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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38752?offset=50</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38541/geneoverlap-an-r-package-to-test-and-visualize-gene-overlaps</guid>
	<pubDate>Thu, 27 Dec 2018 19:45:52 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38541/geneoverlap-an-r-package-to-test-and-visualize-gene-overlaps</link>
	<title><![CDATA[GeneOverlap: An R package to test and visualize gene overlaps]]></title>
	<description><![CDATA[<p>Overlapping gene lists can reveal biological meanings and may lead to novel hypotheses. For example, histone modification is an important cellular mechanism that can pack and re-pack chromatin. By making the chromatin structure more dense or loose, the gene expression can be turned on or off. Tri-methylation on lysine 4 of histone H3 (H3K4me3) is associated with gene activation and its genome-wide enrichment can be mapped by using ChIP-seq experiments. Because of its activating role, if we overlap the genes that are bound by H3K4me3 with the genes that are highly expressed, we should expect a positive association. Similary, we can perform such kind of overlapping between the gene lists of different histone modifications with that of various expression groups and establish each histone modification&rsquo;s role in gene regulation.</p><p>Address of the bookmark: <a href="https://bioconductor.org/packages/release/bioc/vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf" rel="nofollow">https://bioconductor.org/packages/release/bioc/vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes</guid>
	<pubDate>Thu, 13 Aug 2020 10:06:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes</link>
	<title><![CDATA[PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes]]></title>
	<description><![CDATA[<p>PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring&nbsp;O(n2)&nbsp;resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.</p><p>Address of the bookmark: <a href="https://github.com/ryanmelnyk/PyParanoid" rel="nofollow">https://github.com/ryanmelnyk/PyParanoid</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44476/omark-software-for-proteome-protein-coding-gene-repertoire-quality-assessment</guid>
	<pubDate>Wed, 21 Feb 2024 15:01:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44476/omark-software-for-proteome-protein-coding-gene-repertoire-quality-assessment</link>
	<title><![CDATA[OMArk: software for proteome (protein-coding gene repertoire) quality assessment]]></title>
	<description><![CDATA[<p><span>OMArk is a software for proteome (protein-coding gene repertoire) quality assessment. It provides measures of proteome completeness, characterizes the consistency of all protein coding genes with regard to their homologs, and identifies the presence of contamination from other species. OMArk relies on the OMA orthology database, from which it exploits orthology relationships, and on the OMAmer software for fast placement of all proteins into gene families.</span></p><p>Address of the bookmark: <a href="https://github.com/DessimozLab/OMArk" rel="nofollow">https://github.com/DessimozLab/OMArk</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38378/gwaspro-a-high-performance-genome-wide-association-analysis-server</guid>
	<pubDate>Fri, 07 Dec 2018 08:04:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38378/gwaspro-a-high-performance-genome-wide-association-analysis-server</link>
	<title><![CDATA[GWASpro: A High-Performance Genome-Wide Association Analysis Server]]></title>
	<description><![CDATA[<p>GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model (LMM). GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10,000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://bioinfo.noble.org/GWASPRO/" rel="nofollow">https://bioinfo.noble.org/GWASPRO/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38012/cosine-non-seeding-method-for-mapping-long-noisy-sequences</guid>
	<pubDate>Fri, 26 Oct 2018 00:41:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38012/cosine-non-seeding-method-for-mapping-long-noisy-sequences</link>
	<title><![CDATA[COSINE: non-seeding method for mapping long noisy sequences]]></title>
	<description><![CDATA[<p><span>Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors.</span></p><p>Address of the bookmark: <a href="https://github.com/SUwonglab/COSINE" rel="nofollow">https://github.com/SUwonglab/COSINE</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<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/44906/winpca</guid>
	<pubDate>Tue, 23 Sep 2025 03:58:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44906/winpca</link>
	<title><![CDATA[WinPCA]]></title>
	<description><![CDATA[<p><span>A package for windowed principal component analysis. WinPCA performs principal component analyses (PCA) in sliding windows along chromosomes. Both hard-called genotypes (input: VCF or TSV) or genotype likelihoods (input: VCF, TSV or BEAGLE) encoding&nbsp;</span><span>biallellic SNPs</span><span>&nbsp;are accepted. WinPCA uses&nbsp;</span><a href="https://scikit-allel.readthedocs.io/en/stable/stats/decomposition.html?highlight=pca">scikit-allel</a><span>&nbsp;to perfom PCAs on genotype data and&nbsp;</span><a href="https://github.com/Rosemeis/pcangsd">PCAngsd</a><span>&nbsp;methods for genotype likelihood (GL, PL) data.</span></p><p>Address of the bookmark: <a href="https://github.com/MoritzBlumer/winpca" rel="nofollow">https://github.com/MoritzBlumer/winpca</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36635/circlator-automated-circularization-of-genome-assemblies-using-long-sequencing-reads</guid>
	<pubDate>Tue, 15 May 2018 09:42:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36635/circlator-automated-circularization-of-genome-assemblies-using-long-sequencing-reads</link>
	<title><![CDATA[Circlator: automated circularization of genome assemblies using long sequencing reads]]></title>
	<description><![CDATA[A tool to circularize genome assemblies. The algorithm and benchmarks are described in the Genome Biology manuscript. 

Citation: "Circlator: automated circularization of genome assemblies using long sequencing reads", Hunt et al, Genome Biology 2015 Dec 29;16(1):294. doi: 10.1186/s13059-015-0849-0. PMID: 26714481.<p>Address of the bookmark: <a href="http://sanger-pathogens.github.io/circlator/" rel="nofollow">http://sanger-pathogens.github.io/circlator/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42035/pannzer-a-fully-automated-service-for-functional-annotation-of-prokaryotic-and-eukaryotic-proteins-of-unknown-function</guid>
	<pubDate>Thu, 13 Aug 2020 09:57:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42035/pannzer-a-fully-automated-service-for-functional-annotation-of-prokaryotic-and-eukaryotic-proteins-of-unknown-function</link>
	<title><![CDATA[PANNZER: a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function.]]></title>
	<description><![CDATA[<p><strong>PANNZER</strong>&nbsp;(Protein ANNotation with Z-scoRE) is a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function.</p>
<p><strong>PANNZER</strong>&nbsp;(Protein ANNotation with Z-scoRE) is a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function. The tool is designed to predict the functional description (DE) and GO classes.</p>
<p>PANNZER2 processes bacterial proteomes in minutes and eukaryotic proteomes in an hour. You can use&nbsp;<a href="http://ekhidna2.biocenter.helsinki.fi/AAI/">AAI-profiler</a>&nbsp;to summarize a proteome's species neighbors and reveal taxonomic identity or contamination.</p><p>Address of the bookmark: <a href="http://ekhidna2.biocenter.helsinki.fi/sanspanz/#" rel="nofollow">http://ekhidna2.biocenter.helsinki.fi/sanspanz/#</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36017/alpha-a-toolkit-for-automated-local-phylogenomic-analyses</guid>
	<pubDate>Wed, 21 Mar 2018 18:12:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36017/alpha-a-toolkit-for-automated-local-phylogenomic-analyses</link>
	<title><![CDATA[ALPHA: A Toolkit for Automated Local Phylogenomic Analyses]]></title>
	<description><![CDATA[<p><span>Automated Local Phylogenomic Analyses, or ALPHA, is a python-based application that provides an intuitive user interface for phylogenetic analyses and data visualization. It has four distinct modes that are useful for different types of phylogenetic analysis: RAxML, File Converter, MS Comparison, and D-statistic.</span></p><p>Address of the bookmark: <a href="https://github.com/chilleo/ALPHA" rel="nofollow">https://github.com/chilleo/ALPHA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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