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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38892?offset=250</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42198/pggb-the-pangenome-graph-builder</guid>
	<pubDate>Sun, 13 Sep 2020 20:54:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42198/pggb-the-pangenome-graph-builder</link>
	<title><![CDATA[pggb: the pangenome graph builder]]></title>
	<description><![CDATA[<p><span>This pangenome graph construction pipeline renders a collection of sequences into a pangenome graph (in the variation graph model). Its goal is to build a graph that is locally directed and acyclic while preserving large-scale variation. Maintaining local linearity is important for the interpretation, visualization, and reuse of pangenome variation graphs.</span></p>
<p><img src="https://raw.githubusercontent.com/pangenome/pggb/master/data/images/DRB1-3123.fa.gz.pggb-s3000-p70-n10-a70-K11-k8-w10000-j5000-W0-e100.smooth.og.viz.png" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="https://github.com/pangenome/pggb" rel="nofollow">https://github.com/pangenome/pggb</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44497/graphpath-a-graph-attention-model-for-molecular-stratification-with-interpretability-based-on-the-pathway-pathway-interaction-network</guid>
	<pubDate>Wed, 27 Mar 2024 20:51:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44497/graphpath-a-graph-attention-model-for-molecular-stratification-with-interpretability-based-on-the-pathway-pathway-interaction-network</link>
	<title><![CDATA[GraphPath: A graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network]]></title>
	<description><![CDATA[<p><span>Achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. In this paper, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling.</span></p>
<p><span><img src="https://github.com/amazingma/GraphPath/raw/main/Figures/GraphPath.png" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/amazingma/GraphPath" rel="nofollow">https://github.com/amazingma/GraphPath</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36618/lamsa-fast-split-read-alignment-with-long-approximate-matches</guid>
	<pubDate>Tue, 15 May 2018 04:44:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36618/lamsa-fast-split-read-alignment-with-long-approximate-matches</link>
	<title><![CDATA[LAMSA: fast split read alignment with long approximate matches]]></title>
	<description><![CDATA[LAMSA (Long Approximate Matches-based Split Aligner) is a novel split alignment approach with faster speed and good ability of handling SV events. It is well-suited to align long reads (over thousands of base-pairs).

LAMSA takes takes the advantage of the rareness of SVs to implement a specifically designed two-step strategy. That is, LAMSA initially splits the read into relatively long fragments and co-linearly align them to solve the small variations or sequencing errors, and mitigate the effect of repeats. The alignments of the fragments are then used for implementing a sparse dynamic programming (SDP)-based split alignment approach to handle the large or non-co-linear variants.

We benchmarked LAMSA with simulated and real datasets having various read lengths and sequencing error rates, the results demonstrate that it is substantially faster than the state-of-the-art long read aligners; mean-while, it also has good ability to handle various categories of SVs.

LAMSA is open source and free for non-commercial use.

LAMSA is mainly designed by Bo Liu &amp; Yan Gao and developed by Yan Gao in Center for Bioinformatics, Harbin Institute of Technology, China.<p>Address of the bookmark: <a href="https://github.com/hitbc/LAMSA" rel="nofollow">https://github.com/hitbc/LAMSA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler</guid>
	<pubDate>Tue, 14 Jan 2020 06:47:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler</link>
	<title><![CDATA[Shasta long read assembler]]></title>
	<description><![CDATA[<p>The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by&nbsp;<a href="https://nanoporetech.com/">Oxford Nanopore</a>&nbsp;flow cells.</p>
<p>Computational methods used by the Shasta assembler include:</p>
<ul>
<li>Using a&nbsp;<a href="https://en.wikipedia.org/wiki/Run-length_encoding">run-length</a>&nbsp;representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.</li>
<li>Using in some phases of the computation a representation of the read sequence based on&nbsp;<em>markers</em>, a fixed subset of short k-mers (k &asymp; 10).</li>
</ul>
<p>More at&nbsp;<a href="https://chanzuckerberg.github.io/shasta/index.html">https://chanzuckerberg.github.io/shasta/index.html</a></p><p>Address of the bookmark: <a href="https://github.com/chanzuckerberg/shasta" rel="nofollow">https://github.com/chanzuckerberg/shasta</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35883/arcs-scaffolding-genome-drafts-with-linked-reads</guid>
	<pubDate>Tue, 06 Mar 2018 16:35:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35883/arcs-scaffolding-genome-drafts-with-linked-reads</link>
	<title><![CDATA[ARCS: scaffolding genome drafts with linked reads]]></title>
	<description><![CDATA[<p><span>ARCS, an application that utilizes the barcoding information contained in linked reads to further organize draft genomes into highly contiguous assemblies. We show how the contiguity of an ABySS&nbsp;</span><em>H.sapiens</em><span>genome assembly can be increased over six-fold, using moderate coverage (25-fold) Chromium data. We expect ARCS to have broad utility in harnessing the barcoding information contained in linked read data for connecting high-quality sequences in genome assembly drafts.</span></p><p>Address of the bookmark: <a href="https://github.com/bcgsc/ARCS/" rel="nofollow">https://github.com/bcgsc/ARCS/</a></p>]]></description>
	<dc:creator>Rahul Nayak</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/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>

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