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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41405?offset=100</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43439/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite</guid>
	<pubDate>Wed, 06 Oct 2021 07:01:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43439/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite</link>
	<title><![CDATA[MMseqs2: ultra fast and sensitive sequence search and clustering suite]]></title>
	<description><![CDATA[<p><span>MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.</span></p><p>Address of the bookmark: <a href="https://github.com/soedinglab/MMseqs2" rel="nofollow">https://github.com/soedinglab/MMseqs2</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44508/a-web-based-tool-for-sequence-alignment-statistics-and-innovative-visualization</guid>
	<pubDate>Thu, 04 Apr 2024 01:44:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44508/a-web-based-tool-for-sequence-alignment-statistics-and-innovative-visualization</link>
	<title><![CDATA[A web-based tool for sequence alignment statistics and innovative visualization]]></title>
	<description><![CDATA[<p>AlignStatPlot, a new R package and online tool that is well-documented and easy-to usefor MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analy-ses on sequencing data and generates new visualisation methods for MSA results. Whencompared to currently available tools, AlignStatPlot provides a robust ability to handle andvisualise diversity data, while the online version will save time and encourage researchersto focus on explaining their findings. It is a simple tool that can be used in conjunction withpopulation genetics software (PDF) AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data.</p><p>Address of the bookmark: <a href="https://bioinformatics.um6p.ma/AlignStatPlot/" rel="nofollow">https://bioinformatics.um6p.ma/AlignStatPlot/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26587/last</guid>
	<pubDate>Wed, 09 Mar 2016 14:27:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26587/last</link>
	<title><![CDATA[LAST]]></title>
	<description><![CDATA[<p style="text-align: center;"><img src="http://last.cbrc.jp/lastwebfig.png" alt="sketch of  similar regions in sequences" style="border: 0px;"></p>
<p>LAST can:</p>
<ul>
<li>Handle <strong>big</strong> sequence data, e.g:
<ul>
<li>Compare two vertebrate genomes</li>
<li>Align billions of DNA reads to a genome</li>
</ul>
</li>
<li>Indicate the <a href="http://lastweb.cbrc.jp/about.html">reliability</a> of each aligned column.</li>
<li>Use sequence quality data <a href="http://nar.oxfordjournals.org/content/38/7/e100.abstract">properly</a>.</li>
<li>Compare DNA to proteins, with frameshifts.</li>
<li>Compare PSSMs to sequences</li>
<li>Calculate the likelihood of chance similarities between random sequences.</li>
<li>Do split and spliced alignment.</li>
<li><a href="http://last.cbrc.jp/doc/last-train.html">Train</a> alignment parameters for unusual kinds of sequence (e.g. nanopore).</li>
</ul><p>Address of the bookmark: <a href="http://last.cbrc.jp/" rel="nofollow">http://last.cbrc.jp/</a></p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
</item>
<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/37751/kast-perform-alignment-free-k-tuple-frequency-comparisons-from-sequences</guid>
	<pubDate>Thu, 20 Sep 2018 08:56:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37751/kast-perform-alignment-free-k-tuple-frequency-comparisons-from-sequences</link>
	<title><![CDATA[KAST: Perform Alignment-free k-tuple frequency comparisons from sequences]]></title>
	<description><![CDATA[<p><span>Perform Alignment-free k-tuple frequency comparisons from sequences. This can be in the form of two input files (e.g. a reference and a query) or a single file for pairwise comparisons to be made.</span></p><p>Address of the bookmark: <a href="https://github.com/martinjvickers/KAST" rel="nofollow">https://github.com/martinjvickers/KAST</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/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/bookmarks/view/43815/kebabs-package-provides-functionality-for-kernel-based-analysis-of-biological-sequences-via-support-vector-machine-svm-based-methods</guid>
	<pubDate>Fri, 04 Mar 2022 00:14:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43815/kebabs-package-provides-functionality-for-kernel-based-analysis-of-biological-sequences-via-support-vector-machine-svm-based-methods</link>
	<title><![CDATA[kebabs: package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods]]></title>
	<description><![CDATA[<p><span>The&nbsp;</span><tt>kebabs</tt><span>&nbsp;package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods. Biological sequences include DNA, RNA, and amino acid (AA) sequences. Sequence kernels define similarity measures between sequences. The package implements some of the most important kernels for sequence analysis in a very flexible and efficient way and extends the standard position-independent functionality of these kernels in a novel way to take the position of patterns in the sequences into account for the similarity measure.</span></p>
<p>http://www.bioinf.jku.at/software/kebabs/</p>
<p>http://bioconductor.org/packages/release/bioc/vignettes/kebabs/inst/doc/kebabs.pdf</p><p>Address of the bookmark: <a href="http://www.bioinf.jku.at/software/kebabs/" rel="nofollow">http://www.bioinf.jku.at/software/kebabs/</a></p>]]></description>
	<dc:creator>Rahul Nayak</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>

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