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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37737?offset=170</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37416/gfinisher-a-new-strategy-to-refine-and-finish-bacterial-genome-assemblies</guid>
	<pubDate>Thu, 26 Jul 2018 09:31:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37416/gfinisher-a-new-strategy-to-refine-and-finish-bacterial-genome-assemblies</link>
	<title><![CDATA[GFinisher: a new strategy to refine and finish bacterial genome assemblies]]></title>
	<description><![CDATA[<p>GFinisher is an application tools for refinement and finalization of prokaryotic genomes assemblies using the bias of GC Skew to identify assembly errors and organizes the contigs/scaffolds with genomes references.</p>
<pre>java -Xms2G -Xmx4G -jar GenomeFinisher.jar  \
    -i target_contigs.fasta  \
    -ds alternative_assemblies.fasta -ref reference.fasta  \
    -o outputDirectory</pre><p>Address of the bookmark: <a href="http://gfinisher.sourceforge.net" rel="nofollow">http://gfinisher.sourceforge.net</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41831/merqury-reference-free-quality-and-phasing-assessment-for-genome-assemblies</guid>
	<pubDate>Sat, 06 Jun 2020 05:38:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41831/merqury-reference-free-quality-and-phasing-assessment-for-genome-assemblies</link>
	<title><![CDATA[Merqury: reference-free quality and phasing assessment for genome assemblies]]></title>
	<description><![CDATA[<p><span>Often, genome assembly projects have illumina whole genome sequencing reads available for the assembled individual. The k-mer spectrum of this read set can be used for independently evaluating assembly quality without the need of a high quality reference. Merqury provides a set of tools for this purpose.</span></p>
<p><span><a href="https://github.com/marbl/meryl">https://github.com/marbl/meryl</a></span></p><p>Address of the bookmark: <a href="https://github.com/marbl/merqury" rel="nofollow">https://github.com/marbl/merqury</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40359/minipolish-a-tool-for-racon-polishing-of-miniasm-assemblies</guid>
	<pubDate>Tue, 03 Dec 2019 02:40:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40359/minipolish-a-tool-for-racon-polishing-of-miniasm-assemblies</link>
	<title><![CDATA[Minipolish: A tool for Racon polishing of miniasm assemblies]]></title>
	<description><![CDATA[<p><a href="https://github.com/lh3/miniasm">Miniasm</a>&nbsp;is a great long-read assembly tool: straight-forward, effective and very fast. However, it does not include a polishing step, so its assemblies have a high error rate &ndash; they are essentially made of stitched-together pieces of long reads.</p>
<p><a href="https://github.com/isovic/racon">Racon</a>&nbsp;is a great polishing tool that can be used to clean up assembly errors. It's also very fast and well suited for long-read data. However, it operates on FASTA files, not the&nbsp;<a href="https://github.com/GFA-spec/GFA-spec/blob/master/GFA1.md">GFA graphs</a>&nbsp;that miniasm makes.</p>
<p>That's where Minipolish comes in. With a single command, it will use Racon to polish up a miniasm assembly, while keeping the assembly in graph form.</p>
<p>It also takes care of some of the other nuances of polishing a miniasm assembly:</p>
<ul>
<li>Adding read depth information to contigs</li>
<li>Fixing sequence truncation that can occur in Racon</li>
<li>Adding circularising links to circular contigs if not already present (so they display better in&nbsp;<a href="https://github.com/rrwick/Bandage">Bandage</a>)</li>
<li>'Rotating' circular contigs between polishing rounds to ensure clean circularisation</li>
</ul><p>Address of the bookmark: <a href="https://github.com/rrwick/Minipolish" rel="nofollow">https://github.com/rrwick/Minipolish</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41937/merqury-evaluate-genome-assemblies-with-k-mers</guid>
	<pubDate>Fri, 03 Jul 2020 19:29:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41937/merqury-evaluate-genome-assemblies-with-k-mers</link>
	<title><![CDATA[merqury: Evaluate genome assemblies with k-mers]]></title>
	<description><![CDATA[<p><span>Often, genome assembly projects have illumina whole genome sequencing reads available for the assembled individual. The k-mer spectrum of this read set can be used for independently evaluating assembly quality without the need of a high quality reference. Merqury provides a set of tools for this purpose.</span></p>
<p><span>More at&nbsp;<a href="https://www.biorxiv.org/content/10.1101/2020.03.15.992941v1.full">https://www.biorxiv.org/content/10.1101/2020.03.15.992941v1.full</a></span></p><p>Address of the bookmark: <a href="https://github.com/marbl/merqury" rel="nofollow">https://github.com/marbl/merqury</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43888/syri-compares-alignments-between-two-chromosome-level-assemblies-and-identifies-synteny-and-structural-rearrangements</guid>
	<pubDate>Wed, 01 Jun 2022 02:01:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43888/syri-compares-alignments-between-two-chromosome-level-assemblies-and-identifies-synteny-and-structural-rearrangements</link>
	<title><![CDATA[Syri compares alignments between two chromosome-level assemblies and identifies synteny and structural rearrangements.]]></title>
	<description><![CDATA[<p><span>Syri compares alignments between two chromosome-level assemblies and identifies synteny and structural rearrangements.</span></p>
<p><span><img src="https://github.com/schneebergerlab/syri/raw/master/example/ampril_col0_chr3_6600000_10000000.png" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/schneebergerlab/syri" rel="nofollow">https://github.com/schneebergerlab/syri</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36618/lamsa-fast-split-read-alignment-with-long-approximate-matches</guid>
	<pubDate>Tue, 15 May 2018 04:44:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36618/lamsa-fast-split-read-alignment-with-long-approximate-matches</link>
	<title><![CDATA[LAMSA: fast split read alignment with long approximate matches]]></title>
	<description><![CDATA[LAMSA (Long Approximate Matches-based Split Aligner) is a novel split alignment approach with faster speed and good ability of handling SV events. It is well-suited to align long reads (over thousands of base-pairs).

LAMSA takes takes the advantage of the rareness of SVs to implement a specifically designed two-step strategy. That is, LAMSA initially splits the read into relatively long fragments and co-linearly align them to solve the small variations or sequencing errors, and mitigate the effect of repeats. The alignments of the fragments are then used for implementing a sparse dynamic programming (SDP)-based split alignment approach to handle the large or non-co-linear variants.

We benchmarked LAMSA with simulated and real datasets having various read lengths and sequencing error rates, the results demonstrate that it is substantially faster than the state-of-the-art long read aligners; mean-while, it also has good ability to handle various categories of SVs.

LAMSA is open source and free for non-commercial use.

LAMSA is mainly designed by Bo Liu &amp; Yan Gao and developed by Yan Gao in Center for Bioinformatics, Harbin Institute of Technology, China.<p>Address of the bookmark: <a href="https://github.com/hitbc/LAMSA" rel="nofollow">https://github.com/hitbc/LAMSA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38012/cosine-non-seeding-method-for-mapping-long-noisy-sequences</guid>
	<pubDate>Fri, 26 Oct 2018 00:41:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38012/cosine-non-seeding-method-for-mapping-long-noisy-sequences</link>
	<title><![CDATA[COSINE: non-seeding method for mapping long noisy sequences]]></title>
	<description><![CDATA[<p><span>Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors.</span></p><p>Address of the bookmark: <a href="https://github.com/SUwonglab/COSINE" rel="nofollow">https://github.com/SUwonglab/COSINE</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</guid>
	<pubDate>Tue, 27 Nov 2018 04:43:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</link>
	<title><![CDATA[lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data]]></title>
	<description><![CDATA[<p><span>lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.</span></p>
<p>lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/lordfast" rel="nofollow">https://github.com/vpc-ccg/lordfast</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40871/nanopore-adaptor</guid>
	<pubDate>Mon, 03 Feb 2020 00:10:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40871/nanopore-adaptor</link>
	<title><![CDATA[Nanopore adaptor !]]></title>
	<description><![CDATA[<p>Porechop is a tool for finding and removing adapters from&nbsp;<a href="https://nanoporetech.com/">Oxford Nanopore</a>&nbsp;reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity.</p>
<p>Porechop also supports demultiplexing of Nanopore reads that were barcoded with the&nbsp;<a href="https://store.nanoporetech.com/native-barcoding-kit-1d.html">Native Barcoding Kit</a>,&nbsp;<a href="https://store.nanoporetech.com/pcr-barcoding-kit-96.html">PCR Barcoding Kit</a>&nbsp;or&nbsp;<a href="https://store.nanoporetech.com/rapid-barcoding-sequencing-kit.html">Rapid Barcoding Kit</a>.</p>
<p><span>The known Nanopore adapters that Porechop looks for are defined</span></p>
<p><a href="https://github.com/rrwick/Porechop/blob/master/porechop/adapters.py">https://github.com/rrwick/Porechop/blob/master/porechop/adapters.py</a></p>
<p>They are:</p>
<ul>
<li>Ligation kit adapters</li>
<li>Rapid kit adapters</li>
<li>PCR kit adapters</li>
<li>Barcodes</li>
<li>Native barcoding</li>
<li>Rapid barcoding</li>
</ul><p>Address of the bookmark: <a href="https://github.com/rrwick/Porechop/blob/master/porechop/adapters.py" rel="nofollow">https://github.com/rrwick/Porechop/blob/master/porechop/adapters.py</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus</guid>
	<pubDate>Fri, 05 Jan 2018 04:12:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus</link>
	<title><![CDATA[proovread : large-scale high-accuracy PacBio correction through iterative short read consensus]]></title>
	<description><![CDATA[<p>proovread : large-scale high-accuracy PacBio correction through iterative short read consensus</p>
<ul>
<li>outperforms PacBioToCA/LSC in terms of accuracy and contiguity/sensitivity (<a href="http://dx.doi.org/10.1093/bioinformatics/btu392">http://dx.doi.org/10.1093/bioinformatics/btu392</a>)</li>
<li>is easy to install/run/configure</li>
<li>supports various types of dat
<ul>
<li><strong>HiSeq/MiSeq&nbsp;</strong>(100-500bp)</li>
<li><strong>Unitigs</strong></li>
<li>454, ...</li>
</ul>
</li>
</ul>
<p>proovread maps high coverage data to pacbio reads (bwa mem, blasr, daligner) in multiple iterations.</p><p>Address of the bookmark: <a href="https://github.com/BioInf-Wuerzburg/proovread" rel="nofollow">https://github.com/BioInf-Wuerzburg/proovread</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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