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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31714?offset=450</link>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/19540/niab-molecular-biologybioinformatics-scientistra-openings</guid>
  <pubDate>Fri, 12 Dec 2014 21:08:47 -0600</pubDate>
  <link></link>
  <title><![CDATA[NIAB Molecular Biology/Bioinformatics Scientist/RA Openings]]></title>
  <description><![CDATA[
<p>D. No. 1-121/1, 4th and 5th Floors, Axis Clinicals Building, Miyapur, Hyderabad, Telangana, India- 500 049</p>

<p>Email: admin@niab.org.in Telephones: +91 40 2304 9403 Telefax: +91 40 2304 2740<br />Advertisement No: 5/2014</p>

<p>About NIAB National Institute of Animal Biotechnology (NIAB), Hyderabad, an autonomous institute under the aegis of Department of Biotechnology, Government of India, is aimed to harness novel and emerging biotechnologies and create knowledge in the cutting edge areas for improving animal health and productivity.</p>

<p>Applications are invited for the following temporary research positions to work in ongoing DBTBBSRC sponsored research project entitled “Transcriptome Analysis in Indian buffalo and the Genetics of Innate Immunity” at the National Institute of Animal Biotechnology, Hyderabad.</p>

<p>(A) Project Scientist – Level B (One Position)</p>

<p>Emoluments: Rs. 15600 + GP Rs. 5400 + 30 % HRA p.m. (Total emoluments will be Rs. 49,770/-p.m. for the duration of the project)</p>

<p>Essential Qualification: Candidates having M.V.Sc. in Veterinary Microbiology / Veterinary Pathology / Veterinary Public Health / Ph.D. degree in Life Sciences, Biotechnology, Molecular Biology or any other related field from the recognized university are eligible to apply.</p>

<p>The candidate should have a good academic record and research experience as evidenced from published in standard referred journals / patents.</p>

<p>Desirable: Candidates having research experience in the area of tissue culture, genomics, Transcriptomics and Advanced Molecular Biology will be given preference.</p>

<p>Age Limit: Not exceeding 30 years as on last date of the submission of the application.</p>

<p>(B) Research Associate in Bioinformatics (One position)</p>

<p>Fellowship: Rs. 22,000 + 30 % HRA</p>

<p>Essential Qualification: Candidates having Ph.D. degree or M.Tech. with three years of<br />experience in Bioinformatics, Computational Biology, Biotechnology, Life Sciences or any other related field are eligible to apply.</p>

<p>Desirable: Candidate having research experience in the area of next generation sequencing (NGS) data analysis, Genome wide association studies, Genomic selection, advance genomic data analysis etc., will be given preference. The candidate should have a good academic record and research experience as evidenced from published papers in standard journals / patents.</p>

<p>Age Limit: Not exceeding 30 years as on last date of the submission of the application.</p>

<p>Project Duration: The duration of the project is Three years and the positions are co- terminus with the duration of the project. (Initial appointment will be for one year and further extension will be granted based on annual review).</p>

<p>Mode of submission of application: Only online applications are to be submitted through<br />www.niab.org.in on or before 08 December, 2014. Link for online submission of applications will be available from 10 November 2014.</p>

<p>Advertisement: www.niab.org.in/Notifications/Advt_5_2014/Advt_5_2014.pdf</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/19542/bic-pgi-bioinformatics-project-dissertation-program</guid>
  <pubDate>Fri, 12 Dec 2014 21:17:30 -0600</pubDate>
  <link></link>
  <title><![CDATA[BIC-PGI Bioinformatics Project Dissertation Program]]></title>
  <description><![CDATA[
<p>Biomedical Informatics Centre, PGIMER, Chandigarh invites application for a project dissertation program for students who have completed their first year of M.Sc. in Bioinformatics.</p>

<p>This is an exciting opportunity for Master's students to train in modern methods in Bioinformatics. The duration of the training will be four to six months, starting from January 2015.</p>

<p>Education: Pursuing M.Sc. Bioinformatics</p>

<p>Essential: Post graduate applicants should have completed their first year and should be in the third semester or first half of the second year.</p>

<p>Only students who are willing to spend a minimum period of 4 months to a maximum of six months, without any break, would be eligible for the program.</p>

<p>How to Apply: Candidates interested in the above project dissertation program should apply online.</p>

<p>Send your CV, Scanned copy of letter of recommendation from Head of Institution along with Registration form in the given format should be sent to: info@bicpgi.org</p>

<p>Please mention clearly “Project dissertation &amp; your Name” in the Subject.</p>

<p>The last date for application is December 31, 2014</p>

<p>Note: Selected candidates may please note that the program is free of cost and would not provide any financial aid for transport and stay.</p>

<p>Name of the selected candidates would be posted on the centre website by December 31, 2014. Incomplete applications will be rejected.</p>

<p>For more information visit our website: http://www.bic-pgi.org/project_dissertation.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37561/hercules-a-profile-hmm-based-hybrid-error-correction-algorithm-for-long-reads</guid>
	<pubDate>Mon, 20 Aug 2018 14:14:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37561/hercules-a-profile-hmm-based-hybrid-error-correction-algorithm-for-long-reads</link>
	<title><![CDATA[Hercules: a profile HMM-based hybrid error correction algorithm for long reads]]></title>
	<description><![CDATA[<p><span>Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results: We designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platformtextquoterights error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability: </span></p>
<p><span>Hercules source code is available at https://github.com/BilkentCompGen/Hercules</span></p><p>Address of the bookmark: <a href="https://github.com/BilkentCompGen/Hercules" rel="nofollow">https://github.com/BilkentCompGen/Hercules</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/19633/vital-it</guid>
	<pubDate>Thu, 18 Dec 2014 10:46:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/19633/vital-it</link>
	<title><![CDATA[Vital-IT]]></title>
	<description><![CDATA[<p>Vital-IT is a <strong>bioinformatics competence center</strong> that supports and collaborates with life scientists in Switzerland and beyond. The <a href="http://www.vital-it.ch/about/team.php">multi-disciplinary team</a> provides expertise, training and maintains a high-performance computing (HPC) and storage infrastructure, so as to help develop, maintain and extend life science and medical research (<a href="http://www.vital-it.ch/about/activities.php">activities</a>).</p><p>Address of the bookmark: <a href="http://www.vital-it.ch/" rel="nofollow">http://www.vital-it.ch/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37643/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads</guid>
	<pubDate>Thu, 06 Sep 2018 16:21:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37643/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads</link>
	<title><![CDATA[LoRMA: A tool for correcting sequencing errors in long reads]]></title>
	<description><![CDATA[<p><span>An error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of&nbsp;</span><em>k</em><span>-mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75&times;, the throughput of the new method is at least 20% higher.</span></p>
<blockquote>
<p><span>conda install -c atgc-montpellier lorma</span></p>
</blockquote><p>Address of the bookmark: <a href="https://gite.lirmm.fr/lorma/lorma-releases/wikis/home" rel="nofollow">https://gite.lirmm.fr/lorma/lorma-releases/wikis/home</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37959/rainbow-an-integrated-tool-for-efficient-clustering-and-assembling-rad-seq-reads</guid>
	<pubDate>Fri, 19 Oct 2018 08:23:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37959/rainbow-an-integrated-tool-for-efficient-clustering-and-assembling-rad-seq-reads</link>
	<title><![CDATA[Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads]]></title>
	<description><![CDATA[<p><span>Rainbow is developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq. First, Rainbow clusters reads using a spaced seed method. Then, Rainbow implements a heterozygote calling like strategy to divide potential groups into haplotypes in a top&ndash;down manner. And along a guided tree, it iteratively merges sibling leaves in a bottom&ndash;up manner if they are similar enough. Here, the similarity is defined by comparing the 2nd reads of a RAD segment. This approach tries to collapse heterozygote while discriminate repetitive sequences. At last, Rainbow uses a greedy algorithm to locally assemble merged reads into contigs. Rainbow not only outputs the optimal but also suboptimal assembly results. Based on simulation and a real guppy RAD-seq data, we show that Rainbow is more competent than the other tools in dealing with RAD-seq data</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/bio-rainbow/files/" rel="nofollow">https://sourceforge.net/projects/bio-rainbow/files/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/19690/bioinformatics-scientist-at-icar-labs</guid>
  <pubDate>Sun, 21 Dec 2014 23:47:03 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics Scientist at ICAR Labs]]></title>
  <description><![CDATA[
<p>AGRICUL AGRICULTURAL SCIENTISTS RECRUITMENT BOARD TURAL SCIENTISTS RECRUITMENT BOARD<br />KRISHI ANUSANDHAN BHAVAN-I, PUSA, NEW DELHI-110 012</p>

<p>ADVERTISEMENT NO. 03/2014</p>

<p>PRINCIPAL SCIENTIST</p>

<p>Pay Band: Minimum pay of `43,000 in the PB-4 of `37400-67000/- + RGP of `10,000/-.</p>

<p>Age: The candidates must not have attained the age of 52 years as on 19.01.2015. There shall be no age limit for the Council’s employees.</p>

<p>ICAR-Indian Institute for Agricultural Biotechnology, (IIAB) Ranchi (Jharkhand)</p>

<p>151. Principal Scientist (Bioinformatics) (One post)</p>

<p>SENIOR SCIENTIST/PROGRAMME COORDINATOR</p>

<p>Pay Band: PB-4 of ` 37400-67000/- + RGP of ` 9,000/-.</p>

<p>Age: The candidates must not have attained the age of 47 years as on 19.01.2015. There shall be no age limit for the Council’s employees.</p>

<p>National Institute of Biotic Stress Management, Raipur (Chhattishgarh)</p>

<p>166. Senior Scientist (Bioinformatics) (One post)</p>

<p>IMPORTANT NOTE<br />I. (i) CLOSING DATE</p>

<p>THE CLOSING DATE FOR RECEIPT OF APPLICATIONS IN AGRICULTURAL SCIENTISTS RECRUITMENT BOARD IS 19.01.2015 (For applications posted from abroad and in the Andaman and Nicobar Islands, Lakshdweep, Minicoy and Amindivi islands, States/ Union Territories in the North-Eastern Region, Ladakh Division of J &amp; K State, Sikkim, Pangi, Sub-division of Chamba, Lahul and Spiti Districts of Himachal Pradesh, the last date for receipt of application will be 02.02.2015). Non receipt of the application by the closing date will result in rejection of the application.</p>

<p>More Info: http://asrb.org.in/administrator/uploads_dir/1418978057english.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38735/genome-assembly-tutorial-genome-assembly-for-short-and-long-reads</guid>
	<pubDate>Sat, 19 Jan 2019 17:29:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38735/genome-assembly-tutorial-genome-assembly-for-short-and-long-reads</link>
	<title><![CDATA[Genome assembly tutorial &quot;Genome Assembly for short and long reads&quot;]]></title>
	<description><![CDATA[<p>In this lab we will perform de novo genome assembly of a bacterial genome. You will be guided through the genome assembly starting with data quality control, through to building contigs and analysis of the results. At the end of the lab you will know:</p>
<ol>
<li>How to perform basic quality checks on the input data</li>
<li>How to run a short read assembler on Illumina data</li>
<li>How to run a long read assembler on Pacific Biosciences or Oxford Nanopore data</li>
<li>How to improve the accuracy of a long read assembly using short reads</li>
<li>How to assess the quality of an assembly</li>
</ol>
<p>https://bioinformaticsdotca.github.io/high-throughput_biology_2017</p><p>Address of the bookmark: <a href="https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab" rel="nofollow">https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/19786/shrec3d</guid>
	<pubDate>Thu, 25 Dec 2014 23:14:52 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/19786/shrec3d</link>
	<title><![CDATA[ShRec3D]]></title>
	<description><![CDATA[<p><strong>ShRec3D</strong> is a program that aims at reconstructing a genome 3D structure (b) from the sole knowledge of the contacts between different genomic regions (a) as determined by Hi-C (http://www.ncbi.nlm.nih.gov/pubmed/19815776).</p>
<p>There are two options to run ShRec3D (on linuX only so far): the first one uses the Matlab complier runtime environment (MCR), the second one doesn't need any other library to be installed but only works with the latest versions of Linux (equivalent to Fedora 19 and above).</p><p>Address of the bookmark: <a href="https://sites.google.com/site/julienmozziconacci/#TOC-Downloads" rel="nofollow">https://sites.google.com/site/julienmozziconacci/#TOC-Downloads</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads</guid>
	<pubDate>Sun, 05 Jan 2020 04:08:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads</link>
	<title><![CDATA[NextDenovo: string graph-based de novo assembler for TGS long reads]]></title>
	<description><![CDATA[<p>NextDenovo is a string graph-based<span>&nbsp;</span><em>de novo</em><span>&nbsp;</span>assembler for TGS long reads. It uses a "correct-then-assemble" strategy similar to canu, but requires significantly less computing resources and storages. After assembly, the per-base error rate is about 97-98%, to further improve single base accuracy, please use<span>&nbsp;</span><a href="https://github.com/Nextomics/NextPolish">NextPolish</a>.</p>
<p>NextDenovo contains two core modules: NextCorrect and NextGraph. NextCorrect can be used to correct TGS long reads with approximately 15% sequencing errors, and NextGraph can be used to construct a string graph with corrected reads. It also contains a modified version of<span>&nbsp;</span><a href="https://github.com/lh3/minimap2">minimap2</a><span>&nbsp;</span>for adapting input and output and producing more sensitive and accurate dovetail overlaps, and some useful utilities (see<span>&nbsp;</span><a href="https://github.com/Nextomics/NextDenovo/blob/master/doc/UTILITY.md">here</a><span>&nbsp;</span>for more details).</p><p>Address of the bookmark: <a href="https://github.com/Nextomics/NextDenovo" rel="nofollow">https://github.com/Nextomics/NextDenovo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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