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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41673?offset=330</link>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/42206/pollard-lab</guid>
  <pubDate>Fri, 25 Sep 2020 20:20:50 -0500</pubDate>
  <link></link>
  <title><![CDATA[Pollard Lab]]></title>
  <description><![CDATA[
<p>We are a bioinformatics research lab focused on developing novel methods and using them to study genome evolution, organization, and regulation. Our mission is to decode biomedical knowledge that is missed without rigorous statistical approaches.</p>

<p>http://docpollard.org/</p>

<p>Tools</p>

<p>http://docpollard.org/resources/software/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43055/infogenomer-integrative-reconstruction-of-cancer-genome-karyotypes</guid>
	<pubDate>Wed, 05 May 2021 01:02:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43055/infogenomer-integrative-reconstruction-of-cancer-genome-karyotypes</link>
	<title><![CDATA[InfoGenomeR: Integrative reconstruction of cancer genome karyotypes]]></title>
	<description><![CDATA[<p>InfoGenomeR is the Integrative Framework for Genome Reconstruction that uses a breakpoint graph to model the connectivity among genomic segments at the genome-wide scale. InfoGenomeR integrates cancer purity and ploidy, total CNAs, allele-specific CNAs, and haplotype information to identify the optimal breakpoint graph representing cancer genomes.</p>
<p><img src="https://github.com/YeonghunL/InfoGenomeR/raw/master/doc/overview.png" alt="image" style="border: 0px; border: 0px;"></p>
<p>More at&nbsp;https://www.nature.com/articles/s41467-021-22671-6</p><p>Address of the bookmark: <a href="https://github.com/dmcblab/InfoGenomeR" rel="nofollow">https://github.com/dmcblab/InfoGenomeR</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43364/ragtag-a-collection-of-software-tools-for-scaffolding-and-improving-modern-genome-assemblies</guid>
	<pubDate>Sat, 11 Sep 2021 00:28:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43364/ragtag-a-collection-of-software-tools-for-scaffolding-and-improving-modern-genome-assemblies</link>
	<title><![CDATA[RagTag: a collection of software tools for scaffolding and improving modern genome assemblies]]></title>
	<description><![CDATA[<p>RagTag is a collection of software tools for scaffolding and improving modern genome assemblies. Tasks include:</p>
<ul>
<li>Homology-based misassembly&nbsp;<a href="https://github.com/malonge/RagTag/wiki/correct">correction</a></li>
<li>Homology-based assembly&nbsp;<a href="https://github.com/malonge/RagTag/wiki/scaffold">scaffolding</a>&nbsp;and&nbsp;<a href="https://github.com/malonge/RagTag/wiki/patch">patching</a></li>
<li>Scaffold&nbsp;<a href="https://github.com/malonge/RagTag/wiki/merge">merging</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/malonge/RagTag" rel="nofollow">https://github.com/malonge/RagTag</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43661/maftools</guid>
	<pubDate>Fri, 17 Dec 2021 03:18:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43661/maftools</link>
	<title><![CDATA[maftools]]></title>
	<description><![CDATA[<p>With advances in Cancer Genomics, <a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">Mutation Annotation Format</a> (MAF) is being widely accepted and used to store somatic variants detected. <a href="http://cancergenome.nih.gov">The Cancer Genome Atlas</a> Project has sequenced over 30 different cancers with sample size of each cancer type being over 200. <a href="https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files">Resulting data</a> consisting of somatic variants are stored in the form of <a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">Mutation Annotation Format</a>. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner from either TCGA sources or any in-house studies as long as the data is in MAF format.</p>
<p>https://www.bioconductor.org/packages/devel/bioc/vignettes/maftools/inst/doc/maftools.html</p><p>Address of the bookmark: <a href="https://github.com/PoisonAlien/maftools" rel="nofollow">https://github.com/PoisonAlien/maftools</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43770/chromeister-an-ultra-fast-heuristic-approach-to-detect-conserved-signals-in-extremely-large-pairwise-genome-comparisons</guid>
	<pubDate>Thu, 03 Feb 2022 04:01:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43770/chromeister-an-ultra-fast-heuristic-approach-to-detect-conserved-signals-in-extremely-large-pairwise-genome-comparisons</link>
	<title><![CDATA[chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.]]></title>
	<description><![CDATA[<p>chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.</p>
<p dir="auto">USAGE:</p>
<ul dir="auto">
<li>-query: sequence A in fasta format</li>
<li>-db: sequence B in fasta format</li>
<li>-out: output matrix</li>
<li>-kmer Integer: k&gt;1 (default 32) Use 32 for chromosomes and genomes and 16 for small bacteria</li>
<li>-diffuse Integer: z&gt;0 (default 4) Use 4 for everything - if using large plant genomes you can try using 1</li>
<li>-dimension Size of the output matrix and plot. Integer: d&gt;0 (default 1000) Use 1000 for everything that is not full genome size, where 2000 is recommended</li>
</ul><p>Address of the bookmark: <a href="https://github.com/estebanpw/chromeister" rel="nofollow">https://github.com/estebanpw/chromeister</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/43817/bioinfo-lab</guid>
  <pubDate>Fri, 04 Mar 2022 00:17:00 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bioinfo Lab]]></title>
  <description><![CDATA[
<p>The Institute of Bioinformatics conducts internationally renowned research and provides profound education in bioinformatics. Its research focuses on development and application of machine learning and statistical methods in biology and medicine.</p>

<p>Contact:<br />Computer Science Building (Science Park 3)<br />Altenberger Str. 69, A-4040 Linz, Austria<br />Tel. +43 732 2468 4520 / Fax +43 732 2468 4539<br />E-mail secretary@bioinf.jku.at</p>

<p>http://www.bioinf.jku.at/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44375/phyloherb-a-high%E2%80%90throughput-phylogenomic-pipeline-for-processing-genome-skimming-data</guid>
	<pubDate>Wed, 06 Sep 2023 00:14:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44375/phyloherb-a-high%E2%80%90throughput-phylogenomic-pipeline-for-processing-genome-skimming-data</link>
	<title><![CDATA[PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome skimming data]]></title>
	<description><![CDATA[<p dir="auto"><span>Phylo</span>genomic Analysis Pipeline for&nbsp;<span>Herb</span>arium Specimens</p>
<p dir="auto"><span>What is PhyloHerb</span>: PhyloHerb is a wrapper program to process&nbsp;<span>genome skimming</span>&nbsp;data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include&nbsp;<span>low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming</span>.</p>
<p dir="auto"><span>License</span>: GNU General Public License</p>
<p dir="auto"><span>Citation</span>:</p>
<ul dir="auto">
<li>Cai, Liming, Hongrui Zhang, and Charles C. Davis. 2022. PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome‐skimming data. Applications in Plant Sciences 10(3): 1&ndash;9.&nbsp;<a href="https://doi.org/10.1002/aps3.11475">https://doi.org/10.1002/aps3.11475</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/lmcai/PhyloHerb/" rel="nofollow">https://github.com/lmcai/PhyloHerb/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/88/regular-expression-cheat-sheet</guid>
	<pubDate>Tue, 09 Jul 2013 17:38:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/88/regular-expression-cheat-sheet</link>
	<title><![CDATA[Regular Expression Cheat Sheet]]></title>
	<description><![CDATA[<p><span>The Regular Expression are the sole of Perl language, and for bioinformatician it is just a magical stick to resolve gingatic string data. We did not find any good and user friendly regular expression cheat sheet, hence write our own cheat sheet.&nbsp;</span><span>The Regular Expressions Cheat Sheet, a quick reference guide for regular expressions, including symbols, ranges, grouping, assertions and some sample patterns to get you started.</span></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/88" length="14944" type="application/pdf" />
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/42958/claus-peter-stelzer-lab</guid>
  <pubDate>Mon, 15 Mar 2021 15:24:41 -0500</pubDate>
  <link></link>
  <title><![CDATA[Claus-Peter Stelzer Lab]]></title>
  <description><![CDATA[
<p>Interested in various topics at the intersection of ecology and evolution. In my research I use rotifers as model organisms for experimental studies at the individual and population level. Rotifers are ideally suited for this, because populations of thousands can be kept in small containers in the lab, while single individuals can still be handled conveniently. </p>

<p>More at https://www.uibk.ac.at/limno/personnel/stelzer/index.html.en#research</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/6700/tedmed-great-challenges-genomics-and-medicine-where-promise-meets-clinical-practice</guid>
	<pubDate>Fri, 22 Nov 2013 12:05:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/6700/tedmed-great-challenges-genomics-and-medicine-where-promise-meets-clinical-practice</link>
	<title><![CDATA[TEDMED Great Challenges: Genomics and Medicine: Where promise meets clinical practice]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/-VdRMFuB5vo" frameborder="0" allowfullscreen></iframe>November 21, 2013 - NHGRI Director Eric Green, M.D., Ph.D, hosted the TEDMED Google+ Hangout to discuss genomic medicine with an all-star cast that includes Carlos Bustamante, James Evans, Amy McGuire and Sharon Terry.

More: http://www.tedmed.com/greatchallenges]]></description>
	
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