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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36895?offset=30</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36635/circlator-automated-circularization-of-genome-assemblies-using-long-sequencing-reads</guid>
	<pubDate>Tue, 15 May 2018 09:42:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36635/circlator-automated-circularization-of-genome-assemblies-using-long-sequencing-reads</link>
	<title><![CDATA[Circlator: automated circularization of genome assemblies using long sequencing reads]]></title>
	<description><![CDATA[A tool to circularize genome assemblies. The algorithm and benchmarks are described in the Genome Biology manuscript. 

Citation: "Circlator: automated circularization of genome assemblies using long sequencing reads", Hunt et al, Genome Biology 2015 Dec 29;16(1):294. doi: 10.1186/s13059-015-0849-0. PMID: 26714481.<p>Address of the bookmark: <a href="http://sanger-pathogens.github.io/circlator/" rel="nofollow">http://sanger-pathogens.github.io/circlator/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Tue, 02 Apr 2019 21:54:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39213/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 single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.</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>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40701/fastgt-an-alignment-free-method-for-calling-common-snvs-directly-from-raw-sequencing-reads</guid>
	<pubDate>Tue, 28 Jan 2020 03:27:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40701/fastgt-an-alignment-free-method-for-calling-common-snvs-directly-from-raw-sequencing-reads</link>
	<title><![CDATA[FastGT: an alignment-free method for calling common SNVs directly from raw sequencing reads]]></title>
	<description><![CDATA[<p>FastGT is a program package for whole-genome genotyping of genome variants directly from raw sequencing reads. It is written in C and runs in Linux. FastGT uses a list of variant-specific k-mer pairs that are unique in human genome, counts the frequency of k-mers in sequencing data and predicts the genotype. All this takes less than 1 hour on average low-cost Linux server.</p>
<p><a href="http://bioinfo.ut.ee/FastGT/">http://bioinfo.ut.ee/FastGT/</a></p>
<p><strong><a href="https://github.com/bioinfo-ut/GenomeTester4/">https://github.com/bioinfo-ut/GenomeTester4/</a></strong></p><p>Address of the bookmark: <a href="http://bioinfo.ut.ee/FastGT/" rel="nofollow">http://bioinfo.ut.ee/FastGT/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</guid>
	<pubDate>Mon, 04 May 2020 02:04:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</link>
	<title><![CDATA[HASLR: a hybrid assembler which uses both second and third generation sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.</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>BioStar</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/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</guid>
	<pubDate>Thu, 31 May 2018 09:35:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</link>
	<title><![CDATA[BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data]]></title>
	<description><![CDATA[BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes.

The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC.

https://github.com/lh3/bfc<p>Address of the bookmark: <a href="https://github.com/lh3/bfc" rel="nofollow">https://github.com/lh3/bfc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36755/minialign-fast-and-accurate-alignment-tool-for-pacbio-and-nanopore-long-reads</guid>
	<pubDate>Thu, 24 May 2018 08:33:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36755/minialign-fast-and-accurate-alignment-tool-for-pacbio-and-nanopore-long-reads</link>
	<title><![CDATA[minialign: fast and accurate alignment tool for PacBio and Nanopore long reads]]></title>
	<description><![CDATA[Minialign is a little bit fast and moderately accurate nucleotide sequence alignment tool designed for PacBio and Nanopore long reads. It is built on three key algorithms, minimizer-based index of the minimap overlapper, array-based seed chaining, and SIMD-parallel Smith-Waterman-Gotoh extension.<p>Address of the bookmark: <a href="https://github.com/ocxtal/minialign" rel="nofollow">https://github.com/ocxtal/minialign</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore</guid>
	<pubDate>Mon, 12 Nov 2018 05:26:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore</link>
	<title><![CDATA[Pacasus: Correction of palindromes in long reads from PacBio and Nanopore]]></title>
	<description><![CDATA[<p><br>Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder:&nbsp;<a href="https://github.com/swarris/Pacasus/blob/master/vib2017.pdf">https://github.com/swarris/Pacasus/blob/master/vib2017.pdf</a>.</p>
<p>The prepint version is found on&nbsp;<a href="http://www.biorxiv.org/content/early/2017/08/09/173872">http://www.biorxiv.org/content/early/2017/08/09/173872</a></p>
<p>It uses the pyPaSWAS framework for sequence alignment (<a href="https://github.com/swarris/pyPaSWAS">https://github.com/swarris/pyPaSWAS</a>)</p><p>Address of the bookmark: <a href="https://github.com/swarris/Pacasus" rel="nofollow">https://github.com/swarris/Pacasus</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40972/deepbinner-a-signal-level-demultiplexer-for-oxford-nanopore-reads</guid>
	<pubDate>Mon, 10 Feb 2020 02:45:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40972/deepbinner-a-signal-level-demultiplexer-for-oxford-nanopore-reads</link>
	<title><![CDATA[Deepbinner: a signal-level demultiplexer for Oxford Nanopore reads]]></title>
	<description><![CDATA[<p>Deepbinner is a tool for demultiplexing barcoded <a href="https://nanoporetech.com/">Oxford Nanopore</a> sequencing reads. It does this with a deep <a href="https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/">convolutional neural network</a> classifier, using many of the <a href="https://towardsdatascience.com/neural-network-architectures-156e5bad51ba">architectural advances</a> that have proven successful in image classification. Unlike other demultiplexers (e.g. Albacore and <a href="https://github.com/rrwick/Porechop">Porechop</a>), Deepbinner identifies barcodes from the raw signal (a.k.a. squiggle) which gives it greater sensitivity and fewer unclassified reads.</p><p>Address of the bookmark: <a href="https://github.com/rrwick/Deepbinner" rel="nofollow">https://github.com/rrwick/Deepbinner</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36533/mecat-fast-mapping-error-correction-and-de-novo-assembly-for-single-molecule-sequencing-reads</guid>
	<pubDate>Fri, 11 May 2018 05:07:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36533/mecat-fast-mapping-error-correction-and-de-novo-assembly-for-single-molecule-sequencing-reads</link>
	<title><![CDATA[MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads]]></title>
	<description><![CDATA[<p>MECAT is an ultra-fast Mapping, Error Correction and de novo Assembly Tools for single molecula sequencing (SMRT) reads. MECAT employs novel alignment and error correction algorithms that are much more efficient than the state of art of aligners and error correction tools. MECAT can be used for effectively de novo assemblying large genomes. For example, on a 32-thread computer with 2.0 GHz CPU , MECAT takes 9.5 days to assemble a human genome based on 54x SMRT data, which is 40 times faster than the current&nbsp;<a href="http://cbcb.umd.edu/software/pbcr/mhap/">PBcR-Mhap pipeline</a>. MECAT performance were compared with&nbsp;<a href="http://cbcb.umd.edu/software/pbcr/mhap/">PBcR-Mhap pipeline</a>,&nbsp;<a href="https://github.com/PacificBiosciences/falcon">FALCON</a>&nbsp;and&nbsp;<a href="http://canu.readthedocs.io/en/latest/">Canu(v1.3)</a>&nbsp;in five real datasets. The quality of assembled contigs produced by MECAT is the same or better than that of the&nbsp;<a href="http://cbcb.umd.edu/software/pbcr/mhap/">PBcR-Mhap pipeline</a>&nbsp;and&nbsp;<a href="https://github.com/PacificBiosciences/falcon">FALCON</a>.&nbsp;</p>
<p>https://www.nature.com/articles/nmeth.4432</p><p>Address of the bookmark: <a href="https://github.com/xiaochuanle/MECAT" rel="nofollow">https://github.com/xiaochuanle/MECAT</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

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