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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27099?offset=1330</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39869/mfannot-a-program-for-the-annotation-of-mitochondrial-and-plastid-genomes</guid>
	<pubDate>Mon, 26 Aug 2019 11:47:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39869/mfannot-a-program-for-the-annotation-of-mitochondrial-and-plastid-genomes</link>
	<title><![CDATA[MFannot : a program for the annotation of mitochondrial and plastid genomes]]></title>
	<description><![CDATA[<p><span>MFannot is a program for the annotation of mitochondrial and plastid genomes</span></p>
<p>MFannot is a program for the annotation of mitochondrial and plastid genomes. It is a PERL wrapper around a set of diverse, external independent tools.</p>
<p>It makes intense use of RNA/intron detection tools including&nbsp;<a href="http://hmmer.org/">HMMER</a>,&nbsp;<a href="https://github.com/nathanweeks/exonerate">Exonerate</a>,&nbsp;<a href="https://bioinformatics.ca/links_directory/tool/9822/erpin">Erpin</a>&nbsp;and others.</p>
<p><a href="http://megasun.bch.umontreal.ca/cgi-bin/mfannot/mfannotInterface.pl">http://megasun.bch.umontreal.ca/cgi-bin/mfannot/mfannotInterface.pl</a></p><p>Address of the bookmark: <a href="https://github.com/BFL-lab/Mfannot" rel="nofollow">https://github.com/BFL-lab/Mfannot</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31375/cocacola-binning-metagenomic-contigs-using-sequence-composition-read-coverage-co-alignment-and-paired-end-read-linkage</guid>
	<pubDate>Tue, 07 Mar 2017 08:50:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31375/cocacola-binning-metagenomic-contigs-using-sequence-composition-read-coverage-co-alignment-and-paired-end-read-linkage</link>
	<title><![CDATA[COCACOLA (binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment, and paired-end read LinkAge)]]></title>
	<description><![CDATA[<p>COCACOLA is a general framework that combines different types of information: sequence COmposition, CoverAge across multiple samples, CO-alignment to reference genomes and paired-end reads LinkAge to automatically bin contigs into OTUs. Furthermore, COCACOLA seamlessly embraces customized prior knowledge to facilitate binning accuracy.</p>
<p>News: Python version of COCACOLA is available now!</p><p>Address of the bookmark: <a href="https://github.com/younglululu/COCACOLA" rel="nofollow">https://github.com/younglululu/COCACOLA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41602/nucdiff-in-depth-characterization-and-annotation-of-differences-between-two-sets-of-dna-sequences</guid>
	<pubDate>Tue, 05 May 2020 10:35:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41602/nucdiff-in-depth-characterization-and-annotation-of-differences-between-two-sets-of-dna-sequences</link>
	<title><![CDATA[NucDiff: In-depth characterization and annotation of differences between two sets of DNA sequences]]></title>
	<description><![CDATA[<p>NucDiff locates and categorizes differences between two closely related nucleotide sequences. It is able to deal with very fragmented genomes, structural rearrangements and various local differences. These features make NucDiff to be perfectly suitable to compare assemblies with each other or with available reference genomes.</p>
<p>NucDiff provides information about the types of differences and their locations. It is possible to upload the results into genome browser for visualization and further inspection. It was written in Python and uses the NUCmer package from MUMmer[1] for sequence comparison.</p>
<p><br><br></p><p>Address of the bookmark: <a href="https://github.com/uio-cels/NucDiff" rel="nofollow">https://github.com/uio-cels/NucDiff</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31526/sequenceserver</guid>
	<pubDate>Fri, 10 Mar 2017 08:51:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31526/sequenceserver</link>
	<title><![CDATA[sequenceserver]]></title>
	<description><![CDATA[<p><span>SequenceServer lets you rapidly set up a BLAST+ server with an intuitive user interface for use locally or over the web.</span></p>
<p><span><span>More at&nbsp;</span><a href="http://sequenceserver.com/">http://sequenceserver.com</a><span>.</span></span></p><p>Address of the bookmark: <a href="https://github.com/wurmlab/sequenceserver" rel="nofollow">https://github.com/wurmlab/sequenceserver</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies</guid>
	<pubDate>Sat, 20 Sep 2025 09:34:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies</link>
	<title><![CDATA[HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies]]></title>
	<description><![CDATA[<p dir="auto"><code>HiTE</code>&nbsp;is a Python software that uses a dynamic boundary adjustment approach to detect and annotate full-length Transposable Elements in Genome Assemblies. In comparison to other tools, HiTE demonstrates superior performance in detecting a greater number of full-length TEs.</p>
<div dir="auto">
<h2 dir="auto">panHiTE</h2>
<a href="https://github.com/CSU-KangHu/HiTE#panhite"></a></div>
<p dir="auto">We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.</p>
<p dir="auto">For detailed instructions, please refer to the&nbsp;<a href="https://github.com/CSU-KangHu/HiTE/wiki/panHiTE-tutorial">panHiTE tutorial</a>.</p><p>Address of the bookmark: <a href="https://github.com/CSU-KangHu/HiTE" rel="nofollow">https://github.com/CSU-KangHu/HiTE</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/view/138</guid>
	<pubDate>Wed, 10 Jul 2013 16:30:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/138</link>
	<title><![CDATA[Selecting between Python 2 and Python 3?]]></title>
	<description><![CDATA[<p>Which version is best for bioinformatician to work on?</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</guid>
	<pubDate>Mon, 10 Apr 2017 04:16:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</link>
	<title><![CDATA[LoRDEC: a hybrid error correction program for long, PacBio reads]]></title>
	<description><![CDATA[<p>LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.</p>
<p><br> Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.<br> After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.</p>
<p>Why is LoRDEC different?</p>
<ul>
<li>It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.</li>
<li>It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.</li>
</ul><p>Address of the bookmark: <a href="http://www.atgc-montpellier.fr/lordec/" rel="nofollow">http://www.atgc-montpellier.fr/lordec/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/841/research-fellow</guid>
  <pubDate>Sat, 13 Jul 2013 11:23:20 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Fellow]]></title>
  <description><![CDATA[
<p>UNIVERSITY OF CALCUTTA</p>

<p>A walk-in-interview will be held on 15.7.2013 at 12 noon in the Department of Biophysics, Molecular Biology and Bioinformatics, 92 A.P.C Road, Kolkata-700 009 to select one trainee research fellow and two students under DIC. The positions are purely temporary and would be for a period of six months from the date of joining which may be extended by another six months subject to successful performance.</p>

<p>Qualification:</p>

<p>For the Trainee Research Fellow: Should have a master degree in Bioinformatics or allied subjects and should have biological database development experience. Must have at least one publication.</p>

<p>For the Students: Should have a master degree in Bioinformatics or allied subjects and should be proficient in C-programming language. Must be familiar with techniques of Developmental Biology.</p>

<p>The Trainee Research Fellow would be paid a consolidated sum of Rs 10000/- per month and the students would be paid a sum amount Rs. 7000/- per month during the tenure of the project.</p>

<p>Advertisement:<br />www.caluniv.ac.in/News%20&amp;%20Announcement/trainee_rf_DIC.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32154/decostar-detection-of-co-evolution</guid>
	<pubDate>Fri, 14 Apr 2017 06:27:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32154/decostar-detection-of-co-evolution</link>
	<title><![CDATA[DeCoSTAR - Detection of Co-evolution]]></title>
	<description><![CDATA[<p><span>DeCoSTAR is a software which aims at reconstructing ancestral gene or genome organizations, in the form of sets of neighborhood relations -adjacencies- between pairs of ancestral genes or gene domains.</span><br><span>Ancestral genes or domains are deduced from reconciled gene trees in a context of birth, speciation, duplication, loss, transfer, which are either given as input or computed with the&nbsp;</span><a href="http://mbb.univ-montp2.fr/MBB/download_sources/16__TERA">ecceTERA package</a><span>, to which DeCoSTAR is integrated. DeCoSTAR constructs parsimonious scenarios of gains and breakages of adjacencies, and contains in particular all the features of previous software DeCo, DeCoLT, ArtDeCo and DeClone. It provides statistical supports on ancestral adjacencies, or the possibility to handle badly assembled genomes.&nbsp;</span><br><span>DeCoSTAR is able to reconstruct the histories of domains inside genes, including gene fusion and fission events, as well as ancestral genome structures for dozens of whole genomes from all kingdoms of life in a few minutes.</span></p><p>Address of the bookmark: <a href="http://pbil.univ-lyon1.fr/software/DeCoSTAR/" rel="nofollow">http://pbil.univ-lyon1.fr/software/DeCoSTAR/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/32374/ra-bioinformatics-at-jnu-new-delhi-india</guid>
  <pubDate>Thu, 27 Apr 2017 03:29:58 -0500</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at JNU, New Delhi, INDIA]]></title>
  <description><![CDATA[
<p>School of Computational &amp; Integrative Sciences<br />Jawaharlal Nehru University<br />New Delhi-110067, INDIA</p>

<p>Date: April 24th. 2017	Last Date: May 6th 2017<br />PROJECT ID: 632</p>

<p>The following posts are urgently required to be filled for the Department of Biotechnology, Government of India funded project jointly running with IIIT-Hyderabad &amp; JNU, entitled "Computational Core for Plant Metabolomics" administrated by Prof Indira Ghosh, School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110 067.<br />NB: For all the posts, preference will be given to candidates with a good knowledge of Python and/or R in UNIX platform , knowledge of JAVA will also get a special consideration.</p>

<p>1.	RA / Research Associate (Metabolic engineering/Computational Biologist)</p>

<p>Salary: Rs. 36000/- + HRA</p>

<p>Vacancy: 1</p>

<p>Essential Qualifications: PhD in Bioinformatics /Mathematics/Computer Science with experience in analyzing high throughput omics-based data/Analysis of Network Biology/Chemoinformatics/Computational Biology related Software development. Published paper in the field is a must to prove the experience. Special consideration will be given if have experience in Industry, teaching &amp; product development.</p>

<p>Desired Skills: Prior experience in handling and guiding bioinformatics, metabolomics data, planning of new research area in metabolic driven network , collaborating with industry , preparing and filing reports etc. Will be expected to communicate with user groups and coordinate with LIMS group in Hyderabad and the Cheminformatics group in Delhi.</p>

<p>2.	Project SRF (Network model building/Systems biology integration)</p>

<p>Salary*: Rs.18000/- + HRA</p>

<p>Vacancy: 1</p>

<p>Essential Qualifications: M.Tech in Computational Biology with project experience or Masters / B.Tech in Basic Sciences with at least 2yrs of research experience in Bioinformatics/Mathematical Model building using Computational Biology tools &amp; related Database / Network analysis etc. For M.Sc/B.Tech, Published paper in peer-reviewed Journal whereas for M.Tech, the degree obtained in computational biology is a must.</p>

<p>Desired Skills: Will be expected to manage ongoing research activities in LIMS, interact with LIMS group, build network model using data compiled by experimentalist, prepare and file reports and associated project work etc. Familiarity with plant systems biology and genomics /metabolite resources related to plant metabolomics is desirable.</p>

<p>More at http://www.jnu.ac.in/Career/currentjobs.htm</p>
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