<?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/13415?offset=100</link>
	<atom:link href="https://bioinformaticsonline.com/related/13415?offset=100" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40544/ngs-bits-short-read-sequencing-tools</guid>
	<pubDate>Thu, 16 Jan 2020 23:14:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40544/ngs-bits-short-read-sequencing-tools</link>
	<title><![CDATA[ngs-bits - Short-read sequencing tools]]></title>
	<description><![CDATA[<p>Binaries of&nbsp;<em>ngs-bits</em>&nbsp;are available via Bioconda. Alternatively,&nbsp;<em>ngs-bits</em>&nbsp;can be built from sources:</p>
<ul>
<li><span>Binaries</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_bioconda.md">Linux/macOS</a></li>
<li>From&nbsp;<span>sources</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_unix.md">Linux/macOS</a></li>
<li>From&nbsp;<span>sources</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_win.md">Windows</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/imgag/ngs-bits" rel="nofollow">https://github.com/imgag/ngs-bits</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</guid>
	<pubDate>Fri, 31 Jan 2020 05:50:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</link>
	<title><![CDATA[HASLR: a tool for rapid genome assembly of long sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR is a tool for rapid genome assembly of long sequencing reads. HASLR is a hybrid tool which means it requires long reads generated by Third Generation Sequencing technologies (such as PacBio or Oxford Nanopore) together with Next Generation Sequencing reads (such as Illumina) from the same sample.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data</guid>
	<pubDate>Thu, 28 May 2020 21:57:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data</link>
	<title><![CDATA[Parliament2: Runs a combination of tools to generate structural variant calls on whole-genome sequencing data]]></title>
	<description><![CDATA[<p>Parliament2 identifies structural variants in a given sample relative to a reference genome. These structural variants cover large deletion events that are called as Deletions of a region, Insertions of a sequence into a region, Duplications of a region, Inversions of a region, or Translocations between two regions in the genome.</p>
<p>Parliament2 runs a combination of tools to generate structural variant calls on whole-genome sequencing data. It can run the following callers: Breakdancer, Breakseq2, CNVnator, Delly2, Manta, and Lumpy. Because of synergies in how the programs use computational resources, these are all run in parallel. Parliament2 will produce the outputs of each of the tools for subsequent investigation.</p><p>Address of the bookmark: <a href="https://github.com/dnanexus/parliament2" rel="nofollow">https://github.com/dnanexus/parliament2</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</guid>
	<pubDate>Fri, 25 Feb 2022 04:42:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</link>
	<title><![CDATA[Smudgeplot: Inference of ploidy and heterozygosity structure using whole genome sequencing data]]></title>
	<description><![CDATA[<p dir="auto">This tool extracts heterozygous kmer pairs from kmer count databases and performs gymnastics with them. We are able to disentangle genome structure by comparing the sum of kmer pair coverages (CovA + CovB) to their relative coverage (CovB / (CovA + CovB)). Such an approach also allows us to analyze obscure genomes with duplications, various ploidy levels, etc.</p>
<p dir="auto">Smudgeplots are computed from raw or even better from trimmed reads and show the haplotype structure using heterozygous kmer pairs. For example:</p>
<p dir="auto"><a href="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" target="_blank"><img src="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" alt="smudgeexample" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/KamilSJaron/smudgeplot" rel="nofollow">https://github.com/KamilSJaron/smudgeplot</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/856/papenfuss-lab</guid>
  <pubDate>Sun, 14 Jul 2013 12:22:28 -0500</pubDate>
  <link></link>
  <title><![CDATA[Papenfuss Lab]]></title>
  <description><![CDATA[
<p>The human genome project and similar projects in disease-causing organisms such as Plasmodium falciparum, which causes malaria in humans, have provided new tools for discovery in biology and have accelerated the development of understanding in human disease.</p>

<p>Research Area: <br />Analysis of Next Generation sequence data in cancer<br />Methods for analysis of structural variation in cancer genomes<br />Next Generation sequencing in malaria<br />Computational comparative genomics<br />Sensitive genomic sequence search techniques using hidden Markov models<br />Tasmanian devil facial tumour disease</p>

<p>Link @ http://www.wehi.edu.au/faculty_members/dr_tony_papenfuss</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4655/mathivanan-lab</guid>
  <pubDate>Fri, 20 Sep 2013 13:09:38 -0500</pubDate>
  <link></link>
  <title><![CDATA[Mathivanan Lab]]></title>
  <description><![CDATA[
<p>The major research interests are in exploring the role of extracellular matrix components (soluble secreted proteins and membrane vesicles) in cancer and intercellular communication. The lab integrates proteomic, genomic and bioinformatics methodologies to explore cancer cells. </p>

<p>More at http://www.mathivananlab.org/index.html</p>

<p>http://scholar.google.com/citations?user=U6PyEdYAAAAJ&amp;hl=en</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4409/huber-lab</guid>
  <pubDate>Mon, 09 Sep 2013 21:57:03 -0500</pubDate>
  <link></link>
  <title><![CDATA[Huber Lab]]></title>
  <description><![CDATA[
<p>The Huber group develops computational and statistical methods to design and analyse novel experimental approaches in genetics and cell biology. </p>

<p>Future projects and goals</p>

<p>Large-scale systematic maps of gene-gene and gene-environment interactions by automated phenotyping, using image analysis, machine learning, sparse model building and causal inference.<br />DNA-, RNA- and ChIP-Seq and their applications to gene expression regulation: statistical and computational foundations.<br />Cancer genomics, genomes as biomarkers, cancer phylogeny.<br />Image analysis for systems biology: measuring the dynamics of cell cycle and of cell migration of individual cells under normal conditions and many different perturbations (RNAi, drugs).</p>

<p>More @ http://www.embl.de/research/units/genome_biology/huber/index.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4634/immune-response-to-cancer-cells-awesome</guid>
	<pubDate>Fri, 20 Sep 2013 06:20:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4634/immune-response-to-cancer-cells-awesome</link>
	<title><![CDATA[Immune response to cancer cells! AWESOME]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/C6YuBh-wAPQ" frameborder="0" allowfullscreen></iframe><p>Awesome viddeo explaining the way in which the antibody, HuLuc 63, appears to induce anti-tumor effects by binding to a protein that is only expressed on the surface of myeloma cells. This initiates antibody-dependent cellular cytotoxicity activity that kills myeloma cells and leaves healthy cells intact.</p>]]></description>
	
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5623/yau-group</guid>
  <pubDate>Tue, 15 Oct 2013 13:05:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[Yau Group]]></title>
  <description><![CDATA[
<p>Yau Group are a new research group based at the Wellcome Trust Centre for Human Genetics and the Department of Statistics at the University of Oxford.</p>

<p>Yau Group develops statistical and computational methods for the analysis of genomic datasets with a particular interest in cancer sequencing applications and the use of Bayesian Statistics.</p>

<p>Yau Group are currently have projects in somatic mutation analysis of heterogeneous cancers, data fusion or integration techniques and single cell genomics.</p>

<p>More @ http://www.well.ox.ac.uk/~cyau/index.html</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/5946/bioinformatics-tata-memorial-centre-navi-mumbai</guid>
  <pubDate>Mon, 28 Oct 2013 10:40:25 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics @ TATA MEMORIAL CENTRE, NAVI MUMBAI]]></title>
  <description><![CDATA[
<p>TATA MEMORIAL CENTRE<br />ADVANCED CENTRE FOR TREATMENT, RESEARCH AND EDUCATION IN CANCER<br />KHARGHAR, NAVI MUMBAI – 410210</p>

<p>No. ACTREC/Advt./ 72 /2013</p>

<p>WALK IN INTERVIEW</p>

<p>1. JRF*<br />Genome-wide RNAi screen with human pooled tyrosine kinase shRNA libraries in head and neck squamous call carcinoma (HNSCC) cell lines<br />DBT A/C No. 3071, Dr. Amit Dutt</p>

<p>2. JRF<br />IRB Project ACTREC Funds<br />Dr. Amit Dutt</p>

<p>3. RA<br />Defining the cancer genome of Head and Neck Squamous Cell Carcinoma (HNSCC) with SNP arrays and next generation sequencing technology<br />A/C No. 2895, Dr. Amit Dutt</p>

<p>Duration of the Project: One year from the date of appointment, or as and when project terminates.</p>

<p>Consolidated Salary: RA : Rs. 40,000/- p.m.<br />JRF* (DBT): Rs. 20,800/- p.m.<br />JRF: Rs. 16,000/- p.m.<br />Date &amp; Time: 6th November, 2013, at 10.00 a.m.</p>

<p>Venue: Conference Room</p>

<p>Minimum Qualifications and Experience:</p>

<p>RA: The ideal applicant should have a PhD in a relevant field. He/she should have a strong computational biology background, with demonstrated experience in coding using Perl, Python, Java or C++. He/she should be familiar with working in unix enviromnent, devising computational algorithms for data analysis, statistical data analysis in R and matlab and database programming using MySQL. Hands on experience in analyzing high throughput data would be an added advantage.</p>

<p>JRF* (DBT project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Valid UGC-CSIR/DBT/ICMR JRF qualification and laboratory experience in molecular biology. Previous experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team would be desirable.</p>

<p>JRF (ACTREC project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Minimum 2 yrs experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team is essential.</p>

<p>*M.Sc. degree obtained after a one year course will not be considered.</p>

<p>Candidates fulfilling above requirements should send their application by e-mail to<br />‘careers.duttlab@gmail.com. in the format given below so as to reach on or before<br />4th November, 2013.</p>

<p>Advertisement:</p>

<p>http://www.actrec.gov.in/data%20files/2013/AD-RA-JR-TECHN-6-NOV.pdf</p>
]]></description>
</item>

</channel>
</rss>