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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36618?offset=160</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43856/puffaligner-a-fast-efficient-and-accurate-aligner-based-on-the-pufferfish-index</guid>
	<pubDate>Thu, 21 Apr 2022 05:41:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43856/puffaligner-a-fast-efficient-and-accurate-aligner-based-on-the-pufferfish-index</link>
	<title><![CDATA[PuffAligner: a fast, efficient and accurate aligner based on the Pufferfish index]]></title>
	<description><![CDATA[<p><span>PuffAligner, a fast, accurate and versatile aligner built on top of the Pufferfish index. PuffAligner is able to produce highly sensitive alignments, similar to those of Bowtie2, but much more quickly. While exhibiting similar speed to the ultrafast STAR aligner, PuffAligner requires considerably less memory to construct its index and align reads. PuffAligner strikes a desirable balance with respect to the time, space and accuracy tradeoffs made by different alignment tools and provides a promising foundation on which to test new alignment ideas over large collections of sequences.</span></p><p>Address of the bookmark: <a href="https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings" rel="nofollow">https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44904/termal-a-fast-and-interactive-terminal-based-viewer-for-multiple-sequence-alignments</guid>
	<pubDate>Mon, 22 Sep 2025 23:51:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44904/termal-a-fast-and-interactive-terminal-based-viewer-for-multiple-sequence-alignments</link>
	<title><![CDATA[Termal: a fast and interactive terminal-based viewer for multiple sequence alignments]]></title>
	<description><![CDATA[<p>termal, a fast, interactive, terminal-based viewer for multiple sequence alignments (MSAs), designed for use on remote systems such as high-performance computing (HPC) clusters.</p>
<p>https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbaf208/8257678?login=true</p><p>Address of the bookmark: <a href="https://github.com/sib-swiss/termal" rel="nofollow">https://github.com/sib-swiss/termal</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36867/cerulean-a-hybrid-assembly-using-high-throughput-short-and-long-reads</guid>
	<pubDate>Tue, 05 Jun 2018 10:10:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36867/cerulean-a-hybrid-assembly-using-high-throughput-short-and-long-reads</link>
	<title><![CDATA[Cerulean: A hybrid assembly using high throughput short and long reads]]></title>
	<description><![CDATA[Cerulean extends contigs assembled using short read datasets like Illumina paired-end reads using long reads like PacBio RS long reads.

Cerulean v0.1 has been implemented with bacterial genomes in mind.

The method is fully described in Deshpande, V., Fung, E. D., Pham, S., &amp; Bafna, V. (2013). Cerulean: A hybrid assembly using high throughput short and long reads. arXiv preprint arXiv:1307.7933.
http://arxiv.org/abs/1307.7933<p>Address of the bookmark: <a href="https://sourceforge.net/projects/ceruleanassembler/" rel="nofollow">https://sourceforge.net/projects/ceruleanassembler/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</guid>
	<pubDate>Wed, 22 Aug 2018 11:05:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</link>
	<title><![CDATA[LRCstats: a tool for evaluating long reads correction methods]]></title>
	<description><![CDATA[<p><span>LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.</span></p><p>Address of the bookmark: <a href="https://github.com/cchauve/lrcstats" rel="nofollow">https://github.com/cchauve/lrcstats</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37962/wtdbg2-a-de-novo-sequence-assembler-for-long-noisy-reads-produced-by-pacbio-or-oxford-nanopore</guid>
	<pubDate>Fri, 19 Oct 2018 08:48:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37962/wtdbg2-a-de-novo-sequence-assembler-for-long-noisy-reads-produced-by-pacbio-or-oxford-nanopore</link>
	<title><![CDATA[Wtdbg2: a de novo sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore]]></title>
	<description><![CDATA[<p><span>Wtdbg2 is a&nbsp;</span><em>de novo</em><span>&nbsp;sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore Technologies (ONT). It assembles raw reads without error correction and then builds the consensus from intermediate assembly output. Wtdbg2 is able to assemble the human and even the 32Gb&nbsp;</span><a href="https://www.nature.com/articles/nature25458">Axolotl</a><span>&nbsp;genome at a speed tens of times faster than&nbsp;</span><a href="https://github.com/marbl/canu">CANU</a><span>&nbsp;and&nbsp;</span><a href="https://github.com/PacificBiosciences/FALCON">FALCON</a><span>while producing contigs of comparable base accuracy.</span></p><p>Address of the bookmark: <a href="https://github.com/ruanjue/wtdbg2" rel="nofollow">https://github.com/ruanjue/wtdbg2</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/38886/evaluation-of-genome-assembly-software-based-on-long-reads</guid>
	<pubDate>Fri, 01 Feb 2019 11:55:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/file/view/38886/evaluation-of-genome-assembly-software-based-on-long-reads</link>
	<title><![CDATA[Evaluation of genome assembly software based on long reads]]></title>
	<description><![CDATA[<p>TGS technologies have been used to produce highly accurate de novo assemblies of hundreds of microbial genomes and highly contiguous reconstructions of many dozens of plant and animal genomes, enabling new insights into evolution and sequence diversity. They have also been applied to resequencing analyses, to create detailed maps of structural variations in many species. Also, these new technologies have been used to fill in many of the gaps in the human reference genome.</p><p>In this report, we compare and evaluate several genome assembly software based on TSG technology. The experimentation has been performed on 4 reference genomes and the results evaluated with the QUAST software. The 11 software that have been evaluated are: Celera Assembler , Falcon , Miniasm, Newbler , SGA Assembler, Smartdenovo, Abruijn, Ra, DBG2OLC, Spades and Cerulean. The first 8 software use only long reads, while the 3 last software can merge long and short reads</p>]]></description>
	<dc:creator>BioStar</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/38886" length="382699" type="application/pdf" />
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</guid>
	<pubDate>Fri, 31 Jan 2020 05:50:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</link>
	<title><![CDATA[HASLR: a tool for rapid genome assembly of long sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR is a tool for rapid genome assembly of long sequencing reads. HASLR is a hybrid tool which means it requires long reads generated by Third Generation Sequencing technologies (such as PacBio or Oxford Nanopore) together with Next Generation Sequencing reads (such as Illumina) from the same sample.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43384/lncpipea-nextflow-based-pipeline-for-comprehensive-analyses-of-long-non-coding-rnas-from-rna-seq-datasets</guid>
	<pubDate>Fri, 17 Sep 2021 01:57:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43384/lncpipea-nextflow-based-pipeline-for-comprehensive-analyses-of-long-non-coding-rnas-from-rna-seq-datasets</link>
	<title><![CDATA[LncPipe:A Nextflow-based pipeline for comprehensive analyses of long non-coding RNAs from RNA-seq datasets]]></title>
	<description><![CDATA[<p><span>The pipeline was developed based on a popular workflow framework&nbsp;</span><a href="https://github.com/nextflow-io/nextflow">Nextflow</a><span>, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report.&nbsp;</span><a href="https://github.com/likelet/LncPipe">LncPipe</a><span>&nbsp;allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.</span></p>
<p>Ref&nbsp;https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/</p>
<p><img src="https://ars.els-cdn.com/content/image/1-s2.0-S1673852718301176-gr1.jpg" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="https://github.com/likelet/LncPipe" rel="nofollow">https://github.com/likelet/LncPipe</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</guid>
	<pubDate>Wed, 15 Mar 2017 14:31:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</link>
	<title><![CDATA[Software and Tools to detect structure variation with long reads !!]]></title>
	<description><![CDATA[<p>Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio&nbsp;<em>de novo</em>&nbsp;assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.</p><p>With&nbsp;<span style="text-decoration: underline;"><a href="http://www.pacb.com/smrt-science/">Single Molecule, Real-Time (SMRT) Sequencing</a></span>, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:</p><ul>
<li>Uncover missing heritability linked to structural variation</li>
<li>Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease</li>
<li>Resolve structural variation across the complete size spectrum with basepair resolution</li>
</ul><p>Following are the SV tools, which can assist you to achieve your goal.</p><p><strong>Sniffles:</strong>&nbsp;Structural variation caller using third generation sequencing</p><p>Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled!&nbsp;</p><p>More at&nbsp;https://github.com/fritzsedlazeck/Sniffles</p><p><strong style="font-size: 12.8px;"><br />MultiBreak-SV:</strong> It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.</p><p>There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.</p><p>More at&nbsp;https://github.com/raphael-group/multibreak-sv</p><p><strong style="font-size: 12.8px;"><br />Parliament:</strong>&nbsp;A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?</p><p>Publication about the algorithm and &ldquo;&hellip;the first long-read characterization of structural variation in a diploid human personal genome&hellip;&rdquo; (HS1011) -&nbsp;<a href="http://www.biomedcentral.com/1471-2164/16/286">&ldquo;Assessing structural variation in a personal genome&mdash;towards a human reference diploid genome&rdquo;</a></p><p>More at&nbsp;https://sourceforge.net/projects/parliamentsv/</p><p>https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf</p><p><br /><strong>PBHoney:</strong>&nbsp;the structural variation discovery tool&nbsp;<br /><br />PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.</p><p>Read The Paper&nbsp;<a href="http://www.biomedcentral.com/1471-2105/15/180/abstract" target="_blank">http://www.biomedcentral.com/1471-2105/15/180/abstract</a></p><p>More at&nbsp;https://sourceforge.net/projects/pb-jelly/</p><p><strong><br />SMRT-SV:</strong> Structural variant and indel caller for PacBio reads</p><p>Structural variant (SV) and indel caller for PacBio reads based on methods from&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>.</p><p>SMRT-SV provides an official software package for tools described in&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>&nbsp;and adds several key features including the following.</p><ul>
<li>Unified variant calling user interface with built-in cluster compute support</li>
<li>Small indel calling (2-49 bp)</li>
<li>Improved inversion calling (<code>screenInversions</code>)</li>
<li>Quality metric for SV calls based on number of local assemblies supporting each call</li>
<li>Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions</li>
<li>Genotyping of SVs with Illumina paired-end reads from WGS samples</li>
</ul><p>More at&nbsp;https://github.com/EichlerLab/pacbio_variant_caller</p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30966/maftools</guid>
	<pubDate>Thu, 16 Feb 2017 11:16:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30966/maftools</link>
	<title><![CDATA[MafTools]]></title>
	<description><![CDATA[<p>maftools - An R package to summarize, analyze and visualize MAF files. <a href="https://github.com/PoisonAlien/maftools#introduction"></a>Introduction.</p>
<p>With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widley accepted and used to store variants detected. <a href="http://cancergenome.nih.gov">The Cancer Genome Atlas</a> Project has seqenced over 30 different cancers with sample size of each cancer type being over 200. The <a href="https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files">resulting data</a> consisting of genetic variants is stored in the form of <a href="https://wiki.nci.nih.gov/display/TCGA/Mutation+Annotation+Format+%28MAF%29+Specification">Mutation Annotation Format</a>. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner either from TCGA sources or any in-house studies as long as the data is in MAF format. Maftools can also handle ICGC Simple Somatic Mutation format.</p>
<p>maftools is on <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f449.png" alt=":point_right:" width="20" height="20" style="border: 0px;"> <a href="http://biorxiv.org/content/early/2016/05/11/052662">bioRxiv</a> <img src="https://assets-cdn.github.com/images/icons/emoji/bowtie.png" alt=":bowtie:" title=":bowtie:" width="20" height="20" style="border: 0px; text-align: absmiddle;"></p>
<p>Please cite the below if you find this tool useful for you.</p>
<p>Mayakonda, A. and H.P. Koeffler, Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. bioRxiv, 2016. doi: <a href="http://dx.doi.org/10.1101/052662">http://dx.doi.org/10.1101/052662</a></p><p>Address of the bookmark: <a href="https://github.com/PoisonAlien/maftools" rel="nofollow">https://github.com/PoisonAlien/maftools</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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