<?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/37993?offset=460</link>
	<atom:link href="https://bioinformaticsonline.com/related/37993?offset=460" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39872/miropeats-discovers-regions-of-sequence-similarity-amongst-any-set-of-dna-sequences</guid>
	<pubDate>Mon, 26 Aug 2019 17:55:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39872/miropeats-discovers-regions-of-sequence-similarity-amongst-any-set-of-dna-sequences</link>
	<title><![CDATA[Miropeats: discovers regions of sequence similarity amongst any set of DNA sequences]]></title>
	<description><![CDATA[<p><span>Miropeats discovers regions of sequence similarity amongst any set of DNA sequences and then presents this similarity information graphically. Sequence similarity searching is a very general tool that forms the basis of many different biological sequence analyses but it is limited by the verbosity of traditional alignment presentation styles. Miropeats enhances the utility of conventional DNA sequence comparisons when looking at long lengths of sequence similarity by summarizing extensive large scale sequence similarities on a single page of graphics. The latest version of Miropeats can be used as a general pairwise alignment program or in its traditional role sorting out a big mess of overlapping or similar regions.</span></p><p>Address of the bookmark: <a href="http://www.littlest.co.uk/software/bioinf/old_packages/miropeats/" rel="nofollow">http://www.littlest.co.uk/software/bioinf/old_packages/miropeats/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40510/reps-repeat-masked-phrap-with-scaffolding-a-wgs-sequence-assembler</guid>
	<pubDate>Sat, 04 Jan 2020 01:08:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40510/reps-repeat-masked-phrap-with-scaffolding-a-wgs-sequence-assembler</link>
	<title><![CDATA[RePS: Repeat-masked Phrap with scaffolding, a WGS sequence assembler]]></title>
	<description><![CDATA[<p>RePS (Repeat-masked Phrap with scaffolding), a WGS sequence assembler, that explicitly identifies exact kmer repeats from the shotgun data and removes them prior to the assembly. The established software Phrap is used to compute meaningful error probabilities for each base. Clone-end-pairing information is used to construct scaffolds that order and orient the contigs. The updated version of RePS incorporates some of the ideas introduced by Phusion on clustering</p>
<p><img src="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC186573/bin/45793-17f1_F4TT.jpg" alt="image" style="border: 0px;"></p>
<p>More at</p>
<p><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC186573/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC186573/</a></p><p>Address of the bookmark: <a href="ftp://ftp.genomics.org.cn/pub/ricedb/Tools/RePS/RePS-IBM-AIX.tar.gz" rel="nofollow">ftp://ftp.genomics.org.cn/pub/ricedb/Tools/RePS/RePS-IBM-AIX.tar.gz</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41033/clark-fast-accurate-and-versatile-sequence-classification-system</guid>
	<pubDate>Sat, 15 Feb 2020 01:49:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41033/clark-fast-accurate-and-versatile-sequence-classification-system</link>
	<title><![CDATA[CLARK: Fast, accurate and versatile sequence classification system]]></title>
	<description><![CDATA[<p><span></span><a href="http://dx.doi.org/10.1186/s12864-015-1419-2"><strong>CLARK</strong></a><span>, a method based on a supervised sequence classification using discriminative&nbsp;</span><em>k</em><span>-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.</span></p>
<p><span><a href="http://clark.cs.ucr.edu/Spaced/">http://clark.cs.ucr.edu/Spaced/</a></span></p><p>Address of the bookmark: <a href="http://clark.cs.ucr.edu/Spaced/" rel="nofollow">http://clark.cs.ucr.edu/Spaced/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42160/vicuna-a-software-tool-that-enables-consensus-assembly-of-ultra-deep-sequence-derived-from-diverse-viral-or-other-heterogeneous-populations</guid>
	<pubDate>Tue, 25 Aug 2020 03:40:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42160/vicuna-a-software-tool-that-enables-consensus-assembly-of-ultra-deep-sequence-derived-from-diverse-viral-or-other-heterogeneous-populations</link>
	<title><![CDATA[VICUNA: a software tool that enables consensus assembly of ultra-deep sequence derived from diverse viral or other heterogeneous populations.]]></title>
	<description><![CDATA[<p><span>VICUNA</span><span>&nbsp;is a&nbsp;</span><em>de novo</em><span>&nbsp;assembly program targeting populations with high mutation rates. It creates a single linear representation of the mixed population on which intra-host variants can be mapped. For clinical samples rich in contamination (e.g., &gt;95%), VICUNA can leverage existing genomes, if available, to assemble only target-alike reads. After initial assembly, it can also use existing genomes to perform guided merging of contigs. For each data set (e.g., Illumina paired read, 454), VICUNA outputs consensus sequence(s) and the corresponding multiple sequence alignment of constituent reads. VICUNA efficiently handles ultra-deep sequence data with tens of thousands fold coverage.</span></p>
<p><a href="http://software.broadinstitute.org/viral/docs/vicuna_v1.0.pdf">http://software.broadinstitute.org/viral/docs/vicuna_v1.0.pdf</a></p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/viral-genomics/vicuna" rel="nofollow">https://www.broadinstitute.org/viral-genomics/vicuna</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43846/the-complete-sequence-of-a-human-genome</guid>
	<pubDate>Thu, 31 Mar 2022 23:58:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43846/the-complete-sequence-of-a-human-genome</link>
	<title><![CDATA[The complete sequence of a human genome]]></title>
	<description><![CDATA[<p><span>The completed regions include all centromeric satellite arrays, recent segmental duplications, and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies.</span></p><p>Address of the bookmark: <a href="https://www.science.org/doi/10.1126/science.abj6987" rel="nofollow">https://www.science.org/doi/10.1126/science.abj6987</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite</guid>
	<pubDate>Wed, 03 Jan 2018 05:05:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite</link>
	<title><![CDATA[Seal: SEquence ALignment evaluation suite]]></title>
	<description><![CDATA[<p><span>Seal</span>&nbsp;is a comprehensive sequencing simulation and alignment tool evaluation suite. This software (implemented in Java) provides several utilities that can be used to evaluate alignment algorithms, including:</p>
<ul>
<li>Reading a pre-existing reference genome from one or more FASTA files.</li>
<li>Alternatively, generating an artificial reference genome based on input parameters (length, repeat count, repeat length, repeat variability rate).</li>
<li>Simulating reads from random locations in the genome based on input parameters of read length, coverage, sequencing error rate, and indel rate.</li>
<li>Applying alignment tools to the genome and the reads through a standardized interface.</li>
<li>Parsing the output of the alignment tool and calculating the number of reads that were correctly or incorrectly mapped.</li>
<li>Computing run times and measures of accuracy.</li>
</ul>
<p><span>Seal</span>&nbsp;has interfaces to evaluate the following software packages:</p>
<ul>
<li>Bowtie</li>
<li>BWA</li>
<li>MAQ</li>
<li>mrFAST</li>
<li>mrsFAST</li>
<li>Novoalign</li>
<li>SHRiMP</li>
<li>SOAPv2</li>
</ul><p>Address of the bookmark: <a href="http://compbio.case.edu/seal/" rel="nofollow">http://compbio.case.edu/seal/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4191/high-density-sheep-snp-genotyping-chip-released</guid>
	<pubDate>Tue, 03 Sep 2013 13:58:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4191/high-density-sheep-snp-genotyping-chip-released</link>
	<title><![CDATA[High Density Sheep SNP Genotyping Chip released!!!]]></title>
	<description><![CDATA[<p>If you are working on Sheep genomics then there is a good news for you. FarmIQ in conjunction with Illumina and the International Sheep Genomics Consortium (ISGC) are today announcing completion of the &ldquo;Ovine Infinium&reg; HD SNP BeadChip&rdquo;,&nbsp;a high definition SNP chip for ship genome. The OvineSNP50 BeadChip features over 54,241 evenly spaced probes that target SNPs, offering more than sufficient SNP density for genome-wide association studies and other applications such as genome-wide selection, determination of genetic merit, identification of quantitative trait loci, and comparative genetic studies.</p><p>The BeadChip was developed in collaboration with leading ovine researchers from AgResearch, Baylor UCSC, CSIRO, and the USDA as part of the International Sheep Genomics Consortium. It features over 54,241 evenly spaced probes that target single nucleotide polymorphisms (SNPs). More than 18,000 of these markers were discovered through sequencing reduced representation libraries with the Illumina Genome Analyzer IIx. A set of 600 SNPs were identified by BAC end sequencing and validated with Illumina GoldenGate Genotyping Assays over 403 animals from 23 breeds. The remaining SNPs were derived from the draft ovine genome.</p><p>Read more @</p><p><a href="http://res.illumina.com/documents/products/datasheets/datasheet_ovinesnp50.pdf">http://res.illumina.com/documents/products/datasheets/datasheet_ovinesnp50.pdf</a><a href="http://www.scoop.co.nz/stories/SC1309/S00004/high-density-snp-genotyping-chip-for-the-sheep-genome.htm"><br /></a></p><p><a href="http://www.illumina.com/products/ovinesnp50_dna_analysis_kit.ilmn">http://www.illumina.com/products/ovinesnp50_dna_analysis_kit.ilmn</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31976/snpgenie</guid>
	<pubDate>Thu, 30 Mar 2017 17:38:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31976/snpgenie</link>
	<title><![CDATA[SNPGenie]]></title>
	<description><![CDATA[<p>SNPGenie is a Perl script for estimating evolutionary parameters, mainly from pooled next-generation sequencing (NGS) single-nucleotide polymorphism (SNP) variant data. SNP reports (acceptable in a variety of formats) much each correspond to a single population, with variants called relative to a single reference sequence (one sequence in one FASTA file). Just run the main script, <strong>snpgenie.pl</strong>, in a directory containing the necessary <a href="https://github.com/hugheslab/snpgenie#snpgenie-input">input files</a>, and we take care of the rest! For the earlier version, see <a href="http://ww2.biol.sc.edu/~austin/">Hughes Lab Bioinformatics Resource</a>.</p><p>Address of the bookmark: <a href="https://github.com/hugheslab/snpgenie" rel="nofollow">https://github.com/hugheslab/snpgenie</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model</guid>
	<pubDate>Fri, 28 Feb 2020 23:27:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model</link>
	<title><![CDATA[RAINBOWR: Reliable Association INference By Optimizing Weights with R (R package for SNP-set GWAS and multi-kernel mixed model)]]></title>
	<description><![CDATA[<p><code>RAINBOWR</code>(Reliable Association INference By Optimizing Weights with R) is a package to perform several types of <code>GWAS</code> as follows.</p>
<ul>
<li>Single-SNP GWAS with <code>RGWAS.normal</code> function</li>
<li>SNP-set (or gene set) GWAS with <code>RGWAS.multisnp</code> function (which tests multiple SNPs at the same time)</li>
<li>Check epistatic (SNP-set x SNP-set interaction) effects with <code>RGWAS.epistasis</code> (very slow and less reliable)</li>
</ul>
<p>https://github.com/KosukeHamazaki/RAINBOWR</p>
<p>https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007663</p>
<p>https://cran.r-project.org/web/packages/RAINBOWR/index.html</p><p>Address of the bookmark: <a href="https://github.com/KosukeHamazaki/RAINBOWR" rel="nofollow">https://github.com/KosukeHamazaki/RAINBOWR</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment</guid>
	<pubDate>Mon, 05 Aug 2024 23:01:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment</link>
	<title><![CDATA[UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment]]></title>
	<description><![CDATA[<p>UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment</p>
<ul>
<li>
<p>Using state of the art tools, easily extended for other viruses</p>
</li>
<li>
<p>Tool and database updates for critical components via Conda</p>
</li>
<li>
<p>Built using modern design patterns with Conda and Snakemake</p>
</li>
<li>
<p>Extensible and easy to customize</p>
</li>
<li>
<p>Submission Ready Genomes</p>
</li>
<li>
<p>Customizable reporting with comprehensive visualization</p>
</li>
</ul>
<p>https://ikim-essen.github.io/uncovar/</p>
<p>Github&nbsp;https://github.com/IKIM-Essen/uncovar</p>
<p>&nbsp;</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://ikim-essen.github.io/uncovar/" rel="nofollow">https://ikim-essen.github.io/uncovar/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

</channel>
</rss>