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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40204?offset=290</link>
	<atom:link href="https://bioinformaticsonline.com/related/40204?offset=290" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/5958/srfjrf-national-institute-of-immunology</guid>
  <pubDate>Wed, 30 Oct 2013 06:45:57 -0500</pubDate>
  <link></link>
  <title><![CDATA[SRF/JRF @ National Institute of Immunology]]></title>
  <description><![CDATA[
<p>ADVERTISEMENT OF WALK-IN-INTERVIEW</p>

<p>NAME OF THE POST : SRF/JRF (Four Posts only)</p>

<p>DURATION : Indicated with the respective project mentioned below:</p>

<p>NAME OF THE PROJECT : As Mentioned below:</p>

<p>1. Serological diversity and molecular characterization of Dichelobector nodusus and development of vaccine against virulent footroot funded by NAIP. (Tenable upto 31.03.2014)</p>

<p>2. Development of oral vaccine against Clostridium perfringenes employing translational fusion of immunodominant epitopes of beta toxin with heat labile entertoxin B funded by DBT. (Tenable upto 25.02.2014)</p>

<p>3. Indo-Norwegian project, “Evaluation of major porins, ompC and ompR of Areomonas hydrophila as potential vaccine candidates and identification and characterization of immune genes of Indian major carp, Labeo rohita” (Tenable upto 31.03.2014)</p>

<p>EDUCATIONAL QUALIFICATIONS: For JRF- M.Sc/M.Tech in any subject of Biological  Sciences/Life Sciences</p>

<p>For SRF- M.Sc/M.Tech in any subject of Biological Sciences/Life Sciences with 2 years of Research Experience.</p>

<p>JOB DESCRIPTION : The Candidate should have experience in gene Expression, protein purification, molecular biology techniques and bioinformatics<br />EMOLUMENTS : SRF: Rs. 18,000/- per month consolidated plus 30% HRA if /NET/GATE qualified otherwise Rs. 14,000/- per month consolidated + 30% HRA.</p>

<p>JRF: Rs. 16,000/- per month consolidated + 30% HRA if NET/GATE qualified otherwise Rs. 12,000/- per month consolidated + 30% HRA</p>

<p>SCIENTIST NAME : Dr. Lalit C. Garg, SS-VII (Gene Regulation Lab)</p>

<p>SCIENTIST’S EMAIL : lalit@nii.ac.in</p>

<p>WALK IN INTERVIEW ON : October 31st, 2013</p>

<p>REGISTRATION OF CANDIDATES: 10.30 AM to 11.00 AM </p>

<p>Advertisement: http://www1.nii.res.in/sites/default/files/project-Dr.Lalit-31oct2013.pdf</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/7913/the-genome-factory</guid>
	<pubDate>Thu, 16 Jan 2014 02:09:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/7913/the-genome-factory</link>
	<title><![CDATA[The genome factory !!!]]></title>
	<description><![CDATA[<p>Illumina, Inc. announced Tuesday that its new HiSeq X Ten Sequencing System has broken the &ldquo;sound barrier&rdquo; of human genomics by enabling the $1,000 genome. &ldquo;This platform includes dramatic technology breakthroughs that enable researchers to undertake studies of unprecedented scale by providing the throughput to sequence tens of thousands of human whole genomes in a single year in a single lab,&rdquo; Illumina stated.</p><p>Initial customers for the HiSeq X Ten System, which will ship in Q1 2014, include Macrogen, based in Seoul, South Korea and its CLIA laboratory in Rockville, Maryland, the Broad Institute in Cambridge, Massachusetts, and the Garvan Institute of Medical Research in Sydney, Australia.</p><p>&ldquo;For the first time, it looks like it will be possible to deliver the $1,000 genome, which is tremendously exciting,&rdquo; said Eric Lander, founding director of the Broad Institute and a professor of biology at MIT. &ldquo;The HiSeq X Ten should give us the ability to analyze complete genomic information from huge sample populations. Over the next few years, we have an opportunity to learn as much about the genetics of human disease as we have learned in the history of medicine.&rdquo;</p><p>&ldquo;The HiSeq X Ten is an ideal platform for scientists and institutions focused on the discovery of genotypic variation to enable a deeper understanding of human biology and genetic disease,&rdquo; Illumina stated. &ldquo;It can sequence tens of thousands of samples annually with high-quality, high-coverage sequencing, delivering a comprehensive catalog of human variation within and outside coding regions.&rdquo;</p><p>HiSeq X Ten utilizes a number of advanced design features to generate massive throughput. Patterned flow cells, which contain billions of nanowells at fixed locations, combined with a new clustering chemistry deliver a significant increase in data density (6 billion clusters per run). Using state-of-the art optics and faster chemistry, HiSeq X Ten can process sequencing flow cells more quickly than ever before &mdash; generating a 10x increase in daily throughput when compared to current HiSeq 2500 performance.</p><p>The HiSeq X Ten is sold as a set of 10 or more ultra-high throughput sequencing systems, each generating up to 1.8 terabases (Tb) of sequencing data in less than three days or up to 600 gigabases (Gb) per day, per system, providing the throughput to sequence tens of thousands of high-quality, high-coverage genomes per year. Illumina says the $1,000 includes typical instrument depreciation, DNA extraction, library preparation, and estimated labor.</p>]]></description>
	<dc:creator>Madhvan Reddy</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/8108/jrf-institute-of-cytology-preventive-oncology</guid>
  <pubDate>Sat, 01 Feb 2014 13:47:29 -0600</pubDate>
  <link></link>
  <title><![CDATA[JRF @ Institute of Cytology &amp; Preventive Oncology]]></title>
  <description><![CDATA[
<p>Institute of Cytology &amp; Preventive Oncology (ICPO) which was initially established as Cytology Research Centre ( CRC ) by the Indian Council of Medical Research (ICMR) in 1979, came into the existence in 1989 when CRC was elevated to the level of Institute. ICPO was instituted with the main aim of promoting research in the field of cancers that are most prevalent in India with an emphasis on their early detection and prevention.</p>

<p>Candidates having the below mentioned qualifications may appear for Walk in Interview at ICPO on 5th Feb 2014 between 10.00 AM and 12.00 PM under the NIF project entitled "Prediction of drug tragets of chemical constituents present within non-codified medicinal plants" under Dr Subhash M.Agarwal, Scientist C</p>

<p>    Position : JRF<br />    No of Post : One<br />    Pay : Rs 12000/- + 30% HRA </p>

<p>    Desired Profile : M.Sc in Bioinformatics with good academic record. Candidate with experience in database development and scripting would be preferred<br />    Age Limit : Below 28 years<br />    Period : 2 months</p>

<p>Interested candidates may send their applications with bio-data by email (smagarwal@gmail.com) or post addressed to Dr Subhash M Agarwal, Scientist C, Bioinformatics Division, Institute of Cytology and Preventive Oncology (ICPO) I-7, Sector 39, Noida-201301 so as to reach latest by 04.02.14</p>

<p>Deadline : 04.02.14</p>

<p>http://icmr.nic.in/icmrnews/icpo_jrf.pdf</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/8466/srf-position-in-computational-systems-biology-computational-biology-group-iiit-delhi</guid>
  <pubDate>Sun, 23 Feb 2014 20:56:08 -0600</pubDate>
  <link></link>
  <title><![CDATA[SRF position in Computational Systems Biology Computational biology Group, IIIT-Delhi]]></title>
  <description><![CDATA[
<p>An opportunity to perform research in DST supported project that involves building of mathematical models to understand the functional relationship between circadian rhythms and memory formation under stressful condition.  In this project, mathematical model of circadian rhythms based on gene regulatory mechanisms will be unified with the mathematical model of calcium signal transduction pathway to understand and predict the formation of fear memory under stressful conditions. The research scholar will spend full time on this project to build new models and expected to contribute significantly to prepare the results for publication and presentation, and to contribute to grant proposals. </p>

<p>Required Qualifications: Masters in physics/chemistry/mathematics (or) MTech in bioengineering, chemical (or) Masters in any traditional field of science with outstanding performance throughout the program. Candidate should have cleared GATE/UGC-CSIR examinations. Applicant should have done basic mathematics courses like calculus, differential equations, numerical analysis etc in their degree program and have obtained good grades in those courses. Knowledge of MATLAB and C or at least one traditional programming language is absolutely necessary. Strong inclination to understand biological concepts is a must for this research work as this project is about modeling biological systems.     </p>

<p>Salary: A fixed salary of Rs 18000 PM including HRA will be paid. </p>

<p>Last date for application: This advertisement is open until suitable candidate is found for the project. </p>

<p>Preferred Qualifications:  - Expertise in dynamical systems theory, bifurcation theory, numerical simulations, parameter estimation. </p>

<p>Independence and high motivation for carrying out interdisciplinary research. - Excellent communication skills and ability to work independently. - Good working habits. </p>

<p>Interested candidates should submit both curriculum vitae and statement of interest in PDF format to sriramk@iiitd.ac.in and should clearly mention in the subject "Application for SRF".</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/9519/bioinformatics-phd-at-university-of-calcutta</guid>
  <pubDate>Mon, 31 Mar 2014 08:41:04 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics PhD at University of Calcutta]]></title>
  <description><![CDATA[
<p>University of Calcutta<br />Department of Biophysics, Molecular Biology &amp; Bioinformatics</p>

<p>Applications are invited for admission to the Ph.D. programme in the Department of Biophysics, Molecular Biology &amp; Bioinformatics, University of Calcutta for the year 2014 from eligible candidates who would be placed under the departmental teachers or affiliated research supervisors for the pursuance of their Ph.D. programme.</p>

<p>Candidates are requested to download the Ph.D. admission test application form from the University website and apply in the prescribed proforma by paying Rs. 100/- through a challan available through different University Cash counters. The challan is to be duly forwarded through the Head, Department of Biophysics, Molecular Biology &amp; Bioinformatics, University of Calcutta.</p>

<p>The completed application form with a copy of the paid challan is to be submitted to the office of the Department by April 16, 2014.</p>

<p>Syllabus for the Test: The questions for the admission test and interview will be based on topics in the following areas:</p>

<p>Mathematical methods, Molecular and Cellular Biophysics, Molecular and Cell Biology, Biochemistry, Genetics, Plant Biology, Developmental biology, Neurobiology, Biotechnology and Bioinformatics.</p>

<p>However, the interview will be primarily based on the research emphasis of the candidate. Candidates must clearly indicate the program in which they want to apply.</p>

<p>Date of Admission test : April 22, 2014 (Tuesday)</p>

<p>Date of publication of selection list for the interview : April 22, 2014(Tuesday)</p>

<p>Date of Interview : April 23, 2014 (Wednesday)</p>

<p>Number of vacancies for the Ph.D. programme : 12</p>

<p>Reservation policy will be followed as per rules.</p>

<p>Candidates with valid NET/GATE/M.Phil. or equivalent qualifications are not required to appear at the admission test but would need to qualify in the interview.</p>

<p>Advertisement:</p>

<p>http://www.caluniv.ac.in/admission%20notice/PHD_BIO_PHYSICS.pdf</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/11035/bioinformatics-jrfsrf-position-at-nii</guid>
  <pubDate>Sun, 25 May 2014 16:54:04 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics JRF/SRF position at NII]]></title>
  <description><![CDATA[
<p>NATIONAL INSTITUTE OF IMMUNOLOGY, NEW DELHI-110067</p>

<p>Applications are invited for the position of Senior Research Fellow for the following time-bound sponsored project as per the details given below:</p>

<p>1. BTIS project on, “Bioinformatics Center-National Infrastructural Facility in the Area of Immunology” funded by DBT</p>

<p>Senior Research Fellow (P) (One Position only)</p>

<p>Dr. Debasisa Mohanty<br />Staff Scientist-VI<br />deb@nii.res.in</p>

<p>Qualifications: M.Sc in Biological Sciences or Biotechnology with at least 04 years of Research experience in Bioinformatics or computational Biology after the master’s degree is essential.</p>

<p>Emoluments: The selected candidates will draw consolidated emoluments as per Institute Rules, depending upon qualifications &amp; experience</p>

<p>Rs. 18,000/- per month consolidated plus 30% HRA if Leading to Ph.D/NET/GATE Qualified otherwise Rs. 14,000/- per month + 30% HRA.</p>

<p>Job description: The candidate should be well versed in programming in PERL/C++/HTML/CGI, web server and portal development, computational analysis of<br />protein structure &amp; function, molecular dynamics simulations and use of high performance computing systems.</p>

<p>GENERAL TERMS AND CONDITIONS:-</p>

<p>1. The candidates selected for the above posts will be on contract for one year or duration of the project whichever is shorter, at a time.<br />2. No hostel/ housing facility will be provided.<br />3. Number of posts may vary and shall be need based. Advertisement is no commitment.<br />4. Applicants may clearly mention the category they belong to i.e. SC/ST/OBC/PH and attach documentary proof of the same.<br />5. No TA/DA will be paid for attending the interview, if called for.<br />6. Apart from sending application in the prescribed format given below, candidates should send complete Curriculum Vitae along with the names of three referees. Curriculum Vitae should contain details of the experimental expertise.</p>

<p>HOW TO APPLY Interested candidates may apply directly, STRICTLY IN THE PRESCRIBED FORMAT GIVEN BELOW, through e-mail, to the Investigator of the project, clearly indicating the name of the project along with their complete C.V., e-mail id, fax numbers, telephone numbers. Only Short listed candidates will be called for interview and they required to submit attested copies of all their certificates and a Demand Draft of Rs 100/- drawn on Canara Bank or Indian Bank payable at Delhi/New Delhi in favour of the Director, NII (SC / ST and PH candidates are exempted subject to submission of documentary proof), at the time of interview.</p>

<p>LAST DATE OF RECEIPT OF APPLICATIONS: 06th June, 2014</p>

<p>Advertisement</p>

<p>www1.nii.res.in/sites/default/files/projectappointment-Dr.Mohanty-6June2014.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/20368/india-innovates</guid>
	<pubDate>Mon, 12 Jan 2015 02:21:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/20368/india-innovates</link>
	<title><![CDATA[India Innovates]]></title>
	<description><![CDATA[<p>The India Innovation Growth Programme is a joint initiative of the Department of Science and Technology, Govt. of India; Lockheed Martin Corporation; Indo-US Science and Technology Forum, Federation of Indian Chambers of Commerce and Industry; Stanford Graduate School of Business and the IC2 Institute at the University of Texas. More at http://www.indiainnovates.in/B</p><p>Address of the bookmark: <a href="http://www.indiainnovates.in/" rel="nofollow">http://www.indiainnovates.in/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/13014/bioinformatics-jrf-vacancy-at-icgeb-new-delhi</guid>
  <pubDate>Wed, 23 Jul 2014 16:07:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics JRF vacancy at ICGEB, New Delhi]]></title>
  <description><![CDATA[
<p>Junior Research Fellow for a DBT sponsored project entitled "Computational and experimental characterization of stage specific arginine methylation in P. falciparum proteome". </p>

<p>Candidates should have a 1st class MSc/MTech/BTech degree in Bioinformatics. Please send complete CV, quoting Application for RMETH-JRF-2014, by email to Dr. Dinesh Gupta: dinesh@icgeb.res.in</p>

<p>Closing date for applications: 6 August 2014</p>

<p>More at http://www.icgeb.org/tl_files/Vacancies/JRF.pdf</p>
]]></description>
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<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/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>
</item>

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