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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/39869?offset=70</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43090/loretta-a-user-friendly-tool-for-assembling-viral-genomes-from-pacbio-sequence-data</guid>
	<pubDate>Wed, 23 Jun 2021 07:54:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43090/loretta-a-user-friendly-tool-for-assembling-viral-genomes-from-pacbio-sequence-data</link>
	<title><![CDATA[LoReTTA, a user-friendly tool for assembling viral genomes from PacBio sequence data]]></title>
	<description><![CDATA[<p>LoReTTA (Long Read Template-Targeted Assembler), a tool designed for performing <em>de novo</em> assembly of long reads generated from viral genomes on the PacBio platform. LoReTTA exploits a reference genome to guide the assembly process, an approach that has been successful with short reads.</p>
<p>https://academic.oup.com/ve/article/7/1/veab042/6248116</p><p>Address of the bookmark: <a href="https://academic.oup.com/ve/article/7/1/veab042/6248116" rel="nofollow">https://academic.oup.com/ve/article/7/1/veab042/6248116</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44525/synorth-exploring-the-evolution-of-synteny-and-long-range-regulatory-interactions-in-vertebrate-genomes</guid>
	<pubDate>Mon, 06 May 2024 06:21:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44525/synorth-exploring-the-evolution-of-synteny-and-long-range-regulatory-interactions-in-vertebrate-genomes</link>
	<title><![CDATA[Synorth: exploring the evolution of synteny and long-range regulatory interactions in vertebrate genomes]]></title>
	<description><![CDATA[<p><span>Genomic regulatory blocks are chromosomal regions spanned by long clusters of highly conserved noncoding elements devoted to long-range regulation of developmental genes, often immobilizing other, unrelated genes into long-lasting syntenic arrangements. Synorth&nbsp;</span><a href="http://synorth.genereg.net/" target="_blank">http://synorth.genereg.net/</a><span>&nbsp;is a web resource for exploring and categorizing the syntenic relationships in genomic regulatory blocks across multiple genomes, tracing their evolutionary fate after teleost whole genome duplication at the level of genomic regulatory block loci, individual genes, and their phylogenetic context.</span></p>
<p><span>More at&nbsp;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2745767/</span></p><p>Address of the bookmark: <a href="http://synorth.genereg.net/" rel="nofollow">http://synorth.genereg.net/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping</guid>
	<pubDate>Tue, 08 May 2018 04:15:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping</link>
	<title><![CDATA[MITObim - mitochondrial baiting and iterative mapping]]></title>
	<description><![CDATA[<p>This document contains instructions on how to use the MITObim pipeline described in Hahn et al. 2013. The full article can be found&nbsp;<a href="http://nar.oxfordjournals.org/content/41/13/e129" title="MITObim full article at NAR">here</a>. Kindly cite the article if you are using MITObim in your work. The pipeline was originally developed for&nbsp;<span>Illumina</span>&nbsp;data, but thanks to the versatility of the MIRA assembler, MITObim supports in principle also data from the&nbsp;<span>Iontorrent</span>,&nbsp;<span>454</span>&nbsp;and&nbsp;<span>PacBio</span>&nbsp;sequencing platforms.</p>
<p>Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.</p>
<p>I provide further examples&nbsp;<a href="https://github.com/chrishah/MITObim/tree/master/examples">here</a>&nbsp;as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!</p><p>Address of the bookmark: <a href="https://github.com/chrishah/MITObim" rel="nofollow">https://github.com/chrishah/MITObim</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</guid>
	<pubDate>Mon, 10 Apr 2017 04:16:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</link>
	<title><![CDATA[LoRDEC: a hybrid error correction program for long, PacBio reads]]></title>
	<description><![CDATA[<p>LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.</p>
<p><br> Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.<br> After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.</p>
<p>Why is LoRDEC different?</p>
<ul>
<li>It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.</li>
<li>It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.</li>
</ul><p>Address of the bookmark: <a href="http://www.atgc-montpellier.fr/lordec/" rel="nofollow">http://www.atgc-montpellier.fr/lordec/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38501/fgenesh-program-for-predicting-multiple-genes-in-genomic-dna-sequences</guid>
	<pubDate>Thu, 20 Dec 2018 11:55:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38501/fgenesh-program-for-predicting-multiple-genes-in-genomic-dna-sequences</link>
	<title><![CDATA[FGENESH - Program for predicting multiple genes in genomic DNA sequences]]></title>
	<description><![CDATA[<p>FGENESH is the fastest (50-100 times faster than GenScan) and most accurate gene finder available - see the figure and the table below. In recent rice genome sequencing projects, it was cited "the most successful (gene finding) program (Yu&nbsp;<em>et al</em>. (2002) Science 296:79) and was used to produce 87% of all high-evidence predicted genes (Goff&nbsp;<em>et al</em>. (2002) Science 296:79).</p><p>Address of the bookmark: <a href="http://www.softberry.com/berry.phtml?topic=fgenesh&amp;group=help&amp;subgroup=gfind" rel="nofollow">http://www.softberry.com/berry.phtml?topic=fgenesh&amp;group=help&amp;subgroup=gfind</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</guid>
	<pubDate>Mon, 27 Nov 2017 10:18:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</link>
	<title><![CDATA[Opera: An optimal genome scaffolding program]]></title>
	<description><![CDATA[<p><span>Opera (Optimal Paired-End Read Assembler) is a sequence assembly program (</span><a href="http://en.wikipedia.org/wiki/Sequence_assembly" target="_blank">http://en.wikipedia.org/wiki/Sequence_assembly&nbsp;<img src="https://a.fsdn.com/con/img/icons/external_asset.png" alt="image" style="border: 0px;"></a><span>). It uses information from paired-end or long reads to optimally order and orient contigs assembled from shotgun-sequencing reads.</span><br><br><span>An updated version called OPERA-LG has been re-engineered with features for the assembly of large and complex genomes.</span><br><br><span>Song Gao, Denis Bertrand, Burton K. H. Chia and Niranjan Nagarajan. OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees. Genome Biology, May 2016, doi: 10.1186/s13059-016-0951-y.</span><br><br><span>Song Gao, Wing-Kin Sung, Niranjan Nagarajan. Opera: reconstructing optimal genomic scaffolds with high-throughput paired-end sequences. Journal of Computational Biology, Sept. 2011, doi:10.1089/cmb.2011.0170.</span></p>
<p><span>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0951-y</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/operasf/" rel="nofollow">https://sourceforge.net/projects/operasf/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</guid>
	<pubDate>Sat, 02 Jun 2018 07:40:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</link>
	<title><![CDATA[MCMCTREE: a phylogenetic program for Bayesian estimation of species divergence times]]></title>
	<description><![CDATA[<p><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">MCMCTREE</a><span>&nbsp;is a phylogenetic program for Bayesian estimation of species divergence times using soft fossil constraints under various molecular clock models. This is part of the&nbsp;</span><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">PAML</a><span>&nbsp;package. In this tutorial I will analyze an easy example modified from dataset of&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/20551041" target="_blank">Inoue et al. (2010)</a><span>. Here we conduct a commonly used time estimation method, "Approximate Likelihood Method", for the datasets including more than 10 species.</span></p><p>Address of the bookmark: <a href="http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html" rel="nofollow">http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38623/kallisto-a-program-for-quantifying-abundances-of-transcripts-from-bulk-and-single-cell-rna-seq-data</guid>
	<pubDate>Mon, 07 Jan 2019 10:35:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38623/kallisto-a-program-for-quantifying-abundances-of-transcripts-from-bulk-and-single-cell-rna-seq-data</link>
	<title><![CDATA[kallisto: a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data]]></title>
	<description><![CDATA[<p><strong>kallisto</strong>&nbsp;is a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of&nbsp;<em>pseudoalignment</em>&nbsp;for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data,&nbsp;<strong>kallisto</strong>&nbsp;can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and&nbsp;<strong>kallisto</strong>&nbsp;is therefore not only fast, but also as accurate as existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks&nbsp;<strong>kallisto</strong>&nbsp;significantly outperforms existing tools.&nbsp;<strong>kallisto</strong>&nbsp;is described in detail in:</p>
<p>Nicolas L Bray, Harold Pimentel, P&aacute;ll Melsted and Lior Pachter,&nbsp;<a href="http://www.nature.com/nbt/journal/v34/n5/full/nbt.3519.html">Near-optimal probabilistic RNA-seq quantification</a>, Nature Biotechnology&nbsp;<strong>34</strong>, 525&ndash;527 (2016), doi:10.1038/nbt.3519</p><p>Address of the bookmark: <a href="https://pachterlab.github.io/kallisto/about" rel="nofollow">https://pachterlab.github.io/kallisto/about</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39837/cactus-a-reference-free-whole-genome-multiple-alignment-program</guid>
	<pubDate>Mon, 12 Aug 2019 07:52:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39837/cactus-a-reference-free-whole-genome-multiple-alignment-program</link>
	<title><![CDATA[Cactus: a reference-free whole-genome multiple alignment program]]></title>
	<description><![CDATA[<p>Cactus is a reference-free whole-genome multiple alignment program. The principal algorithms are described here:&nbsp;<a href="https://doi.org/10.1101/gr.123356.111">https://doi.org/10.1101/gr.123356.111</a></p>
<p><span>Cactus uses substantial resources. For primate-sized genomes (3 gigabases each), you should expect Cactus to use approximately 120 CPU-days of compute per genome, with about 120 GB of RAM used at peak. The requirements scale roughly quadratically, so aligning two 1-megabase bacterial genomes takes only 1.5 CPU-hours and 14 GB RAM.</span>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/ComparativeGenomicsToolkit/cactus" rel="nofollow">https://github.com/ComparativeGenomicsToolkit/cactus</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43722/crossmap-program-for-genome-coordinates-conversion-between-different-assemblies</guid>
	<pubDate>Tue, 25 Jan 2022 17:59:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43722/crossmap-program-for-genome-coordinates-conversion-between-different-assemblies</link>
	<title><![CDATA[CrossMap: program for genome coordinates conversion between different assemblies]]></title>
	<description><![CDATA[<p><span>CrossMap is a program for genome coordinates conversion between&nbsp;</span><em>different assemblies</em><span>&nbsp;(such as&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/assembly/2928/">hg18 (NCBI36)</a><span>&nbsp;&lt;=&gt;&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/assembly/2758/">hg19 (GRCh37)</a><span>). It supports commonly used file formats including&nbsp;</span><a href="https://samtools.github.io/hts-specs/SAMv1.pdf">BAM</a><span>,&nbsp;</span><a href="https://en.wikipedia.org/wiki/CRAM_(file_format)">CRAM</a><span>,&nbsp;</span><a href="https://en.wikipedia.org/wiki/SAM_(file_format)">SAM</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/goldenPath/help/wiggle.html">Wiggle</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/goldenPath/help/bigWig.html">BigWig</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/FAQ/FAQformat.html#format1">BED</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/FAQ/FAQformat.html#format3">GFF</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/FAQ/FAQformat.html#format4">GTF</a><span>,&nbsp;</span><a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">MAF</a><span>&nbsp;</span><a href="https://samtools.github.io/hts-specs/VCFv4.2.pdf">VCF</a><span>, and&nbsp;</span><a href="https://sites.google.com/site/gvcftools/home/about-gvcf">gVCF</a><span>.</span></p><p>Address of the bookmark: <a href="http://crossmap.sourceforge.net/" rel="nofollow">http://crossmap.sourceforge.net/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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