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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/1897?offset=170</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34445/inc-seq-accurate-single-molecule-reads-using-nanopore-sequencing</guid>
	<pubDate>Mon, 27 Nov 2017 10:38:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34445/inc-seq-accurate-single-molecule-reads-using-nanopore-sequencing</link>
	<title><![CDATA[INC-Seq: accurate single molecule reads using nanopore sequencing]]></title>
	<description><![CDATA[<p><span>INC-Seq reads enabled accurate species-level classification, identification of species at 0.1&nbsp;% abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.</span></p><p>Address of the bookmark: <a href="https://github.com/CSB5/INC-Seq" rel="nofollow">https://github.com/CSB5/INC-Seq</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36271/heap-a-highly-sensitive-and-accurate-snp-detection-tool-for-low-coverage-high-throughput-sequencing-data</guid>
	<pubDate>Thu, 19 Apr 2018 08:06:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36271/heap-a-highly-sensitive-and-accurate-snp-detection-tool-for-low-coverage-high-throughput-sequencing-data</link>
	<title><![CDATA[Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data]]></title>
	<description><![CDATA[<p><span>Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both end of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from&nbsp;</span><a href="https://github.com/meiji-bioinf/heap">https://github.com/meiji-bioinf/heap</a><span>&nbsp;and our web site (</span><a href="http://bioinf.mind.meiji.ac.jp/lab/en/tools.html">http://bioinf.mind.meiji.ac.jp/lab/en/tools.html</a><span>).</span></p><p>Address of the bookmark: <a href="https://github.com/meiji-bioinf/heap" rel="nofollow">https://github.com/meiji-bioinf/heap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36621/hapcut2-robust-and-accurate-haplotype-assembly-for-diverse-sequencing-technologies</guid>
	<pubDate>Tue, 15 May 2018 07:35:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36621/hapcut2-robust-and-accurate-haplotype-assembly-for-diverse-sequencing-technologies</link>
	<title><![CDATA[HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies]]></title>
	<description><![CDATA[HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to:

NGS short reads (Illumina HiSeq)
clone-based sequencing (Fosmid or BAC clones)
SMRT reads (PacBio)
Oxford Nanopore reads
10X Genomics Linked-Reads
proximity-ligation (Hi-C) reads
high-coverage sequencing (&gt;40x coverage-per-SNP) using above technologies
combinations of the above technologies (e.g. scaffold long reads with Hi-C reads)
See below for specific examples of command line options and best practices for some of these technologies.

NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants.

If you use HapCUT2 in your research, please cite:

Edge, P., Bafna, V. &amp; Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116<p>Address of the bookmark: <a href="https://github.com/vibansal/HapCUT2" rel="nofollow">https://github.com/vibansal/HapCUT2</a></p>]]></description>
	<dc:creator>Jit</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</guid>
	<pubDate>Wed, 22 Aug 2018 10:40:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</link>
	<title><![CDATA[SimLoRD: A read simulator for third generation sequencing reads]]></title>
	<description><![CDATA[<p>SimLoRD is a read simulator for third generation sequencing reads and is currently focused on the Pacific Biosciences SMRT error model.</p>
<p>Reads are simulated from both strands of a provided or randomly generated reference sequence.</p>
<div id="rst-header-features">
<ul>
<li>The reference can be read from a FASTA file or randomly generated with a given GC content. It can consist of several chromosomes, whose structure is respected when drawing reads. (Simulation of genome rearrangements may be incorporated at a later stage.)</li>
<li>The read lengths can be determined in four ways: drawing from a log-normal distribution (typical for genomic DNA), sampling from an existing FASTQ file (typical for RNA), sampling from a a text file with integers (RNA), or using a fixed length</li>
<li>Quality values and number of passes depend on fragment length.</li>
<li>Provided subread error probabilities are modified according to number of passes</li>
<li>Outputs reads in FASTQ format and alignments in SAM format</li>
</ul>
</div><p>Address of the bookmark: <a href="https://bitbucket.org/genomeinformatics/simlord/" rel="nofollow">https://bitbucket.org/genomeinformatics/simlord/</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37674/qualimap2-evaluating-next-generation-sequencing-alignment-data</guid>
	<pubDate>Tue, 11 Sep 2018 04:44:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37674/qualimap2-evaluating-next-generation-sequencing-alignment-data</link>
	<title><![CDATA[Qualimap2: Evaluating next generation sequencing alignment data]]></title>
	<description><![CDATA[<p><strong>Qualimap 2</strong><span>&nbsp;is a platform-independent application written in Java and R that provides both a Graphical User Inteface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.&nbsp;</span><br><br><span>Supported types of experiments include:</span></p>
<ul>
<li>Whole-genome sequencing</li>
<li>Whole-exome sequencing</li>
<li>RNA-seq (speical mode available)</li>
<li>ChIP-seq</li>
</ul><p>Address of the bookmark: <a href="http://qualimap.bioinfo.cipf.es/" rel="nofollow">http://qualimap.bioinfo.cipf.es/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38169/amstat-display-statistics-of-large-sequence-files-from-next-generation-sequencing-projects</guid>
	<pubDate>Fri, 09 Nov 2018 13:34:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38169/amstat-display-statistics-of-large-sequence-files-from-next-generation-sequencing-projects</link>
	<title><![CDATA[AMStat: display statistics of large sequence files from next generation sequencing projects]]></title>
	<description><![CDATA[<p><span>SAMStat is an efficient C program to quickly display statistics of large sequence files from next generation sequencing projects. When applied to&nbsp;</span><a href="http://samstat.sourceforge.net/#about">SAM/BAM</a><span>&nbsp;files all statistics are reported for unmapped, poorly and accurately mapped reads separately. This allows for identification of a variety of problems, such as remaining linker and adaptor sequences, causing poor mapping. Apart from this SAMStat can be used to verify individual processing steps in large analysis pipelines.</span></p><p>Address of the bookmark: <a href="http://samstat.sourceforge.net/" rel="nofollow">http://samstat.sourceforge.net/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38593/excavator-detecting-copy-number-variants-from-whole-exome-sequencing-data</guid>
	<pubDate>Fri, 04 Jan 2019 10:10:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38593/excavator-detecting-copy-number-variants-from-whole-exome-sequencing-data</link>
	<title><![CDATA[EXCAVATOR: detecting copy number variants from whole-exome sequencing data]]></title>
	<description><![CDATA[<p><span>EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at&nbsp;</span><span><a href="http://sourceforge.net/projects/excavatortool/" target="_blank"><span>http://sourceforge.net/projects/excavatortool/</span></a></span><span>.</span><br><br><br><span>EXCAVATOR is a novel software package for the detection of copy number variants (CNVs) from whole-exome sequencing data.</span><br><span>EXCAVATOR has been published on Genome Biology (</span><a href="http://genomebiology.com/2013/14/10/R120/abstract" target="_blank">http://genomebiology.com/2013/14/10/R120/abstract<span></span></a><span>).</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/excavatortool/" rel="nofollow">https://sourceforge.net/projects/excavatortool/</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39671/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Sat, 06 Jul 2019 03:48:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39671/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>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40544/ngs-bits-short-read-sequencing-tools</guid>
	<pubDate>Thu, 16 Jan 2020 23:14:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40544/ngs-bits-short-read-sequencing-tools</link>
	<title><![CDATA[ngs-bits - Short-read sequencing tools]]></title>
	<description><![CDATA[<p>Binaries of&nbsp;<em>ngs-bits</em>&nbsp;are available via Bioconda. Alternatively,&nbsp;<em>ngs-bits</em>&nbsp;can be built from sources:</p>
<ul>
<li><span>Binaries</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_bioconda.md">Linux/macOS</a></li>
<li>From&nbsp;<span>sources</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_unix.md">Linux/macOS</a></li>
<li>From&nbsp;<span>sources</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_win.md">Windows</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/imgag/ngs-bits" rel="nofollow">https://github.com/imgag/ngs-bits</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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