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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42826?offset=90</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42413/liftoff-an-accurate-gff3gtf-lift-over-pipeline</guid>
	<pubDate>Sun, 20 Dec 2020 01:36:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42413/liftoff-an-accurate-gff3gtf-lift-over-pipeline</link>
	<title><![CDATA[Liftoff: An accurate GFF3/GTF lift over pipeline]]></title>
	<description><![CDATA[<p><span>Liftoff is a tool that accurately maps annotations in GFF or GTF between assemblies of the same, or closely-related species. Unlike current coordinate lift-over tools which require a pre-generated &ldquo;chain&rdquo; file as input, Liftoff is a standalone tool that takes two genome assemblies and a reference annotation as input and outputs an annotation of the target genome.</span></p><p>Address of the bookmark: <a href="https://github.com/agshumate/Liftoff" rel="nofollow">https://github.com/agshumate/Liftoff</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44894/dna2bit-an-ultra-fast-and-accurate-genomic-distance-estimation-software</guid>
	<pubDate>Sun, 31 Aug 2025 06:24:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44894/dna2bit-an-ultra-fast-and-accurate-genomic-distance-estimation-software</link>
	<title><![CDATA[dna2bit: an ultra-fast and accurate genomic distance estimation software]]></title>
	<description><![CDATA[<p><span>dna2bit is a software tool developed in C++11, leveraging the capabilities of OpenMP for parallel computing and the popcount technique for efficient bit manipulation. It has been thoroughly tested using the g++ and clang compilers on both Linux and MacOS platforms.</span></p><p>Address of the bookmark: <a href="https://github.com/lijuzeng/dna2bit" rel="nofollow">https://github.com/lijuzeng/dna2bit</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37842/rapclust-accurate-lightweight-clustering-of-de-novo-transcriptomes-using-fragment-equivalence-classes</guid>
	<pubDate>Thu, 04 Oct 2018 17:57:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37842/rapclust-accurate-lightweight-clustering-of-de-novo-transcriptomes-using-fragment-equivalence-classes</link>
	<title><![CDATA[RapClust: Accurate, Lightweight Clustering of de novo Transcriptomes using Fragment Equivalence Classes]]></title>
	<description><![CDATA[<p><span>RapClust is a tool for clustering contigs from&nbsp;</span><em>de novo</em><span>&nbsp;transcriptome assemblies. RapClust is designed to be run downstream of the&nbsp;</span><a href="https://github.com/kingsfordgroup/sailfish">Sailfish</a><span>&nbsp;or&nbsp;</span><a href="https://github.com/COMBINE-lab/salmon">Salmon</a><span>&nbsp;tools for rapid transcript-level quantification. Specifically, RapClust relies on the&nbsp;</span><em>fragment equivalence classes</em><span>&nbsp;computed by these tools in order to determine how seqeunce is shared across the transcriptome, and how reads map to potentially-related contigs across different conditions.</span></p><p>Address of the bookmark: <a href="https://github.com/COMBINE-lab/RapClust" rel="nofollow">https://github.com/COMBINE-lab/RapClust</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/44515/cleaner-blast-databases-for-more-accurate-results</guid>
	<pubDate>Tue, 23 Apr 2024 01:23:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/44515/cleaner-blast-databases-for-more-accurate-results</link>
	<title><![CDATA[Cleaner BLAST Databases for More Accurate Results]]></title>
	<description><![CDATA[<p>Do you use&nbsp;<a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=blast-cleaner-20240422">BLAST</a><span style="font-size: 12.8px; font-weight: normal;">&nbsp;to identify a sequence or the evolutionary scope of a gene? That can be challenging if contaminated and misclassified sequences are in the BLAST databases and show up in your search results. To address</span><span style="font-size: 12.8px; font-weight: normal;">&nbsp;this problem</span><span style="font-size: 12.8px; font-weight: normal;">, we now use the NCBI quality assurance tools listed below to systematically remove these misleading sequences from the default nucleotide (nt) and protein (nr) BLAST databases.</span><span style="font-size: 12.8px; font-weight: normal;">&nbsp;</span></p><div><ul>
<li><a href="https://github.com/ncbi/fcs">Foreign Contamination Screen tool for genome cross-species screening (FCS-GX)</a>&nbsp;detects contamination from foreign organisms in genomes and other sequences using the genome cross-species aligner (GX)&nbsp;</li>
<li><a href="https://ncbiinsights.ncbi.nlm.nih.gov/2022/05/27/ani-for-assembly-validation?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=blast-cleaner-20240422">Average Nucleotide Identity (ANI)</a>&nbsp;evaluates the taxonomic classification of prokaryotic genome assemblies. Sequences from genomes marked up as &lsquo;unverified source organism&rsquo; are considered suspect and removed.&nbsp;</li>
</ul><p>Ref&nbsp;https://ncbiinsights.ncbi.nlm.nih.gov/2024/04/22/cleaner-blast-databases-more-accurate-results/</p></div>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34420/rita-rapid-identification-of-high-confidence-taxonomic-assignments-for-metagenomic-data</guid>
	<pubDate>Mon, 27 Nov 2017 08:25:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34420/rita-rapid-identification-of-high-confidence-taxonomic-assignments-for-metagenomic-data</link>
	<title><![CDATA[RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data]]></title>
	<description><![CDATA[<p>RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.</p>
<p>This work has been accepted for publication:</p>
<p>MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to&nbsp;<em>Nucleic Acids Research</em>&nbsp;April 4, 2012.</p>
<p>If you have any questions or bug reports, please let us know at &lt;beiko@cs.dal.ca&gt;.</p><p>Address of the bookmark: <a href="http://kiwi.cs.dal.ca/Software/RITA" rel="nofollow">http://kiwi.cs.dal.ca/Software/RITA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35272/biocircosjs-is-an-open-source-interactive-javascript-library-to-interactive-display-biological-data-on-the-web</guid>
	<pubDate>Fri, 19 Jan 2018 15:03:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35272/biocircosjs-is-an-open-source-interactive-javascript-library-to-interactive-display-biological-data-on-the-web</link>
	<title><![CDATA[BioCircos.js is an open source interactive Javascript library to interactive display biological data on the web]]></title>
	<description><![CDATA[<p><a href="http://bioinfo.ibp.ac.cn/biocircos/index.php">BioCircos.js</a>&nbsp;is an open source interactive&nbsp;<code>Javascript</code>&nbsp;library which provides an easy way to interactive display biological data on the web. It implements a raster-based&nbsp;<code>SVG</code>&nbsp;visualization using the open source Javascript framework jquery.js. BioCircos.js is multiplatform and works in all major internet browsers (<strong>Internet Explorer</strong>,&nbsp;<strong>Mozilla Firefox</strong>,&nbsp;<strong>Google Chrome</strong>,&nbsp;<strong>Safari</strong>,&nbsp;<strong>Opera</strong>). Its speed is determined by the client&rsquo;s hardware and internet browser. For smoothest user experience, we recommend&nbsp;<strong>Google Chrome</strong>.</p>
<p>BioCircos.js provides&nbsp;<strong>SNP</strong>,&nbsp;<strong>CNV</strong>,&nbsp;<strong>HEATMAP</strong>,&nbsp;<strong>LINK</strong>,&nbsp;<strong>LINE</strong>,&nbsp;<strong>SCATTER</strong>,&nbsp;<strong>ARC</strong>,&nbsp;<strong>TEXT</strong>, and&nbsp;<strong>HISTGRAM</strong>modules to display genome-wide genetic variations (SNPs, CNVs and chromosome rearrangement), gene expression and biomolecule interactions. BioCircos.js also provides&nbsp;<strong>BACKGROUND</strong>&nbsp;module to display background and axis circles. Tooltips showing detailed information of SVG elements are also provided.</p>
<p><a href="http://bioinfo.ibp.ac.cn/biocircos/document/demo/pages/paper01.html">Demo</a></p><p>Address of the bookmark: <a href="http://bioinfo.ibp.ac.cn/biocircos/document/index.html" rel="nofollow">http://bioinfo.ibp.ac.cn/biocircos/document/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37259/epiviz-an-interactive-visualization-tool-for-functional-genomics-data</guid>
	<pubDate>Mon, 09 Jul 2018 05:27:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37259/epiviz-an-interactive-visualization-tool-for-functional-genomics-data</link>
	<title><![CDATA[Epiviz: an interactive visualization tool for functional genomics data.]]></title>
	<description><![CDATA[<p><span>Epiviz is an interactive visualization tool for functional genomics data. It supports genome navigation like other genome browsers, but allows multiple visualizations of data within genomic regions using scatterplots, heatmaps and other user-supplied visualizations. It also includes data from the&nbsp;</span><a href="http://barcode.luhs.org/" target="_blank">Gene Expression Barcode project</a><span>&nbsp;for transcriptome visualization. It has a flexible plugin framework so users can add</span><a href="http://d3js.org/" target="_blank">d3</a><span>&nbsp;visualizations. You can see a video tour&nbsp;</span><a href="http://youtu.be/099c4wUxozA" target="_blank">here</a><span>.</span></p>
<p><span>https://bioconductor.org/packages/release/bioc/html/epivizr.html</span></p>
<p><span>https://github.com/epiviz</span></p>
<p><span>https://github.com/epiviz/epiviz</span></p><p>Address of the bookmark: <a href="https://epiviz.github.io/" rel="nofollow">https://epiviz.github.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38829/nquire-a-statistical-framework-for-ploidy-estimation-using-ngs-short-read-data</guid>
	<pubDate>Thu, 31 Jan 2019 05:12:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38829/nquire-a-statistical-framework-for-ploidy-estimation-using-ngs-short-read-data</link>
	<title><![CDATA[nQuire: A statistical framework for ploidy estimation using NGS short-read data]]></title>
	<description><![CDATA[<p>nQuire implements a set of commands to estimate ploidy level of individuals from species, where recent polyploidization occurred and intraspecific ploidy variation is observed. Specifically, nQuire uses next-generation sequencing data to distinguish between diploids, triploids and tetraploids, on the basis of frequency distributions at variant sites where only two bases are segregating.</p>
<p>For more background see also the publication at&nbsp;<a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2128-z">BMC Bioinformatics</a>.</p>
<p>https://github.com/clwgg/nQuire</p><p>Address of the bookmark: <a href="https://github.com/clwgg/nQuire" rel="nofollow">https://github.com/clwgg/nQuire</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40583/trelliscope-flexibly-visualize-large-complex-data-in-great-detail-from-within-the-r-statistical-programming-environment</guid>
	<pubDate>Tue, 21 Jan 2020 04:22:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40583/trelliscope-flexibly-visualize-large-complex-data-in-great-detail-from-within-the-r-statistical-programming-environment</link>
	<title><![CDATA[Trelliscope: flexibly visualize large, complex data in great detail from within the R statistical programming environment.]]></title>
	<description><![CDATA[<p>Trelliscope provides a way to flexibly visualize large, complex data in great detail from within the R statistical programming environment. Trelliscope is a component in the<span>&nbsp;</span><a href="http://deltarho.org/docs-trelliscope/deltarho.org">DeltaRho</a><span>&nbsp;</span>environment.</p>
<p>For those familiar with<span>&nbsp;</span><a href="http://cm.bell-labs.com/cm/ms/departments/sia/project/trellis/">Trellis Display</a>,<span>&nbsp;</span><a href="http://docs.ggplot2.org/0.9.3.1/facet_wrap.html">faceting in ggplot</a>, or the notion of<span>&nbsp;</span><a href="http://en.wikipedia.org/wiki/Small_multiple">small multiples</a>, Trelliscope provides a scalable way to break a set of data into pieces, apply a plot method to each piece, and then arrange those plots in a grid and interactively sort, filter, and query panels of the display based on metrics of interest. With Trelliscope, we are able to create multipanel displays on data with a very large number of subsets and view them in an interactive and meaningful way.</p><p>Address of the bookmark: <a href="http://deltarho.org/docs-trelliscope/#introduction" rel="nofollow">http://deltarho.org/docs-trelliscope/#introduction</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/41562/submit-your-sars-cov-2-sequence-data-to-genbank</guid>
	<pubDate>Thu, 09 Apr 2020 18:28:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/41562/submit-your-sars-cov-2-sequence-data-to-genbank</link>
	<title><![CDATA[Submit your SARS-CoV-2 sequence data to GenBank]]></title>
	<description><![CDATA[<div dir="auto">Submit your SARS-CoV-2 sequence data to GenBank and SRA with our new submission landing page. Submission is simple and streamlined *and* there&rsquo;s a rapid turnaround. <span><a href="https://l.facebook.com/l.php?u=https%3A%2F%2Fsubmit.ncbi.nlm.nih.gov%2Fsarscov2%2F%3Ffbclid%3DIwAR3p-OzZPe2yx4CZMoZxiWMF3kUQjXyVVduNQhBdehWmFTJ3cPBstsOLypI&amp;h=AT2d-umit7ciXRW-nrRYVL3gJSLKY4Hte8W8cXw8Wl94n6PGmoHmVqvvhgQj-mTo6A5lpMP9JDV_lRSq9RRLT5KeVVAAfcuRgJOeA6QhApIB2B9nFxUfDCD3sio4HYidpRwpmng&amp;__tn__=-UK-R&amp;c[0]=AT2zWGa1K5EvV4UcnB0b7HHvkBtX-wAyh7AF8_fZ9uI2y-02nOHQHT_Um3xgnto5KEZ26wRG0xNgUWTA1W-7HF0E25E23XtIL5XGOhloBXaDIcHw30AVjTCkQi7aFk4dN7aBCmVJeSbH37urtbM2kmMfyTCbdTvMU8FGlnX-DNVuCaZr4XfXnf_jvPNdxe9sBH84oXJ-uJz5kbqlHGAHDoqK" target="_blank">https://submit.ncbi.nlm.nih.gov/sarscov2/</a></span></div><div dir="auto">&nbsp;</div><div dir="auto"><span><span>Quickly and easily add your SARS-CoV-2 sequence data to the growing public archive with new, special features and support from NCBI. </span><a href="https://submit.ncbi.nlm.nih.gov/sarscov2/">new SARS-CoV-2 sequence submission landing page</a><span>&nbsp;will help you get started. GenBank submissions are accessioned and released in approximately 1-2 working days, and&nbsp;</span><a href="https://www.ncbi.nlm.nih.gov/sra" target="_blank">Sequence Read Archive</a><span>&nbsp;(SRA) submissions typically processed and released within hours. Submission is simple!</span></span></div><div><div dir="auto">&nbsp;</div><div dir="auto">More information is available on NCBI Insights. <span><a href="https://l.facebook.com/l.php?u=https%3A%2F%2Fncbiinsights.ncbi.nlm.nih.gov%2F2020%2F04%2F09%2Fsars-cov2-data-streamlined-submission-rapid-turnaround%2F%3Ffbclid%3DIwAR1OuLu3oDjz3VX4fDq5Jg316td9foTOUGNqnoN1eI2nFXTf4EBv28JiXD4&amp;h=AT0ah_epxwAc-nM6QiPBYvKSQ-kWmiPgHKO1w7SnxnnRiTI4etJJfNAWyzcR7snIdtxtcErAFRdHPBH2j0EY77gUPDdnBVnAsxnVbSgZnrrOPfnni331A37Xvytgnye0ArnUuWk&amp;__tn__=-UK-R&amp;c[0]=AT2zWGa1K5EvV4UcnB0b7HHvkBtX-wAyh7AF8_fZ9uI2y-02nOHQHT_Um3xgnto5KEZ26wRG0xNgUWTA1W-7HF0E25E23XtIL5XGOhloBXaDIcHw30AVjTCkQi7aFk4dN7aBCmVJeSbH37urtbM2kmMfyTCbdTvMU8FGlnX-DNVuCaZr4XfXnf_jvPNdxe9sBH84oXJ-uJz5kbqlHGAHDoqK" target="_blank">https://ncbiinsights.ncbi.nlm.nih.gov/2020/04/09/sars-cov2-data-streamlined-submission-rapid-turnaround/</a></span></div></div>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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