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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44234?offset=20</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37561/hercules-a-profile-hmm-based-hybrid-error-correction-algorithm-for-long-reads</guid>
	<pubDate>Mon, 20 Aug 2018 14:14:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37561/hercules-a-profile-hmm-based-hybrid-error-correction-algorithm-for-long-reads</link>
	<title><![CDATA[Hercules: a profile HMM-based hybrid error correction algorithm for long reads]]></title>
	<description><![CDATA[<p><span>Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results: We designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platformtextquoterights error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability: </span></p>
<p><span>Hercules source code is available at https://github.com/BilkentCompGen/Hercules</span></p><p>Address of the bookmark: <a href="https://github.com/BilkentCompGen/Hercules" rel="nofollow">https://github.com/BilkentCompGen/Hercules</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37650/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads</guid>
	<pubDate>Fri, 07 Sep 2018 05:19:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37650/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads</link>
	<title><![CDATA[P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads]]></title>
	<description><![CDATA[<p><span>P_RNA_scaffolder is a novel scaffolding tool using Pair-end RNA-seq to scaffold genome fragments. The method is suitable for most genomes. The program could utilize Illumina Paired-end RNA-sequencing reads from target speciesies. Our method provides another practical alternative to existing mate-pair_based approaches or other Protein-based approaches (for instance,&nbsp;</span><a href="http://www.fishbrowser.org/software/PEP_scaffolder/">PEP_scaffolder&nbsp;</a><span>) for scaffolding genome sequences. The most important feature of this method is to improve the completeness of gene regions and long-coding gene regions (for instance,&nbsp;</span><a href="http://circrna.org/">circRNA</a><span>).</span></p><p>Address of the bookmark: <a href="http://www.fishbrowser.org/software/P_RNA_scaffolder/#" rel="nofollow">http://www.fishbrowser.org/software/P_RNA_scaffolder/#</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</guid>
	<pubDate>Fri, 19 Oct 2018 07:25:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</link>
	<title><![CDATA[BASE: a practical de novo assembler for large genomes using long NGS reads]]></title>
	<description><![CDATA[<p><span>new&nbsp;</span><em>de novo</em><span>&nbsp;assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.</span></p><p>Address of the bookmark: <a href="https://github.com/dhlbh/BASE" rel="nofollow">https://github.com/dhlbh/BASE</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38481/arcs-scaffolding-genome-drafts-with-linked-reads</guid>
	<pubDate>Mon, 17 Dec 2018 17:40:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38481/arcs-scaffolding-genome-drafts-with-linked-reads</link>
	<title><![CDATA[ARCS: scaffolding genome drafts with linked reads]]></title>
	<description><![CDATA[<p>ARCS requires two input files:</p>
<ul>
<li>Draft assembly fasta file</li>
<li>Interleaved linked reads file (Barcode sequence expected in the BX tag of the read header or in the form "@readname_barcode" ; Run&nbsp;<a href="https://support.10xgenomics.com/genome-exome/software/pipelines/latest/what-is-long-ranger">Long Ranger basic</a>&nbsp;on raw chromium reads to produce this interleaved file)</li>
<li></li>
</ul><p>Address of the bookmark: <a href="https://github.com/bcgsc/ARCS/" rel="nofollow">https://github.com/bcgsc/ARCS/</a></p>]]></description>
	<dc:creator>Jit</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/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/41669/filtlong-quality-filtering-tool-for-long-reads</guid>
	<pubDate>Wed, 13 May 2020 10:23:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41669/filtlong-quality-filtering-tool-for-long-reads</link>
	<title><![CDATA[Filtlong: quality filtering tool for long reads]]></title>
	<description><![CDATA[<p>Filtlong is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.</p>
<p>Filtlong builds into a stand-alone executable:</p>
<pre><code>git clone https://github.com/rrwick/Filtlong.git
cd Filtlong
make -j
bin/filtlong -h
</code></pre><p>Address of the bookmark: <a href="https://github.com/rrwick/Filtlong" rel="nofollow">https://github.com/rrwick/Filtlong</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42132/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</guid>
	<pubDate>Mon, 17 Aug 2020 05:25:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42132/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins</link>
	<title><![CDATA[SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins]]></title>
	<description><![CDATA[<p>SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:</p>
<ol>
<li>Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome</li>
<li>Co-assembly of a large number of metagenomes via merging of individual metagenomes</li>
<li>Includes binning and bin checking, for retrieving individual genomes</li>
<li>The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.</li>
<li>Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.</li>
<li>Metatranscriptomic support via mapping of cDNA reads against reference metagenomes</li>
</ol><p>Address of the bookmark: <a href="https://github.com/jtamames/SqueezeMeta" rel="nofollow">https://github.com/jtamames/SqueezeMeta</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42946/aligngraph2-similar-genome-assisted-reassembly-pipeline-for-pacbio-long-reads</guid>
	<pubDate>Sun, 14 Mar 2021 09:42:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42946/aligngraph2-similar-genome-assisted-reassembly-pipeline-for-pacbio-long-reads</link>
	<title><![CDATA[AlignGraph2: similar genome-assisted reassembly pipeline for PacBio long reads]]></title>
	<description><![CDATA[<p><span>AlignGraph2 is the second version of&nbsp;</span><a href="https://github.com/baoe/AlignGraph">AlignGraph</a><span>&nbsp;for PacBio long reads. It extends and refines contigs assembled from the long reads with a published genome similar to the sequencing genome.</span></p>
<p><span>More at&nbsp;https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbab022/6146772</span></p><p>Address of the bookmark: <a href="https://github.com/huangs001/AlignGraph2" rel="nofollow">https://github.com/huangs001/AlignGraph2</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

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