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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37473?offset=10</link>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36950/salsa-a-tool-to-scaffold-long-read-assemblies-with-hi-c</guid>
	<pubDate>Fri, 15 Jun 2018 04:01:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36950/salsa-a-tool-to-scaffold-long-read-assemblies-with-hi-c</link>
	<title><![CDATA[SALSA: A tool to scaffold long read assemblies with Hi-C]]></title>
	<description><![CDATA[This code is used to scaffold your assemblies using Hi-C data. This version implements some improvements in the original SALSA algorithm. If you want to use the old version, it can be found in the old_salsa branch.

To use the latest version, first run the following commands:

  cd SALSA
  make
To run the code, you will need Python 2.7, BOOST libraries and Networkx(version lower than 1.2).

If you consider using this tool, please cite our publication which describes the methods used for scaffolding.

Ghurye, J., Pop, M., Koren, S., Bickhart, D., &amp; Chin, C. S. (2017). Scaffolding of long read assemblies using long range contact information. BMC genomics, 18(1), 527. Link

Ghurye, J., Rhie, A., Walenz, B.P., Schmitt, A., Selvaraj, S., Pop, M., Phillippy, A.M. and Koren, S., 2018. Integrating Hi-C links with assembly graphs for chromosome-scale assembly. bioRxiv, p.261149 Link

For any queries, please either ask on github issue page or send an email to Jay Ghurye (jayg@cs.umd.edu).<p>Address of the bookmark: <a href="https://github.com/machinegun/SALSA" rel="nofollow">https://github.com/machinegun/SALSA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35059/lrcstats-long-read-correction-statistics</guid>
	<pubDate>Fri, 05 Jan 2018 04:04:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35059/lrcstats-long-read-correction-statistics</link>
	<title><![CDATA[LRCstats: Long Read Correction Statistics]]></title>
	<description><![CDATA[<p>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.</p>
<p>Of course, some long read correction algorithms are better than others, and developers of long read correction algorithms will wish to compare their algorithm with others currently available. LRCstats benchmarks long read correction algorithms using long reads produced by simulators (such as SimLoRD or PBSim) where the two-way alignments between the uncorrected long reads (uLR) and the corresponding sequences in the reference genome (Ref) are given in some sort of alignment file and then aligning the corrected long reads (cLR) to the Ref-uLR two-way alignments to create three-way alignments using a dynamic programming algorithm. Statistics on these three-way alignments are then collected, such as the overall error rates of the corrected long reads.</p>
<p>https://www.healthcare.uiowa.edu/labs/au/LSC/</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>Jit</dc:creator>
</item>
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	<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/37643/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads</guid>
	<pubDate>Thu, 06 Sep 2018 16:21:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37643/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads</link>
	<title><![CDATA[LoRMA: A tool for correcting sequencing errors in long reads]]></title>
	<description><![CDATA[<p><span>An error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of&nbsp;</span><em>k</em><span>-mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75&times;, the throughput of the new method is at least 20% higher.</span></p>
<blockquote>
<p><span>conda install -c atgc-montpellier lorma</span></p>
</blockquote><p>Address of the bookmark: <a href="https://gite.lirmm.fr/lorma/lorma-releases/wikis/home" rel="nofollow">https://gite.lirmm.fr/lorma/lorma-releases/wikis/home</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37776/rhat-a-seed-and-extension-based-noisy-long-read-alignment-tool</guid>
	<pubDate>Sun, 23 Sep 2018 05:12:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37776/rhat-a-seed-and-extension-based-noisy-long-read-alignment-tool</link>
	<title><![CDATA[rHAT: a seed-and-extension-based noisy long read alignment tool]]></title>
	<description><![CDATA[<p><span>rHAT is a seed-and-extension-based noisy long read alignment tool. It is suitable for aligning 3rd generation sequencing reads which are in large read length with relatively high error rate, especially Pacbio's Single Molecule Read-time (SMRT) sequencing reads.</span></p><p>Address of the bookmark: <a href="https://github.com/dfguan/rHAT" rel="nofollow">https://github.com/dfguan/rHAT</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</guid>
	<pubDate>Thu, 24 Jan 2019 10:35:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</link>
	<title><![CDATA[NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads]]></title>
	<description><![CDATA[<p>NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.</p>
<p>https://nxrepair.readthedocs.io/en/latest/tutorial.html</p>
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<pre>nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta</pre>
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<div>&nbsp;</div><p>Address of the bookmark: <a href="https://github.com/rebeccaroisin/nxrepair" rel="nofollow">https://github.com/rebeccaroisin/nxrepair</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</guid>
	<pubDate>Mon, 06 Aug 2018 17:19:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</link>
	<title><![CDATA[gSearch: a fast and flexible general search tool for whole-genome sequencing]]></title>
	<description><![CDATA[<p><span>gSearch compares sequence variants in the Genome Variation Format (GVF) or Variant Call Format (VCF) with a pre-compiled annotation or with variants in other genomes. Its search algorithms are subsequently optimized and implemented in a multi-threaded manner.&nbsp;</span></p><p>Address of the bookmark: <a href="http://ml.ssu.ac.kr/gSearch/index.html" rel="nofollow">http://ml.ssu.ac.kr/gSearch/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<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>
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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>
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