<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/11582?offset=170</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/10741/managing-and-analyzing-next-generation-sequence-data</guid>
	<pubDate>Sat, 10 May 2014 06:28:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/10741/managing-and-analyzing-next-generation-sequence-data</link>
	<title><![CDATA[Managing and Analyzing Next-Generation Sequence Data]]></title>
	<description><![CDATA[<p>Centralized Bioinformatics Core Facilities provide shared resources for the computational and IT requirements of the investigators in their department or institution. As such, they must be able to effectively react to new types of experimental technology. Recently faced with an unprecedented flood of data generated by the next generation of DNA sequencers, these groups found it necessary to respond quickly and efficiently to the informatics and infrastructure demands. Centralized Facilities newly facing this challenge need to anticipate time and design considerations of necessary components, including infrastructure upgrades, staffing, and tools for data analyses and management ...</p>
<p>More at http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369</p><p>Address of the bookmark: <a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369" rel="nofollow">http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/12593/visiting-scientist-computational-genomics-two-positions</guid>
  <pubDate>Mon, 07 Jul 2014 22:53:41 -0500</pubDate>
  <link></link>
  <title><![CDATA[Visiting Scientist - Computational Genomics (two positions)]]></title>
  <description><![CDATA[
<p>Scientific/Managerial &amp; International Recruitment</p>

<p>ICRISAT seeks applications from Indian nationals Visiting Scientist-Computational Genomics (2 positions), to be part of a team of Centre of Excellence in Genomics (CEG), (www.icrisat.org/ceg) to work on legume genomics projects.  The positions will be based at ICRISAT’s Headquarters in Patancheru, Hyderabad, India.</p>

<p>ICRISAT is a non-profit, non-political organization that conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. Covering 6.5 million square kilometers of land in 55 countries, the semi-arid tropics is home to over 2 billion people, with 650 million of these are the poorest of the poor. ICRISAT and its partners help empower those living in the semi-arid tropics, especially smallholder farmers, to overcome poverty, hunger, malnutrition and a degraded environment through more efficient and profitable agriculture. ICRISAT is headquartered in Greater Hyderabad, Andhra Pradesh, India and belongs to the Consortium of Centers supported by the Consultative Group on International Agricultural Research (CGIAR).</p>

<p>The Job: Responsibilities for these positions include:</p>

<p>    Analyzing and handling large-scale next generation sequencing DNA and RNA data<br />    Data mining and development of pipelines and troubleshooting<br />    Genome diversity analysis such as SNPs, Indels, Structural Variations, population structure<br />    Genome wide association study (GWAS) related analysis- LD analysis, hapmap and trait mapping<br />    Expression analysis based on RNA-Seq data, annotation, gene ontology and metabolic pathway analysis<br />    Epigenome analysis, small RNA identification<br />    Gene family analysis, sequence level protein analysis, orthology/paralogy and molecular modelling<br />    Compiling and analysis of results, writing reports and research papers</p>

<p>The Person:  Ph.D. or MSc/MTech/PGDCA with two years research experience in Biotechnology, Computational biology, Agricultural/ Plant Biotechnology, Genetics, Molecular Biology or related discipline. Good knowledge of programming/scripting in at least two of following languages: Perl, C, C++, R, Shell Scripting and Python is plus.</p>

<p>How to apply: Please apply latest by 20 July 2014.  The application should include the name of the position applied for, a letter of motivation, a full Curriculum Vita (CV), and the names and contact information of three references that are knowledgeable of the candidate’s professional qualifications and work experience. Technical details and more information about these positions can be obtained from R.K.VARSHNEY@CGIAR.ORG. All applications will be acknowledged, however only short listed candidates will be contacted.</p>

<p>Apply here https://recruit.zoho.com/ats/Portal.na?digest=T642sgLYWZOStExJ77cPrcM*sIMGZETWw4yPxngbmHA-</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/11030/r-programming-and-jobs-website</guid>
	<pubDate>Sun, 25 May 2014 14:43:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/11030/r-programming-and-jobs-website</link>
	<title><![CDATA[R programming and Jobs website]]></title>
	<description><![CDATA[<p>Welcome to the R Jobs section of ProgrammingR.com. If your organization has an R employment opportunity that you would like to have posted here, submit it via the <a href="http://www.programmingr.com/contact" title="contact page">contact page</a>. Prospective employees: use the contact information provided in the position listing to apply or contact the hiring organization.</p><p>Address of the bookmark: <a href="http://www.programmingr.com/category/stype/r-job-listings/" rel="nofollow">http://www.programmingr.com/category/stype/r-job-listings/</a></p>]]></description>
	<dc:creator>Pragati Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/11181/perl-one-liner-for-bioinformatician</guid>
	<pubDate>Fri, 30 May 2014 05:49:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11181/perl-one-liner-for-bioinformatician</link>
	<title><![CDATA[Perl one-liner for bioinformatician !!!]]></title>
	<description><![CDATA[<p>With the emergence of NGS technologies, and sequencing data most of the bioinformaticians mung and wrangle around massive amounts of genomics text. There are several "standardized" file formats (FASTQ, SAM, VCF, etc.) and some tools for manipulating them (fastx toolkit, samtools, vcftools, etc.), there are still times where knowing a little bit of Perl onliner is extremely helpful.</p><p>Perl one-liners are small and awesome Perl programs that fit in a single line of code and they do one thing really well. These things include changing line spacing, numbering lines, doing calculations, converting and substituting text, deleting and printing certain lines, parsing logs, editing files in-place, doing statistics, carrying out system administration tasks, updating a bunch of files at once, and many more. Perl one-liners will make you the shell warrior. Anything that took you minutes to solve, will now take you seconds!<br /><br />perl -pe '$\="\n"'&nbsp; &nbsp;<br />#double space a file<br /><br />perl -pe '$_ .= "\n" unless /^$/' <br />#double space a file except blank lines<br /><br />perl -pe '$_.="\n"x7' <br />#7 space in a line.<br /><br />perl -ne 'print unless /^$/' <br />#remove all blank lines<br /><br />perl -lne 'print if length($_) &lt; 20' <br />#print all lines with length less than 20.<br /><br />perl -00 -pe '' <br />#If there are multiple spaces, delete all leaving one(make the file a single spaced file).<br /><br />perl -00 -pe '$_.="\n"x4' <br />#Expand single blank lines into 4 consecutive blank lines<br /><br />perl -pe '$_ = "$. $_"'<br />#Number all lines in a file<br /><br />perl -pe '$_ = ++$a." $_" if /./' <br />#Number only non-empty lines in a file<br /><br />perl -ne 'print ++$a." $_" if /./' <br />#Number and print only non-empty lines in a file<br /><br />perl -pe '$_ = ++$a." $_" if /regex/' <br />#Number only lines that match a pattern<br /><br />perl -ne 'print ++$a." $_" if /regex/' <br />#Number and print only lines that match a pattern<br /><br />perl -ne 'printf "%-5d %s", $., $_ if /regex/' <br />#Left align lines with 5 white spaces if matches a pattern (perl -ne 'printf "%-5d %s", $., $_' : for all the lines)<br /><br />perl -le 'print scalar(grep{/./}&lt;&gt;)' <br />#prints the total number of non-empty lines in a file<br /><br />perl -lne '$a++ if /regex/; END {print $a+0}' <br />#print the total number of lines that matches the pattern<br /><br />perl -alne 'print scalar @F' <br />#print the total number fields(words) in each line.<br /><br />perl -alne '$t += @F; END { print $t}' <br />#Find total number of words in the file<br /><br />perl -alne 'map { /regex/ &amp;&amp; $t++ } @F; END { print $t }' <br />#find total number of fields that match the pattern<br /><br />perl -lne '/regex/ &amp;&amp; $t++; END { print $t }' <br />#Find total number of lines that match a pattern<br /><br />perl -le '$n = 20; $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $m' <br />#will calculate the GCD of two numbers.<br /><br />perl -le '$a = $n = 20; $b = $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $a*$b/$m' <br />#will calculate lcd of 20 and 35.<br /><br />perl -le '$n=10; $min=5; $max=15; $, = " "; print map { int(rand($max-$min))+$min } 1..$n' <br />#Generates 10 random numbers between 5 and 15.<br /><br />perl -le 'print map { ("a".."z",&rdquo;0&rdquo;..&rdquo;9&rdquo;)[rand 36] } 1..8'<br />#Generates a 8 character password from a to z and number 0 &ndash; 9.<br /><br />perl -le 'print map { ("a",&rdquo;t&rdquo;,&rdquo;g&rdquo;,&rdquo;c&rdquo;)[rand 4] } 1..20'<br />#Generates a 20 nucleotide long random residue.<br /><br />perl -le 'print "a"x50'<br />#generate a string of &lsquo;x&rsquo; 50 character long<br /><br />perl -le 'print join ", ", map { ord } split //, "hello world"'<br />#Will print the ascii value of the string hello world.<br /><br />perl -le '@ascii = (99, 111, 100, 105, 110, 103); print pack("C*", @ascii)'<br />#converts ascii values into character strings.<br /><br />perl -le '@odd = grep {$_ % 2 == 1} 1..100; print "@odd"'<br />#Generates an array of odd numbers.<br /><br />perl -le '@even = grep {$_ % 2 == 0} 1..100; print "@even"'<br />#Generate an array of even numbers<br /><br />perl -lpe 'y/A-Za-z/N-ZA-Mn-za-m/' file <br />#Convert the entire file into 13 characters offset(ROT13)<br /><br />perl -nle 'print uc' <br />#Convert all text to uppercase:<br /><br />perl -nle 'print lc' <br />#Convert text to lowercase:<br /><br />perl -nle 'print ucfirst lc' <br />#Convert only first letter of first word to uppercas<br /><br />perl -ple 'y/A-Za-z/a-zA-Z/' <br />#Convert upper case to lower case and vice versa<br /><br />perl -ple 's/(\w+)/\u$1/g' <br />#Camel Casing<br /><br />perl -pe 's|\n|\r\n|' <br />#Convert unix new lines into DOS new lines:<br /><br />perl -pe 's|\r\n|\n|' <br />#Convert DOS newlines into unix new line<br /><br />perl -pe 's|\n|\r|' <br />#Convert unix newlines into MAC newlines:<br /><br />perl -pe '/regexp/ &amp;&amp; s/foo/bar/' <br />#Substitute a foo with a bar in a line with a regexp.</p><p>Reference/Sources:</p><p>http://genomics-array.blogspot.in/2010/11/some-unixperl-oneliners-for.html</p><p><a href="http://genomespot.blogspot.com/2013/08/a-selection-of-useful-bash-one-liners.html">http://genomespot.blogspot.com/2013/08/a-selection-of-useful-bash-one-liners.html</a></p><p><a href="http://biowize.wordpress.com/2012/06/15/command-line-magic-for-your-gene-annotations/">http://biowize.wordpress.com/2012/06/15/command-line-magic-for-your-gene-annotations/</a></p><p><a href="http://genomics-array.blogspot.com/2010/11/some-unixperl-oneliners-for.html">http://genomics-array.blogspot.com/2010/11/some-unixperl-oneliners-for.html</a></p><p><a href="http://bioexpressblog.wordpress.com/2013/04/05/split-multi-fasta-sequence-file/">http://bioexpressblog.wordpress.com/2013/04/05/split-multi-fasta-sequence-file/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</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>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/11494/postdoc-position-at-centre-mediterraneen-de-medecine-moleculaire-nice-france</guid>
  <pubDate>Wed, 04 Jun 2014 07:20:57 -0500</pubDate>
  <link></link>
  <title><![CDATA[Postdoc position at Centre Méditerranéen de Médecine Moléculaire - Nice - France]]></title>
  <description><![CDATA[
<p>The research group of Dr. Michele Trabucchi at the Centre Méditerranéen de Médecine Moléculaire (C3M) at INSERM U1065 (University of Nice Sophia-Antipolis, France) is seeking candidates for a Postdoctoral fellow position to start on October 2014 for 3 years funded by FRM (Fondation pour la Recherche Médicale).<br />The broad interest of the lab is in understanding the expression control and function of small RNAs in activated myeloid cells (visit our webpage to check research interests and publications of the group : http://www.unice.fr/c3m/EN/Equipe10.html ). </p>

<p>The work will focus on the functional studies of small RNAs by using next-generation sequencing approaches.<br /> <br />Candidates should hold a Ph.D. degree and have strong background in bioinformatics.<br />The University of Nice Sophia-Antipolis provides a wide range of facilities and training essential for biomedical research.</p>

<p>Interested applicants should send a PDF with a cover letter stating research interests and qualifications, an updated CV, a summary of previous research experience and contact information for two references to Michele Trabucchi ( mtrabucchi@unice.fr )</p>

<p>Homepage: http://www.unice.fr/c3m/EN/Equipe10.html</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/12206/bioinformatics-algorithms-tutorials</guid>
	<pubDate>Tue, 24 Jun 2014 00:10:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/12206/bioinformatics-algorithms-tutorials</link>
	<title><![CDATA[Bioinformatics algorithms tutorials]]></title>
	<description><![CDATA[<p>Useful bioinformatics tutorial, such as</p>
<p>De Bruijn Graphs for NGS Assembly<br>Algorithms for PacBio Reads<br>Software and Hardware Concepts for Bioinformatics<br>Finding us in Homolog.us (Search Algorithms)<br>NGS Genome and RNAseq Assembly - a Hands on Primer<br>Introduction to PERL, Python, R and C/C++ for Bioinformatics</p><p>Address of the bookmark: <a href="http://www.homolog.us/Tutorials/" rel="nofollow">http://www.homolog.us/Tutorials/</a></p>]]></description>
	<dc:creator>John Parker</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/12567/workshop-on-molecular-modeling-and-dynamics-simulation-analyses</guid>
  <pubDate>Fri, 04 Jul 2014 13:38:13 -0500</pubDate>
  <link></link>
  <title><![CDATA[Workshop On Molecular Modeling and Dynamics Simulation Analyses]]></title>
  <description><![CDATA[
<p>Workshop On Molecular Modeling and Dynamics Simulation Analyses</p>

<p>August1-2, 2014</p>

<p>Organised By</p>

<p>Centre of Excellence in Bioinformatics<br />Bioinformatics Infrastructure Facility<br />Department of Biochemistry<br />University of Lucknow<br />Lucknow-226007</p>

<p>Course Contents</p>

<p>Molecular Modeling<br /> Homology Modeling<br />Molecular Docking<br />Post-structural Analyses</p>

<p>Molecular Dynamics (MD)<br />Simulation<br />Linux Introduction<br />Gromacs Installation</p>

<p>MD Simulation of Protein ligand complex<br />Analyses of MD<br />Trajectories<br />Visualization of Dynamic<br />complexes</p>

<p>Important Dates</p>

<p>Registration Begins June 25, 2014<br />Registration Closes July 25, 2014</p>

<p>Brochure : www.lkouniv.ac.in/conference/Brochure_August,%202014.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/12944/orione-%E2%80%93-a-web-based-framework-for-ngs-analysis-in-microbiology</guid>
	<pubDate>Wed, 23 Jul 2014 06:43:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/12944/orione-%E2%80%93-a-web-based-framework-for-ngs-analysis-in-microbiology</link>
	<title><![CDATA[Orione – a web-based framework for NGS analysis in microbiology]]></title>
	<description><![CDATA[<p>End-to-end NGS microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult due to a lack of interoperability, reproducibility, and transparency. To overcome these limitations researchers at <a href="http://www.crs4.it/" target="_blank">CRS4</a>, Italy have developed Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for NGS microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.</p>
<p>Reference</p>
<p>Cuccuru G1, Orsini M, Pinna A, Sbardellati A, Soranzo N, Travaglione A, Uva P, Zanetti G, Fotia G. (2014)<strong> Orione, a web-based framework for NGS analysis in microbiology.</strong> <em>Bioinformatics</em> [Epub ahead of print]. [<a href="http://bioinformatics.oxfordjournals.org/content/early/2014/03/10/bioinformatics.btu135.long" target="_blank">article</a>]</p><p>Address of the bookmark: <a href="http://orione.crs4.it/" rel="nofollow">http://orione.crs4.it/</a></p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/12940/ra-at-iiser-kolkata-computational-biologybioinformatics</guid>
  <pubDate>Wed, 23 Jul 2014 06:24:28 -0500</pubDate>
  <link></link>
  <title><![CDATA[RA at IISER Kolkata Computational Biology/Bioinformatics]]></title>
  <description><![CDATA[
<p>Applications are invited from suitable candidates for research associate (post-doc; Rs. 22000-32000)/research fellow (16000-18000)/project assistant (Rs. 10000-14000) positions in the Department of Biological Sciences, Indian Institute for Science Education and Research Kolkata in the extramural project. Condition to satisfactory performance, the positions is for a period of upto 2 years (or funding of the project).</p>

<p>Brief description: We are looking for suitable candidates in the area o computational biology/bioinformatics/genomics or related field for next-generation sequencing (NGS) data analysis for small-RNAs, RNA-Seq and targeted resequencing of plants and associated organisms. We are an interdisciplinary group where projects equally involve bioinformatics and systems biology (specially microarrays and next-generation sequencing (NGS) data analysis and its use), along with plant molecular biology, genetic engineering, field biology, and analytical plant chemistry for understanding response of plants to biotic stresses.</p>

<p>Essential qualification: MSc/BTech/MTech/PhD (or other suitable qualification) in disciplines preferable to bioinformatics, computational biology, computer application (or equivalent)/ ‘Advance Post-Graduate Diploma in Bioinformatics’. Proficiency in programming languages (such as Perl, C++) and/or statistics (proficient in R for example) is compulsory.</p>

<p>Desirable qualification: Experience in the field of genomics e.g. microarray analysis, NGS, genome annotation, database development and management, software development, systems and network biology (or related fields) will be preferred.</p>

<p>Application process: Applications should contain CV along with brief description (maximum 1 page) of research conducted (highlighting skills and experience) till now. Applications should be sent by e-mail to Shree Prakash Pandey, Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur Campus, WB, India within 14 days of this advertisement.</p>

<p>E-mail: sppiiserkol@gmail.com, sppandey@iiserkol.ac.in</p>

<p>Advertisement:</p>

<p>http://www.iiserkol.ac.in/announcements/adverts/671-advt_ra_shree_prakash_july_2014</p>
]]></description>
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