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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42965?offset=110</link>
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	<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/38670/ltr-finder-an-efficient-program-for-finding-full-length-ltr-retrotranspsons-in-genome-sequences</guid>
	<pubDate>Sun, 13 Jan 2019 07:05:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38670/ltr-finder-an-efficient-program-for-finding-full-length-ltr-retrotranspsons-in-genome-sequences</link>
	<title><![CDATA[LTR_Finder: an efficient program for finding full-length LTR retrotranspsons in genome sequences.]]></title>
	<description><![CDATA[<p>LTR_Finder is an efficient program for finding full-length LTR retrotranspsons in genome sequences.</p>
<p>The Program first constructs all exact match pairs by a suffix-array based algorithm and extends them to long highly similar pairs. Then Smith-Waterman algorithm is used to adjust the ends of LTR pair candidates to get alignment boundaries. These boundaries are subject to re-adjustment using supporting information of TG..CA box and TSRs and reliable LTRs are selected. Next, LTR_FINDER tries to identify PBS, PPT and RT inside LTR pairs by build-in aligning and counting modules. RT identification includes a dynamic programming to process frame shift. For other protein domains, LTR_FINDER calls ps_scan (from PROSITE,&nbsp;<a href="http://www.expasy.org/prosite/">http://www.expasy.org/prosite/</a>) to locate cores of important enzymes if they occur.</p><p>Address of the bookmark: <a href="https://github.com/xzhub/LTR_Finder" rel="nofollow">https://github.com/xzhub/LTR_Finder</a></p>]]></description>
	<dc:creator>Neel</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/43826/tiara-deep-learning-based-classification-system-for-eukaryotic-sequences</guid>
	<pubDate>Mon, 14 Mar 2022 23:02:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43826/tiara-deep-learning-based-classification-system-for-eukaryotic-sequences</link>
	<title><![CDATA[Tiara: deep learning-based classification system for eukaryotic sequences]]></title>
	<description><![CDATA[<p><span>With a large number of metagenomic datasets becoming available, eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step toward a better understanding of eukaryotic diversity.</span></p><p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article/38/2/344/6375939" rel="nofollow">https://academic.oup.com/bioinformatics/article/38/2/344/6375939</a></p>]]></description>
	<dc:creator>Rahul Nayak</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27080/mrfast-micro-read-fast-alignment-search-tool</guid>
	<pubDate>Tue, 26 Apr 2016 03:50:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27080/mrfast-micro-read-fast-alignment-search-tool</link>
	<title><![CDATA[mrFAST:  Micro Read Fast Alignment Search Tool]]></title>
	<description><![CDATA[<p><span>mrFAST is a read mapper that is designed to map short reads to reference genome with a special emphasis on the discovery of structural variation and segmental duplications. mrFAST maps short reads with respect to user defined error threshold, including indels up to 4+4 bp. This manual, describes how to choose the parameters and tune mrFAST with respect to the library settings. mrFAST is designed to find&nbsp;</span><strong><span style="text-decoration: underline;">'all'</span></strong><span>&nbsp; mappings for a given set of reads, however it can return one "best" map location if the relevant parameter is invoked.</span></p>
<p><span>More at&nbsp;http://mrfast.sourceforge.net/manual.html</span></p><p>Address of the bookmark: <a href="http://mrfast.sourceforge.net/manual.html" rel="nofollow">http://mrfast.sourceforge.net/manual.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27461/maftools</guid>
	<pubDate>Sat, 21 May 2016 22:40:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27461/maftools</link>
	<title><![CDATA[mafTools]]></title>
	<description><![CDATA[<p><span>Bioinformatics tools for dealing with Multiple Alignment Format (MAF) files.</span></p><p>Address of the bookmark: <a href="https://github.com/dentearl/mafTools" rel="nofollow">https://github.com/dentearl/mafTools</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30557/speedseq</guid>
	<pubDate>Fri, 20 Jan 2017 06:05:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30557/speedseq</link>
	<title><![CDATA[SpeedSeq]]></title>
	<description><![CDATA[<p>A flexible framework for rapid genome analysis and interpretation</p>
<p>C Chiang, R M Layer, G G Faust, M R Lindberg, D B Rose, E P Garrison, G T Marth, A R Quinlan, and I M Hall. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat Meth (2015). doi:10.1038/nmeth.3505.</p>
<p><a href="http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html">http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html</a></p><p>Address of the bookmark: <a href="https://github.com/hall-lab/speedseq" rel="nofollow">https://github.com/hall-lab/speedseq</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31014/sockeye</guid>
	<pubDate>Fri, 17 Feb 2017 08:51:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31014/sockeye</link>
	<title><![CDATA[sockeye]]></title>
	<description><![CDATA[<p>This sockeye&nbsp;software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization.</p><p>Address of the bookmark: <a href="http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3" rel="nofollow">http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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