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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37576?offset=110</link>
	<atom:link href="https://bioinformaticsonline.com/related/37576?offset=110" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads</guid>
	<pubDate>Mon, 27 Feb 2017 09:49:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads</link>
	<title><![CDATA[FinisherSC: a repeat-aware and scalable tool for upgrading de novo assembly using long reads]]></title>
	<description><![CDATA[<p><span>FinisherSC, a repeat-aware and scalable tool for upgrading&nbsp;</span><em>de novo</em><span>&nbsp;assembly using long reads. Experiments with real data suggest that FinisherSC can provide longer and higher quality contigs than existing tools while maintaining high concordance.</span></p><p>Address of the bookmark: <a href="http://kakitone.github.io/finishingTool/" rel="nofollow">http://kakitone.github.io/finishingTool/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32905/bigmac-breaking-inaccurate-genomes-and-merging-assembled-contigs-for-long-read-metagenomic-assembly</guid>
	<pubDate>Mon, 22 May 2017 05:43:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32905/bigmac-breaking-inaccurate-genomes-and-merging-assembled-contigs-for-long-read-metagenomic-assembly</link>
	<title><![CDATA[BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly]]></title>
	<description><![CDATA[<p>This tool is for users to upgrade their metagenomics assemblies using long reads. This includes fixing mis-assemblies and scaffolding/gap-filling. If you encounter any issues, please contact me at&nbsp;<a href="mailto:kklam@eecs.berkeley.edu">kklam@eecs.berkeley.edu</a>. My name is Ka-Kit Lam.</p>
<p>https://github.com/kakitone/MetaFinisherSC</p>
<p>https://github.com/kakitone/BIGMAC</p><p>Address of the bookmark: <a href="https://github.com/kakitone/BIGMAC" rel="nofollow">https://github.com/kakitone/BIGMAC</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40711/vg-variation-graph-data-structures-interchange-formats-alignment-genotyping-and-variant-calling-methods</guid>
	<pubDate>Tue, 28 Jan 2020 03:53:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40711/vg-variation-graph-data-structures-interchange-formats-alignment-genotyping-and-variant-calling-methods</link>
	<title><![CDATA[VG: variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods]]></title>
	<description><![CDATA[<p><em>Variation graphs</em>&nbsp;provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:</p>
<ul>
<li><em>nodes</em>, which are labeled by sequences and ids</li>
<li><em>edges</em>, which connect two nodes via either of their respective ends</li>
<li><em>paths</em>, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges</li>
</ul><p>Address of the bookmark: <a href="https://github.com/vgteam/vg" rel="nofollow">https://github.com/vgteam/vg</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34702/run-miniasm-assembler-on-nanopore-reads</guid>
	<pubDate>Mon, 18 Dec 2017 04:07:50 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34702/run-miniasm-assembler-on-nanopore-reads</link>
	<title><![CDATA[Run miniasm assembler on nanopore reads !]]></title>
	<description><![CDATA[<p>Miniasm is a very fast OLC-based&nbsp;<em>de novo</em>&nbsp;assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by&nbsp;<a href="https://github.com/lh3/minimap">minimap</a>) as input and outputs an assembly graph in the&nbsp;<a href="https://github.com/pmelsted/GFA-spec/blob/master/GFA-spec.md">GFA</a>&nbsp;format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final&nbsp;<a href="http://wgs-assembler.sourceforge.net/wiki/index.php/Celera_Assembler_Terminology">unitig</a>&nbsp;sequences. Thus the per-base error rate is similar to the raw input reads.</p><p>Find the detail of the reads repeats:</p><blockquote><p>fq2fa ONT_A.fastq ONT_A.fasta&nbsp;<br /><br />minimap2 -xava-ont ONT_A.fasta ONT_A.fasta -t10 -X &gt; AONT.paf&nbsp;<br /><br />awk '{if($1==$6){print}}' AONT.paf &gt; AONTself.paf&nbsp;<br /><br />awk '$5=="-"' AONTself.paf | awk '{print $1}'| sort|uniq &gt; invertedrepeat.list</p></blockquote><p>Generated a few palindrome and repeats plots (highlighting only repeats largest than 10, 20 and 30 kb)</p><blockquote><p>minidot -f 5 -m 30000 AONTself.paf &gt; AONTself30000.eps&nbsp;<br />sed 's/_template_pass_FAH31515//' AONTself30000.eps &gt; AONTself30000final.eps&nbsp;<br /><br />minidot -f 5 -m 20000 AONTself.paf &gt; AONTself20000.eps&nbsp;<br />sed 's/_template_pass_FAH31515//' AONTself20000.eps &gt; AONTself20000final.eps&nbsp;<br /><br />minidot -f 5 -m 10000 AONTself.paf &gt; AONTself10000.eps&nbsp;<br />sed 's/_template_pass_FAH31515//' AONTself10000.eps &gt; AONTself10000final.eps&nbsp;</p></blockquote><p>Assemble with miniasm:</p><blockquote><p>miniasm -f ONT_A.fasta AONT.paf &gt; AONT.gfa&nbsp;</p><p>grep '^S' AONT.gfa |awk '{print "&gt;"$2"\n"$3}' &gt; AONT_miniasm.fasta&nbsp;<br /><br />minimap2 -xasm10 AONT_miniasm.fasta AONT_miniasm.fasta -t1 -X &gt; AONT_miniasm.paf&nbsp;<br /><br />awk '{if($1==$6){print}}' AONT_miniasm.paf &gt; AONT_miniasm_self.paf&nbsp;<br /><br />minidot -f 5 -m 10000 AONT_miniasm_self.paf &gt; AONT_miniasm_self10000.eps&nbsp;</p></blockquote><p>Njoy the assembly !</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36476/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Fri, 04 May 2018 19:16:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36476/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 long and noisy reads, such as those produced by PacBio and Oxford Nanopore Technologies. The algorithm uses an A-Bruijn graph to find the overlaps between reads and does not require them to be error-corrected. After the initial assembly, Flye performs an extra repeat classification and analysis step to improve the structural accuracy of the resulting sequence. The package also includes a polisher module, which produces the final assembly of high nucleotide-level quality.</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>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36630/frequent-paired-end-reads-pe-2x100-mapping-command-lines</guid>
	<pubDate>Tue, 15 May 2018 08:59:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36630/frequent-paired-end-reads-pe-2x100-mapping-command-lines</link>
	<title><![CDATA[Frequent Paired-end reads (PE 2x100) mapping command lines]]></title>
	<description><![CDATA[
<p>bowtie2 -x hs37m -X 650 -q -1 r1.fq -2 r2.fq -S r12.bowtie2.sam  </p>

<p>bwa aln hs37m.fa r1.fq &gt; r1.sai &amp;&amp; bwa aln hs37m.fa r2.fq &gt; r2.sai \  <br />    &amp;&amp; bwa sampe hs37m r1.sai r2.sai r1.fq r2.fq &gt; r12.bwa.sam  </p>

<p>bwa bwasw ../index/bwa/hs37m.fa r12.fq &gt; r12.bwasw.sam  </p>

<p>gsnap -A sam -d hs37m r1.fq r2.fq &gt; r12.gsnap.sam  </p>

<p>novoalign -r Random -o SAM -f r1.fq r2.fq -i 500 50 -d hs37m-k14s3.novo &gt; r12.novo.sam  </p>

<p>smalt map -f samsoft -i 650 -o r12.smalt-k20s13.sam hs37m-k20s13 r1.fq r2.fq  </p>

<p>stampy.py -g hs37m -h hs37m -o r12.stampy.sam -M r1.fq,r2.fq  </p>

<p>soap -D hs37m.fa.index -a r1.fq -b r2.fq -l 32 -g 3 -u dummy -2 dummy -o r12.soap</p>
]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file</guid>
	<pubDate>Thu, 24 May 2018 10:06:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file</link>
	<title><![CDATA[pbalign: maps PacBio reads to reference sequences and saves alignments to a BAM file]]></title>
	<description><![CDATA[pbalign aligns PacBio reads to reference sequences, filters aligned reads according to user-specific filtering criteria, and converts the output to either the SAM format or PacBio Compare HDF5 (e.g., .cmp.h5) format. The output Compare HDF5 file will be compatible with Quiver if --forQuiver option is specified.<p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/pbalign" rel="nofollow">https://github.com/PacificBiosciences/pbalign</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36921/breakpointer-using-local-mapping-artifacts-to-support-sequence-breakpoint-discovery-from-single-end-reads</guid>
	<pubDate>Tue, 12 Jun 2018 12:41:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36921/breakpointer-using-local-mapping-artifacts-to-support-sequence-breakpoint-discovery-from-single-end-reads</link>
	<title><![CDATA[Breakpointer: using local mapping artifacts to support sequence breakpoint discovery from single-end reads]]></title>
	<description><![CDATA[Breakpointer is a fast tool for locating sequence breakpoints from the alignment of single end reads (SE) produced by next generation sequencing (NGS). It adopts a heuristic method in searching for local mapping signatures created by insertion/deletions (indels) or more complex structural variants(SVs).<p>Address of the bookmark: <a href="https://github.com/ruping/Breakpointer" rel="nofollow">https://github.com/ruping/Breakpointer</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/38299/deepbinner-a-signal-level-demultiplexer-for-oxford-nanopore-reads</guid>
	<pubDate>Tue, 27 Nov 2018 03:38:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38299/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&nbsp;<a href="https://nanoporetech.com/">Oxford Nanopore</a>&nbsp;sequencing reads. It does this with a deep&nbsp;<a href="https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/">convolutional neural network</a>&nbsp;classifier, using many of the&nbsp;<a href="https://towardsdatascience.com/neural-network-architectures-156e5bad51ba">architectural advances</a>&nbsp;that have proven successful in image classification. Unlike other demultiplexers (e.g. Albacore and&nbsp;<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>
<ul>
<li><span>Reasons to use Deepbinner</span>:
<ul>
<li>To minimise the number of unclassified reads (use Deepbinner by itself).</li>
<li>To minimise the number of misclassified reads (use Deepbinner in conjunction with Albacore demultiplexing).</li>
<li>You plan on running signal-level downstream analyses, like&nbsp;<a href="https://github.com/jts/nanopolish">Nanopolish</a>. Deepbinner can&nbsp;<a href="https://github.com/rrwick/Deepbinner#using-deepbinner-before-basecalling">demultiplex the fast5 files</a>which makes this easier.</li>
</ul>
</li>
<li><span>Reasons to&nbsp;<em>not</em>&nbsp;use Deepbinner</span>:
<ul>
<li>You only have basecalled reads not the raw fast5 files (which Deepbinner requires).</li>
<li>You have a small/slow computer. Deepbinner is more computationally intensive than&nbsp;<a href="https://github.com/rrwick/Porechop">Porechop</a>.</li>
<li>You used a sequencing/barcoding kit other than&nbsp;<a href="https://github.com/rrwick/Deepbinner/blob/master/models">the ones Deepbinner was trained on</a>.</li>
</ul>
</li>
</ul><p>Address of the bookmark: <a href="https://github.com/rrwick/Deepbinner" rel="nofollow">https://github.com/rrwick/Deepbinner</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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