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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26309?offset=110</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/37905/phased-human-genome-assembly</guid>
	<pubDate>Mon, 08 Oct 2018 09:10:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/37905/phased-human-genome-assembly</link>
	<title><![CDATA[Phased Human Genome Assembly !]]></title>
	<description><![CDATA[<p>The new publicly available assembly (PacBio&nbsp;<a href="https://www.globenewswire.com/Tracker?data=IM2cKfZgtHafORdb9VSstujBjyW-aIzFILCtXNAkcY_yqVmxdjvG01R_FZQC7zLxs-alqquXwsW6MG98G9-g-ym8Nue2pmUZMtkIg3FIat2mYbJ-z2Ra367GlinbO13x" target="_blank" title=""><span style="text-decoration: underline;">HG00733</span></a>) has the fewest gaps of any human genome assembly, with more than half of the genome contained in gapless sequence at least 27 Mb long. The primary contig assembly is 2.89 Gb long and consists of 865 contigs that were assembled with PacBio data generated with the company&rsquo;s Sequel<span>&reg;</span>&nbsp;System. Using the&nbsp;<a href="https://www.globenewswire.com/Tracker?data=jOa6mE1Y5r8VbU1CaCgx1A0HsoVzJ7waxOiDKgvmKL6cwJq_eH4nWrGj2vLkNpxHl1-5CH4htDB4113PXT8WU60hvHQ-KKpvAwQwveEGvz3N4d0q7QHSa_X97LW8_9xEiYqfsc4d24ca-IpVYZsf7Ue-XL7fSIIZw_EHK-F96t1aaQNRcD-z1PP5qvlZbVwX" target="_blank" title=""><span style="text-decoration: underline;">FALCON-Unzip assembler</span></a>, maternal and paternal haplotypes were resolved over more than 80% of the genome. Maternal and paternal haplotype blocks were then further phased using Hi-C technology and the&nbsp;<a href="https://www.globenewswire.com/Tracker?data=jOa6mE1Y5r8VbU1CaCgx1IrQmRcKvNQm83FLTqQE6OGzutM-fEggnm4Z-nsniK0D_YmDKS_UKWE0NHtHbgvbL973Y2-9NhrWhYKizXQ4lpiTvlqPf1UZdjqVs7BDjISgDnovv8foYw8es8jQzAg5Xfq1CH36NOnWQgA_X04XSvyEEEj0q801Im6cV5M5K4eL15vb_ZgUayccOvDY_fc6lxxPAAAyA4h16-zUN44Y81KdujciCrJrv5xynMIXEjRsaIKCf6eCX_Q1j_uZlN5TD0MVr6HulTYG8lGgyL0x-eQ=" target="_blank" title=""><span style="text-decoration: underline;">FALCON-Phase method</span></a>developed in collaboration with Phase Genomics. The genome was then&nbsp;<em>de novo</em>&nbsp;scaffolded using Phase Genomics&rsquo;&nbsp;<a href="https://www.globenewswire.com/Tracker?data=4wcqEWHJpCHRJARQkC0oVkYT9htT14iVebujxcW1nMpAjmigHGQ46ObCGetRfyaZm1ADIHaV1-30B9izTAhjJ-efhFlxorUxs08kdV-9AAzQyuHJ9S7wxnRRnyegsTZd" target="_blank" title=""><span style="text-decoration: underline;">Proximo Hi-C platform</span></a>, resulting in the first chromosome-scale diploid assembly of a single individual accomplished with only two technologies. More specific details about the assembly are included on the PacBio blog.</p><p>The data are available using NCBI accession IDs: BioProject: (<a href="https://www.globenewswire.com/Tracker?data=YZtCuhY2wu5H0yIso9jtUufPXbwyHh1QOZ1jBggGpK5NtXaU_JGC9X39F3uHZ96uVmu6hW5OB2Qq805hUEW2OhSNCm630yFiEF6_nsAwYB0=" target="_blank" title=""><span style="text-decoration: underline;">PRJNA483067</span></a>), assembly: [<a href="https://www.globenewswire.com/Tracker?data=CEXZ7E56JOsRgfH4Wq3r5LVbv4QH_UIekV9idYBys9l8K7pFft824jmYWNzJqK7lQ9fMbaAtbURpm8gM7zqUbpPUrydFwrkJGGtG-NBHctjyjddiFY-p06xZPm2mHXE2" target="_blank" title=""><span style="text-decoration: underline;">RBJD00000000</span></a>] and sequence data (<a href="https://www.globenewswire.com/Tracker?data=pELP2RpqTqTRaPF9yN1N7GZYlQmTxpY0aW-B8xaNw6iyD-Lylw7X3UzMDK3YS4AIYgLtD13em2XsbzOwKhXuNbI4Ks6-LSyXl1_yVdFoB0U=" target="_blank" title=""><span style="text-decoration: underline;">SRP155659</span></a>).</p><p><span>Additional Resources</span></p><ul>
<li><a href="http://globenewswire.com/Tracker?data=zXpdadphSgIAIEWeq46yRPm5-TU0H7wTkL48ue4I9GsaHd5mJyMb9PgXgAsElREkLOCOdWdJ8uW9DHB-LyQ7xhzbd97Qis6CuAlqD0ubGgY%3D" target="_blank" title=""><span style="text-decoration: underline;">Interactive map</span></a>&nbsp;showcasing global initiatives underway to generate reference-quality human genome assemblies for diverse populations</li>
<li><a href="http://globenewswire.com/Tracker?data=EQ8NIaaa8k1Nw1MPRJYIHYrqgsDy92kU8W0siJdGQhq5IJ0dcb890PFFm-C1SrAlFf0xkxUVRxZefFK5ebhoIzmS-6OjR1G9sTxOkCOwRHCAZWmHL-e7uGSuZYcw1VsDp8AeDWO0RwcepMMB6hAoR6BBCJDiJVVZtdFlWBn2uxs%3D" target="_blank" title=""><span style="text-decoration: underline;">BioReport Podcast</span></a>&nbsp;on the value of ethnic-specific reference genomes</li>
<li><em>Nature Reviews Genetics</em>&nbsp;paper from NHGRI:&nbsp;<a href="http://globenewswire.com/Tracker?data=dffu-wPD_JX1_KVeCA6VFy-kP1tlAUbn7d85saXD59dnnJfT2BE3N_Rbm6kT4BvifA_XEs49ioa75cy4HyFi90RA_LRa2QFF6Y4mr-dcoMucljZw0K4JNDZuwWkWPE51cVC2Lqq3E3C1aZ8un6Bq3i-OO_NiVH0hh23hUw4wC84%3D" target="_blank" title=""><span style="text-decoration: underline;">Prioritizing&nbsp;diversity&nbsp;in human genomics research</span></a></li>
<li>Article in&nbsp;<em>The Journal of Precision Medicine</em>: &ldquo;<a href="http://globenewswire.com/Tracker?data=yokLqO2TCBLCdj6uZl-GYbqcGMWBerBYjSPrLMumNrWF2p5XlXq9yl5p-1b5xx3Ckfn5ZjQWkdhxLttbiNae5gccUCP-9RWPUqvTu9MuU9zgJ1c8e14lAladCuEOiVZ2oVRiqssPtLu9hgQWw4ad5EUxZemevsHE4BHC6IiFmMZ6DS6ApwZu-IonFgCFBIcjWOpitQthDASosfaqkMi9LsKgLU9F0WGVJDDOzHXpddhjfCUdEEJ7xC1p8uh9TSiCZgZV6XPlUJSe8n0C_9TtOw%3D%3D" target="_blank" title=""><span style="text-decoration: underline;">Minority Report &ndash; Ethnic Diversity and the Real Promise for Precision Medicine</span></a>&rdquo;</li>
<li>Article&nbsp;in&nbsp;<em>Bio-IT World</em>: &ldquo;<a href="http://globenewswire.com/Tracker?data=rLp1pKetctTPitNEnRjOVDZ3Cvw3FUdL6_ybXncvhjR4ksOrX3y6HUK8WtLlKHT7XZzq_woUjZ-uw20YNvsP0GZAmy5lVqETt27oBLi02wFtTH_6ubELIHtBu8vfVyKnqKp-YhosFG5K7y0RUtzmNjOAlCYPAeVXabn2a2AiSePxUXA_tSy_g79hjYm63x9dPN9oFQGYedOsyHD_ls8DKw%3D%3D" target="_blank" title=""><span style="text-decoration: underline;">Genomic Data Standards Are a Necessity</span></a>&rdquo;</li>
<li>NHGRI Project Award:&nbsp;<a href="http://globenewswire.com/Tracker?data=FbqTEeRffJ88lFryYX6MiOefXvIXFdZDAyW4nrFoYNHaJyMEYIcb7I4BIcEQmxzsKOjrlf9F8irfRJeJLOqG8KFsl-kvkhakUkg3BfYdKGnpLzKYyWbUFR0aKMeEXirHBi7oDLEUSDO45qxANwxyee-pqZXfzAIwF1Wcuaf7EIzNqRqmBUJ3TyNyI05lwAo9gDKmApMnJo5VxPj5P_6rY8lisuv1PNSAh_kJPOuhVBk%3D" target="_blank" title=""><span style="text-decoration: underline;">High Quality Human and Non-Human Primate Genome Assemblies</span></a></li>
</ul><p>More details are available on the PacBio website:</p><ul>
<li>Blog post:&nbsp;<a href="http://globenewswire.com/Tracker?data=ycj-ujgsKzVyljNa11buVmIS5tk9B733VsFZEw77nBXo-IkBvcoG16dN9vuTiY3nm2G5dJZS5Iva3w_znrEtJVDuU8cVlFpozY2ibinKwrMGxkXZVSqW8_uD8fbySRjM5Q_cjuPU22ARFSSLCc9vHJx9WHnb9Rza-qPbuWgewa0rWWStq2fQY5mLpeaQf5fcDJnyQkvDAMI3fauXdzyThg%3D%3D" target="_blank" title=""><span style="text-decoration: underline;">Data Release: Highest-Quality, Most Contiguous Individual Human Genome Assembly to Date</span></a></li>
<li>Blog post:&nbsp;<a href="http://globenewswire.com/Tracker?data=GlZZ9nyp5mDSjJPPfhVD1-dZ_W2l8s0eAUox3TQs949zyGjzO7dx9xodyvyqerdqPC-G3ZhdPEs9xNhJwflrwgHPYQL3kTofprKHBBq3O4gn9E75YUBweJw9b6tTE89sMLUQzF-vRNNDjero3mibm_uG-fSHoYBTm2ZlyEmwzZ5E9tXVd5_RjG0Xnej2E0scA0SncEItAF6Q7vdOydTV_Yr9yYT2TmKY5jtyAt6ZrNGn3McqfV9mMRkR-8dYJLqrQln9JiEkWTwUae6Blj56HyjyXKl6Dfa_CyNuy4r-EWU%3D" target="_blank" title=""><span style="text-decoration: underline;">For Reference-Grade Human Genome Assemblies, SMRT Sequencing Yields Optimal Results</span></a></li>
<li>Webinar: &nbsp;<a href="http://globenewswire.com/Tracker?data=xlnfDwMNLGZZvtexJYsUgMe-DV8HNrYx2QqjwIjfj40dToVtqrBi-gvhknHZmIe8GV_3WU3_9LIlP6GzG3ZoajnDIpwECzdMV5Vyy8Ast4Y2AiHJckf7rBhZVEU4_mV4JB0k3I9XjN2jHK8Cp5uBxyIWWqPdI6qBBdCYYhYLXUTkKpaZEV98oCfC5ET2Q7OSwUM7NieKa75yzMHwaPEYwg%3D%3D" target="_blank" title=""><span style="text-decoration: underline;">Assembling High-Quality Human Reference Genomes for Global Populations</span></a></li>
<li>FALCON-Phase&nbsp;<a href="http://globenewswire.com/Tracker?data=4Z9LDdRq3w2zYFQXEFGmz6u-Vrbfh96syfzrQMKhegLRo2PUvk7s3Xz_y1o--NuTLoCQMrHsqOEBUHIL1IPeOmhyf6Eqwdp8dv8xYo9gSVI%3D" target="_blank" title=""><span style="text-decoration: underline;">press release</span></a>&nbsp;and article&nbsp;<a href="http://globenewswire.com/Tracker?data=4Z9LDdRq3w2zYFQXEFGmz9Ts_IJqHWWrKd33x_ldJEU9mSKXpcVTTi9ioY0kVqrbrXHeCKDf4TdPnAoPJaGBK3YeZtYp-nXZacgyPESZ1XboSUZEJ9rIhDyW7bTLL5HN" target="_blank" title=""><span style="text-decoration: underline;">preprint</span></a></li>
<li>PacBio research focus webpage about&nbsp;<a href="http://globenewswire.com/Tracker?data=E-zzUkw4N01KR4muPun47qg4HX8ToDvLS4sX953hLM2wRyQZ2upkLR4WidyXTFDRLWQORpqxnkbD-CNzsOJyIfH8mJPbrLwRf04J4yjuNdem-Fulc8QIT3OCi4wx5LpqgC2ymLE0rYX5UOpbFPBgvA%3D%3D" target="_blank" title=""><span style="text-decoration: underline;">Human Population Genetics</span></a></li>
</ul><p>&nbsp;Ref:&nbsp;https://stockguru.com/2018/10/08/pacific-biosciences-releases-highest-quality-most-contiguous-individual-human-genome-assembly-to-date/</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41905/research-associate-bioinformatics-in-iisc-recruitment-2020</guid>
  <pubDate>Tue, 23 Jun 2020 21:53:34 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate Bioinformatics in IISc Recruitment 2020]]></title>
  <description><![CDATA[
<p>Research Associate Bioinformatics in IISc Recruitment 2020</p>

<p>Essential Qualifications: Ph.D. (Bioinformatics/ Biophysics/ Biotechnology or any other stream of biological/ physical sciences) with a minimum of two publications in reputed peer reviewed journals in the area of structural bioinformatics or biophysics or biomolecular modeling/ simulation.</p>

<p>Job description: Development of bioinformatics tools and algorithms/software for structure based analysis of biomolecular systems. Programmatic access to major biomolecular databases using APIs Knowledge based prediction and analysis of biomolecular structure, function and interactions. Docking/simulations for inhibitor design.</p>

<p>Desirable Qualifications (Research Associate/s): i)  Strong computer programming skills (in Python/PERL/PHP or C++ or object oriented database management systems like MySQL etc or scripting languages under LINUX/UNIX environment). </p>

<p>ii) Extensive experience in computational analysis of biomolecular structure/interactions and usage of advanced biomolecular simulation softwares. iii) Adequate knowledge of major databases, webservers and softwares in the area of biomolecular structure/function and drug design. iv)  Familiarity with Parallel Programming environments and experience in usage of high-end HPC clusters.</p>

<p>The candidates must highlight their experience in above mentioned fields/topics in their CV. Initial appointment will be for a period of 1 year, subject to extension after review of performance.</p>

<p>Emoluments: As per DST, GOI norms and commensurate with experience.</p>

<p>More at https://www.iisc.ac.in/positions-open/</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4873/vveks-lab</guid>
  <pubDate>Thu, 26 Sep 2013 11:11:39 -0500</pubDate>
  <link></link>
  <title><![CDATA[Vvek's Lab]]></title>
  <description><![CDATA[
<p>Broad Area of Research: RNA biology (microRNA, lncRNA), Stem cells, Functional genomics, Epigenomics and Cancer</p>

<p>RNAs, especially non-coding RNAs (such as microRNA, long ncRNAs) are recently identified to be very abundant in mammalian organisms and play some key roles in gene expression regulation, gene silencing, and also implicated in disease progression, stem cell pluripotency etc. Current research activities of our lab include analysis of expression pattern of ncRNAs by microarray and next-gen sequencing data and understanding the role of miRNAs or other regulatory RNAs in various diseases, especially cancer and validation by reporter assays (renilla/luciferase) and other experimental tools.</p>

<p>More @ http://vvekslab.in/index.html</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5748/troyanskaya-lab</guid>
  <pubDate>Fri, 18 Oct 2013 10:57:40 -0500</pubDate>
  <link></link>
  <title><![CDATA[Troyanskaya  Lab]]></title>
  <description><![CDATA[
<p>In our research, we combine computational methods with an experimental component in a unified effort to develop comprehensive descriptions of genetic systems of cellular controls, including those whose malfunctioning becomes the basis of genetic disorders, such as cancer, and others whose failure might produce developmental defects in model systems.</p>

<p>Research Interest<br />Genomic Data Integration</p>

<p>Microarray Analysis</p>

<p>Gene and Protein Function Prediction</p>

<p>Detection and Analysis of Chromosomal Abnormalities and Functional Evolution</p>

<p>Integration of Computation and Experiments</p>

<p>Identification of Biological Networks and Pathways</p>

<p>Evaluation and Validation of Computational Predictions</p>

<p>Scalable Visualization-Based Data Analysis</p>

<p>More @ http://reducio.princeton.edu/cm/<br />PI page @ http://reducio.princeton.edu/cm/ogt</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/researchlabs/view/8943/roth-lab</guid>
  <pubDate>Tue, 11 Mar 2014 17:43:45 -0500</pubDate>
  <link></link>
  <title><![CDATA[Roth Lab]]></title>
  <description><![CDATA[
<p>The Roth Lab seeks insight into biological systems through genome- and proteome-scale experimentation and analysis.</p>

<p>Current computational interests:</p>

<p>Systematic analysis of genetic epistasis to identify redundant or compensatory systems and to reveal order of action in genetic pathways.<br />Using knockout, knockdown, or overexpression, or other perturbation experiments in combinations of genes in S. cerevisiae, C. elegans or mouse.<br />Using genome-scale genotyping of natural polymorphisms in S. cerevisiae and human populations.<br />Alternative splicing and its relationship to protein interaction networks.<br />Integrating large-scale studies including phenotype, genetic epistasis, protein-protein and transcription-regulatory interactions and sequence patterns to quantitatively assign function to genes and guide experimentation.</p>

<p>More at http://llama.mshri.on.ca/index.html</p>
]]></description>
</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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  <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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/poll/view/15000/which-mathstatistics-programming-languageapplication-do-you-most-frequently-use-in-bioinformatics</guid>
	<pubDate>Thu, 04 Sep 2014 17:46:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/poll/view/15000/which-mathstatistics-programming-languageapplication-do-you-most-frequently-use-in-bioinformatics</link>
	<title><![CDATA[Which math/statistics programming language/application do you most frequently use in bioinformatics?]]></title>
	<description><![CDATA[<p>I'm doing a bit more statistical analysis on some bioinformatics things lately, and I'm curious if there are any programming languages that are particularly good for this NGS computation. What suggestions do you guys have? Are there any languages that have exceptionally good libraries?</p>]]></description>
	<dc:creator>John Parker</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/19187/srbioinformatics-analyst-ngs-at-ocimum-biosolution</guid>
  <pubDate>Sat, 15 Nov 2014 04:46:10 -0600</pubDate>
  <link></link>
  <title><![CDATA[Sr.Bioinformatics Analyst (NGS) at Ocimum Biosolution]]></title>
  <description><![CDATA[
<p>“Ocimum Biosolution” is a comprehensive Integrated Life Science Informatics solutions provider with service offerings that span Sample and Data Management (LIMS, Biologics Data Management), Genomics Data Analysis Services such as Gene Expression, Genotyping, and Next Gen Sequencing, Bioinformatics and Genomics Databases (BioExpress®, ToxExpress®) and Bio-IT consulting services.</p>

<p>Experience Required: 3-5 years of experience</p>

<p>No of Positions : Multiple</p>

<p>Qualifications: Candidates with minimum qualification as M.Sc Bioinformatics with 3-5  years of experience in Life sciences R&amp;D or Pharma Industry.</p>

<p>Ph.D candidates with research experience in Bioinformatics with publications in International journal and minimum 2 years of  industry experience in clinical genomics will be preferred for this position.</p>

<p>Requirement:</p>

<p>1.  Must have basic understanding of molecular biology and Genomics.</p>

<p>2. Experience in application development or must have expertise in programming using either of Perl/Python.</p>

<p>3.  Experience in statistical programming using R/Bioconductor/Matlab.</p>

<p>4. Strong concept in statistical and mathematical modelling.</p>

<p>5.  Experience in designing and developing the bioinformatics pipeline.</p>

<p>6.  Must have minimum 2+ years of hands on experience in NSG data analysis such as RNA-Seq,Exome-Seq ,Chip-Seq and downstream analysis.</p>

<p>7. Knowledge in WGS ,WES, Targeted re-sequencing,GWAS and population genomics will be preferred.</p>

<p>8. Must have experience working on opensource software/Framework and commercial software for NGS data analysis and reporting.</p>

<p>9. Should be aware of handling big data and guiding team members on multiple projects simultaneously.</p>

<p>10. Should have experience coordinating with different groups of clinical research scientist for various project requirements.</p>

<p>11. Ability to work as team as well as independently with minimal support.</p>

<p>More at http://www.ocimumbio.com/careers1/</p>
]]></description>
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