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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40546?offset=360</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</guid>
	<pubDate>Fri, 24 Jan 2020 04:09:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</link>
	<title><![CDATA[MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization]]></title>
	<description><![CDATA[<p><span>MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from&nbsp;</span><code>https://github.com/linzhi2013/MitoZ</code><span>.</span></p>
<p><span><a href="https://academic.oup.com/nar/article/47/11/e63/5377471">https://academic.oup.com/nar/article/47/11/e63/5377471</a></span></p><p>Address of the bookmark: <a href="https://github.com/linzhi2013/MitoZ" rel="nofollow">https://github.com/linzhi2013/MitoZ</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links</guid>
	<pubDate>Thu, 13 Feb 2020 00:20:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links</link>
	<title><![CDATA[genomics public data links !]]></title>
	<description><![CDATA[<p>List of publically available databases on google server.</p>
<p>More at <a href="https://software.broadinstitute.org/gatk/download/bundle">https://software.broadinstitute.org/gatk/download/bundle</a></p>
<p><a href="ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/">ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/</a>.</p>
<p><a href="ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/">ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/</a></p><p>Address of the bookmark: <a href="https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1" rel="nofollow">https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34420/rita-rapid-identification-of-high-confidence-taxonomic-assignments-for-metagenomic-data</guid>
	<pubDate>Mon, 27 Nov 2017 08:25:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34420/rita-rapid-identification-of-high-confidence-taxonomic-assignments-for-metagenomic-data</link>
	<title><![CDATA[RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data]]></title>
	<description><![CDATA[<p>RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.</p>
<p>This work has been accepted for publication:</p>
<p>MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to&nbsp;<em>Nucleic Acids Research</em>&nbsp;April 4, 2012.</p>
<p>If you have any questions or bug reports, please let us know at &lt;beiko@cs.dal.ca&gt;.</p><p>Address of the bookmark: <a href="http://kiwi.cs.dal.ca/Software/RITA" rel="nofollow">http://kiwi.cs.dal.ca/Software/RITA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35272/biocircosjs-is-an-open-source-interactive-javascript-library-to-interactive-display-biological-data-on-the-web</guid>
	<pubDate>Fri, 19 Jan 2018 15:03:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35272/biocircosjs-is-an-open-source-interactive-javascript-library-to-interactive-display-biological-data-on-the-web</link>
	<title><![CDATA[BioCircos.js is an open source interactive Javascript library to interactive display biological data on the web]]></title>
	<description><![CDATA[<p><a href="http://bioinfo.ibp.ac.cn/biocircos/index.php">BioCircos.js</a>&nbsp;is an open source interactive&nbsp;<code>Javascript</code>&nbsp;library which provides an easy way to interactive display biological data on the web. It implements a raster-based&nbsp;<code>SVG</code>&nbsp;visualization using the open source Javascript framework jquery.js. BioCircos.js is multiplatform and works in all major internet browsers (<strong>Internet Explorer</strong>,&nbsp;<strong>Mozilla Firefox</strong>,&nbsp;<strong>Google Chrome</strong>,&nbsp;<strong>Safari</strong>,&nbsp;<strong>Opera</strong>). Its speed is determined by the client&rsquo;s hardware and internet browser. For smoothest user experience, we recommend&nbsp;<strong>Google Chrome</strong>.</p>
<p>BioCircos.js provides&nbsp;<strong>SNP</strong>,&nbsp;<strong>CNV</strong>,&nbsp;<strong>HEATMAP</strong>,&nbsp;<strong>LINK</strong>,&nbsp;<strong>LINE</strong>,&nbsp;<strong>SCATTER</strong>,&nbsp;<strong>ARC</strong>,&nbsp;<strong>TEXT</strong>, and&nbsp;<strong>HISTGRAM</strong>modules to display genome-wide genetic variations (SNPs, CNVs and chromosome rearrangement), gene expression and biomolecule interactions. BioCircos.js also provides&nbsp;<strong>BACKGROUND</strong>&nbsp;module to display background and axis circles. Tooltips showing detailed information of SVG elements are also provided.</p>
<p><a href="http://bioinfo.ibp.ac.cn/biocircos/document/demo/pages/paper01.html">Demo</a></p><p>Address of the bookmark: <a href="http://bioinfo.ibp.ac.cn/biocircos/document/index.html" rel="nofollow">http://bioinfo.ibp.ac.cn/biocircos/document/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37205/afterqc-automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data</guid>
	<pubDate>Fri, 29 Jun 2018 03:26:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37205/afterqc-automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data</link>
	<title><![CDATA[AfterQC: Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data]]></title>
	<description><![CDATA[Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data
AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair.
Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats

The author has reimplemented this tool in C++ with multithreading support to make it much faster. The new tool is called fastp and can be found at: https://github.com/OpenGene/fastp . If you prefer a C++ based tool, please use fastp instead.

https://github.com/OpenGene/AfterQC<p>Address of the bookmark: <a href="https://github.com/OpenGene/AfterQC" rel="nofollow">https://github.com/OpenGene/AfterQC</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37672/seqmonka-tool-to-visualise-and-analyse-high-throughput-mapped-sequence-data</guid>
	<pubDate>Tue, 11 Sep 2018 04:39:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37672/seqmonka-tool-to-visualise-and-analyse-high-throughput-mapped-sequence-data</link>
	<title><![CDATA[SeqMonk:A tool to visualise and analyse high throughput mapped sequence data]]></title>
	<description><![CDATA[<p>SeqMonk is a program to enable the visualisation and analysis of mapped sequence data. It was written for use with mapped next generation sequence data but can in theory be used for any dataset which can be expressed as a series of genomic positions. It's main features are:</p>
<ul>
<li>Import of mapped data from mapped data (BAM/SAM/bowtie etc)</li>
<li>Creation of data groups for visualisation and analysis</li>
<li>Visualisation of mapped regions against an annotated genome.</li>
<li>Flexible quantitation of the mapped data to allow comparisons between data sets</li>
<li>Statistical analysis of data to find regions of interest</li>
<li>Creation of reports containing data and genome annotation</li>
</ul><p>Address of the bookmark: <a href="http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/" rel="nofollow">http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40611/deepvariant-an-analysis-pipeline-that-uses-a-deep-neural-network-to-call-genetic-variants-from-next-generation-dna-sequencing-data</guid>
	<pubDate>Sat, 25 Jan 2020 13:28:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40611/deepvariant-an-analysis-pipeline-that-uses-a-deep-neural-network-to-call-genetic-variants-from-next-generation-dna-sequencing-data</link>
	<title><![CDATA[DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.]]></title>
	<description><![CDATA[<p><span>DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.</span></p>
<p><span><span>DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on&nbsp;</span><a href="https://github.com/google/nucleus">Nucleus</a><span>, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the&nbsp;</span><a href="https://www.tensorflow.org/">TensorFlow</a><span>&nbsp;machine learning framework.</span></span></p>
<p><span><a href="https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html">https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html</a></span></p>
<p><span><a href="https://www.biorxiv.org/content/10.1101/092890v6">https://www.biorxiv.org/content/10.1101/092890v6</a></span></p>
<p><span><img src="https://4.bp.blogspot.com/-2KlXZO60sWE/WiGc8qlZfxI/AAAAAAAACOs/s1pNiKI8jsAvJLr1E_po5udDO8eObm_awCLcBGAs/s640/image3.png" width="640" height="427" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/google/deepvariant" rel="nofollow">https://github.com/google/deepvariant</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41148/pbmm2a-minimap2-frontend-for-pacbio-native-data-formats</guid>
	<pubDate>Tue, 18 Feb 2020 03:36:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41148/pbmm2a-minimap2-frontend-for-pacbio-native-data-formats</link>
	<title><![CDATA[pbmm2:A minimap2 frontend for PacBio native data formats]]></title>
	<description><![CDATA[<p><em>pbmm2</em> is a SMRT C++ wrapper for <a href="https://github.com/lh3/minimap2">minimap2</a>'s C API. Its purpose is to support native PacBio in- and output, provide sets of recommended parameters, generate sorted output on-the-fly, and postprocess alignments. Sorted output can be used directly for polishing using GenomicConsensus, if BAM has been used as input to <em>pbmm2</em>. Benchmarks show that <em>pbmm2</em> outperforms BLASR in sequence identity, number of mapped bases, and especially runtime. <em>pbmm2</em> is the official replacement for BLASR.</p><p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/pbmm2" rel="nofollow">https://github.com/PacificBiosciences/pbmm2</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42310/dada2-fast-and-accurate-sample-inference-from-amplicon-data-with-single-nucleotide-resolution</guid>
	<pubDate>Tue, 10 Nov 2020 20:26:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42310/dada2-fast-and-accurate-sample-inference-from-amplicon-data-with-single-nucleotide-resolution</link>
	<title><![CDATA[DADA2: Fast and accurate sample inference from amplicon data with single-nucleotide resolution]]></title>
	<description><![CDATA[<p>The&nbsp;<a href="https://benjjneb.github.io/dada2/tutorial.html">DADA2 tutorial</a>&nbsp;goes through a typical workflow for paired end Illumina Miseq data: raw amplicon sequencing data is processed into the table of exact&nbsp;<strong>amplicon sequence variants (ASVs)</strong>&nbsp;present in each sample.</p>
<p>The&nbsp;<a href="https://benjjneb.github.io/dada2/bigdata.html">DADA2 Workflow on Big Data</a>&nbsp;goes through workflow optimized to run on large datasets (10s of millions to billions of reads).</p>
<p>An&nbsp;<a href="https://benjjneb.github.io/dada2/ITS_workflow.html">ITS-specific version of the DADA2 workflow</a>&nbsp;identifies and verifiably removes primers on both ends of each ITS read, a key step due to the variable length of the ITS region.</p>
<p>Short demonstrations of&nbsp;<a href="https://benjjneb.github.io/dada2/assign.html">assigning taxonomy</a>&nbsp;and&nbsp;<a href="https://benjjneb.github.io/dada2/assign.html">assigning species</a>&nbsp;to sequences.</p><p>Address of the bookmark: <a href="https://benjjneb.github.io/dada2/index.html" rel="nofollow">https://benjjneb.github.io/dada2/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42826/ktrim-an-extra-fast-and-accurate-adapter-and-quality-trimmer-for-sequencing-data</guid>
	<pubDate>Thu, 11 Feb 2021 21:39:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42826/ktrim-an-extra-fast-and-accurate-adapter-and-quality-trimmer-for-sequencing-data</link>
	<title><![CDATA[Ktrim: an extra-fast and accurate adapter- and quality-trimmer for sequencing data]]></title>
	<description><![CDATA[<p>Ktrim&nbsp;is written in&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">C++</code>&nbsp;for GNU Linux/Unix platforms. After uncompressing the source package, you can find an executable file&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">ktrim</code>&nbsp;under&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">bin/</code>&nbsp;directory compiled using&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">g++ v4.8.5</code>&nbsp;and linked with&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">libz v1.2.7</code>&nbsp;for Linux x86_64 system. If you could not run it (which is usually caused by low version of&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">libc++</code>&nbsp;or&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">libz</code>&nbsp;library) or you want to build a version optimized for your system, you can re-compile the programs:</p>
<p>user@linux$ make clean &amp;&amp; make</p><p>Address of the bookmark: <a href="https://github.com/hellosunking/Ktrim" rel="nofollow">https://github.com/hellosunking/Ktrim</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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