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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/28809?offset=1100</link>
	<atom:link href="https://bioinformaticsonline.com/related/28809?offset=1100" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome</guid>
	<pubDate>Thu, 31 Aug 2023 02:43:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome</link>
	<title><![CDATA[Steps to find all the repeats in the genome !]]></title>
	<description><![CDATA[<div><p>To find repeats in a genome from 2 to 9 length using a Perl script, you can use the RepeatMasker tool with the "--length" option<a href="https://mobilednajournal.biomedcentral.com/articles/10.1186/1759-8753-5-13" target="_blank">[0]</a>. Here's a step-by-step guide:</p></div><div><ol>
<li>Install RepeatMasker: First, you need to install RepeatMasker on your system. You can download it from the RepeatMasker website<a href="https://mobilednajournal.biomedcentral.com/articles/10.1186/1759-8753-5-13" target="_blank">[0]</a>.</li>
</ol></div><div><ol>
<li>Prepare the genome sequence: Make sure you have the genome sequence in a FASTA file format. Let's assume the file is named "genome.fasta".</li>
</ol><blockquote><p>./RepeatMasker -pa &lt;number_of_processors&gt; -nolow -norna -no_is -div &lt;divergence_value&gt; -lib RepeatMaskerLib.embl -gff -xsmall -small -poly -species &lt;species_name&gt; -dir &lt;output_directory&gt; -length &lt;min_length&gt;-&lt;max_length&gt; genome.fasta</p></blockquote><div><p>Replace the following placeholders with appropriate values:</p><ul>
<li><code>&lt;number_of_processors&gt;</code>: The number of processors/threads you want to use for parallel processing.</li>
<li><code>&lt;divergence_value&gt;</code>: The divergence value for the species you are analyzing. You can find divergence values for different species in the RepeatMasker documentation<a href="https://mobilednajournal.biomedcentral.com/articles/10.1186/1759-8753-5-13" target="_blank">[0]</a>.</li>
<li><code>&lt;species_name&gt;</code>: The name of the species you are analyzing.</li>
<li><code>&lt;output_directory&gt;</code>: The directory where you want the output files to be saved.</li>
<li><code>&lt;min_length&gt;</code>&nbsp;and&nbsp;<code>&lt;max_length&gt;</code>: The minimum and maximum lengths of the repeats you want to find (in this case, 2 and 9).</li>
</ul></div><div><ol>
<li>Analyze the output: RepeatMasker will generate several output files, including a .out file. You can parse this file to extract the information you need. There is a Perl tool called "one_code_to_find_them_all.pl" that can help you parse RepeatMasker output files<a href="https://mobilednajournal.biomedcentral.com/articles/10.1186/1759-8753-5-13" target="_blank">[0]</a>. You can download it from the source provided.</li>
</ol></div><div><ol>
<li>Use the provided Perl script: Once you have the "one_code_to_find_them_all.pl" script, you can run it to conveniently parse the RepeatMasker output files. Here's an example of how to use it:</li>
</ol><blockquote><p>perl one_code_to_find_them_all.pl --rm &lt;RepeatMasker_out_file&gt; --length &lt;length_file&gt;</p></blockquote></div><p>&nbsp;</p></div><div><div><p>Replace&nbsp;<code>&lt;RepeatMasker_out_file&gt;</code>&nbsp;with the path to your RepeatMasker .out file, and&nbsp;<code>&lt;length_file&gt;</code>&nbsp;with the path to a file containing the lengths of the reference elements.</p></div><div><p>This script will generate several output files, including .log.txt and .copynumber.csv, which contain quantitative information about the identified repeat elements.</p></div><div><p>Remember to adjust the parameters and options according to your specific needs and the characteristics of your genome.</p></div></div>]]></description>
	<dc:creator>Neel</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17186/urdip-pune-bioinformatics-srfpa-openings</guid>
  <pubDate>Sat, 20 Sep 2014 20:48:50 -0500</pubDate>
  <link></link>
  <title><![CDATA[URDIP Pune Bioinformatics SRF/PA Openings]]></title>
  <description><![CDATA[
<p>CSIR UNIT FOR RESEARCH AND DEVELOPMENT OF INFORMATION PRODUCTS<br />NCL Campus, S.No.113,114, Pashan, Pune 411 008</p>

<p>ADVERTISEMENT NO. - URDIP/ 5/2014</p>

<p>Learning opportunity for young Science and Engineering professionals to make a career in Information Science Industry CSIR has set up a Unit for Research and Development of Information Products (CSIR-URDIP) at Pune to work in the area of Scientific Informatics (ChemBioinformatics/Patent Informatics/Phytoinformatics/Toxinformatics) and related<br />software development projects.</p>

<p>Applications are invited from CSIR - UGC NET Qualified Candidates for consideration as Project Fellow (PF) and/or Senior Project Fellow (SPF) based on the experience to work on existing and new projects at CSIRURDIP.</p>

<p>Project Fellow</p>

<p>    Remuneration - (Rs. 16,000.00 + 20% HRA)</p>

<p>    M. Sc. In Biochemistry/Microbiology/Bioinformatics [Post-code A02] only with minimum of 55% marks</p>

<p>Senior Project Fellow</p>

<p>    Remuneration - (Rs. 18,000.00 + 20% HRA)</p>

<p>    M. Sc. in Biochemistry/Microbiology/Bioinformatics [Post-code A05] only with minimum of 55% marks plus two years research or relevant informatics experience</p>

<p>Please visit www.urdip.res.in/career.htm to apply online by 30th September, 2014.</p>

<p>Successful candidates who have appeared for NET exam in 2012 and 2013 are only eligible to apply.</p>

<p>Advertisement: http://115.112.95.114/urhr/download/Advt5_2014.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44637/tools-to-access-the-quality-of-your-assembled-genome</guid>
	<pubDate>Thu, 08 Aug 2024 23:31:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44637/tools-to-access-the-quality-of-your-assembled-genome</link>
	<title><![CDATA[Tools to access the quality of your assembled genome !]]></title>
	<description><![CDATA[<ul dir="auto">
<li><a href="https://github.com/linsalrob/fasta_validator">FASTA VALIDATOR</a>&nbsp;+&nbsp;<a href="https://github.com/shenwei356/seqkit">SEQKIT RMDUP</a>: FASTA validation</li>
<li><a href="https://genometools.org/tools/gt_gff3validator.html">GENOMETOOLS GT GFF3VALIDATOR</a>: GFF3 validation</li>
<li><a href="https://github.com/PlantandFoodResearch/assemblathon2-analysis/blob/a93cba25d847434f7eadc04e63b58c567c46a56d/assemblathon_stats.pl">ASSEMBLATHON STATS</a>: Assembly statistics</li>
<li><a href="https://genometools.org/tools/gt_stat.html">GENOMETOOLS GT STAT</a>: Annotation statistics</li>
<li><a href="https://github.com/ncbi/fcs">NCBI FCS ADAPTOR</a>: Adaptor contamination pass/fail</li>
<li><a href="https://github.com/ncbi/fcs">NCBI FCS GX</a>: Foreign organism contamination pass/fail</li>
<li><a href="https://gitlab.com/ezlab/busco">BUSCO</a>: Gene-space completeness estimation</li>
<li><a href="https://github.com/tolkit/telomeric-identifier">TIDK</a>: Telomere repeat identification</li>
<li><a href="https://github.com/oushujun/LTR_retriever/blob/master/LAI">LAI</a>: Continuity of repetitive sequences</li>
<li><a href="https://github.com/DerrickWood/kraken2">KRAKEN2</a>: Taxonomy classification</li>
<li><a href="https://github.com/igvteam/juicebox.js">HIC CONTACT MAP</a>: Alignment and visualisation of HiC data</li>
<li><a href="https://github.com/mummer4/mummer">MUMMER</a>&nbsp;&rarr;&nbsp;<a href="http://circos.ca/documentation/">CIRCOS</a>&nbsp;+&nbsp;<a href="https://plotly.com/">DOTPLOT</a>&nbsp;&amp;&nbsp;<a href="https://github.com/lh3/minimap2">MINIMAP2</a>&nbsp;&rarr;&nbsp;<a href="https://github.com/schneebergerlab/plotsr">PLOTSR</a>: Synteny analysis</li>
<li><a href="https://github.com/marbl/merqury">MERQURY</a>: K-mer completeness, consensus quality and phasing assessment</li>
</ul>]]></description>
	<dc:creator>LEGE</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17189/bioinformatics-svims-project-assistant-walk-in</guid>
  <pubDate>Sat, 20 Sep 2014 21:02:29 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics SVIMS Project Assistant Walk IN]]></title>
  <description><![CDATA[
<p>SRI VENKATESWARA INSTITUTE OF MEDICAL SCIENCES<br />TIRUPATI, ANDHRA PRADESH, INDIA- 517 507<br />BIOINFORMATICS CENTRE, DEPARTMENT OF BIOINFORMATICS</p>

<p>Eligible candidates are invited for a walk-in-interview for recruitment of Project Assistant in SVIMS Bioinformatics centre under the BTISnet Project entitled “Creation of Bioinformatics Infrastructure Facility for promotion of Biology teaching through Bioinformatics” on 25.09.2014 at 11 AM in SVIMS, Tirupati. The engagement will be made purely on temporary basis for a period of one year and it can be terminated at any time without notice or without assigning any reason thereof by the Coordinator of the Project. The person engaged shall not be entitled for any claim implicit or explicit for absorption in the University.</p>

<p>1. Name of the post : Project Assistant</p>

<p>2. Qualification :<br />i) Essential : MSc Bioinformatics/MTech (Biotechnology/Bioinformatics)</p>

<p>ii) Desirable : Experience in Bioinformatics research work (Preference will be given to candidates  qualified in BINC/UGC/CSIR/NET/GATE)</p>

<p>3. Remuneration : 16000 + 10% HRA for NET/GATE candidates 14000 + 10% HRA for M. Tech / M.Sc. Candidates</p>

<p>4. Place of posting : Tirupati</p>

<p>5. Duration of the Project : One year</p>

<p>Terms and conditions:</p>

<p>1. Candidates are required to submit the Biodata relevant certificates in support of their age and educational qualification etc., before the interview committee, SVIMS University, Tirupati.</p>

<p>2. Candidates called for interview will attend the interview at their own cost.<br />3. Interim enquiries will not be entertained.<br />4. The maximum age limit for Project Assistant is 28 years for general category and 33 years for SC and ST category candidates as on 25th September, 2014.</p>

<p>Advertisement:</p>

<p>http://svr98.ehostpros.com/~svimsb98/Project%20Assistant_notification.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44766/genome-simulation-with-slim-and-msprime</guid>
	<pubDate>Fri, 31 Jan 2025 12:47:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44766/genome-simulation-with-slim-and-msprime</link>
	<title><![CDATA[Genome Simulation with SLiM and msprime]]></title>
	<description><![CDATA[<p>Genome simulation is an essential tool in population genetics, enabling researchers to model evolutionary processes and study genetic variation. Two widely used simulation tools in this field are <strong style="font-size: 12.8px;">SLiM</strong><span style="font-size: 12.8px; font-weight: normal;"> and </span><strong style="font-size: 12.8px;">msprime</strong><span style="font-size: 12.8px; font-weight: normal;">. While both serve different purposes, they can be used together with the </span><strong style="font-size: 12.8px;">slendr</strong><span style="font-size: 12.8px; font-weight: normal;"> framework to compare simulation outputs effectively.</span></p><h2>Overview of SLiM and msprime</h2><h3>SLiM: Forward Genetic Simulator</h3><p>SLiM is a <strong>free, open-source</strong> tool designed for forward genetic simulations. It allows researchers to model complex evolutionary scenarios, including selection, recombination, and demographic events, making it particularly useful for studying adaptation and selection in populations.</p><p><strong>Key Features of SLiM:</strong></p><ul>
<li>
<p>Simulates population evolution forward in time</p>
</li>
<li>
<p>Supports custom evolutionary models using an embedded scripting language</p>
</li>
<li>
<p>Allows modeling of spatial and ecological dynamics</p>
</li>
<li>
<p>Provides high flexibility and extensibility for user-defined scenarios</p>
</li>
<li>
<p>Available on GitHub as an open-source project</p>
</li>
</ul><h3>msprime: Ancestry and Mutation Simulator</h3><p>msprime is an efficient, <strong>open-source</strong> tool that simulates ancestry and mutations using a coalescent framework. It is known for its high-speed performance and low memory requirements, making it a popular choice for large-scale genomic simulations.</p><p><strong>Key Features of msprime:</strong></p><ul>
<li>
<p>Implements coalescent simulations for ancestry modeling</p>
</li>
<li>
<p>Efficiently simulates large population histories</p>
</li>
<li>
<p>Supports the addition of mutations to genealogies</p>
</li>
<li>
<p>Developed using an open-source community model</p>
</li>
<li>
<p>Often faster and more memory-efficient than alternative simulators</p>
</li>
</ul><h2>Using SLiM and msprime with slendr</h2><p>Both SLiM and msprime can be integrated with <strong>slendr</strong>, a framework that facilitates structured population genetic simulations. This integration allows for seamless comparison of simulation outputs.</p><h3>How They Work Together:</h3><ul>
<li>
<p>SLiM and msprime simulations can be analyzed within slendr.</p>
</li>
<li>
<p>The <strong>ts_read()</strong> function in slendr enables loading and comparing tree sequence outputs from both simulators.</p>
</li>
<li>
<p>This integration allows researchers to validate simulation results and gain deeper insights into evolutionary processes.</p>
</li>
</ul><h2>Performance Considerations</h2><p>While SLiM offers powerful forward simulations with extensive customization, msprime is often preferred for its <strong>speed and memory efficiency</strong> when simulating ancestry and mutations. The choice between the two depends on the research goals:</p><ul>
<li>
<p><strong>For detailed evolutionary modeling with selection and recombination:</strong> Use SLiM.</p>
</li>
<li>
<p><strong>For large-scale coalescent simulations with mutations:</strong> Use msprime.</p>
</li>
<li>
<p><strong>For comparing different simulation models and their outputs:</strong> Use slendr to integrate SLiM and msprime results.</p>
</li>
</ul><h2>Conclusion</h2><p>SLiM and msprime are valuable tools for genome simulation, each serving distinct but complementary purposes in population genetics research. By leveraging the strengths of both simulators with slendr, researchers can conduct robust and efficient evolutionary simulations, enhancing our understanding of genetic diversity and adaptation.</p><p>For more information, check out the official GitHub repositories for <strong>SLiM</strong> and <strong>msprime</strong>, and explore the <strong>slendr</strong> framework for streamlined simulation workflow</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/view/2044</guid>
	<pubDate>Mon, 12 Aug 2013 12:19:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/2044</link>
	<title><![CDATA[Does anyone have Nanopore latest updates?]]></title>
	<description><![CDATA[<p>There was a lot of buzz about&nbsp;<span>Oxford Nanopore Technologies&reg; is developing the GridION&trade; system and miniaturised MinION&trade; device. These are a new generation of electronic molecular analysis system for use in scientific research, personalised medicine, crop science, security/defence and more. The platform technology uses nanopores to analyse single molecules including DNA/RNA and proteins. With a broad patent portfolio, the Oxford Nanopore pipeline includes biological nanopores and solid-state nanopores.</span></p><p>Is this available, or still under trial mode?&nbsp;</p><p><a href="https://www.nanoporetech.com/">https://www.nanoporetech.com/</a></p><p><a href="https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system">https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/18741/a-powerful-yet-simple-gene-set-analysis-tool-for-interpreting-rna-seq-and-ngs-results</guid>
	<pubDate>Thu, 30 Oct 2014 09:19:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/18741/a-powerful-yet-simple-gene-set-analysis-tool-for-interpreting-rna-seq-and-ngs-results</link>
	<title><![CDATA[A powerful, yet simple, gene set analysis tool for interpreting RNA-seq and NGS results.]]></title>
	<description><![CDATA[<p>LifeMap Sciences is introducing&nbsp;<a href="http://geneanalytics.genecards.org/">GeneAnalytics</a>, our new gene set analysis tool, which is applicable for NGS results and differentially expressed gene lists from variable sources. GeneAnalytics provides&nbsp;gene associations with tissues &amp; cells, diseases, pathways, GO terms and compounds.</p><p>Our main advantages over other similar tools are:</p><ul>
<li>GeneAnalytics is very simple and intuitive to use.</li>
<li>GeneAnalytics is based on our proprietary databases &ndash;&nbsp;<strong>GeneCards</strong>, MalaCards, PathCards and LifeMap Discovery, each of them integrates information from a very large number of resources.</li>
<li>GeneAnalytics supplies links for extensive background information on each of the matched results.</li>
</ul><p>&nbsp;</p><p>I invite you to try it out for free at&nbsp;geneanalytics.genecards.org, and would be happy to hear your comments and thoughts on how we can improve.</p><p>&nbsp;</p><p>Yours,</p><p>Shani Ben-Ari Fuchs</p><p>LifeMap Sciences Team</p>]]></description>
	<dc:creator>Shani</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</guid>
	<pubDate>Fri, 02 Feb 2018 03:24:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</link>
	<title><![CDATA[karyoploteR: plot whole genomes with arbitrary data]]></title>
	<description><![CDATA[<p><span><a href="http://bioconductor.org/packages/karyoploteR">karyoploteR</a></span><span>&nbsp;is an R package to create karyoplots, that is, representations of whole genomes with arbitrary data plotted on them. It is inspired by the R base graphics system and does not depend on other graphics packages. The aim of karyoploteR is to offer the user an easy way to plot data along the genome to get broad genome-wide view to facilitate the identification of genome wide relations and distributions.</span></p><p>Address of the bookmark: <a href="https://bernatgel.github.io/karyoploter_tutorial/" rel="nofollow">https://bernatgel.github.io/karyoploter_tutorial/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18819/jrfsrf-at-jawaharlal-nehru-institute-ofadvanced-studies-jnias-hyderabad</guid>
  <pubDate>Fri, 31 Oct 2014 08:48:23 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF/SRF at Jawaharlal Nehru Institute ofAdvanced Studies (JNIAS), Hyderabad]]></title>
  <description><![CDATA[
<p>Applications for Academic Projects in Biotechnology, Bioinformatics, Environmental Sciences and Computer Science &amp; Engineering</p>

<p>About JNIAS<br />Jawaharlal Nehru Institute of Advanced Studies (JNIAS), Hyderabad has been established by Dr. D. Swaminadhan Research Foundation (DSRF), Hyderabad as a Research and Educational Institution with a view to contribute in developing advanced technologies and build „core competence‟ in specific areas. The activities of JNIAS involves: Education, Research Training and Innovations in the fields of Sciences, Technologies, Humanities and Social Sciences. It aims to blossom into an Advanced Institute of education and research with a reservoir of expertise and experience in the relevant fields and the necessary capability to harness multi-disciplinary research and studies. JNIAS has been recognized as an Advanced Research Institute by Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad and Jawaharlal Nehru Technological University Anantapur (JNTUA), for offering Ph.D., P.G M.Phil, P.G Diploma and Training Programmes in Sciences and Engineering &amp; Technology.</p>

<p>Jawaharlal Nehru Architecture and Fine Arts University (JNAFAU) Hyderabad also recognized JNIAS for offering UG, PG degree in Architecture.</p>

<p>Projects &amp; Facilities</p>

<p>JNIAS offers wide range of projects:</p>

<p>Biotechnology area:</p>

<p>Molecular Biology<br />Microbiology<br />Nanotechnology<br />Bioinformatics (Schrodinger Software)<br />In Silico studies &amp; Drug Designing<br />Sequence analysis<br />Protein structure function studies</p>

<p>Registration<br />Tuition Fees: Interested students need to pay the following tuition fees:<br />1. Six Month’s Project: Rs. 20,000/-<br />2. Four Month’s Project: Rs. 15,000/-<br />3. Three Month’s Project: Rs. 10,000/-<br />4. One Month - Hands on Training : Rs. 8,000/-</p>

<p>For enquires call:<br />91-7893203414 (Biotechnology), 91-9949582263 (Environmental Sciences) 91-8977369305 (Computer Science)</p>

<p>Interested student may download the application from the website (www.jnias.in) and send the hard copy of the completed application forms and Curriculum Vitae along with the Demand Draft drawn on any nationalized Banks in favor of “The Registrar, JNIAS, Secunderabad”. Application forms can be sent through email to academicprojects@jnias.in</p>

<p>Address<br />Jawaharlal Nehru Institute of Advanced Studies (JNIAS)<br />6th Floor, Buddha Bhavan, M.G Road,<br />Secunderabad - 500 003<br />Andhra Pradesh, India<br />Tele/Fax: 040- 27541551; 27541553<br />Mobile: 08885541554<br />Web site: www.jnias.in</p>

<p>Brochure : https://drive.google.com/file/d/0B3zPwhgA-u-nU0dyMFd2OWcxNUpSTWNYc0xDSGs5UDI4UDNB/view?usp=sharing</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</guid>
	<pubDate>Tue, 23 May 2017 05:20:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</link>
	<title><![CDATA[GRASS: a generic algorithm for scaffolding next-generation sequencing assemblies.]]></title>
	<description><![CDATA[<p><span>GRASS (GeneRic ASsembly Scaffolder)-a novel algorithm for scaffolding second-generation sequencing assemblies capable of using diverse information sources. GRASS offers a mixed-integer programming formulation of the contig scaffolding problem, which combines contig order, distance and orientation in a single optimization objective. The resulting optimization problem is solved using an expectation-maximization procedure and an unconstrained binary quadratic programming approximation of the original problem. We compared GRASS with existing HTS scaffolders using Illumina paired reads of three bacterial genomes. Our algorithm constructs a comparable number of scaffolds, but makes fewer errors. This result is further improved when additional data, in the form of related genome sequences, are used.</span></p><p>Address of the bookmark: <a href="https://github.com/AlexeyG/GRASS" rel="nofollow">https://github.com/AlexeyG/GRASS</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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