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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38067?offset=90</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34391/taxoblast-taxoblast-is-a-pipeline-to-identify-contamination-in-genomic-sequence</guid>
	<pubDate>Thu, 23 Nov 2017 08:37:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34391/taxoblast-taxoblast-is-a-pipeline-to-identify-contamination-in-genomic-sequence</link>
	<title><![CDATA[Taxoblast : Taxoblast is a pipeline to identify contamination in genomic sequence]]></title>
	<description><![CDATA[<p><span>Modern genome sequencing strategies are highly sensitive to contamination making the detection of foreign DNA sequences an important part of analysis pipelines. Here we use Taxoblast, a simple pipeline with a graphical user interface, for the post-assembly detection of contaminating sequences in the published genome of the kelp&nbsp;</span><em>Saccharina japonica</em><span>. Analyses were based on multiple blastn searches with short sequence fragments. They revealed a number of probable bacterial contaminations as well as hybrid scaffolds that contain both bacterial and algal sequences. This or similar types of analysis, in combination with manual curation, may thus constitute a useful complement to standard bioinformatics analyses prior to submission of genomic data to public repositories. Our analysis pipeline is open-source and freely available at&nbsp;</span><a href="http://sdittami.altervista.org/taxoblast" title="">http://sdittami.altervista.org/taxoblast</a><span>&nbsp;and via SourceForge (</span><a href="https://sourceforge.net/projects/taxoblast" title="">https://sourceforge.net/projects/taxoblast</a><span>).</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/taxoblast/files/" rel="nofollow">https://sourceforge.net/projects/taxoblast/files/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35292/pgap-x-extension-on-pan-genome-analysis-pipeline</guid>
	<pubDate>Tue, 23 Jan 2018 11:41:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35292/pgap-x-extension-on-pan-genome-analysis-pipeline</link>
	<title><![CDATA[PGAP-X: Extension on pan-genome analysis pipeline]]></title>
	<description><![CDATA[<p>PGAP-X is a microbial comparative genomic analysis platform with graphic interface. Serials of algorithms and methodologies have been developed and integrated to analyze and visualize genomics structure variation, gene distribution with different conservative levels, and genetic variation from pan-genome sight. At the same time, analytical result data from many other programs, including genome alignment result and orthologs clusters, are also supported to be further analyzed or visualized in PGAP-X. The workflow and feature snapshot in PGAP-X were shown as Fig.1 and Fig.2.</p>
<div><img src="https://pgapx.ybzhao.com/image/f1.jpg" alt="image" style="border: 0px; border: 0px;"></div>
<div>&nbsp;</div>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://pgapx.ybzhao.com/" rel="nofollow">https://pgapx.ybzhao.com/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39236/causel-an-epigenome-and-genome-editing-pipeline-for-establishing-function-of-noncoding-gwas-variants</guid>
	<pubDate>Tue, 09 Apr 2019 07:23:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39236/causel-an-epigenome-and-genome-editing-pipeline-for-establishing-function-of-noncoding-gwas-variants</link>
	<title><![CDATA[CAUSEL: an epigenome- and genome-editing pipeline for establishing function of noncoding GWAS variants]]></title>
	<description><![CDATA[<p><span>Validated a widely accessible approach that can be used to establish functional causality for noncoding sequence variants identified by GWASs.</span></p>
<p><a href="https://www.nature.com/articles/nm.3975">https://www.nature.com/articles/nm.3975</a></p><p>Address of the bookmark: <a href="https://www.nature.com/articles/nm.3975" rel="nofollow">https://www.nature.com/articles/nm.3975</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40409/haplotypo-a-variant-calling-pipeline-for-phased-genomes</guid>
	<pubDate>Thu, 19 Dec 2019 07:33:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40409/haplotypo-a-variant-calling-pipeline-for-phased-genomes</link>
	<title><![CDATA[HaploTypo: a variant-calling pipeline for phased genomes]]></title>
	<description><![CDATA[<p>An increasing number of phased (i.e. with resolved haplotypes) reference genomes are available. However, most genetic variant calling tools do not explicitly account for haplotype structure. Here, we present HaploTypo, a pipeline tailored to resolve haplotypes in genetic variation analyses. HaploTypo infers the haplotype correspondence for each heterozygous variant called on a phased reference genome.</p>
<div>Availability and Implementation</div>
<p>HaploTypo is implemented in Python 2.7 and Python 3.5, and is freely available at&nbsp;<a href="https://github.com/gabaldonlab/haplotypo" target="">https://github.com/gabaldonlab/haplotypo</a>, and as a Docker image.</p><p>Address of the bookmark: <a href="https://github.com/gabaldonlab/haplotypo" rel="nofollow">https://github.com/gabaldonlab/haplotypo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41030/slr-superscaffolder-a-scaffold-assemble-pipeline-for-stlfr-reads</guid>
	<pubDate>Fri, 14 Feb 2020 14:23:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41030/slr-superscaffolder-a-scaffold-assemble-pipeline-for-stlfr-reads</link>
	<title><![CDATA[SLR-superscaffolder: A scaffold assemble pipeline for stLFR reads.]]></title>
	<description><![CDATA[<p>This is a scaffold assembler designed for stLFR reads[1]. It uses the link-reads information from stLFR reads to assemble contigs to scaffolds.</p>
<p>Here is an illustration of this pipeline:</p>
<p>&nbsp;<img src="https://github.com/BGI-Qingdao/SLR-superscaffolder/raw/master/image.png" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="https://github.com/BGI-Qingdao/SLR-superscaffolder" rel="nofollow">https://github.com/BGI-Qingdao/SLR-superscaffolder</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes</guid>
	<pubDate>Thu, 13 Aug 2020 10:06:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes</link>
	<title><![CDATA[PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes]]></title>
	<description><![CDATA[<p>PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring&nbsp;O(n2)&nbsp;resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.</p><p>Address of the bookmark: <a href="https://github.com/ryanmelnyk/PyParanoid" rel="nofollow">https://github.com/ryanmelnyk/PyParanoid</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps</guid>
	<pubDate>Fri, 10 Dec 2021 06:22:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps</link>
	<title><![CDATA[Illumina based assembly pipeline steps !]]></title>
	<description><![CDATA[<h3 id="illumina">Illumina<a href="https://nf-co.re/viralrecon#illumina"><span></span></a></h3><ol>
<li>Merge re-sequenced FastQ files (<a href="http://www.linfo.org/cat.html"><code>cat</code></a>)</li>
<li>Read QC (<a href="https://www.bioinformatics.babraham.ac.uk/projects/fastqc/"><code>FastQC</code></a>)</li>
<li>Adapter trimming (<a href="https://github.com/OpenGene/fastp"><code>fastp</code></a>)</li>
<li>Removal of host reads (<a href="http://ccb.jhu.edu/software/kraken2/"><code>Kraken 2</code></a>; <em>optional</em>)</li>
<li>Variant calling<ol>
<li>Read alignment (<a href="http://bowtie-bio.sourceforge.net/bowtie2/index.shtml"><code>Bowtie 2</code></a>)</li>
<li>Sort and index alignments (<a href="https://sourceforge.net/projects/samtools/files/samtools/"><code>SAMtools</code></a>)</li>
<li>Primer sequence removal (<a href="https://github.com/andersen-lab/ivar"><code>iVar</code></a>; <em>amplicon data only</em>)</li>
<li>Duplicate read marking (<a href="https://broadinstitute.github.io/picard/"><code>picard</code></a>; <em>optional</em>)</li>
<li>Alignment-level QC (<a href="https://broadinstitute.github.io/picard/"><code>picard</code></a>, <a href="https://sourceforge.net/projects/samtools/files/samtools/"><code>SAMtools</code></a>)</li>
<li>Genome-wide and amplicon coverage QC plots (<a href="https://github.com/brentp/mosdepth/"><code>mosdepth</code></a>)</li>
<li>Choice of multiple variant calling and consensus sequence generation routes (<a href="https://github.com/andersen-lab/ivar"><code>iVar variants and consensus</code></a>; <em>default for amplicon data</em> <em>||</em> <a href="http://samtools.github.io/bcftools/bcftools.html"><code>BCFTools</code></a>, <a href="https://github.com/arq5x/bedtools2/"><code>BEDTools</code></a>; <em>default for metagenomics data</em>)
<ul>
<li>Variant annotation (<a href="http://snpeff.sourceforge.net/SnpEff.html"><code>SnpEff</code></a>, <a href="http://snpeff.sourceforge.net/SnpSift.html"><code>SnpSift</code></a>)</li>
<li>Consensus assessment report (<a href="http://quast.sourceforge.net/quast"><code>QUAST</code></a>)</li>
<li>Lineage analysis (<a href="https://github.com/cov-lineages/pangolin"><code>Pangolin</code></a>)</li>
<li>Clade assignment, mutation calling and sequence quality checks (<a href="https://github.com/nextstrain/nextclade"><code>Nextclade</code></a>)</li>
<li>Individual variant screenshots with annotation tracks (<a href="https://asciigenome.readthedocs.io/en/latest/"><code>ASCIIGenome</code></a>)</li>
</ul>
</li>
<li>Intersect variants across callers (<a href="http://samtools.github.io/bcftools/bcftools.html"><code>BCFTools</code></a>)</li>
</ol></li>
<li><em>De novo</em> assembly<ol>
<li>Primer trimming (<a href="https://cutadapt.readthedocs.io/en/stable/guide.html"><code>Cutadapt</code></a>; <em>amplicon data only</em>)</li>
<li>Choice of multiple assembly tools (<a href="http://cab.spbu.ru/software/spades/"><code>SPAdes</code></a> <em>||</em> <a href="https://github.com/rrwick/Unicycler"><code>Unicycler</code></a> <em>||</em> <a href="https://github.com/GATB/minia"><code>minia</code></a>)
<ul>
<li>Blast to reference genome (<a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch"><code>blastn</code></a>)</li>
<li>Contiguate assembly (<a href="https://www.sanger.ac.uk/science/tools/pagit"><code>ABACAS</code></a>)</li>
<li>Assembly report (<a href="https://github.com/BU-ISCIII/plasmidID"><code>PlasmidID</code></a>)</li>
<li>Assembly assessment report (<a href="http://quast.sourceforge.net/quast"><code>QUAST</code></a>)</li>
</ul>
</li>
</ol></li>
<li>Present QC and visualisation for raw read, alignment, assembly and variant calling results (<a href="http://multiqc.info/"><code>MultiQC</code></a>)</li>
</ol>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44561/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes</guid>
	<pubDate>Sat, 08 Jun 2024 16:25:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44561/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes</link>
	<title><![CDATA[Bactopia: a flexible pipeline for complete analysis of bacterial genomes]]></title>
	<description><![CDATA[<p>Bactopia is a flexible pipeline for complete analysis of bacterial genomes. The goal of Bactopia is process your data with a broad set of tools, so that you can get to the fun part of analyses quicker!</p>
<p>Bactopia was inspired by&nbsp;<a href="https://staphopia.github.io/">Staphopia</a>, a workflow we (Tim Read and myself) released that is targeted towards&nbsp;<em>Staphylococcus aureus</em>&nbsp;genomes. Using what we learned from Staphopia and user feedback, Bactopia was developed from scratch with usability, portability, and speed in mind from the start.</p>
<p>Bactopia uses&nbsp;<a href="https://www.nextflow.io/">Nextflow</a>&nbsp;to manage the workflow, allowing for support of many types of environments (e.g. cluster or cloud). Bactopia allows for the usage of many public datasets as well as your own datasets to further enhance the analysis of your sequencing. Bactopia only uses software packages available from&nbsp;<a href="https://bioconda.github.io/">Bioconda</a>&nbsp;and&nbsp;<a href="https://conda-forge.org/">Conda-Forge</a>&nbsp;to make installation as simple as possible for&nbsp;<em>all</em>&nbsp;users.</p>
<p>To highlight the use of&nbsp;<a href="https://bactopia.github.io/latest/full-guide/">Bactopia</a>&nbsp;and&nbsp;<a href="https://bactopia.github.io/latest/bactopia-tools/">Bactopia Tools</a>, we performed an analysis of 1,664 public&nbsp;<em>Lactobacillus</em>&nbsp;genomes, focusing on&nbsp;<em>Lactobacillus crispatus</em>, a species that is a common part of the human vaginal microbiome. The results from this analysis are published in mSystems under the title:&nbsp;<em><a href="https://doi.org/10.1128/mSystems.00190-20">Bactopia: a flexible pipeline for complete analysis of bacterial genomes</a></em></p>
<p><a href="https://bactopia.github.io/latest/assets/bactopia-workflow.png"><img src="https://bactopia.github.io/latest/assets/bactopia-workflow.png" alt="Bactopia Workflow" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://bactopia.github.io/latest/" rel="nofollow">https://bactopia.github.io/latest/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35762/genome-assembly-stats-plotting</guid>
	<pubDate>Wed, 28 Feb 2018 03:45:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35762/genome-assembly-stats-plotting</link>
	<title><![CDATA[Genome assembly stats plotting]]></title>
	<description><![CDATA[<p>A&nbsp;<em>de novo</em>&nbsp;genome assembly can be summarised b</p>
<p>y a number of metrics, including:</p>
<ul>
<li>Overall assembly length</li>
<li>Number of scaffolds/contigs</li>
<li>Length of longest scaffold/contig</li>
<li>Scaffold/contig N50 and N90Assembly base composition, in particular percentage GC and percentage Ns</li>
<li>CEGMA completeness</li>
<li>Scaffold/contig length/count distribution</li>
</ul>
<p>assembly-stats supports two widely used presentations of these values, tabular and cumulative length plots, and introduces an additional circular plot that summarises most commonly used assembly metrics in a single visualisation. Each of these presentations is generated using javascript from a common (JSON) data structure, allowing toggling between alternative views, and each can be applied to a single or multiple assemblies to allow direct comparison of alternate assemblies.</p>
<p>Tabular presentation allows direct comparison of exact values between assemblies, the limitations of this approach lie in the necessary omission of distributions and the challenge of interpreting ratios of values that may vary by several orders of magnitude.</p><p>Address of the bookmark: <a href="https://github.com/rjchallis/assembly-stats" rel="nofollow">https://github.com/rjchallis/assembly-stats</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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