<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/29614?offset=770</link>
	<atom:link href="https://bioinformaticsonline.com/related/29614?offset=770" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43260/bioinformatics-tools-for-telomere-to-telomere-assembly</guid>
	<pubDate>Tue, 17 Aug 2021 13:17:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43260/bioinformatics-tools-for-telomere-to-telomere-assembly</link>
	<title><![CDATA[Bioinformatics tools for telomere to telomere assembly !]]></title>
	<description><![CDATA[<p>●&nbsp;<a href="https://github.com/arangrhie/merfin" target="_blank">Merfin</a>&nbsp;&ndash; k-mer-based assembly and variant calling evaluation for improved consensus accuracy (Arang Rhie)<br />●&nbsp;<a href="https://www.biorxiv.org/content/10.1101/2020.11.11.378133v1" target="_blank">PanGenie</a>&nbsp;&ndash; algorithm that leverages a pangenome reference built from haplotype-resolved genome assemblies in conjunction with k-mer count information from raw, short-read sequencing data to genotype a wide spectrum of genetic variation (Tobias Marschall)<br />●&nbsp;<a href="https://github.com/ConesaLab/SQANTI3" target="_blank">SQANTI3</a>&nbsp;&ndash; an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline (Roc&iacute;o Amor&iacute;n de Heged&uuml;s&nbsp;<a href="https://twitter.com/rocioadh" target="_blank">@rocioadh</a>)<br />●&nbsp;<a href="https://github.com/GenomeRIK/tama" target="_blank">tama</a>&nbsp;(Transcriptome Annotation by Modular Algorithms) &ndash; software designed for processing Iso-Seq data and other long-read transcriptome data (Richard Kuo&nbsp;<a href="https://twitter.com/GenomeRIK" target="_blank">@GenomeRIK</a>)<br />●&nbsp;<a href="https://github.com/PacificBiosciences/pbAA" target="_blank">pbaa</a>&nbsp;(PacBio Amplicon Analysis) &ndash; separates complex mixtures of amplicon targets from genomic samples to cluster and generate high-quality consensus sequences from HiFi reads (Zev Kronenberg&nbsp;<a href="https://twitter.com/zevkronenberg" target="_blank">@zevkronenberg</a>)<br />●&nbsp;<a href="https://github.com/yuanyuan929/bellerophon" target="_blank">bellerophon</a>&nbsp;&ndash; analyzes MHC typing and other low-complexity gene amplicon data; performs allele calling while detecting polymorphic sites within the sequences and removing potential chimeric sequence variants (Yuanyuan Cheng&nbsp;<a href="https://twitter.com/Yuanyuan929" target="_blank">@Yuanyuan929</a>)<br />●&nbsp;<a href="https://github.com/amwenger/svpack" target="_blank">svpack</a>&nbsp;&ndash; tools for filtering, comparing, and annotating structural variant (SV) calls in VCF format (Aaron Wenger)<br />●&nbsp;<a href="https://github.com/AntonBankevich/jumboDB" target="_blank">JumboDB</a>&nbsp;&ndash; tool for de Bruijn graph construction (Anton Bankevich&nbsp;<a href="https://twitter.com/AntonBankevich" target="_blank">@AntonBankevich</a>)<br />●&nbsp;<a href="https://github.com/ksahlin/ultra" target="_blank">uLTRA</a>&nbsp;&ndash; tool for splice alignment of long transcriptomic reads to a genome, guided by a database of exon annotations. (Kristoffer Sahlin&nbsp;<a href="https://twitter.com/krsahlin" target="_blank">@krsahlin</a>)<br />●&nbsp;<a href="https://www.biorxiv.org/content/10.1101/2021.01.25.428044v1.full.pdf" target="_blank">LeafGo</a>&nbsp;&ndash; workflow to rapidly produce high-quality de novo plant genomes (Luca Ermini&nbsp;<a href="https://twitter.com/ermini_luca" target="_blank">@ermini_luca</a>)</p><p>Reference:</p><p>https://www.pacb.com/blog/young-investigators-share-stellar-science-career-advice-and-bioinformatics-tools-at-smrt-leiden-2021/</p><p>&nbsp;</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/19811/jnu-neurosciencesystems-biologymathematical-modeling-jrf-vacancies</guid>
  <pubDate>Fri, 26 Dec 2014 11:22:20 -0600</pubDate>
  <link></link>
  <title><![CDATA[JNU Neuroscience/Systems Biology/Mathematical modeling JRF Vacancies]]></title>
  <description><![CDATA[
<p>School of Computational and Integrative Sciences<br />Jawaharlal Nehru University<br />New Delhi 110067</p>

<p>Recruitment for Project</p>

<p>Applications were invited from the citizens of India for filling up the following temporary position for the CSIR sponsored Fellowship in the School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067. This position is temporary for a period of two and half years or tenable only for the duration of the project. The requisite qualifications &amp; experience are given below.</p>

<p>Project Title : "Understanding Complex dynamics and Information processing in Brain Networks"<br />Funding Agency : CSIR</p>

<p>Principal Investigator : Dr. R.K. Brojen Singh</p>

<p>Position : Junior Research Fellow(One post)</p>

<p>Salary : As per CSIR rules and guidelines for JRF.</p>

<p>Qualifications &amp; Experience : M.Sc. in Physics/Mathematics/Biology/B.Tech. In Eng. Physics/Comp. Sc. and desirable CSIR-UGC NET Qualified. Candidates should also have at least one years research experience after M. Sc./B.Tech. in works related to Neuroscience/Mathematical modeling.</p>

<p>Candidates possessing requisite qualifications may apply either on plain paper stating the project title along with CV and send to the following address or send as email attachment (pdf or word format) so as to reach on or before 8 January, 2014.</p>

<p>Dr. R.K. Brojen Singh<br />School of Computational and Integrative Sciences<br />Jawharlal Nehru University<br />New Delhi 110067<br />Email: brojen@jnu.ac.in, brojen@mail.jnu.ac.in</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44234/steps-to-find-palindrome-in-genomes</guid>
	<pubDate>Thu, 09 Mar 2023 02:56:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44234/steps-to-find-palindrome-in-genomes</link>
	<title><![CDATA[Steps to find palindrome in genomes !]]></title>
	<description><![CDATA[<div><div><div><div><div><div><div><div><div><div><p>Palindromes are sequences of nucleotides that read the same backward as forward. They can be present in genomes and have various biological functions. Here are some methods for discovering palindromes in genomes:</p><ol>
<li>
<p>Direct sequence search: One of the simplest ways to discover palindromes is to search the genome sequence directly for palindromic sequences using pattern matching tools, such as regular expressions or string algorithms. This approach can be useful for discovering simple palindromes, but may miss more complex palindromic structures.</p>
</li>
<li>
<p>Dot plot analysis: Dot plot analysis is a graphical method that can be used to identify palindromic regions in a genome. It involves plotting the genome sequence against itself and examining the diagonal patterns that emerge. Palindromic regions will appear as symmetrical patterns along the diagonal.</p>
</li>
<li>
<p>Restriction enzyme analysis: Some restriction enzymes, such as EcoRI and HindIII, recognize palindromic sequences and cleave DNA at these sites. By digesting the genome with these enzymes and examining the resulting fragments, palindromic regions can be identified.</p>
</li>
<li>
<p>Next-generation sequencing: High-throughput sequencing technologies, such as PacBio and Oxford Nanopore, can generate long reads that can span entire palindromic regions. By mapping these reads to the genome, palindromic regions can be identified and characterized.</p>
</li>
<li>
<p>Comparative genomics: Comparing the genomes of related species can also reveal palindromic regions that are conserved across evolutionarily divergent lineages. This approach can help identify functional palindromes that are under selective pressure.</p>
</li>
</ol><p>Overall, the discovery of palindromic sequences in genomes can be accomplished using a variety of methods, each with their own advantages and limitations. A combination of these methods can provide a comprehensive understanding of the palindromic landscape of a genome.</p></div></div></div></div></div></div></div></div></div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34235/quorum-an-error-corrector-for-illumina-reads</guid>
	<pubDate>Wed, 08 Nov 2017 11:40:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34235/quorum-an-error-corrector-for-illumina-reads</link>
	<title><![CDATA[QuorUM: An Error Corrector for Illumina Reads]]></title>
	<description><![CDATA[<p><span><span>Illumina Sequencing data can provide high coverage of a genome by relatively short (most often 100 bp to 150 bp) reads at a low cost. Even with low (advertised 1%) error rate, 100 &times; coverage Illumina data on average has an error in some read at every base in the genome. These errors make handling the data more complicated because they result in a large number of low-count erroneous&nbsp;</span><em>k</em><span>-mers in the reads. However, there is enough information in the reads to correct most of the sequencing errors, thus making subsequent use of the data (e.g. for mapping or assembly) easier. Here we use the term &ldquo;error correction&rdquo; to denote the reduction in errors due to both changes in individual bases and trimming of unusable sequence. We developed an error correction software called QuorUM. QuorUM is mainly aimed at error correcting Illumina reads for subsequent assembly. It is designed around the novel idea of minimizing the number of distinct erroneous&nbsp;</span><em>k</em><span>-mers in the output reads and preserving the most true&nbsp;</span><em>k</em><span>-mers, and we introduce a composite statistic &pi; that measures how successful we are at achieving this dual goal. We evaluate the performance of QuorUM by correcting actual Illumina reads from genomes for which a reference assembly is available.</span></span></p>
<p><span>QuorUM is distributed as an independent software package and as a module of the MaSuRCA assembly software. Both are available under the GPL open source license at&nbsp;</span><a href="http://www.genome.umd.edu/">http://www.genome.umd.edu</a><span>.</span></p><p>Address of the bookmark: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130821" rel="nofollow">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130821</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/20015/illumina-smartphone-chip</guid>
	<pubDate>Tue, 30 Dec 2014 23:19:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/20015/illumina-smartphone-chip</link>
	<title><![CDATA[Illumina Smartphone Chip !!!]]></title>
	<description><![CDATA[<p>Illumina, the company that claims it brought human genome sequencing down to $1000 prices, has now turned its attention to a consumer product - a chip that you can plug into your smartphone and have it read your genetic information.<br /><br />The biggest challenge ahead of Illumina is simplifying the process of genetic sequencing. Currently, Illumina&rsquo;s DNA sequencers are gigantic machines that use techinques like colorimetry to work, but while the core technology is computational, it takes some 30 steps to extract genetic data and run it through. This process will likely have to be hugely simplified on mobile devices, given the fact that some studies require extracting 10 mililiters of blood. Illumina researchers are also working on finding the optimal technology for this on-chip DNA sequencing - be it electrical, optical, or other.<br /><br />Illumina is one of the most prominent names in genetics, often said to be the Intel of genetic sequencing, as just like Intel it provides the algorithms, the processing brain that runs a DNA reading task.<br /><br />In other recent smartphone-related biotech news, drug company Pfizer launched its REMOTE project, a new type of clinical trial that does not require going to a hospital for checks - targeted at patients with overactive bladder problems, the FDA-approved REMOTE project allowed to gather data from patients from over 10 states remotely, via mobile devices.<br /><br /></p><p>This is indeed the Illumina answer to Apple's Health app, HealthBook, Google HealthFit.</p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</guid>
	<pubDate>Thu, 24 Jan 2019 10:35:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</link>
	<title><![CDATA[NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads]]></title>
	<description><![CDATA[<p>NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.</p>
<p>https://nxrepair.readthedocs.io/en/latest/tutorial.html</p>
<div>
<div>
<div id="js-repo-pjax-container">
<div>
<div>
<div id="readme">
<div>
<div>
<pre>nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta</pre>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<div>&nbsp;</div><p>Address of the bookmark: <a href="https://github.com/rebeccaroisin/nxrepair" rel="nofollow">https://github.com/rebeccaroisin/nxrepair</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/20449/walk-in-interview-for-the-post-of-jrf-and-project-assistant-cift</guid>
  <pubDate>Tue, 20 Jan 2015 23:03:20 -0600</pubDate>
  <link></link>
  <title><![CDATA[WALK-IN-INTERVIEW for the post of JRF and Project Assistant @ CIFT]]></title>
  <description><![CDATA[
<p>Eligible candidates are invited to attend a walk-in-Interview with all relevant documents for the following positions of Project Fellows (on contractual basis) to work in the Project “ Genetic Diversity of Clostridium botulinum in seafood and Development of Lateral Flow Immuno Assay (LFIA) for toxinotyping  funded by Department of Biotechnology.   The duration of the project is 3 years / co-terminus with the scheme.</p>

<p>Jr. Research Fellow – 2 posts</p>

<p>    Fellowship    :   Rs. 25000/- + 20% HRA pm  for Ist &amp; 2nd year and Rs.28000/- + HRA on 3rd year</p>

<p>    Qualification :    Ist class Masters Degree in Microbiology/Fishery Microbiology/Bio-technology.</p>

<p>    Desirable        :  </p>

<p>    1. CSIR/UGC NET/JRF qualified</p>

<p>    2. Excellent analytical skills and computer documentation</p>

<p>    3. Prior experience in handling microbial cultures and molecular techniques</p>

<p>Project Assistant – 1 post</p>

<p>Fellowship    :    Rs.8000/- p.m (consolidated)</p>

<p>Qualification:   Masters degree in Microbiology/Biotechnology with skill in Bioinformatics</p>

<p>Desirable:   Excellent analytical skills in Bioinformatics and computer documentation</p>

<p>Terms &amp; Conditions:</p>

<p>Registration will begin at 8.30 a.m and will close at 11.00 am<br />Age limit (as on 29.1.2015):  Below 35 years for men and 40 years for women.<br />Age relaxation of 3 year for OBC candidates and 5 years for SC/ST candidates is permissible.<br />Candidates are required to submit self-attested copies of all the Certificates in support of their claims    regarding age, educational qualifications, scheduled caste/scheduled tribe/OBC etc.  The original certificates shall be produced for verification before the interview.<br />Candidates should bring detailed bio-data (in the enclosed format)  affixing a recent passport size photograph.<br />The selected candidate will be recruited on contract basis under ICAR norms.  The post is purely temporary and is co-terminus with the project.<br />The candidates attending the interview should ensure that they fulfil all the eligibility conditions.  No correspondence will be entertained from the candidates for selection/test/appointment.<br />No TA/DA will be paid to attend the interview.<br />Canvassing in any form will render the candidate disqualified for the post.<br />The Director’s decision will be final and binding in all aspects regarding the selection to the post.</p>

<p>Venue: CIFT, Matsyapuri.P.O, Cochin                  Date of interview:  29.01.2015          Time:  10.00 am</p>

<p>http://www.cift.res.in/uploads/userfiles/file/file/srf%20appn.doc</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44593/bear-better-emulation-for-artificial-reads</guid>
	<pubDate>Sat, 06 Jul 2024 04:27:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44593/bear-better-emulation-for-artificial-reads</link>
	<title><![CDATA[BEAR: Better Emulation for Artificial Reads]]></title>
	<description><![CDATA[<p dir="auto">Created by Stephen Johnson, Brett Trost, Dr. Jeffrey R. Long, Dr. Anthony Kusalik University of Saskatchewan, Department of Computer Science</p>
<p dir="auto">BEAR is intended to be an easy-to-use collection of scripts for generating simulated WGS metagenomic reads with read lengths, quality scores, error profiles, and species abundances derived from real user-supplied WGS data.</p><p>Address of the bookmark: <a href="https://github.com/sej917/BEAR" rel="nofollow">https://github.com/sej917/BEAR</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/20387/walk-in-interview-for-project-assistant-bharathidasan-university</guid>
  <pubDate>Mon, 12 Jan 2015 21:54:10 -0600</pubDate>
  <link></link>
  <title><![CDATA[WALK-IN-INTERVIEW FOR PROJECT ASSISTANT @ BHARATHIDASAN UNIVERSITY]]></title>
  <description><![CDATA[
<p>BHARATHIDASAN UNIVERSITY<br />DEPARTMENT OF BIOINFORMATICS, SCHOOL OF LIFE SCIENCES, TIRUCHIRAPPALLI – 620024</p>

<p>Project title: “Genome-scale metabolic modeling and simulation of rumen methanogens An in silico attempt to methane attenuation”</p>

<p>Funding Agency: University Grants Commission, New Delhi</p>

<p>Tenure of the project: Two years or till the end of the project period.</p>

<p>Position: Project Assistant (1 no.)</p>

<p>Essential qualification: First class M.Sc. in Bioinformatics/Microbiology/ Biotechnology and other related discipline.</p>

<p>Desirable qualification: Experience in an area relevant (Molecular Systems Engineering) to the project.</p>

<p>Fellowship: Rs. 5000 per month as per the UGC norms.</p>

<p>Upper age limit: 28 years</p>

<p>Date of Venue of interview: 22.01.2015, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli -620 024, Tamil Nadu</p>

<p>The above post is purely temporary and will be terminated with three month notice.</p>

<p>The Terms and the condition of the appointment shall be governed according to UGC, Govt. of India. The eligible candidates will bring their original certificates and documents at the time of interview. No TA/DA will be paid for attending the interview.</p>

<p>Dr. P. CHELLAPANDI<br />UGC-Research Awardee,<br />Department of Bioinformatics,<br />School of Life Sciences,<br />Bharathidasan University,<br />Tiruchirappalli -620 024, Tamil Nadu</p>

<p>Advertisement: www.bdu.ac.in/adv/PA_UGC_Bioinformatics.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41941/svengine-allele-specific-and-haplotype-aware-structural-variants-simulator</guid>
	<pubDate>Sat, 04 Jul 2020 05:52:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41941/svengine-allele-specific-and-haplotype-aware-structural-variants-simulator</link>
	<title><![CDATA[SVEngine: Allele Specific and Haplotype Aware Structural Variants Simulator]]></title>
	<description><![CDATA[<p>SVEngine (Structural Variants Engine)</p>
<ul>
<li>SVEngine is a multi-purpose and self-contained simulator for whole genome scale spike-in of thousands of SV events of various types in both single-sample and matched sample scenarios.</li>
<li>SVEngine takes as input reference contigs in FASTA files, variant meta distribution as specified in META files (see Manual) or specific variant information as specified in VAR files (see Manual) and NEWICK files for specifying clonal phylogenetic trees in cancer.</li>
<li>SVEngine outpus alterred contigs in FASTA files, spiked-in variants in VAR files (see Manual), simulated short read in FASTQ files and aligned short reads in BAM files.</li>
</ul>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://bitbucket.org/charade/svengine/src/master/" rel="nofollow">https://bitbucket.org/charade/svengine/src/master/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>