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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38526?offset=240</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36476/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Fri, 04 May 2018 19:16:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36476/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</link>
	<title><![CDATA[Flye: Fast and accurate de novo assembler for single molecule sequencing reads]]></title>
	<description><![CDATA[<p><span>Flye is a de novo assembler for long and noisy reads, such as those produced by PacBio and Oxford Nanopore Technologies. The algorithm uses an A-Bruijn graph to find the overlaps between reads and does not require them to be error-corrected. After the initial assembly, Flye performs an extra repeat classification and analysis step to improve the structural accuracy of the resulting sequence. The package also includes a polisher module, which produces the final assembly of high nucleotide-level quality.</span></p><p>Address of the bookmark: <a href="https://github.com/fenderglass/Flye" rel="nofollow">https://github.com/fenderglass/Flye</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36837/ranbow-a-haplotype-assembler-for-polyploid-genomes</guid>
	<pubDate>Fri, 01 Jun 2018 07:21:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36837/ranbow-a-haplotype-assembler-for-polyploid-genomes</link>
	<title><![CDATA[Ranbow: a haplotype assembler for polyploid genomes]]></title>
	<description><![CDATA[Ranbow is a haplotype assembler for polyploid genomes. It has been developed for the haplotype assembly of the hexaploid sweet potato genome, which is highly heterozygous. Ranbow can also be applied to other polyploid genomes. After a first phasing, Ranbow utilizes the assembled haplotypes to improve the accuracy of variant calling results and to infer the evolutionary history of the organism´s genome. Ranbow has three main modes of function:

ranbow hap: for haplotyping
ranbow eval: for evaluating of the assemble haplotypes by gold standard (long) reads 
ranbow phylo: for the phylogenetic analysis<p>Address of the bookmark: <a href="https://www.molgen.mpg.de/ranbow" rel="nofollow">https://www.molgen.mpg.de/ranbow</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41452/apollo-a-sequencing-technology-independent-scalable-and-accurate-assembly-polishing-algorithm</guid>
	<pubDate>Mon, 16 Mar 2020 10:09:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41452/apollo-a-sequencing-technology-independent-scalable-and-accurate-assembly-polishing-algorithm</link>
	<title><![CDATA[Apollo: A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm]]></title>
	<description><![CDATA[<p><span>Apollo is an assembly polishing algorithm that attempts to correct the errors in an assembly. It can take multiple set of reads in a single run and polish the assemblies of genomes of any size. Described by Firtina et al. (preliminary version at&nbsp;</span><a href="https://arxiv.org/pdf/1902.04341.pdf">https://arxiv.org/pdf/1902.04341.pdf</a></p>
<p>More at&nbsp;<a href="https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa179/5804978?rss=1">https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa179/5804978?rss=1</a></p><p>Address of the bookmark: <a href="https://github.com/CMU-SAFARI/Apollo" rel="nofollow">https://github.com/CMU-SAFARI/Apollo</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44474/claw-chloroplast-long-read-assembly-workflow</guid>
	<pubDate>Wed, 21 Feb 2024 12:37:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44474/claw-chloroplast-long-read-assembly-workflow</link>
	<title><![CDATA[CLAW: Chloroplast Long-read Assembly Workflow]]></title>
	<description><![CDATA[<p dir="auto">CLAW (Chloroplast Long-read Assembly Workflow) is an mostly-automated Snakemake-based workflow for the assembly of chloroplast genomes. CLAW uses chloroplast long-reads, which are baited out of larger read libraries (e.g., an Oxford Nanopore Technologies MinION read library derived from photosynthetic tissue), for assembly with Flye and/or Unicycler. CLAW was designed with the novice bioinformatician in mind - it is easy to install and easy to use, requiring only minimal user input.</p><p>Address of the bookmark: <a href="https://github.com/aaronphillips7493/CLAW" rel="nofollow">https://github.com/aaronphillips7493/CLAW</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite</guid>
	<pubDate>Wed, 03 Jan 2018 05:05:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite</link>
	<title><![CDATA[Seal: SEquence ALignment evaluation suite]]></title>
	<description><![CDATA[<p><span>Seal</span>&nbsp;is a comprehensive sequencing simulation and alignment tool evaluation suite. This software (implemented in Java) provides several utilities that can be used to evaluate alignment algorithms, including:</p>
<ul>
<li>Reading a pre-existing reference genome from one or more FASTA files.</li>
<li>Alternatively, generating an artificial reference genome based on input parameters (length, repeat count, repeat length, repeat variability rate).</li>
<li>Simulating reads from random locations in the genome based on input parameters of read length, coverage, sequencing error rate, and indel rate.</li>
<li>Applying alignment tools to the genome and the reads through a standardized interface.</li>
<li>Parsing the output of the alignment tool and calculating the number of reads that were correctly or incorrectly mapped.</li>
<li>Computing run times and measures of accuracy.</li>
</ul>
<p><span>Seal</span>&nbsp;has interfaces to evaluate the following software packages:</p>
<ul>
<li>Bowtie</li>
<li>BWA</li>
<li>MAQ</li>
<li>mrFAST</li>
<li>mrsFAST</li>
<li>Novoalign</li>
<li>SHRiMP</li>
<li>SOAPv2</li>
</ul><p>Address of the bookmark: <a href="http://compbio.case.edu/seal/" rel="nofollow">http://compbio.case.edu/seal/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36373/tools-to-predict-the-impact-of-missense-variants</guid>
	<pubDate>Mon, 23 Apr 2018 12:57:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36373/tools-to-predict-the-impact-of-missense-variants</link>
	<title><![CDATA[Tools to Predict the Impact of Missense Variants !]]></title>
	<description><![CDATA[<p><span>Prioritizing missense variants for further experimental investigation is a key challenge in current sequencing studies for exploring complex and Mendelian diseases. A large number of&nbsp;</span><em>in silico</em><span>&nbsp;tools have been employed for the task of pathogenicity prediction, including PolyPhen‐2, SIFT, FatHMM, MutationTaster‐2, MutationAssessor, Combined Annotation Dependent Depletion, LRT, phyloP, and GERP++, as well as optimized methods of combining tool scores, such as Condel and Logit. Due to the wealth of these methods, an important practical question to answer is which of these tools generalize best, that is, correctly predict the pathogenic character of new variants. </span></p><p><span>Study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. Comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.</span></p><p><span>Following tools are useful for mis sense muation detection ...&nbsp;</span></p><p>PolyPhen‐2 (PP2)<br />&ldquo;Predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations&rdquo;</p><p>MutationTaster‐2 (MT2)<br />&ldquo;Evaluation of the disease‐causing potential of DNA sequence alterations&rdquo;</p><p>MutationAssessor (MASS)<br />&ldquo;Predicts the functional impact of amino acid substitutions in proteins, such as mutations discovered in cancer or missense polymorphisms&rdquo;</p><p>LRT<br />&ldquo;Identify a subset of deleterious mutations that disrupt highly conserved amino acids within protein‐coding sequences, which are likely to be unconditionally deleterious&rdquo;</p><p>SIFT<br />&ldquo;Predicts whether an amino acid substitution affects protein function&rdquo;</p><p>GERP++<br />&ldquo;Identifies constrained elements in multiple alignments by quantifying substitution deficits. These deficits represent substitutions that would have occurred if the element were neutral DNA, but did not occur because the element has been under functional constraint. We refer to these deficits as &ldquo;rejected substitutions.&rdquo; Rejected substitutions are a natural measure of constraint that reflects the strength of past purifying selection on the element&rdquo;</p><p>phyloP<br />&ldquo;Compute conservation or acceleration P values based on an alignment and a model of neutral evolution&rdquo;</p><p>FatHMM unweighted (FatHMM‐U)<br />Predicts &ldquo;functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants&rdquo;</p><p>FatHMM weighted (FatHMM‐W)<br />Predicts &ldquo;functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants&rdquo; and its weighting scheme attributes higher tolerance scores to SNVs in proteins, related proteins, or domains that already include a high fraction of pathogenic variantsh</p><p>Combined Annotation Dependent Depletion (CADD)<br />&ldquo;CADD is a tool for scoring the deleteriousness of single‐nucleotide variants as well as insertion/deletions variants in the human genome&rdquo;</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced</guid>
	<pubDate>Tue, 17 Sep 2013 16:48:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced</link>
	<title><![CDATA[Tigers genome sequenced]]></title>
	<description><![CDATA[<p>Fifteen scientists led by Dr Jong Bhak of Genome Research Foundation, South Korea, decoded as many as 3 billion nucleotides (organic molecules that form the basic building blocks of nucleic acids, such as DNA). They identified 20,000 genes related to various functions of the tiger.&nbsp;</p><p>The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.</p><p>The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.</p><p>Reference:</p><p><a href="http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690">http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690</a></p><p><a href="http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms">http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms</a></p><p>Paper:</p><p><a href="http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html">http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34488/scripts-for-the-analysis-of-hgt-in-genome-sequence-data</guid>
	<pubDate>Wed, 29 Nov 2017 16:44:10 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34488/scripts-for-the-analysis-of-hgt-in-genome-sequence-data</link>
	<title><![CDATA[Scripts for the analysis of HGT in genome sequence data.]]></title>
	<description><![CDATA[<p><span>Scripts for the analysis of HGT in genome sequence data</span></p><p>Address of the bookmark: <a href="https://github.com/reubwn/hgt" rel="nofollow">https://github.com/reubwn/hgt</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34571/mugsy-multiple-whole-genome-alignment-tool</guid>
	<pubDate>Fri, 08 Dec 2017 17:41:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34571/mugsy-multiple-whole-genome-alignment-tool</link>
	<title><![CDATA[Mugsy: multiple whole genome alignment tool]]></title>
	<description><![CDATA[<p><span>Mugsy is a multiple whole genome aligner. Mugsy uses Nucmer for pairwise alignment, a custom graph based segmentation procedure for identifying collinear regions, and the segment-based progressive multiple alignment strategy from Seqan::TCoffee. Mugsy accepts draft genomes in the form of multi-FASTA files and does not require a reference genome.</span></p>
<p>To cite Mugsy, use:</p>
<p>Angiuoli SV and Salzberg SL.&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/27/3/334">Mugsy: Fast multiple alignment of closely related whole genomes.</a><em>Bioinformatics</em>&nbsp;2011 27(3):334-4</p><p>Address of the bookmark: <a href="http://mugsy.sourceforge.net/" rel="nofollow">http://mugsy.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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