<?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/34088?offset=220</link>
	<atom:link href="https://bioinformaticsonline.com/related/34088?offset=220" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41959/rna-bloom-a-fast-and-memory-efficient-de-novo-transcript-sequence-assembler</guid>
	<pubDate>Thu, 09 Jul 2020 03:13:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41959/rna-bloom-a-fast-and-memory-efficient-de-novo-transcript-sequence-assembler</link>
	<title><![CDATA[RNA-Bloom: a fast and memory-efficient de novo transcript sequence assembler]]></title>
	<description><![CDATA[<p><strong>RNA-Bloom</strong><span>&nbsp;</span>is a fast and memory-efficient<span>&nbsp;</span><em>de novo</em><span>&nbsp;</span>transcript sequence assembler. It is designed for the following sequencing data types:</p>
<ul>
<li>single-end/paired-end bulk RNA-seq (strand-specific/agnostic)</li>
<li>paired-end single-cell RNA-seq (strand-specific/agnostic)</li>
<li>nanopore RNA-seq (PCR cDNA/direct cDNA/direct RNA)</li>
</ul>
<p>Written by<span>&nbsp;</span><a>Ka Ming Nip</a><span>&nbsp;</span>✉️</p><p>Address of the bookmark: <a href="https://github.com/bcgsc/RNA-Bloom" rel="nofollow">https://github.com/bcgsc/RNA-Bloom</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis</guid>
	<pubDate>Wed, 18 Aug 2021 00:02:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43268/kmer-a-suite-of-tools-for-dna-sequence-analysis</link>
	<title><![CDATA[Kmer: a suite of tools for DNA sequence analysis]]></title>
	<description><![CDATA[<p>More at&nbsp;https://help.rc.ufl.edu/doc/Kmer</p>
<p>This also includes:</p>
<ul>
<li>A2Amapper: ATAC, Assembly to Assembly Comparision tool:
<ul>
<li>Comparative mapping between two genome assemblies (same species), or between two different genomes (cross species).</li>
</ul>
</li>
</ul>
<ul>
<li>Sim4db:
<ul>
<li>Spliced alignment of cDNA and genomic sequences, from the same (sim4) or related (sim4cc) species. Optimized for high-throughput batched alignment.</li>
</ul>
</li>
</ul>
<ul>
<li>LEAFF:
<ul>
<li>LEAFF (ahem, Let's Extract Anything From Fasta) is a utility program for working with multi-fasta files. In addition to providing random access to the base level, it includes several analysis functions.</li>
</ul>
</li>
</ul>
<ul>
<li>Meryl:
<ul>
<li>An out-of-core k-mer counter. The amount of sequence that can be processed for any size k depends only on the amount of free disk space.</li>
</ul>
</li>
</ul><p>Address of the bookmark: <a href="https://help.rc.ufl.edu/doc/Kmer" rel="nofollow">https://help.rc.ufl.edu/doc/Kmer</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44481/unialigner-a-parameter-free-framework-for-fast-sequence-alignment</guid>
	<pubDate>Fri, 08 Mar 2024 23:36:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44481/unialigner-a-parameter-free-framework-for-fast-sequence-alignment</link>
	<title><![CDATA[UniAligner: a parameter-free framework for fast sequence alignment]]></title>
	<description><![CDATA[<p>UniAligner (formerly, TandemAligner) is the first parameter-free algorithm for sequence alignment that introduces a sequence-dependent alignment scoring that automatically changes for any pair of compared sequences. Classical alignment approaches, such as the Smith-Waterman algorithm, that work well for most sequences, fail to construct biologically adequate alignments of extra-long tandem repeats (ETRs), such as human centromeres and immunoglobulin loci. This limitation was overlooked in the previous studies since the sequences of the centromeres and other ETRs across multiple genomes only became available recently.</p>
<p>More at https://www.nature.com/articles/s41592-023-01970-4</p><p>Address of the bookmark: <a href="https://github.com/seryrzu/unialigner" rel="nofollow">https://github.com/seryrzu/unialigner</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/38682/bourque-lab</guid>
  <pubDate>Mon, 14 Jan 2019 15:39:25 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bourque Lab]]></title>
  <description><![CDATA[
<p>The goal of the lab is to understand mammalian genomes using comparative genomic and epigenomic analyses. Areas of interest include: the evolution of regulatory sequences, the role of transposable elements in gene regulation and the impact of genome rearrangements in evolution and cancer.</p>

<p>As a computational genomicists our work involves examining billions of DNA base pairs and interpreting how variation impacts basic biology and disease. We develop computational methods and resources for the functional annotation of genomes with a special emphasis on sequencing-based assays (e.g. ChIP-seq, RNA-Seq, exome- and whole-genome sequencing, single-cell analysis).</p>

<p>http://www.computationalgenomics.ca</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</guid>
	<pubDate>Mon, 04 Jul 2016 00:44:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</link>
	<title><![CDATA[4DGenome]]></title>
	<description><![CDATA[<p><span>Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.&nbsp;</span><br><br><span>All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.&nbsp;</span></p><p>Address of the bookmark: <a href="http://4dgenome.research.chop.edu/" rel="nofollow">http://4dgenome.research.chop.edu/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41978/senior-scientist-computational-biology-at-nipgr</guid>
  <pubDate>Sun, 19 Jul 2020 23:30:04 -0500</pubDate>
  <link></link>
  <title><![CDATA[Senior Scientist Computational Biology at NIPGR]]></title>
  <description><![CDATA[
<p>Senior Scientist Computational Biology	<br />Level 13</p>

<p>₹ 1,66,378 (Consolidated)</p>

<p>01</p>

<p>(UR)</p>

<p>Ph.D. in the area of Computational Biology/Bioinformatics/Biotechnology/Life Sciences with at least 6 years of relevant experience.<br />OR<br />M. Tech with 8 years of relevant experience.</p>

<p>The relevant experience shall be in the area of sequencing/genome assembly and annotation and high throughput genotyping for facilitating Genomic assisted Breeding<br />Age: Not exceeding 50 years</p>

<p>• The incumbent will assist the Programme Director of the Facility to discharge various activities of the NGGF</p>

<p>• Co-ordination with the service provider, DBT/academic institutions/anchoring institute (NIPGR) for execution of activities of the Facility.</p>

<p>• The incumbent will be expected to identify the requirements of Private Sector and Government laboratories in the area of Marker assisted selection and develop linkages to facilitate the same.</p>

<p>• Interact with multiple stake holders including Government and Private Sector in the area of agriculture Biotechnology.</p>

<p>• Oversee the establishment of relevant Standard Operational Procedures (SOP), Quality Accreditation of Genomics and Genotyping facility.</p>

<p>More at http://www.nipgr.ac.in/careers/vacancies_latest.php#vacancy2</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/40503/3-phd-positions-available-in-the-area-of-bioinformaticscomputational-biology-at-ulsteracuk</guid>
  <pubDate>Thu, 02 Jan 2020 12:41:10 -0600</pubDate>
  <link></link>
  <title><![CDATA[3 PhD positions available in the area of Bioinformatics/Computational Biology at ulster.ac.uk]]></title>
  <description><![CDATA[
<p>3 PhD positions available in the area of Bioinformatics/Computational Biology, Machine Learning (ML)/Artificial Intelligence (AI), Biomarker Discovery, Stratified/Personalized Medicine in Mental Health, Diabetes and Multimorbidity. Please see details (weblinks) below:</p>

<p>1. https://www.ulster.ac.uk/doctoralcollege/find-a-phd/510894<br />2. https://www.ulster.ac.uk/doctoralcollege/find-a-phd/511458<br />3. https://www.ulster.ac.uk/doctoralcollege/find-a-phd/512618</p>

<p>Looking for students with good computational/programming skills (preferable in Linux/Shell, Python and/or R) and knowledge in computational biology and statistics. However, students from more biology oriented background but strong interest to learn bioinformatics and programming are also encouraged to apply.</p>

<p>Informal inquiries are welcomed at: p.shukla@ulster.ac.uk</p>

<p>Dr Priyank Shukla PhD FHEA FCHERP<br />Lecturer (Asst Prof) in Stratified Medicine (Bioinformatics)</p>

<p>Northern Ireland Centre for Stratified Medicine<br />Biomedical Sciences Research Institute<br />University of Ulster (Magee Campus)<br />C-TRIC Building, Altnagelvin Area Hospital<br />Glenshane Road, Derry/Londonderry<br />BT47 6SB, Northern Ireland, United Kingdom</p>

<p>T: +44 28 7167 5690<br />E: p.shukla@ulster.ac.uk<br />W: https://www.ulster.ac.uk/staff/p-shukla</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/35267/a-computational-postdoc-position-and-a-bioinformatician-position</guid>
  <pubDate>Thu, 18 Jan 2018 16:29:42 -0600</pubDate>
  <link></link>
  <title><![CDATA[A computational postdoc position and a bioinformatician position]]></title>
  <description><![CDATA[
<p>A computational postdoc position and a bioinformatician position are available in Alessandro Romanel's Lab recently established at the Centre for Integrative Biology (CIBIO) in Trento, Italy. The positions are in the context of an AIRC grant and are immediately available.<br /> <br />Successful candidates will be involved in the design and implementation of strategies to study the role of inherited polymorphisms in combination with timedependent variables and somatic events on cancer genesis, progression and resistance.<br />The ideal postdoc candidate will have a PhD in Computer Science, Bioinformatics, Computational Biology or equivalent, experience in the analysis of next generation sequencing and high-density array data from human cells, strong analytical and quantitative background and programming skills. Background in cancer genomics is recommended.<br />The ideal bioinformatician candidate will have a four or five years degree in Computer Science, Bioinformatics or equivalent, experience in the management of large datasets, implementation of processing pipelines and strong programming skills. Background in biology/genomics is a plus.<br />Highly motivated individuals are invited to send a detailed CV, a cover letter describing research interests and experience, and contact information for two references to Alessandro Romanel (alessandro.romanel@unitn.it).</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42201/rosettaantibodydesign-rabd-a-general-framework-for-computational-antibody-design</guid>
	<pubDate>Sun, 20 Sep 2020 06:03:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42201/rosettaantibodydesign-rabd-a-general-framework-for-computational-antibody-design</link>
	<title><![CDATA[RosettaAntibodyDesign (RAbD): A general framework for computational antibody design]]></title>
	<description><![CDATA[<p><strong>RosettaAntibodyDesign (RAbD)</strong>&nbsp;is a generalized framework for the design of antibodies, in which a user can easily tailor the run to their project needs.&nbsp;<strong>The algorithm is meant to sample the diverse sequence, structure, and binding space of an antibody-antigen complex.</strong>&nbsp;It can be used for a multitude of project types, from denovo design to redesigns that improve binding affinity, optimize stability, or manipulate function.</p>
<p>The framework is based on rigorous bioinformatic analysis and rooted very much on our&nbsp;<a href="https://www.ncbi.nlm.nih.gov/pubmed/21035459">recent clustering</a>&nbsp;of antibody CDR regions. It uses the&nbsp;<strong>North/Dunbrack CDR definition</strong>&nbsp;as outlined in the North/Dunbrack clustering paper.</p>
<p>More at</p>
<p>https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign</p>
<p>https://bio-jade.readthedocs.io/en/latest/installation.html</p><p>Address of the bookmark: <a href="https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign" rel="nofollow">https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5254/mike-ritchie-lab</guid>
  <pubDate>Wed, 02 Oct 2013 15:25:45 -0500</pubDate>
  <link></link>
  <title><![CDATA[Mike Ritchie Lab]]></title>
  <description><![CDATA[
<p>Mike Ritchie Lab primary research focus is the detection of susceptibility genes for common diseases such as cancer, diabetes, hypertension, and cardiovascular disease, among others. The approaches will involve the development and application of new statistical methods with a focus on the detection of gene-gene interactions associated with human disease.</p>

<p>Gene expression and protein expression patterns between normal and non-normal tissues is a growing area of research that may lead to the identification of candidate genes for understanding the etiology of common, complex diseases. </p>

<p>Lab homepage @ http://ritchielab.psu.edu/ritchielab/</p>
]]></description>
</item>

</channel>
</rss>