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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34543?offset=70</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40856/3d-de-novo-assembly-3d-dna-pipeline</guid>
	<pubDate>Sun, 02 Feb 2020 13:41:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40856/3d-de-novo-assembly-3d-dna-pipeline</link>
	<title><![CDATA[3D de novo assembly (3D DNA) pipeline]]></title>
	<description><![CDATA[<p>For a detailed description of the pipeline and how it integrates with other tools designed by the Aiden Lab see&nbsp;<a href="http://aidenlab.org/assembly/manual_180322.pdf">Genome Assembly Cookbook</a>&nbsp;on&nbsp;<a href="http://aidenlab.org/assembly">http://aidenlab.org/assembly</a>.</p>
<p>For the original version of the pipeline and to reproduce the Hs2-HiC and the AaegL4 genomes reported in&nbsp;<a href="http://science.sciencemag.org/content/356/6333/92">(Dudchenko et al.,&nbsp;<em>Science</em>, 2017)</a>&nbsp;see the&nbsp;<a href="https://github.com/theaidenlab/3d-dna/tree/745779bdf64db6e55bddb70c24e9b58825938c33">original commit</a>.</p>
<p>For the detailed description of the merge section see&nbsp;<a href="https://github.com/theaidenlab/AGWG-merge">https://github.com/theaidenlab/AGWG-merge</a>.</p><p>Address of the bookmark: <a href="https://github.com/theaidenlab/3d-dna" rel="nofollow">https://github.com/theaidenlab/3d-dna</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools</guid>
	<pubDate>Sat, 08 Aug 2020 08:25:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools</link>
	<title><![CDATA[ENCODE3: A collection of research articles and related content describing the Encyclopedia of DNA Elements, its datasets and tools.]]></title>
	<description><![CDATA[<p>How cells, tissues and organisms interpret the information encoded in the genome has vital implications for our understanding of development, health and disease. Launched in 2003, the ENCyclopedia Of DNA Elements (ENCODE) project has the aim of mapping the functional elements in the human genome (later expanded to include model organisms).</p><p>During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.</p><p>ENCODE&rsquo;s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.</p><p><span>The&nbsp;</span><a href="https://www.nature.com/articles/s41586-020-2493-4">Encyclopedia paper of ENCODE 3</a><span>, published in&nbsp;</span><em>Nature</em><span>, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate&nbsp;</span><em>cis</em><span>-regulatory elements (cCREs).</span></p><p>More at&nbsp;<a href="https://www.nature.com/immersive/d42859-020-00027-2/index.html">https://www.nature.com/immersive/d42859-020-00027-2/index.html</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44329/metabuli-%EB%B6%84%EB%A6%AC-improves-metagenomic-read-classification</guid>
	<pubDate>Sat, 03 Jun 2023 20:15:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44329/metabuli-%EB%B6%84%EB%A6%AC-improves-metagenomic-read-classification</link>
	<title><![CDATA[Metabuli 분리 improves metagenomic read classification]]></title>
	<description><![CDATA[<p><span>Metabuli 분리 improves metagenomic read classification through metamers, DNA-AA k-mers, to be sensitive and specific, recovering 99% and 98% of DNA or AA classifiers.</span></p>
<p>&nbsp;</p>
<p><span><span>Metabuli is metagenomic classifier that jointly analyze both DNA and amino acid (AA) sequences. DNA-based classifiers can make specific classifications, exploiting point mutations to distinguish close taxa. AA-based classifiers have higher sensitivity in detecting homology between query and reference sequences, leverageing higher conservation of AA sequences. Metabuli combines the information of both sequence types using a novel k-mer structure,&nbsp;</span><em>metamer</em><span>, to enable both specific and sensitive characterization of metagenomic samples. In addition, it can classify reads against a database of any size as long as it fits in the hard disk.</span> </span></p><p>Address of the bookmark: <a href="https://github.com/steineggerlab/Metabuli" rel="nofollow">https://github.com/steineggerlab/Metabuli</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40994/biological-databases</guid>
	<pubDate>Wed, 12 Feb 2020 01:16:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40994/biological-databases</link>
	<title><![CDATA[Biological databases !]]></title>
	<description><![CDATA[<p>Now a days there are a lots of genomics databases available around the world. This bookmark is created to provide all links in one place ...</p>
<p>ftp://ftp.ncbi.nih.gov/genomes/</p>
<p>https://hgdownload.soe.ucsc.edu/downloads.html</p><p>Address of the bookmark: <a href="ftp://ftp.ncbi.nih.gov/genomes/" rel="nofollow">ftp://ftp.ncbi.nih.gov/genomes/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26499/katju-lab</guid>
  <pubDate>Fri, 26 Feb 2016 03:25:32 -0600</pubDate>
  <link></link>
  <title><![CDATA[Katju Lab]]></title>
  <description><![CDATA[
<p>TheLab seek to understand the genetic factors contributing to genomic variation and phenotypic diversity.  To this end, we employ molecular and bioinformatic tools to study evolutionary processes at the level of populations, both experimental and natural, and genomes.  Our research interests encompass a wide range of topics, including the evolution of organellar and nuclear genomes, gene duplication and the origin of novel function, and the fitness and phenotypic consequences of mutation in evolution. For details regards ongoing projects, please see the Research page.</p>

<p>http://katjulab.com/research.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38310/sisrs-site-identification-from-short-read-sequences</guid>
	<pubDate>Wed, 28 Nov 2018 08:56:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38310/sisrs-site-identification-from-short-read-sequences</link>
	<title><![CDATA[SISRS: Site Identification from Short Read Sequences]]></title>
	<description><![CDATA[<p>Next-gen sequence data such as Illumina HiSeq reads. Data must be sorted into folders by taxon (e.g. species or genus). Paired reads in fastq format must be specified by _R1 and _R2 in the (otherwise identical) filenames. Paired and unpaired reads must have a fastq file extension.</p><p>Address of the bookmark: <a href="https://github.com/rachelss/SISRS" rel="nofollow">https://github.com/rachelss/SISRS</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/44364/genbank-release-2570-is-now-available</guid>
	<pubDate>Wed, 23 Aug 2023 00:23:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/44364/genbank-release-2570-is-now-available</link>
	<title><![CDATA[GenBank release 257.0 is now available!]]></title>
	<description><![CDATA[<p><span>GenBank release 257.0 is now available! This release has 25.10 trillion bases and 3.69 billion records. Learn more:&nbsp;https://ncbiinsights.ncbi.nlm.nih.gov/2023/08/21/genbank-release-257/</span><a href="https://ow.ly/zHbV50PBE5o"><br /></a></p><p><a href="https://www.ncbi.nlm.nih.gov/genbank/?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=genbank-release-20230821">GenBank</a>&nbsp;release 257.0 (8/15/2023) is now available on the&nbsp;<a href="https://ftp.ncbi.nlm.nih.gov/genbank/">NCBI FTP site</a>. This release has 25.10 trillion bases and 3.69 billion records.</p><p><strong>The current release has:</strong></p><ul>
<li>246,119,175 traditional records containing 2,112,058,517,945 base pairs of sequence data</li>
<li>2,631,493,489 WGS records containing 22,294,446,104,543 base pairs of sequence data</li>
<li>686,271,945 bulk-oriented TSA records containing 646,176,166,908 base pairs of sequence data</li>
<li>124,421,006 bulk-oriented TLS records containing 48,289,699,026 base pairs of sequence data</li>
</ul>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37788/s-plot2-creates-an-interactive-two-dimensional-heatmap-of-sequences</guid>
	<pubDate>Fri, 28 Sep 2018 05:36:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37788/s-plot2-creates-an-interactive-two-dimensional-heatmap-of-sequences</link>
	<title><![CDATA[S-plot2: creates an interactive, two-dimensional heatmap of sequences]]></title>
	<description><![CDATA[<p><span>S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.</span></p>
<p><span>http://www.putonti-lab.com/uploads/4/5/3/0/45307835/s-plot2_tutorial.pdf</span></p>
<p><span>http://journals.sagepub.com/doi/pdf/10.1177/1176934318797354</span></p><p>Address of the bookmark: <a href="https://bitbucket.org/lkalesinskas/splot" rel="nofollow">https://bitbucket.org/lkalesinskas/splot</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38452/silix-implements-an-ultra-efficient-algorithm-for-the-clustering-of-homologous-sequences</guid>
	<pubDate>Wed, 12 Dec 2018 09:22:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38452/silix-implements-an-ultra-efficient-algorithm-for-the-clustering-of-homologous-sequences</link>
	<title><![CDATA[SiLiX: implements an ultra-efficient algorithm for the clustering of homologous sequences]]></title>
	<description><![CDATA[<p>The software package SiLiX implements<strong>&nbsp;an ultra-efficient algorithm for the clustering of homologous sequences</strong>, based on single transitive links (<em>single linkage</em>) with alignment coverage constraints.</p>
<p>SiLiX adopts a graph-theoretical framework to interpret similarity pairs as edges of a network. A very efficient algorithm, based on the&nbsp;<em>Disjoint Sets Data Structure</em>, allows the computation of sequence families with&nbsp;<strong>low time and space requirements</strong>.</p>
<p><strong>A parallel version</strong>&nbsp;of SiLiX, based on MPI, is also available in this package and has been proved to be scalable, so that its allows the study of&nbsp;<strong>very large datasets</strong>.</p>
<p>SiLiX is already included in the analysis pipeline for&nbsp;<a href="http://pbil.univ-lyon1.fr/databases/hogenom/acceuil.php">HOGENOM</a>.</p><p>Address of the bookmark: <a href="http://lbbe.univ-lyon1.fr/SiLiX?lang=fr" rel="nofollow">http://lbbe.univ-lyon1.fr/SiLiX?lang=fr</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39867/gepard-allows-the-calculation-of-dotplots-even-for-large-sequences-like-chromosomes-or-bacterial-genomes</guid>
	<pubDate>Mon, 26 Aug 2019 11:38:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39867/gepard-allows-the-calculation-of-dotplots-even-for-large-sequences-like-chromosomes-or-bacterial-genomes</link>
	<title><![CDATA[Gepard: allows the calculation of dotplots even for large sequences like chromosomes or bacterial genomes]]></title>
	<description><![CDATA[<p>Gepard (German: "cheetah", Backronym for "GEnome PAir - Rapid Dotter") allows the calculation of dotplots even for large sequences like chromosomes or bacterial genomes. Reference: Krumsiek J, Arnold R, Rattei T. Gepard: A rapid and sensitive tool for creating dotplots on genome scale. Bioinformatics 2007; 23(8): 1026-8. PMID:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/17309896" target="_blank">17309896</a></p>
<p><a href="http://cube.univie.ac.at/gepard">http://cube.univie.ac.at/gepard</a></p><p>Address of the bookmark: <a href="https://github.com/univieCUBE/gepard" rel="nofollow">https://github.com/univieCUBE/gepard</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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