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	<title><![CDATA[BOL: Related items]]></title>
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<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/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/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>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/19090/deeptools</guid>
	<pubDate>Sat, 08 Nov 2014 15:02:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/19090/deeptools</link>
	<title><![CDATA[deepTools]]></title>
	<description><![CDATA[<p>deepTools addresses the challenge of handling the large amounts of data that are now routinely generated from DNA sequencing centers. To do so, deepTools contains useful modules to process the mapped reads data to create coverage files in standard bedGraph and bigWig file formats. By doing so, deepTools allows the creation of normalized coverage files or the comparison between two files (for example, treatment and control). Finally, using such normalized and standardized files, multiple visualizations can be created to identify enrichments with functional annotations of the genome.<br /><br />Publicaton: http://nar.oxfordjournals.org/content/early/2014/05/05/nar.gku365.full<br /><br />Source Code and Wiki: https://github.com/fidelram/deepTools/wiki<br /><br />Galaxy Tool Shed repository: http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools<br /><br />and example Galaxy workflows: http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools_workflows</p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/19648/mit-computational-biology-group</guid>
  <pubDate>Thu, 18 Dec 2014 14:47:01 -0600</pubDate>
  <link></link>
  <title><![CDATA[MIT Computational Biology Group]]></title>
  <description><![CDATA[
<p>My research group consists primarily of computer science graduate students and postdocs with expertise in algorithms, statistical inferences and machine learning, and sharing a passion for understanding fundamental biological problems.</p>

<p>We work in a highly interdisciplinary environment at the interface of Computer Science and Biology. Since its inception, our lab has eagerly engaged in collaborative research partnerships with biological and experimental collaborators, facilitated by our affiliation with the Broad Institute and the Computational and Systems Biology initiative (CSBi) at MIT, our participation in the Epigenome Roadmap, ENCODE, and modENCODE consortia, and by several other ongoing collaborations at MIT, Harvard, and the Harvard Medical School affiliated hospitals.</p>

<p>http://compbio.mit.edu/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/21444/a-guide-for-complete-r-beginners-installing-r-packages</guid>
	<pubDate>Tue, 24 Feb 2015 20:23:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/21444/a-guide-for-complete-r-beginners-installing-r-packages</link>
	<title><![CDATA[A guide for complete R beginners :- Installing R packages]]></title>
	<description><![CDATA[<p>Part of the reason R has become so popular is the vast array of packages available at the <a href="http://cran.r-project.org/" target="_blank">cran</a> and <a href="http://www.bioconductor.org/" target="_blank">bioconductor</a> repositories. In the last few years, the number of packages has grown <a href="http://blog.revolutionanalytics.com/2010/09/what-can-other-languages-learn-from-r.html" target="_blank">exponentially</a>!</p><p>This is a short post giving steps on how to actually install R packages. Let&rsquo;s suppose you want to install the <a href="http://had.co.nz/ggplot2/" target="_blank">ggplot2</a> package. Well nothing could be easier. We just fire up an R shell and type:<br /><code><br />&gt; install.packages("ggplot2")</code></p><p>In theory the package should just install, however:</p><ul>
<li>if you are using Linux and don&rsquo;t have root access, this command won&rsquo;t work.</li>
<li>you will be asked to select your local mirror, i.e. which server should you use to download the package.</li>
</ul><h4>Installing packages without root access</h4><p>First, you need to designate a directory where you will store the downloaded packages. On my machine, I use the directory <code>/data/Rpackages/</code> After creating a package directory, to install a package we use the command:<br /><code><br />&gt; install.packages("ggplot2"</code><code>, lib="/data/Rpackages/")<br />&gt; library(ggplot2, lib.loc="/data/Rpackages/")<br /></code></p><p>It&rsquo;s a bit of a pain having to type <code>/data/Rpackages/</code> all the time. To avoid this burden,&nbsp; we create a file <code>.Renviron</code> in our home area, and add the line <code>R_LIBS=/data/Rpackages/</code> to it. This means that whenever you start R, the directory <code>/data/Rpackages/</code> is added to the list of places to look for R packages and so:</p><p><code>&gt; install.packages("ggplot2"</code><code>)<br />&gt; library(ggplot2)</code></p><p>just works!</p><h4>Setting the repository</h4><p>Every time you install a R package, you are asked which repository R should use. To set the repository and avoid having to specify this at every package install, simply:</p><ul>
<li>create a file <code>.Rprofile</code> in your home area.</li>
<li>Add the following piece of code to it:</li>
</ul><p><code><br />cat(".Rprofile: Setting UK repositoryn")<br />r = getOption("repos") # hard code the UK repo for CRAN<br />r["CRAN"] = "http://cran.uk.r-project.org"<br />options(repos = r)<br />rm(r)<br /></code></p><p>I found this tip in a stackoverflow <a href="http://stackoverflow.com/questions/1189759/expert-r-users-whats-in-your-rprofile/1189826#1189826" target="_blank">answer </a>.</p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/22133/r-320-is-released</guid>
	<pubDate>Sat, 18 Apr 2015 05:06:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/22133/r-320-is-released</link>
	<title><![CDATA[R 3.2.0 is released]]></title>
	<description><![CDATA[<p>R 3.2.0 (codename &ldquo;Full of Ingredients&rdquo;)&nbsp;was <a href="http://r.789695.n4.nabble.com/R-3-2-0-is-released-td4705933.html" target="_blank">released yesterday</a>.&nbsp;You can get the latest binaries version <strong><a href="http://cran.rstudio.com/" target="_blank">from here</a>.</strong>&nbsp;(or the .tar.gz&nbsp;<strong>source</strong> code from <a href="http://cran.r-project.org/src/base/R-3/R-3.2.0.tar.gz" target="_blank">here</a>).&nbsp;The full list of new features and bug fixes is provided below.</p><h3>Upgrading to R 3.2.0 on Windows</h3><p>If you are using <strong>Windows&nbsp;</strong>you can easily upgrade to the latest version of R using <a href="http://cran.r-project.org/web/packages/installr/" target="_blank">the installr package</a>. Simply run the following code:</p><div><table>
<tbody>
<tr id="p612572">
<td id="p61257code2">
<pre><span style="color: #228b22;"># installing/loading the latest installr package:</span>
<span style="color: #0000ff; font-weight: bold;">install.<span>packages</span></span><span style="color: #080;">(</span><span style="color: #ff0000;">"installr"</span><span style="color: #080;">)</span><span style="color: #080;">;</span> <span style="color: #0000ff; font-weight: bold;">library</span><span style="color: #080;">(</span>installr<span style="color: #080;">)</span> <span style="color: #228b22;">#load / install+load installr</span>
&nbsp;
updateR<span style="color: #080;">(</span><span style="color: #080;">)</span> <span style="color: #228b22;"># updating R.</span></pre>
</td>
</tr>
</tbody>
</table></div><p><span>Running &ldquo;updateR()&rdquo; will detect if there is a new R version available, and if so it will download+install it (etc.).</span></p><p><span><strong>If you are an R blogger yourself</strong> you are invited to <a href="http://www.r-bloggers.com/add-your-blog/">add your own R content feed to this site</a> (<strong>Non-English</strong> R bloggers should add themselves- <a href="http://www.r-bloggers.com/lang/add-your-blog">here</a>)</span></p><h4>NEW FEATURES</h4><ul>
<li><code>anyNA()</code> gains a <code>recursive</code> argument.</li>
<li>When <code>x</code> is missing and <code>names</code> is not false (including the default value), <code>Sys.getenv(x, names)</code> returns an object of class <code>"Dlist"</code> and hence prints tidily.</li>
<li>(Windows.) <code>shell()</code> no longer consults the environment variable <span>SHELL</span>: too many systems have been encountered where it was set incorrectly (usually to a path where software was compiled, not where it was installed). <span>R_SHELL</span>, the preferred way to select a non-default shell, can be used instead.</li>
<li>Some unusual arguments to <code>embedFonts()</code> can now be specified as character vectors, and the defaults have been changed accordingly.</li>
<li>Functions in the <code>Summary</code> group duplicate less. (<a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=15798" target="_blank">PR#15798</a>)</li>
<li>(Unix-alikes.) <code>system(cmd, input = )</code> now uses &lsquo;shell-execution-environment&rsquo; redirection, which will be more natural if <code>cmd</code> is not a single command (but requires a POSIX-compliant shell). (Wish of <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=15508" target="_blank">PR#15508</a>)</li>
<li><code>read.fwf()</code> and <code>read.DIF()</code> gain a <code>fileEncoding</code> argument, for convenience.</li>
<li>Graphics devices can add attributes to their description in <code>.Device</code> and <code>.Devices</code>. Several of those included with <strong>R</strong> use a <code>"filepath"</code> attribute.</li>
<li><code>pmatch()</code> uses hashing in more cases and so is faster at the expense of using more memory. (<a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=15697" target="_blank">PR#15697</a>)</li>
<li><code>pairs()</code> gains new arguments to select sets of variables to be plotted against each other.</li>
<li><code>file.info(, extra_cols = FALSE)</code> allows a minimal set of columns to be computed on Unix-alikes: on some systems without properly-configured caching this can be significantly faster with large file lists.</li>
<li>New function <code>dir.exists()</code> in package <span>base</span> to test efficiently whether one or more paths exist and are directories.</li>
<li><code>dput()</code> and friends gain new controls <span>hexNumeric</span> and <span>digits17</span> which output double and complex quantities as, respectively, binary fractions (exactly, see <code>sprintf("%a")</code>) and as decimals with up to 17 significant digits.</li>
<li><code>save()</code>, <code>saveRDS()</code> and <code>serialize()</code> now support <code>ascii = NA</code> which writes ASCII files using <code>sprintf("%a")</code> for double/complex quantities. This is read-compatible with <code>ascii = TRUE</code> but avoids binary-&gt;decimal-&gt;binary conversions with potential loss of precision. Unfortunately the Windows C runtime&rsquo;s lack of C99 compliance means that the format cannot be read correctly there in <strong>R</strong> before 3.1.2.</li>
<li>The default for <code>formatC(decimal.mark =)</code> has been changed to be <code>getOption("OutDec")</code>; this makes it more consistent with <code>format()</code> and suitable for use in print methods, e.g. those for classes <code>"density"</code>, <code>"ecdf"</code>, <code>"stepfun"</code> and <code>"summary.lm"</code>.
<p><code>getOption("OutDec")</code> is now consulted by the print method for class <code>"kmeans"</code>, by <code>cut()</code>, <code>dendrogram()</code>, <code>plot.ts()</code> and <code>quantile()</code> when constructing labels and for the report from<code>legend(trace = TRUE)</code>.</p>
<p>(In part, wish of <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=15819" target="_blank">PR#15819</a>.)</p>
</li>
<li><code>printNum()</code> and hence <code>format()</code> and <code>formatC()</code> give a warning if <code>big.mark</code> and <code>decimal.mark</code> are set to the same value (period and comma are not uncommonly used for each, and this is a check that conventions have not got mixed).</li>
<li><code>merge()</code> can create a result which uses long vectors on 64-bit platforms.</li>
<li><code>dget()</code> gains a new argument <code>keep.source</code> which defaults to <code>FALSE</code> for speed (<code>dput()</code> and <code>dget()</code> are most often used for data objects where this can make <code>dget()</code> many times faster).</li>
<li>Packages may now use a file of common macro definitions in their help files, and may import definitions from other packages.</li>
<li>A number of macros have been added in the new &lsquo;<span>share/Rd</span>&rsquo; directory for use in package overview help pages, and <code>promptPackage()</code> now makes use of them.</li>
<li><code>tools::parse_Rd()</code> gains a new <code>permissive</code> argument which converts unrecognized macros into text. This is used by <code>utils:::format.bibentry</code> to allow LaTeX markup to be ignored.</li>
<li><code>options(OutDec =)</code> can now specify a multi-byte character, e.g., <code>options(OutDec = "u00b7")</code> in a UTF-8 locale.</li>
<li><code>is.recursive(x)</code> is no longer true when <code>x</code> is an external pointer, a weak reference or byte code; the first enables <code>all.equal(x, x)</code> when <code>x .</code></li>
<li><code>ls()</code> (aka <code>objects()</code>) and <code>as.list.environment()</code> gain a new argument <code>sorted</code>.</li>
<li>The <code>"source"</code> attribute (which has not been added to functions by <strong>R</strong> since before <strong>R</strong> version 2.14.0) is no longer treated as special.</li>
<li>Function <code>returnValue()</code> has been added to give <code>on.exit()</code> code access to a function&rsquo;s return value for debugging purposes.</li>
<li><code>crossprod(x, y)</code> allows more matrix coercions when <code>x</code> or <code>y</code> are vectors, now equalling <code>t(x) %*% y</code> in these cases (also reported by Radford Neal). Similarly, <code>tcrossprod(x,y)</code> and <code>%*%</code> work in more cases with vector arguments.</li>
<li>Utility function <code>dynGet()</code> useful for detecting cycles, aka infinite recursions.</li>
<li>The byte-code compiler and interpreter include new instructions that allow many scalar subsetting and assignment and scalar arithmetic operations to be handled more efficiently. This can result in significant performance improvements in scalar numerical code.</li>
<li><code>apply(m, 2, identity)</code> is now the same as the matrix <code>m</code> when it has <em>named</em> row names.</li>
<li>A new function <code>debuggingState()</code> has been added, allowing to temporarily turn off debugging.</li>
<li><code>example()</code> gets a new optional argument <code>run.donttest</code> and <code>tools::Rd2ex()</code> a corresponding <code>commentDonttest</code>, with a default such that <code>example(..)</code> in help examples will run <code>donttest</code> code only if used interactively (a change in behaviour).</li>
<li><code>rbind.data.frame()</code> gains an optional argument <code>make.row.names</code>, for potential speedup.</li>
<li>New function <code>extSoftVersion()</code> to report on the versions of third-party software in use in this session. Currently reports versions of <code>zlib</code>, <code>bzlib</code>, the <code>liblzma</code> from <code>xz</code>, PCRE, ICU, TRE and the <code>iconv</code> implementation.
<p>A similar function <code>grSoftVersion()</code> in package <span>grDevices</span> reports on third-party graphics software.</p>
<p>Function <code>tcltk::tclVersion()</code> reports the Tcl/Tk version.</p>
</li>
<li>Calling <code>callGeneric()</code> without arguments now works with primitive generics to some extent.</li>
<li><code>vapply(x, FUN, FUN.VALUE)</code> is more efficient notably for large <code>length(FUN.VALUE)</code>; as extension of <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16061" target="_blank">PR#16061</a>.</li>
<li><code>as.table()</code> now allows tables with one or more dimensions of length 0 (such as <code>as.table(integer())</code>).</li>
<li><code>names(x) now clears the names of call and <code>...</code> objects.</code></li>
<li><code>library()</code> will report a warning when an insufficient dependency version is masking a sufficient one later on the library search path.</li>
<li>A new <code>plot()</code> method for class <code>"raster"</code> has been added.</li>
<li>New <code>check_packages_in_dir_changes()</code> function in package <span>tools</span> for conveniently analyzing how changing sources impacts the check results of their reverse dependencies.</li>
<li>Speed-up from Peter Haverty for <code>ls()</code> and <code>methods:::.requirePackage()</code> speeding up package loading. (<a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16133" target="_blank">PR#16133</a>)</li>
<li>New <code>get0()</code> function, combining <code>exists()</code> and <code>get()</code> in one call, for efficiency.</li>
<li><code>match.call()</code> gains an <code>envir</code> argument for specifying the environment from which to retrieve the <code>...</code> in the call, if any; this environment was wrong (or at least undesirable) when the<code>definition</code> argument was a function.</li>
<li><code>topenv()</code> has been made <code>.Internal()</code> for speedup, based on Peter Haverty&rsquo;s proposal in <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16140" target="_blank">PR#16140</a>.</li>
<li><code>getOption()</code> no longer calls <code>options()</code> in the main case.</li>
<li>Optional use of <code>libcurl</code> (version 7.28.0 from Oct 2012 or later) for Internet access:
<ul>
<li><code>capabilities("libcurl")</code> reports if this is available.</li>
<li><code>libcurlVersion()</code> reports the version in use, and other details of the <code>"libcurl"</code> build including which URL schemes it supports.</li>
<li><code>curlGetHeaders()</code> retrieves the headers for <code>http://</code>, <code>https://</code>, <code>ftp://</code> and <code>ftps://</code> URLs: analysis of these headers can provide insights into the &lsquo;existence&rsquo; of a URL (it might for example be permanently redirected) and is so used in <code>R CMD check --as-cran</code>.</li>
<li><code>download.file()</code> has a new optional method <code>"libcurl"</code> which will handle more URL schemes, follow redirections, and allows simultaneous downloads of multiple URLs.</li>
<li><code>url()</code> has a new method <code>"libcurl"</code> which handles more URL schemes and follows redirections. The default method is controlled by a new option <code>url.method</code>, which applies also to the opening of URLs <em>via</em> <code>file()</code> (which happens implicitly in functions such as <code>read.table</code>.)</li>
<li>When <code>file()</code> or <code>url()</code> is invoked with a <code>https://</code> or <code>ftps://</code> URL which the current method cannot handle, it switches to a suitable method if one is available.</li>
</ul>
</li>
<li>(Windows.) The DLLs &lsquo;<span>internet.dll</span>&rsquo; and &lsquo;<span>internet2.dll</span>&rsquo; have been merged. In this version it is safe to switch (repeatedly) between the internal and Windows internet functions within an <strong>R</strong>session.
<p>The Windows internet functions are still selected by flag <span>&ndash;internet2</span> or <code>setInternet2()</code>. This can be overridden for an <code>url()</code> connection <em>via</em> its new <code>method</code> argument.</p>
<p><code>download.file()</code> has new method <code>"wininet"</code>, selected as the default by <span>&ndash;internet2</span> or <code>setInternet2()</code>.</p>
</li>
<li><code>parent.env&lt;-</code> can no longer modify the parent of a locked namespace or namespace imports environment. Contributed by Karl Millar.</li>
<li>New function <code>isLoadedNamespace()</code> for readability and speed.</li>
<li><code>names(env)</code> now returns all the object names of an <code>environment</code> <code>env</code>, equivalently to <code>ls(env, all.names = TRUE, sorted = FALSE)</code> and also to the names of the corresponding list,<code>names(as.list(env, all.names = TRUE))</code>. Note that although <code>names()</code> returns a character vector, the names have no particular ordering.</li>
<li>The memory manager now grows the heap more aggressively. This reduces the number of garbage collections, in particular while data or code are loaded, at the expense of slightly increasing the memory footprint.</li>
<li>New function <code>trimws()</code> for removing leading/trailing whitespace.</li>
<li><code>cbind()</code> and <code>rbind()</code> now consider S4 inheritance during S3 dispatch and also obey <code>deparse.level</code>.</li>
<li><code>cbind()</code> and <code>rbind()</code> will delegate recursively to <code>methods::cbind2</code> (<code>methods::rbind2</code>) when at least one argument is an S4 object and S3 dispatch fails (due to ambiguity).</li>
<li>(Windows.) <code>download.file(quiet = FALSE)</code> now uses text rather than Windows progress bars in non-interactive use.</li>
<li>New function <code>hsearch_db()</code> in package <span>utils</span> for building and retrieving the help search database used by <code>help.search()</code>, along with functions for inspecting the concepts and keywords in the help search database.</li>
<li>New function <code>.getNamespaceInfo()</code>, a no-check version of <code>getNamespaceInfo()</code> mostly for internal speedups.</li>
<li>The help search system now takes <span>keyword</span> entries in Rd files which are not standard keywords (as given in &lsquo;<span>KEYWORDS</span>&rsquo; in the <strong>R</strong> documentation directory) as concepts. For standard keyword entries the corresponding descriptions are additionally taken as concepts.</li>
<li>New <code>lengths()</code> function for getting the lengths of all elements in a list.</li>
<li>New function <code>toTitleCase()</code> in package <span>tools</span>, tailored to package titles.</li>
<li>The matrix methods of <code>cbind()</code> and <code>rbind()</code> allow matrices as inputs which have <em>2^31</em> or more elements. (For <code>cbind()</code>, wish of <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16198" target="_blank">PR#16198</a>.)</li>
<li>The default method of <code>image()</code> has an explicit check for a numeric or logical matrix (which was always required).</li>
<li><code>URLencode()</code> will not by default encode further URLs which appear to be already encoded.</li>
<li><code>BIC(mod)</code> and <code>BIC(mod, mod2)</code> now give non-NA numbers for <code>arima()</code> fitted models, as <code>nobs(mod)</code> now gives the number of &ldquo;used&rdquo; observations for such models. This fixes <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16198" target="_blank">PR#16198</a>, quite differently than proposed there.</li>
<li>The <code>print()</code> methods for <code>"htest"</code>, <code>"pairwise.htest"</code> and <code>"power.htest"</code> objects now have a <code>digits</code> argument defaulting to (a function of) <code>getOption("digits")</code>, and influencing all printed numbers coherently. Unavoidably, this changes the display of such test results in some cases.</li>
<li>Code completion for namespaces now recognizes all loaded namespaces, rather than only the ones that are also attached.</li>
<li>The code completion mechanism can now be replaced by a user-specified completer function, for (temporary) situations where the usual code completion is inappropriate.</li>
<li><code>unzip()</code> will now warn if it is able to detect truncation when unpacking a file of 4GB or more (related to <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16243" target="_blank">PR#16243</a>).</li>
<li><code>methods()</code> reports S4 in addition to S3 methods; output is simplified when the <code>class</code> argument is used. <code>.S3methods()</code> and <code>methods::.S4methods()</code> report S3 and S4 methods separately.</li>
<li>Higher order functions such as the <code>apply</code> functions and <code>Reduce()</code> now force arguments to the functions they apply in order to eliminate undesirable interactions between lazy evaluation and variable capture in closures. This resolves <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16093" target="_blank">PR#16093</a>.</li>
</ul><p>More at http://cran.rstudio.com/</p><p>Reference: http://www.r-bloggers.com/r-3-2-0-is-released-using-the-installr-package-to-upgrade-in-windows-os/</p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/22410/nicolas-corradi-lab</guid>
  <pubDate>Tue, 26 May 2015 16:19:02 -0500</pubDate>
  <link></link>
  <title><![CDATA[Nicolas Corradi Lab]]></title>
  <description><![CDATA[
<p>The goal of our research is to better understand the biology of microbial organisms of significant ecological, veterinary and medical importance.<br />To achieve this goal, our team combines the power of next generation DNA sequencing and  bioinformatics with molecular biology and experimental procedures.</p>

<p>Main research topics:<br />- Comparative and Population Genomics of Plant Symbionts<br />- Parasite Genome Evolution<br />- Experimental Evolution of Microbial Symbionts and Parasites<br />- Phylogenomics of Early Branching Fungi</p>

<p>More at http://corradilab.weebly.com/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26319/n50plottingtools</guid>
	<pubDate>Mon, 08 Feb 2016 15:39:04 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26319/n50plottingtools</link>
	<title><![CDATA[n50PlottingTools]]></title>
	<description><![CDATA[<p><span>Tools to create plots showing N-statistics for genome assemblies </span></p>
<p><span>More at https://github.com/dentearl/n50PlottingTools</span></p><p>Address of the bookmark: <a href="https://github.com/dentearl/n50PlottingTools" rel="nofollow">https://github.com/dentearl/n50PlottingTools</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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