employment</p>

<p>The Department of Informatics is a vacant position as a researcher in computer science, related to Computational Biology Unit (CBU), for 3 years.<br /> <br />The position is part of CBU Service Group and will focus on bioinformatic analysis project and especially the analysis of high-throughput data, including NGS (sequencing), and proteomics data.<br /> <br />The successful candidate will be part of the Norwegian bioinformatics platform's national helpdesk within the project ELIXIR.NO<br /> <br />Applicants must hold a PhD in a relevant subject such as computer science, mathematics, molecular biology and also possess expertise and experience in bioinformatics statistics and analysis of data from high-throughput molecular experiment.<br /> <br />Basic programming or scripting skills are required. Experience in Python, R, Perl, Linux-based operating systems and moreover knowledge of databases and web programming will be a strength for applicants.<br /> <br />We expect enthusiasm and independence and moreover the ability to work in an interdisciplinary team environment.<br /> <br />Good knowledge of English is required.<br /> <br />Salaries start at level 57 (code 1109/LR 24.1) by appointment. Further promotion occurs after<br />service seniority in the position (at grade 57-65). Of particularly highly qualified applicants may be considered a higher salary.<br /> <br />Further information about the position is available from the chair of the CBU, <br />Professor Inge Jonassen, e-mail: Inge.Jonassen @ ii.uib.no<br /> <br />The successful applicant must comply with the guidelines that apply at any given time the position.<br /> <br />State employment shall as far as possible reflect the diversity of the population. It is therefore an objective to achieve a balanced age and sex composition and the recruitment of persons with immigrant backgrounds. Persons with immigrant background are requested to apply for the position.<br /> <br />Women are particularly encouraged to apply. If the experts find that several applicants have approximately equivalent qualifications, the rules on equal in the Personnel Regulations for Academic Positions will be applied.<br /> <br />University of Bergen applies the principles of public openness when recruiting staff to scientific positions.<br /> <br />Information about the applicant may be made public even though the applicant has requested not to be named in the list of applicants. If the request does not host admitted to the result, the applicant shall be notified of this.<br /> <br />Send application, CV, certificates, diplomas, undergraduate work and a list of publications (list of publications) online by clicking on https://www.jobbnorge.no/jobbsoknet/login.aspx?returnurl=/jobbsoknet/jobapplication.aspx?jobid=95196<br /> <br />You need to upload certified translations into English or a Scandinavian language of appendices, such as diplomas and transcripts.<br /> <br />Applications sent by email to individuals at the institute will not be considered.<br /> <br />Deadline: 9 August 2013</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/6458/bigre-lab</guid>
  <pubDate>Sun, 17 Nov 2013 10:35:49 -0600</pubDate>
  <link></link>
  <title><![CDATA[BIGRE Lab]]></title>
  <description><![CDATA[
<p>The Laboratoire de Bioinformatique des Génomes et des Réseaux (Genome and Network Bioinformatics) is specialized in the conception, implementation, evaluation and application of bioinformatics approaches for the analysis of genome, transcriptome, proteome and metabolism.<br />Our main activities include</p>

<p>Analysis of regulatory sequences (RSAT project)<br />Classification and analysis of mobile genetic elements (ACLAME project).<br />Analysis of molecular interaction networks (NeAT project)<br />Inference of metabolic pathways from genomic and post-genomic data <br />(metabolic pathfinding, see also metabolic pathfinding in NeAT)<br />Critical assesment of protein interactions (CAPRI)</p>

<p>Lab Page http://www.bigre.ulb.ac.be/</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/6961/research-assistant-national-bureau-of-animal-genetic-resources</guid>
  <pubDate>Tue, 03 Dec 2013 06:17:34 -0600</pubDate>
  <link></link>
  <title><![CDATA[Research Assistant @ NATIONAL BUREAU OF ANIMAL GENETIC RESOURCES]]></title>
  <description><![CDATA[
<p>NATIONAL BUREAU OF ANIMAL GENETIC RESOURCES<br />Near Basant Vihar G.T. Road Bypass<br />P.O. Box No.129, Karnal-132001 (Haryana)</p>

<p>WALK-IN-INTERVIEW</p>

<p>A walk-in-Interview is proposed to be held at National Bureau of Animal Genetic Resources, Karnal (Haryana)-132001 at 11:30 AM on 18.12.2013 to select One RA and One SRF as per details given below:</p>

<p>1. One post of Research Associate under DBT sponsored Support under BIPP for the “SanGenix: A comprehensive Next Generation Sequence (NGS) data analysis solution” as Grants in AID. Thepost duration is Upto 31st March 2015 or earlier.</p>

<p>2. One post of Senior Research Fellow under NAIP (Component-4) Bioprospecting of genes and allele mining for abiotic stress tolerance. The post duration is Upto 31st March 2014 or earlier</p>

<p>Essential Qualifications: Ph.D. in Bioinformatics/ Computer Application or<br />First Class Masters degree in Bioinformatics/ Computer Application with two years experience as evidenced by Publications.</p>

<p>Desirable: Experience in the field of handling Next generation Sequencing Data.</p>

<p>Emolument: Rs. 22,000/- per month + HRA as per admissibility</p>

<p>Age Limit:</p>

<p>40 years for Men<br />45 years for women as on date of interview</p>

<p>Research Associate: ONE</p>

<p>Duration of engagement: Upto</p>

<p>31st March 2015 or earlier &amp; Coterminus with the project</p>

<p>Responsibilities: To help the PI for Beta testing and development of the SanGenix Tool for NGS data.</p>

<p>Essential Qualifications: First Class Masters’ degree in Bioinformatics/Biotechnology.</p>

<p>Desirable: Experience in the field of Biotechnology/ Bioinformatics</p>

<p>Emoluments:</p>

<p>Rs. 16,000/- per month + HRA as per admissibility.<br />Senior Research Fellow: ONE<br />Duration of engagement: Upto 31st March 2014 or earlier &amp; Coterminus with the project</p>

<p>Age Limit</p>

<p>35 years for men<br />40 years for women as on date of interview</p>

<p>Note: Relaxation in age will be admissible for SC/ST &amp; OBC candidates as per Govt. of India /ICAR norms</p>

<p>1. The applicants must bring with them original documents and brief of research work done during post graduation along with a set of photocopy and latest two passport size photographs.<br />2. A panel of selected candidates will also be made which may be utilized for filling of positions of shorter durations in future if demand arises.<br />3. Experience certificate in original, if any 4. The above positions are purely on temporary basis and are co-terminus with the project. No TA/DA will be paid to attend the interview.<br />5. Any other clarifications can be had on the date of interview.<br />6. The Director’s decision will be final and binding on all respects.</p>

<p>Advertisement: http://210.212.93.85/rasrfadvertise.pdf</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/11528/post-doctoral-research-assistant-in-genetics</guid>
  <pubDate>Thu, 05 Jun 2014 16:01:39 -0500</pubDate>
  <link></link>
  <title><![CDATA[Post-doctoral Research Assistant in Genetics]]></title>
  <description><![CDATA[
<p>Post-doctoral Research Assistant in Genetics<br />Camden, North London<br />£31.1K per annum inclusive of London Weighting</p>

<p>This is a fixed term post for 36 months.</p>

<p>We wish to recruit a highly motivated, postdoctoral scientist to carry out a BBSRC funded project in the laboratory of Dr. Denis Larkin. The project is focused on developing and applying new algorithms to study genome and chromosome evolution in birds, mammals and other vertebrate species using whole-genome sequences and existing algorithms. The post holder will use cutting edge computational and laboratory approaches to generate chromosomal assemblies for sequenced genomes, study chromosomal structures and genome differences between bird and other vertebrate species in attempt to identify species- and clade-specific genome signatures.</p>

<p>Applicants must have a Ph.D. and a track record of success, as indicated by first-author publications in international journals. They must possess excellent organisation skills and be capable of individual initiative and of interacting as part of a team. Applicants with extensive practical experience in bioinformatics or computer science, programming, visualization, handling of large data sets, high-performance computing are encouraged to apply. The post will involve collaboration with a wide range of academic partners both within the UK, EU and worldwide. In addition to leading their own project the post holder will have opportunities to contribute to multiple international genome initiatives.</p>

<p>Experience in programming, bioinformatics and comparative genome analysis is essential. Applicants should have a minimum of a degree and preferably a higher degree in a relevant subject.</p>

<p>The Royal Veterinary College has the largest range of veterinary, para-veterinary and animal science undergraduate and postgraduate courses of any veterinary school in the world and is one of the largest veterinary schools in Europe.</p>

<p>Prospective applicants are encouraged to contact Dr. Denis Larkin, Comparative Biomedical Sciences Department on +442071211906 or email: dlarkin@rvc.ac.uk</p>

<p>We offer a generous reward package.</p>

<p>For further information and to apply on-line please visit our website: www.rvc.ac.uk<br />Job reference CBS-0025-14A</p>

<p>Closing date: 4 July 2014<br />Interviews are likely to be held in July 2014</p>

<p>We promote equality of opportunity and diversity within the workplace and welcome applications from all sections of the community.</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/10925/a-brief-bioinformatics-tutorial</guid>
	<pubDate>Wed, 21 May 2014 12:50:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/10925/a-brief-bioinformatics-tutorial</link>
	<title><![CDATA[A Brief Bioinformatics Tutorial]]></title>
	<description><![CDATA[<p>This is about how to use a computer to find what is known about a gene of interest and also how to get new insights about it.</p>
<p>The tutorial is divided in three main parts:</p>
<ul>
<li>In the <strong>Sequence </strong>part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.</li>
<li>The <strong>Structure </strong>part is about molecular visualization, homology modeling and structural domain prediction.</li>
<li>In the <strong>Function </strong>part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...</li>
</ul>
<p>During all the three parts, we will use the <em>S. cerevisiae </em>VPS36 protein as an example.</p><p>Address of the bookmark: <a href="http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html" rel="nofollow">http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/11399/next-generation-sequencing-in-r-or-bioconductor-environment</guid>
	<pubDate>Mon, 02 Jun 2014 18:03:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11399/next-generation-sequencing-in-r-or-bioconductor-environment</link>
	<title><![CDATA[Next generation sequencing in R or bioconductor environment]]></title>
	<description><![CDATA[<p>There are many R software and bioconductor packages for NGS data analysis, some of them are as follows</p><h3><a name="TOC-Biostrings" id="TOC-Biostrings"></a>Biostrings</h3><p>The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It contains many speed and memory effective string containers, string matching algorithms, and other utilities, for fast manipulation of large sets of biological sequences. The objects and functions provided by Biostrings form the basis for many other sequence analysis packages. <a href="http://bioconductor.org/packages/release/bioc/html/Biostrings.html">Documentation</a></p><div><div style="text-align: left;"><div style="color: #000000;"><h4><a name="TOC-IRanges-Overview" id="TOC-IRanges-Overview"></a>IRanges Overview</h4><p>IRanges provides the low-level infrastructure and containers for handling sets of integer ranges within Bioconductor's BioC-Seq domain. Its classes and methods provide support for many more high-level packages like GenomicRanges, ShortRead, Rsamtools, etc. <a href="http://bioconductor.org/packages/release/bioc/html/IRanges.html">Documentation</a></p><div style="text-align: right;"><div style="text-align: left;"><h4><a name="TOC-GenomicRanges-Overview" id="TOC-GenomicRanges-Overview"></a>GenomicRanges Overview</h4><p>The <em>GenomicRanges</em> package serves as the foundation for representing genomic locations within the Bioconductor project. It is built upon the <em>IRanges</em> infrastructure and defines three major data containers - <em>GRanges, GRangesList</em> and <em>GappedAlignments</em> - which are supporting other important BioC-Seq packages including <em>ShortRead, Rsamtools, rtracklayer, GenomicFeatures</em> and <em>BSgenome</em>.&nbsp; Compared to the IRanges container, the GRanges/<em>GRangesList</em> classes are more flexible and extensible to store additional information about sequence ranges, such as chromosome identifiers (sequence space), strand information and annotation data. <a href="http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html">Documentation</a></p></div></div></div></div><h3><a name="TOC-Motif-Discovery" id="TOC-Motif-Discovery"></a>Motif Discovery</h3><h4><a name="TOC-cosmo" id="TOC-cosmo"></a>cosmo</h4><p>The cosmo package allows to search a set of unaligned DNA sequences for a shared motif that may function as transcription factor binding site. The algorithm extends the popular motif discovery tool MEME (Bailey and Elkan, 1995) in that it allows the search to be supervised by specifying a set of constraints that the motif to be discovered must satisfy. <a href="http://bioconductor.org/packages/release/bioc/html/cosmo.html">Documentation</a></p></div><div>
<p><span></span><span></span></p>
<div style="color: #0000ff;"><h4><a name="TOC-BCRANK" id="TOC-BCRANK"></a>BCRANK</h4><p>BCRANK is a method that takes a ranked list of genomic regions as input and outputs short DNA sequences that are overrepresented in some part of the list. The algorithm was developed for detecting transcription factor (TF) binding sites in a large number of enriched regions from high-throughput ChIP-chip or ChIP-seq experiments, but it can be applied to any ranked list of DNA sequences. Documentation</p>
<p><a href="http://bioconductor.org/packages/release/bioc/html/BCRANK.html"></a></p>
<p>rGADEM: <a href="http://bioconductor.org/packages/devel/bioc/html/rGADEM.html">Documentation</a></p><p>MotIV: <a href="http://bioconductor.org/packages/devel/bioc/html/MotIV.html">Documentation</a></p></div><h3><a name="TOC-ShortRead" id="TOC-ShortRead"></a>ShortRead</h3><p>The ShortRead package provides input, quality control, filtering, parsing, and manipulation functionality for short read sequences produced by high throughput sequencing technologies. While support is provided for many sequencing technologies, this package is primairly focused on Solexa/Illumina reads. <a href="http://bioconductor.org/packages/release/bioc/html/ShortRead.html">Documentation</a></p><h3><a name="TOC-Rsamtools" id="TOC-Rsamtools"></a>Rsamtools</h3><p>Rsamtools provides functions for parsing and inspecting samtools BAM formatted binary alignment data. SAM/BAM is quickly becoming a universal standard alignment format, and is now supported by a wide variety of alignment tools. <a href="http://bioconductor.org/help/bioc-views/2.7/bioc/html/Rsamtools.html">Documentation</a></p>
<p><a href="http://samtools.sourceforge.net/">Samtools Website</a><br /> <a href="http://bio-bwa.sourceforge.net/">BWA (Burrows-Wheeler Alignment) Website</a><br /><span style="color: #0000ff;"></span></p>
<div style="color: #000000;">&nbsp;</div></div><div>
<p><span style="color: #000000;">Additional tools for SNP analysis:&nbsp;</span></p>
<p><a href="http://bioconductor.org/help/bioc-views/release/bioc/html/snpMatrix.html">snpMatrix</a></p><h3><a name="TOC-BSgenome" id="TOC-BSgenome"></a>BSgenome</h3><p>BSgenome provides an object oriented infrastructure for interacting with a Biostring based genome sequence. BSgenome packages exist for many common genomes, and can be created to represent custom genomes. See the "How to forge a BSgenome data package" Vignette for instructions to create a new BSgenome package if a prebuilt package does not exist for your organism. <a href="http://bioconductor.org/packages/release/bioc/html/BSgenome.html">Documentation</a></p><h3><a name="TOC-rtracklayer" id="TOC-rtracklayer"></a>rtracklayer</h3><p>rtracklayer provides an interface for exporting annotation feature data to various genome browsers and file formats (such as GFF). See the Small RNA Profiling exercise for an example of using rtracklayer to visualize alignment coverage. <a href="http://bioconductor.org/packages/release/bioc/html/rtracklayer.html">Documentation</a></p><h3><a name="TOC-biomaRt" id="TOC-biomaRt"></a>biomaRt</h3><p>The biomaRt package, provides an interface to a growing collection of databases implementing the BioMart software suite (http:// www.biomart.org). The package enables online retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas. This data is retrieved automatically via the Internet, so it's recommended that you cache the data locally, or check versions if your code will be adversely affected by updates to these data. <a href="http://bioconductor.org/packages/release/bioc/html/biomaRt.html">Documentation</a></p><h3><a name="TOC-ChIP-Seq-Analysis-Packages" id="TOC-ChIP-Seq-Analysis-Packages"></a>ChIP-Seq Analysis Packages</h3><p>Bioconductor provides various packages for analyzing and visualizing ChIP-Seq data. Only a small selection of these packages is introduced here. Additional useful introductions to this topic are: <a href="http://www.bioconductor.org/workshops/2009/SeattleJan09/ChIP-seq/">BioC ChIP-seq Case Study</a> and BioC <a href="http://www.bioconductor.org/help/course-materials/2009/SeattleNov09/ChIP-seq/">ChIP-Seq</a>.</p><h4><a name="TOC-chipseq" id="TOC-chipseq"></a>chipseq</h4><p>The chipseq package combines a variety of HT-Seq packages to a pipeline for ChIP-Seq data analysis. <a href="http://bioconductor.org/packages/release/bioc/html/chipseq.html">Documentation</a></p><h4><a name="TOC-BayesPeak" id="TOC-BayesPeak"></a>BayesPeak</h4><p>BayesPeak is a peak calling package for identifying DNA binding sites of proteins in ChIP-Seq experiments. Its algorithm uses hidden Markov models (HMM) and Bayesian statistical methods. The following sample code introduces the identification of peaks with the BayesPeak package as well as the incorporation of read coverage information obtained by the chipseq package. <a href="http://bioconductor.org/packages/release/bioc/html/BayesPeak.html">Documentation</a> [ <a href="http://www.biomedcentral.com/1471-2105/10/299">Publication</a> ]</p><h4><a name="TOC-PICS" id="TOC-PICS"></a>PICS</h4><p>The PICS package applies probabilistic inference to aligned-read ChIP-Seq data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. The following sample code uses the test data set from the above BayesPeak package in order to compare the results from both methods by identifying their consensus peak set. <a href="http://www.bioconductor.org/packages/release/bioc/html/PICS.html">Documentation</a> [ <a href="http://www.hubmed.org/display.cgi?uids=20528864">Publication</a> ]</p><h4><a name="TOC-ChIPpeakAnno" id="TOC-ChIPpeakAnno"></a>ChIPpeakAnno</h4><p>The ChIPpeakAnno package provides. batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments. It includes functions to retrieve the sequences around peaks, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. The package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. <a href="http://bioconductor.org/packages/release/bioc/html/ChIPpeakAnno.html">Documentation</a></p><h4><a name="TOC-Additional-ChIP-Seq-Packages" id="TOC-Additional-ChIP-Seq-Packages"></a>Additional ChIP-Seq Packages</h4><p>DiffBind: <a href="http://www.bioconductor.org/packages/release/bioc/html/DiffBind.html">Documentation</a></p><p>MOSAICS: <a href="http://bioconductor.org/packages/devel/bioc/html/mosaics.html">Documentation</a></p><p>iSeq: <a href="http://bioconductor.org/packages/release/bioc/html/iSeq.html">Documentation</a></p><p>ChIPseqR: <a href="http://bioconductor.org/packages/release/bioc/html/ChIPseqR.html">Documentation</a></p><p>ChiPsim: <a href="http://bioconductor.org/packages/release/bioc/html/ChIPsim.html">Documentation</a></p><p>CSAR: <a href="http://www.bioconductor.org/packages/devel/bioc/html/CSAR.html">Documentation</a></p><p>ChIP-Seq Pipeline: <a href="http://www.bioconductor.org/packages/release/bioc/html/PICS.html">PICS</a>, rGADEM and MotIV (<a href="http://www.rglab.org/pics-and-bioconductor/">developer web site</a>)</p><p>SPP: <a href="http://compbio.med.harvard.edu/Supplements/ChIP-seq/">ChIP-seq processing pipeline</a></p><p><a href="http://compbio.med.harvard.edu/Supplements/ChIP-seq/tutorial.html">SPP Tutorial</a></p><p><a href="http://liulab.dfci.harvard.edu/MACS/index.html">MACS</a></p><p><a href="http://gmdd.shgmo.org/Computational-Biology/ChIP-Seq/download/SIPeS">SIPeS</a></p><h3><a name="TOC-RNA-Seq-Analysis" id="TOC-RNA-Seq-Analysis"></a>RNA-Seq Analysis</h3><h4><a name="TOC-Counting-Reads-that-Overlap-with-Annotation-Ranges-" id="TOC-Counting-Reads-that-Overlap-with-Annotation-Ranges-"></a>Counting Reads that Overlap with Annotation Ranges&nbsp;</h4><p>The GenomicRanges package provides support for importing into R short read alignment data in BAM format (via Rsamtools) and associating them with genomic feature ranges, such as exons or genes. This way one can quantify the number of reads aligning to annotated genomic regions. The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome. The two main functions for read counting provided by this infrastructure are <span>countOverlaps <span style="color: #000000;"><span>and</span></span> summarizeOverlaps</span>. For their proper usage, it is important to read the corresponding <a href="http://www.bioconductor.org/packages/devel/bioc/vignettes/GenomicRanges/inst/doc/summarizeOverlaps.pdf">PDF manual</a>. <a href="http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html">Documentation</a></p><h4><a name="TOC-Differential-Gene-Expression-Analysis-with-DESeq" id="TOC-Differential-Gene-Expression-Analysis-with-DESeq"></a>Differential Gene Expression Analysis with DESeq</h4><p>The DESeq package contains functions to call differentially expressed genes (DEGs) in count tables based on a model using the negative binomial distribution. It expects as input a data frame with the raw read counts per region/gene of interest (rows) for each test sample (columns).&nbsp; Such a count table can be imported into R or generated from BAM alignment files using the <span>countOverlaps</span> function as introduced above. <a href="http://www.bioconductor.org/packages/release/bioc/html/DESeq.html">Documentation</a></p><h4><a name="TOC-Differential-Gene-Expression-Analysis-with-edgeR" id="TOC-Differential-Gene-Expression-Analysis-with-edgeR"></a>Differential Gene Expression Analysis with edgeR</h4><p>The edgeR package uses empirical Bayes estimation and exact tests based on the negative binomial distribution to call differentially expressed genes (DEGs) in count data.&nbsp;</p>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/edgeR.html">Documentation</a></p>
<p><span style="color: #000000;">A variety of additional R packages are available for normalizing RNA-Seq read count data and identifying differentially expressed genes (DEG): <br /> </span></p><p><a href="http://bioconductor.org/packages/devel/bioc/html/easyRNASeq.html">easyRNASeq</a> (simplifies read counting per genome feature)</p><p><a href="http://www.bioconductor.org/packages/release/bioc/html/DEXSeq.html">DEXSeq</a> (Inference of differential exon usage);&nbsp;<a href="http://www.bioconductor.org/packages/release/data/experiment/html/parathyroidSE.html">parathyroidSE</a> explains how to generate exon read counts in R</p><p><a href="http://bioconductor.org/packages/release/bioc/html/DEGseq.html">DEGseq</a></p><p><a href="http://www.bioconductor.org/packages/release/bioc/html/baySeq.html">baySeq</a> (also see: <a href="http://www.bioconductor.org/packages/release/bioc/html/segmentSeq.html">segmentSeq</a>)</p><p><a href="http://bioconductor.org/packages/release/bioc/html/Genominator.html">Genominator</a> (<a href="http://www.hubmed.org/display.cgi?uids=20167110">Bullard et al. 2010</a>)</p><div style="text-align: right;"><div style="text-align: left;"><h4><a name="TOC-Detection-of-Alternative-Splice-Junctions" id="TOC-Detection-of-Alternative-Splice-Junctions"></a>Detection of Alternative Splice Junctions</h4>
<p><span style="color: #000000;">Another utility of RNA-Seq experiments is the analysis of splice junctions. The following software suggestions provide this utility:</span></p>
<p><a href="http://woldlab.caltech.edu/rnaseq/">ERANGE<br /> </a><a href="http://tophat.cbcb.umd.edu/">TopHat</a></p><p><a href="http://biogibbs.stanford.edu/%7Ekinfai/SpliceMap/">SpliceMap</a></p><p><a href="http://solidsoftwaretools.com/gf/project/splitseek/">SplitSeek</a></p><h3><a name="TOC-DNA-Methylation-Data-Analysis" id="TOC-DNA-Methylation-Data-Analysis"></a>DNA-Methylation Data Analysis</h3><div><ul>
<li><span style="font-size: 10pt;"><a href="http://www.bioconductor.org/help/course-materials/2012/BiocEurope2012/mattia_pelizzola_methylPipe.pdf">methylPipe</a></span></li>
<li><span style="font-size: 10pt;"><a href="http://www.bioconductor.org/packages/devel/bioc/html/bsseq.html">bsseq</a></span></li>
<li><a href="http://www.bioconductor.org/packages/devel/bioc/html/BiSeq.html">BiSeq</a></li>
<li>Much more under <a href="http://www.bioconductor.org/packages/devel/BiocViews.html#___DNAMethylation">BiocViews</a></li>
</ul></div></div></div><h3><a name="TOC-HT-Seq-Data-Visualization" id="TOC-HT-Seq-Data-Visualization"></a>HT-Seq Data Visualization</h3>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/ggbio.html">ggbio</a>: ggplot2 extension for genomics data (<a href="http://tengfei.github.com/ggbio/">online manual</a>) <a href="http://www.bioconductor.org/packages/devel/bioc/html/Gviz.html">Gviz</a>:&nbsp;Plotting data and annotation information along genomic coordinates <a href="http://bioconductor.org/packages/release/bioc/html/HilbertVis.html">HilbertVis</a>: Hilbert genome plots</p>
<p><a href="http://bioconductor.org/packages/release/bioc/html/GenomeGraphs.html">GenomeGraphs</a>: Plotting genomic information from Ensembl</p><p><a href="http://www.hubmed.org/display.cgi?uids=18507856">TileQC</a>: Flow Cell Quality Visualization</p><p><a href="http://bioconductor.org/packages/release/bioc/html/rtracklayer.html">rtracklayer</a>: R interface to genome browsers</p><p><a href="http://genoplotr.r-forge.r-project.org/">genoPlotR</a>: Plotting maps of genes and genomes</p><p><a href="http://bioconductor.org/packages/release/bioc/html/Genominator.html">Genominator</a>: Tools for storing, accessing, analyzing and visualizing genomic data.</p><p>&nbsp;</p><p>To install all packages</p><blockquote><p>source("http://bioconductor.org/biocLite.R")<br />biocLite()<br />biocLite(c("ShortRead", "Biostrings", "IRanges", "BSgenome", "rtracklayer", "biomaRt", "chipseq", "ChIPpeakAnno", "Rsamtools", "BayesPeak", "PICS", "GenomicRanges", "DESeq", "edgeR", "leeBamViews", "GenomicFeatures", "BSgenome.Celegans.UCSC.ce2"))</p></blockquote></div>]]></description>
	<dc:creator>John Parker</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/12870/nuclear-dynamics-lab</guid>
  <pubDate>Thu, 17 Jul 2014 15:03:27 -0500</pubDate>
  <link></link>
  <title><![CDATA[Nuclear Dynamics Lab]]></title>
  <description><![CDATA[
<p>Lab focus is to elucidate fundamental principles, new mechanisms, machineries and emergent properties that are involved in maintaining the genome and gene expression programmes for improvements in lifelong health and well-being for all.</p>

<p>More at http://www.babraham.ac.uk/our-research/nuclear-dynamics/</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/14218/pimp-your-brain-bioinformatics</guid>
	<pubDate>Wed, 20 Aug 2014 22:09:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/14218/pimp-your-brain-bioinformatics</link>
	<title><![CDATA[Pimp your brain: Bioinformatics]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/KqelGy6Q8nE" frameborder="0" allowfullscreen></iframe>Jan Lisec from the Max Planck Institute of Molecular Plant Physiology explains, in this "pimp your brain" episode, what bioinformatics is and why bioinformatics is so important and indispensable for biological research.

In the video serial "Pimp your brain" scientists from the Max Planck Institute of Molecular Plant Physiology describe their research. More videos from the 'Pimp your brain' serial are available on www.youtube.com/playlist?list=PL-l9VItC9Gn2Ur2Xj6PTOAkjLUlVPbIOO

More videos are available on www.mpimp-golm.mpg.de]]></description>
	
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/poll/view/14927/which-of-the-following-programming-language-is-best-for-a-bioinformatics-beginner</guid>
	<pubDate>Thu, 04 Sep 2014 07:51:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/poll/view/14927/which-of-the-following-programming-language-is-best-for-a-bioinformatics-beginner</link>
	<title><![CDATA[Which of the following programming language is best for a bioinformatics beginner?]]></title>
	<description><![CDATA[<p>I will be doing NGS in the course of my research work and I will like to learn a programming language which is compatible with most bioinformatics tools or software. I basically want to do de-novo assembly, map reads, align reads, and expression analysis. Recommendations welcomed. Which languages would you recommend to a student wishing to enter the world of bioinformatics?</p>]]></description>
	<dc:creator>Manisha Mishra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/19633/vital-it</guid>
	<pubDate>Thu, 18 Dec 2014 10:46:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/19633/vital-it</link>
	<title><![CDATA[Vital-IT]]></title>
	<description><![CDATA[<p>Vital-IT is a <strong>bioinformatics competence center</strong> that supports and collaborates with life scientists in Switzerland and beyond. The <a href="http://www.vital-it.ch/about/team.php">multi-disciplinary team</a> provides expertise, training and maintains a high-performance computing (HPC) and storage infrastructure, so as to help develop, maintain and extend life science and medical research (<a href="http://www.vital-it.ch/about/activities.php">activities</a>).</p><p>Address of the bookmark: <a href="http://www.vital-it.ch/" rel="nofollow">http://www.vital-it.ch/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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