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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43826?offset=10</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38668/gvolante-completeness-assessment-of-genometranscriptome-sequences</guid>
	<pubDate>Sun, 13 Jan 2019 07:03:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38668/gvolante-completeness-assessment-of-genometranscriptome-sequences</link>
	<title><![CDATA[gVolante: Completeness Assessment of Genome/Transcriptome Sequences]]></title>
	<description><![CDATA[<p><span>A brand-new web server, gVolante, which provides an online tool for (i) on-demand completeness assessment of sequence sets by means of the previously developed pipelines CEGMA and BUSCO and (ii) browsing pre-computed completeness scores for publicly available data in its database section</span></p><p>Address of the bookmark: <a href="https://gvolante.riken.jp/analysis.html" rel="nofollow">https://gvolante.riken.jp/analysis.html</a></p>]]></description>
	<dc:creator>Neel</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/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file</guid>
	<pubDate>Thu, 24 May 2018 10:06:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file</link>
	<title><![CDATA[pbalign: maps PacBio reads to reference sequences and saves alignments to a BAM file]]></title>
	<description><![CDATA[pbalign aligns PacBio reads to reference sequences, filters aligned reads according to user-specific filtering criteria, and converts the output to either the SAM format or PacBio Compare HDF5 (e.g., .cmp.h5) format. The output Compare HDF5 file will be compatible with Quiver if --forQuiver option is specified.<p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/pbalign" rel="nofollow">https://github.com/PacificBiosciences/pbalign</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37796/grsr-a-tool-for-deriving-genome-rearrangement-scenarios-from-multiple-unichromosomal-genome-sequences</guid>
	<pubDate>Fri, 28 Sep 2018 09:35:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37796/grsr-a-tool-for-deriving-genome-rearrangement-scenarios-from-multiple-unichromosomal-genome-sequences</link>
	<title><![CDATA[GRSR: a tool for deriving genome rearrangement scenarios from multiple unichromosomal genome sequences]]></title>
	<description><![CDATA[<p>GRSR is a Tool for Deriving Genome Rearrangement Scenarios for Multiple Uni-chromosomal Genomes. This tool will do the following steps:</p>
<ul>
<li>Step 1. Run mugsy to get multiple sequence alignment results.</li>
<li>Step 2 &amp; 3. Extraction of the Coordinates of Core Blocks, Construction of Synteny Blocks and Generating Signed Permutations.</li>
<li>Step 4. Generate pairwise genome rearrangement scenarios and find repeats at the breakpoints of each rearrangement events.</li>
<li></li>
<li></li>
</ul>
<p>https://github.com/DanwangJessica/GRSR</p><p>Address of the bookmark: <a href="https://github.com/DanwangJessica/GRSR" rel="nofollow">https://github.com/DanwangJessica/GRSR</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/39624/cogent-a-tool-for-reconstructing-the-coding-genome-using-high-quality-full-length-transcriptome-sequences</guid>
	<pubDate>Tue, 18 Jun 2019 05:33:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39624/cogent-a-tool-for-reconstructing-the-coding-genome-using-high-quality-full-length-transcriptome-sequences</link>
	<title><![CDATA[Cogent: a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences.]]></title>
	<description><![CDATA[<div id="yui_3_14_1_1_1560853173251_3865">Cogent is a tool that identifies gene&nbsp;families and reconstructs the coding genome using high-quality transcriptome data without a reference genome, and can be used to check&nbsp;assemblies&nbsp;for the presence of&nbsp;these known coding sequences.</div>
<div>&nbsp;</div>
<div>
<p>Cogent is a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences. It is designed to be used on&nbsp;<a href="https://github.com/PacificBiosciences/cDNA_primer/wiki">Iso-Seq data</a>&nbsp;and in cases where there is no reference genome or the ref genome is highly incomplete.</p>
<p>See a&nbsp;<a href="https://www.dropbox.com/s/mn6hwhguh0pqceu/20160106_Cogent_developers_conference_slides_Cuttlefish.pdf?dl=0">recent presentation</a>&nbsp;on Cogent being applied to the Cuttlefish Iso-Seq data.</p>
<p><a href="https://www.dropbox.com/s/kz0gi7qg0w82k9a/20161026_Cogent_manuscript_forGitHub.pdf?dl=0">Cogent preliminary draft paper (updated 2016Dec version)</a>,&nbsp;<a href="https://www.dropbox.com/s/37412o8glvnfhf9/20161026_Cogent_ManuscriptPlusSupplement_forGitHub.pdf?dl=0">Supplementary</a></p>
<p>Please see&nbsp;<a href="https://github.com/Magdoll/Cogent/wiki">wiki</a>&nbsp;for details on usage.</p>
</div><p>Address of the bookmark: <a href="https://github.com/Magdoll/Cogent" rel="nofollow">https://github.com/Magdoll/Cogent</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/41230/curated-set-of-ribosomal-rna-rrna-reference-sequences-targeted-loci-with-verifiable-organism</guid>
	<pubDate>Sun, 23 Feb 2020 02:17:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/41230/curated-set-of-ribosomal-rna-rrna-reference-sequences-targeted-loci-with-verifiable-organism</link>
	<title><![CDATA[Curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism]]></title>
	<description><![CDATA[<p>MCBI have a curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism sources and current names. This set is critical for correctly identifying and classifying prokaryotic (bacteria and archaea) and fungal samples. To provide easy access to these sequences, we recently added a separate rRNA/ITS databases section on the nucleotide BLAST page for these targeted sequences that makes it convenient to quickly identify source organisms. The new databases are: </p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *16S ribosomal RNA (Bacteria and Archaea)</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *18S ribosomal RNA sequences (SSU) from Fungi type and reference material&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *28S ribosomal RNA sequences (LSU) from Fungi type and reference material</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *Internal transcribed spacer region (ITS) from Fungi type and reference material</p><p>You can also download these from the BLAST db FTP area.&nbsp; See the <a href="https://go.usa.gov/xdEBX" target="_blank">NCBI Insights post</a> for more detail. </p><p>Useful links</p><p>-----------------</p><p><a href="https://go.usa.gov/xdEj5" target="_blank">BLAST form with rRNA/ITS databases</a></p><p><a href="https://ftp.ncbi.nlm.nih.gov/blast/db/" target="_blank">BLAST db download</a></p><p><a href="https://www.ncbi.nlm.nih.gov/refseq/targetedloci/" target="_blank">Targeted loci</a></p><p><span style="color: black;">If you have any questions or concerns, please contact <a href="mailto:blast-help@ncbi.nlm.nih.gov" target="_blank" title="Follow link">blast-help@ncbi.nlm.nih.gov<sup><span style="color: black; text-decoration: none;"><img src="https://mail.google.com/mail/u/0?ui=2&amp;ik=024a8aa0b9&amp;attid=0.1&amp;permmsgid=msg-f:1659255165855446848&amp;th=1706dbc8408bb740&amp;view=fimg&amp;sz=s0-l75-ft&amp;attbid=ANGjdJ_drW2ArYDNLoHrQh36gm6rp2Std8ZUSplCzP6bYQSQYBsQfZ_85vOujXOdTRdaLxrR7QeEBVUbyACPBJHhFUeIglX8G7Ew7TcclzhvO7fJhiz7sIdkkDgZ7QA&amp;disp=emb" alt="https://jira.ncbi.nlm.nih.gov/images/icons/mail_small.gif" width="13" height="12" style="border: 0px;"></span></sup></a></span></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42296/igblast-117-is-now-available-with-improved-identification-of-productive-v-gene-sequences</guid>
	<pubDate>Sun, 01 Nov 2020 16:52:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42296/igblast-117-is-now-available-with-improved-identification-of-productive-v-gene-sequences</link>
	<title><![CDATA[IgBLAST 1.17 is now available with improved identification of productive V gene sequences]]></title>
	<description><![CDATA[<p>A new release of&nbsp;<a href="https://go.usa.gov/x7WMc" target="_blank">IgBLAST</a>&nbsp;(1.17), the popular package for classifying and analyzing immunoglobulin and T cell receptor sequences, is now available on the&nbsp;<a href="https://go.usa.gov/x7WMc" target="_blank">web</a>&nbsp;and from the&nbsp;<a href="https://ftp.ncbi.nih.gov/blast/executables/igblast/release/LATEST" target="_blank">FTP site</a>. The updated package is better at identifying productive V gene sequences. We added a new field , &ldquo;V frame shift&rdquo;, to the IgBLAST output to indicate whether the V gene translation frame contains a frame-shift. We have also updated the definition of a productive V(D)J sequence to now exclude those with internal frame shifts.</p><p>See the&nbsp;<a href="https://ncbi.github.io/igblast/" target="_blank">new IgBLAST manual</a>&nbsp;on the NCBI GitHub site for more information on setting up and running IgBLAST.</p><p>If you have any questions or concerns, please email us at&nbsp;<a href="mailto:blast-help@ncbi.nlm.nih.gov" target="_blank">blast-help@ncbi.nlm.nih.gov</a></p><p>&nbsp;</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44479/doubletrouble-identify-duplicated-genes-from-whole-genome-protein-sequences-and-classify</guid>
	<pubDate>Tue, 05 Mar 2024 00:23:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44479/doubletrouble-identify-duplicated-genes-from-whole-genome-protein-sequences-and-classify</link>
	<title><![CDATA[doubletrouble: identify duplicated genes from whole-genome protein sequences and classify]]></title>
	<description><![CDATA[<p><span>doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks.</span></p><p>Address of the bookmark: <a href="https://bioconductor.org/packages/release/bioc/html/doubletrouble.html" rel="nofollow">https://bioconductor.org/packages/release/bioc/html/doubletrouble.html</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44896/jaeger-an-accurate-and-fast-deep-learning-tool-to-detect-bacteriophage-sequences</guid>
	<pubDate>Sun, 31 Aug 2025 06:30:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44896/jaeger-an-accurate-and-fast-deep-learning-tool-to-detect-bacteriophage-sequences</link>
	<title><![CDATA[Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences]]></title>
	<description><![CDATA[<p><span>Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.</span></p><p>Address of the bookmark: <a href="https://github.com/MGXlab/Jaeger" rel="nofollow">https://github.com/MGXlab/Jaeger</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

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