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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34445?offset=30</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads</guid>
	<pubDate>Mon, 11 Jun 2018 05:14:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads</link>
	<title><![CDATA[npScarf: real-time scaffolder using SPAdes contigs and Nanopore sequencing reads]]></title>
	<description><![CDATA[npScarf (jsa.np.npscarf) is a program that connect contigs from a draft genomes to generate sequences that are closer to finish. These pipelines can run on a single laptop for microbial datasets. In real-time mode, it can be integrated with simple structural analyses such as gene ordering, plasmid forming.<p>Address of the bookmark: <a href="http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html" rel="nofollow">http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads</guid>
	<pubDate>Mon, 20 Aug 2018 14:17:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads</link>
	<title><![CDATA[CoLoRMap: Correcting Long Reads by Mapping short reads]]></title>
	<description><![CDATA[<p><span>Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and often lead to highly fragmented assemblies. The recent developments in long reads sequencing methods offer a promising way to address this issue. However, so far long reads are characterized by a high error rate, and assembling from long reads require a high depth of coverage. This motivates the development of hybrid approaches that leverage the high quality of short reads to correct errors in long reads.We introduce CoLoRMap, a hybrid method for correcting noisy long reads, such as the ones produced by PacBio sequencing technology, using high-quality Illumina paired-end reads mapped onto the long reads. Our algorithm is based on two novel ideas: using a classical shortest path algorithm to find a sequence of overlapping short reads that minimizes the edit score to a long read and extending corrected regions by local assembly of unmapped mates of mapped short reads. Our results on bacterial, fungal and insect data sets show that CoLoRMap compares well with existing hybrid correction methods.The source code of CoLoRMap is freely available for non-commercial use at https://github.com/sfu-compbio/colormap</span></p>
<p><span>ehaghshe@sfu.ca or cedric.chauve@sfu.ca</span></p><p>Address of the bookmark: <a href="https://github.com/sfu-compbio/colormap" rel="nofollow">https://github.com/sfu-compbio/colormap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</guid>
	<pubDate>Fri, 13 Aug 2021 07:44:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</link>
	<title><![CDATA[QuasR: Quantification and annotation of short reads in R]]></title>
	<description><![CDATA[<p>The <em><a href="https://bioconductor.org/packages/3.14/QuasR">QuasR</a></em> package (short for <em>Qu</em>antify and <em>a</em>nnotate <em>s</em>hort reads in <em>R</em>) integrates the functionality of several <strong>R</strong> packages (such as <em><a href="https://bioconductor.org/packages/3.14/IRanges">IRanges</a></em> <span>(Lawrence et al. 2013)</span> and <em><a href="https://bioconductor.org/packages/3.14/Rsamtools">Rsamtools</a></em>) and external software (e.g.&nbsp;<code>bowtie</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rbowtie">Rbowtie</a></em> package, and <code>HISAT2</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rhisat2">Rhisat2</a></em> package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single <strong>R</strong> script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.</p><p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html" rel="nofollow">https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35055/jabba-hybrid-error-correction-for-long-sequencing-reads</guid>
	<pubDate>Fri, 05 Jan 2018 03:58:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35055/jabba-hybrid-error-correction-for-long-sequencing-reads</link>
	<title><![CDATA[Jabba: Hybrid Error Correction for Long Sequencing Reads]]></title>
	<description><![CDATA[<p>Jabba is a hybrid error correction tool to correct third generation (PacBio / ONT) sequencing data, using second generation (Illumina) data.</p>
<p>Input</p>
<p>Jabba takes as input a concatenated de Bruijn graph and a set of sequences:</p>
<p>the de Bruijn graph should appear in fasta format with 1 entry per node, the meta information should be in the format:<br>&gt;NODE <br>the set of sequences should be in fasta or fastq format. These sequences will be corrected (e.g. PacBio reads). The corrections will be written to a file Jabba fasta.<br>The output is a file in fasta format with corrections of the long reads, and additionally a file in the input format containing uncorrected reads.</p>
<p>https://github.com/biointec/jabba/wiki</p>
<p>https://almob.biomedcentral.com/articles/10.1186/s13015-016-0075-7</p><p>Address of the bookmark: <a href="https://github.com/biointec/jabba" rel="nofollow">https://github.com/biointec/jabba</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39689/msaprobs-parallel-and-accurate-multiple-sequence-alignment</guid>
	<pubDate>Tue, 09 Jul 2019 23:58:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39689/msaprobs-parallel-and-accurate-multiple-sequence-alignment</link>
	<title><![CDATA[MSAProbs - Parallel and accurate multiple sequence alignment]]></title>
	<description><![CDATA[<p><strong>MSAProbs</strong><span>&nbsp;is a well-established state-of-the-art multiple sequence alignment algorithm for protein sequences. The design of MSAProbs is based on a combination of pair hidden Markov models and partition functions to calculate posterior probabilities. Assessed using the popular benchmarks: BAliBASE, PREFAB, SABmark and OXBENCH, MSAProbs achieves statistically significant accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons and Probalign. In addition, MSAProbs is optimized for shared-memory CPUs by employing a multi-threaded design, and further parallelized for distributed-memory systems using MPI to overcome high memory overhead barrier and achieve good parallel and data-size scalability.</span></p><p>Address of the bookmark: <a href="http://msaprobs.sourceforge.net/homepage.htm#latest" rel="nofollow">http://msaprobs.sourceforge.net/homepage.htm#latest</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41033/clark-fast-accurate-and-versatile-sequence-classification-system</guid>
	<pubDate>Sat, 15 Feb 2020 01:49:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41033/clark-fast-accurate-and-versatile-sequence-classification-system</link>
	<title><![CDATA[CLARK: Fast, accurate and versatile sequence classification system]]></title>
	<description><![CDATA[<p><span></span><a href="http://dx.doi.org/10.1186/s12864-015-1419-2"><strong>CLARK</strong></a><span>, a method based on a supervised sequence classification using discriminative&nbsp;</span><em>k</em><span>-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.</span></p>
<p><span><a href="http://clark.cs.ucr.edu/Spaced/">http://clark.cs.ucr.edu/Spaced/</a></span></p><p>Address of the bookmark: <a href="http://clark.cs.ucr.edu/Spaced/" rel="nofollow">http://clark.cs.ucr.edu/Spaced/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<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/44641/heliano-a-fast-and-accurate-tool-for-detection-of-helitron-like-elements</guid>
	<pubDate>Tue, 13 Aug 2024 07:16:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44641/heliano-a-fast-and-accurate-tool-for-detection-of-helitron-like-elements</link>
	<title><![CDATA[HELIANO: A fast and accurate tool for detection of Helitron-like elements]]></title>
	<description><![CDATA[<p><span>Helitron-like elements (HLE1 and HLE2) are DNA transposons. They have been found in diverse species and seem to play significant roles in the evolution of host genomes. Although known for over twenty years, Helitron sequences are still challenging to identify. Here, we propose HELIANO (Helitron-like elements annotator) as an efficient solution for detecting Helitron-like elements.</span></p>
<p>https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkae679/7730539?login=true</p><p>Address of the bookmark: <a href="https://github.com/Zhenlisme/heliano/" rel="nofollow">https://github.com/Zhenlisme/heliano/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34324/orthognc-a-software-for-accurate-identification-of-orthologs-based-on-gene-neighborhood-conservation</guid>
	<pubDate>Tue, 14 Nov 2017 09:30:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34324/orthognc-a-software-for-accurate-identification-of-orthologs-based-on-gene-neighborhood-conservation</link>
	<title><![CDATA[OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation]]></title>
	<description><![CDATA[<div>
<p id="sp0005">Orthology relations can be used to transfer annotations from one gene (or protein) to another. Hence, detecting orthology relations has become an important task in the post-genomic era. Various genomic events, such as duplication and horizontal gene transfer, can cause erroneous assignment of orthology relations. In closely-related species, gene neighborhood information can be used to resolve many ambiguities in orthology inference. Here we present OrthoGNC, a software for accurately predicting pairwise orthology relations based on gene neighborhood conservation. Analyses on simulated and real data reveal the high accuracy of OrthoGNC. In addition to orthology detection, OrthoGNC can be employed to investigate the conservation of genomic context among potential orthologs detected by other methods. OrthoGNC is freely available online at http://bs.ipm.ir/softwares/orthognc and http://tinyurl.com/orthoGNC.</p>
<p>http://www.comp.nus.edu.sg/~wongls/projects/orthoGNC/</p>
</div><p>Address of the bookmark: <a href="http://www.sciencedirect.com/science/article/pii/S1672022917301663" rel="nofollow">http://www.sciencedirect.com/science/article/pii/S1672022917301663</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43904/jasmine-jointly-accurate-sv-merging-with-intersample-network-edges</guid>
	<pubDate>Sat, 02 Jul 2022 11:41:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43904/jasmine-jointly-accurate-sv-merging-with-intersample-network-edges</link>
	<title><![CDATA[JASMINE: Jointly Accurate Sv Merging with Intersample Network Edges]]></title>
	<description><![CDATA[<p><span>This tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. Jasmine represents the set of all SVs across samples as a network, and uses a modified minimum spanning forest algorithm to determine the best way of merging the variants such that each merged variants represents a set of analogous variants occurring in different samples.</span></p><p>Address of the bookmark: <a href="https://github.com/mkirsche/Jasmine" rel="nofollow">https://github.com/mkirsche/Jasmine</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>

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