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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38212?offset=140</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</guid>
	<pubDate>Fri, 19 Oct 2018 07:25:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</link>
	<title><![CDATA[BASE: a practical de novo assembler for large genomes using long NGS reads]]></title>
	<description><![CDATA[<p><span>new&nbsp;</span><em>de novo</em><span>&nbsp;assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.</span></p><p>Address of the bookmark: <a href="https://github.com/dhlbh/BASE" rel="nofollow">https://github.com/dhlbh/BASE</a></p>]]></description>
	<dc:creator>Rahul Nayak</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/22761/pit-bioinformatics-group</guid>
  <pubDate>Tue, 16 Jun 2015 14:34:26 -0500</pubDate>
  <link></link>
  <title><![CDATA[PIT Bioinformatics Group]]></title>
  <description><![CDATA[
<p>PIT Bioinformatics Group solves problems in bioinformatics and  computational biology. Recent developed online tools:</p>

<p>- Budapest Reference Connectome: View a parametrizable connectome (brain graph).<br />- AmphoraNet: The webserver implementation of the AMPHORA2 workflow for phylogenetic analysis of metagenomic shotgun sequencing data.<br />- AmphoraVizu: Chart visualization for metagenomics analysis tools AMPHORA2 and AmphoraNet.<br />- SCARF: Free online association rule mining tool.</p>

<p>More at: http://pitgroup.org</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42132/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</guid>
	<pubDate>Mon, 17 Aug 2020 05:25:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42132/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</link>
	<title><![CDATA[SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins]]></title>
	<description><![CDATA[<p>SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:</p>
<ol>
<li>Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome</li>
<li>Co-assembly of a large number of metagenomes via merging of individual metagenomes</li>
<li>Includes binning and bin checking, for retrieving individual genomes</li>
<li>The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.</li>
<li>Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.</li>
<li>Metatranscriptomic support via mapping of cDNA reads against reference metagenomes</li>
</ol><p>Address of the bookmark: <a href="https://github.com/jtamames/SqueezeMeta" rel="nofollow">https://github.com/jtamames/SqueezeMeta</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44595/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</guid>
	<pubDate>Sat, 06 Jul 2024 04:29:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44595/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</link>
	<title><![CDATA[SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins]]></title>
	<description><![CDATA[<p dir="auto">SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:</p>
<ol dir="auto">
<li>Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome</li>
<li>Co-assembly of a large number of metagenomes via merging of individual metagenomes</li>
<li>Includes binning and bin checking, for retrieving individual genomes</li>
<li>The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.</li>
<li>Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.</li>
<li>Metatranscriptomic support via mapping of cDNA reads against reference metagenomes</li>
</ol><p>Address of the bookmark: <a href="https://github.com/jtamames/SqueezeMeta" rel="nofollow">https://github.com/jtamames/SqueezeMeta</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34515/metasim-a-sequencing-simulator-for-genomics-and-metagenomics</guid>
	<pubDate>Mon, 04 Dec 2017 07:18:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34515/metasim-a-sequencing-simulator-for-genomics-and-metagenomics</link>
	<title><![CDATA[MetaSim A Sequencing Simulator for Genomics and Metagenomics.]]></title>
	<description><![CDATA[<p><span>Our software can be used to&nbsp;</span><strong>generate collections of synthetic reads</strong><span>&nbsp;that reflect the diverse taxonomical composition of typical metagenome data sets. Based on a database of given genomes, the program allows the user to&nbsp;</span><strong>design a metagenome</strong><span>&nbsp;by specifying the number of genomes present at different levels of the NCBI taxonomy, and then to collect reads from the metagenome using a&nbsp;</span><strong>simulation of a number of different sequencing technologies</strong><span>. A population sampler optionally produces evolved sequences based on source genomes and a given evolutionary tree.&nbsp;</span></p><p>Address of the bookmark: <a href="http://ab.inf.uni-tuebingen.de/software/metasim/" rel="nofollow">http://ab.inf.uni-tuebingen.de/software/metasim/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/22569/reverse-complement-problem-solved-with-perl</guid>
	<pubDate>Tue, 09 Jun 2015 23:37:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/22569/reverse-complement-problem-solved-with-perl</link>
	<title><![CDATA[Reverse Complement Problem Solved with Perl]]></title>
	<description><![CDATA[<p>Question at http://rosalind.info/problems/1b/</p><p>#Find the reverse complement of a DNA string.<br />#Given: A DNA string Pattern.<br />#Return: Pattern, the reverse complement of Pattern.<br /><br />use strict;<br />use warnings;<br /><br />my $string="AAAACCCGGT";<br />my $finalString="";<br />my %hash = (<br />&nbsp;&nbsp; &nbsp;"C" =&gt; "G", <br />&nbsp;&nbsp; &nbsp;"A" =&gt; "T", <br />&nbsp;&nbsp; &nbsp;"T" =&gt; "A", <br />&nbsp;&nbsp; &nbsp;"G" =&gt; "C",<br />);<br /><br />for (my $aa=0; $aa&lt;=(length($string)-1); $aa++) {<br />&nbsp;&nbsp; &nbsp;my $char=substr $string, $aa, 1;<br />&nbsp;&nbsp; &nbsp;#print $hash{$char};<br />&nbsp;&nbsp; &nbsp;$finalString="$hash{$char}"."$finalString";<br />}<br /><br />print $finalString;<br />print "\n";</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/22961/bioscripts</guid>
	<pubDate>Sun, 28 Jun 2015 07:46:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/22961/bioscripts</link>
	<title><![CDATA[BioScripts]]></title>
	<description><![CDATA[<p>You are requested to please bookmark collection of bioinformatics tools, scripts, codes that can be pieced together in a very easy and flexible manner to perform both simple and complex bioinformatics tasks.</p>
<p>The next-generation sequencing included whole genome sequencing(WGS), transcriptome sequencing (whole cDNA sequencing, RNA-seq), digital gene expression sequencing (Tag-Seq), ChIP-Seq, and so on. And there are many sequencing platform to generate sequece, as well know Sanger/ABi(the frist generation), Solexa/illumina, SOLiD/ABi, 454/Roche. But thier sequence format is different, also they have different error type. High quality data is very important for further analysis or data mining. There are many pipeline for raw sequence quality analysis and control with few of process for reporting reads quality statistical details, trimming, filtering, and error correction. Please bookmarks them for the benefits of bioinformatics community.</p>
<p>https://code.google.com/p/biowiki/</p>
<p>https://code.google.com/p/ngs-pipeline/source/browse/#svn%2Ftrunk</p>
<p>NGSand Perl scripts https://code.google.com/hosting/search?q=NGS+perl&amp;projectsearch=Search+projects</p>
<p>NGS and Python scripts https://code.google.com/hosting/search?q=NGS+Python&amp;projectsearch=Search+projects</p><p>Address of the bookmark: <a href="https://code.google.com/hosting/search?q=bioinformatics&amp;sa=Search" rel="nofollow">https://code.google.com/hosting/search?q=bioinformatics&amp;sa=Search</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42280/urmap-an-ultra-fast-read-mapper</guid>
	<pubDate>Thu, 29 Oct 2020 23:03:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42280/urmap-an-ultra-fast-read-mapper</link>
	<title><![CDATA[URMAP, an ultra-fast read mapper]]></title>
	<description><![CDATA[<p><span>URMAP, a new read mapping algorithm. URMAP is an order of magnitude faster than BWA with comparable accuracy on several validation tests. On a Genome in a Bottle (GIAB) variant calling test with 30&times; coverage 2&times;150 reads, URMAP achieves high accuracy (precision 0.998, sensitivity 0.982 and F-measure 0.990) with the strelka2 caller. However, GIAB reference variants are shown to be biased against repetitive regions which are difficult to map and may therefore pose an unrealistically easy challenge to read mappers and variant callers.</span></p>
<p><span>More at&nbsp;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320720/</span></p><p>Address of the bookmark: <a href="https://github.com/rcedgar/urmap" rel="nofollow">https://github.com/rcedgar/urmap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34246/unicycler-hybrid-assembly-pipeline-for-bacterial-genomes</guid>
	<pubDate>Fri, 10 Nov 2017 03:58:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34246/unicycler-hybrid-assembly-pipeline-for-bacterial-genomes</link>
	<title><![CDATA[Unicycler: Hybrid assembly pipeline for bacterial genomes]]></title>
	<description><![CDATA[<p><span>Unicycler is an assembly pipeline for bacterial genomes. It can assemble&nbsp;</span><a href="http://www.illumina.com/">Illumina</a><span>-only read sets where it functions as a&nbsp;</span><a href="http://cab.spbu.ru/software/spades/">SPAdes</a><span>-optimiser. It can also assembly long-read-only sets (</span><a href="http://www.pacb.com/">PacBio</a><span>&nbsp;or&nbsp;</span><a href="https://nanoporetech.com/">Nanopore</a><span>) where it runs a&nbsp;</span><a href="https://github.com/lh3/miniasm">miniasm</a><span>+</span><a href="https://github.com/isovic/racon">Racon</a><span>&nbsp;pipeline. For the best possible assemblies, give it both Illumina reads&nbsp;</span><em>and</em><span>&nbsp;long reads, and it will conduct a hybrid assembly.</span></p><p>Address of the bookmark: <a href="https://github.com/rrwick/Unicycler" rel="nofollow">https://github.com/rrwick/Unicycler</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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