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	<title><![CDATA[BOL: Related items]]></title>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44229/common-steps-for-reads-mapping</guid>
	<pubDate>Thu, 09 Mar 2023 02:48:02 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44229/common-steps-for-reads-mapping</link>
	<title><![CDATA[Common steps for reads mapping !]]></title>
	<description><![CDATA[<div><div><div><div><div><div><div><div><div><div><p>Mapping reads to a reference genome is an essential step in many types of genomic analysis, such as variant calling and gene expression analysis. Here are some general steps to follow for mapping reads to a genome:</p><ol>
<li>
<p>Choose a read mapper: There are many read mappers available, such as BWA, Bowtie, and HISAT2. Choose a mapper that is appropriate for your type of data and research question.</p>
</li>
<li>
<p>Index the reference genome: Before mapping reads, the reference genome needs to be indexed. This involves creating an index of the genome sequence that allows the mapper to quickly find matches to the reads. Most mappers have their own indexing tools.</p>
</li>
<li>
<p>Prepare the read data: The reads should be in a format that is compatible with the mapper. Most mappers accept FASTQ or BAM files. Depending on the quality of the data, it may need to be filtered or trimmed before mapping.</p>
</li>
<li>
<p>Run the mapper: The mapper is run with the command-line interface or using a graphical user interface. The specific command depends on the mapper being used, but typically involves specifying the input data, reference genome, and output file format.</p>
</li>
<li>
<p>Evaluate the mapping results: After the mapping is complete, the results should be evaluated. This includes assessing the quality of the mapping, such as the mapping rate, the number of mapped reads, and the mapping quality score.</p>
</li>
<li>
<p>Post-processing: Depending on the analysis being performed, post-processing of the mapped reads may be necessary. This can include filtering reads based on quality, removing duplicate reads, and calling variants.</p>
</li>
</ol><p>Overall, mapping reads to a reference genome is a complex process that requires careful consideration of the type of data, the research question, and the specific mapper being used.</p></div></div></div></div></div></div></div></div></div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26535/svelter</guid>
	<pubDate>Mon, 29 Feb 2016 17:33:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26535/svelter</link>
	<title><![CDATA[svelter]]></title>
	<description><![CDATA[<p>This software is designed to identify both simple and complex rearrangements from paired-end sequencing data. Users could ran it easily by just alling&nbsp;<em>SVelter.py</em>&nbsp;with proper parameters. It's also possible to ran it on multiple cores by calling different sub-functions separately.</p>
<p>More at&nbsp;https://github.com/mills-lab/svelter/</p><p>Address of the bookmark: <a href="https://github.com/mills-lab/svelter/" rel="nofollow">https://github.com/mills-lab/svelter/</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41604/synteny-and-rearrangement-identifier-syri</guid>
	<pubDate>Tue, 05 May 2020 10:37:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41604/synteny-and-rearrangement-identifier-syri</link>
	<title><![CDATA[Synteny and Rearrangement Identifier (SyRI)]]></title>
	<description><![CDATA[<p>SyRI is a comprehensive tool for predicting genomic differences between related genomes using whole-genome assemblies (WGA). The assemblies are aligned using whole-genome alignment tools, and these alignments are then used as input to SyRI. SyRI identifies syntenic path (longest set of co-linear regions), structural rearrangements (inversions, translocations, and duplications), local variations (SNPs, indels, CNVs etc) within syntenic and structural rearrangements, and un-aligned regions.</p><p>Address of the bookmark: <a href="https://schneebergerlab.github.io/syri/" rel="nofollow">https://schneebergerlab.github.io/syri/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37796/grsr-a-tool-for-deriving-genome-rearrangement-scenarios-from-multiple-unichromosomal-genome-sequences</guid>
	<pubDate>Fri, 28 Sep 2018 09:35:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37796/grsr-a-tool-for-deriving-genome-rearrangement-scenarios-from-multiple-unichromosomal-genome-sequences</link>
	<title><![CDATA[GRSR: a tool for deriving genome rearrangement scenarios from multiple unichromosomal genome sequences]]></title>
	<description><![CDATA[<p>GRSR is a Tool for Deriving Genome Rearrangement Scenarios for Multiple Uni-chromosomal Genomes. This tool will do the following steps:</p>
<ul>
<li>Step 1. Run mugsy to get multiple sequence alignment results.</li>
<li>Step 2 &amp; 3. Extraction of the Coordinates of Core Blocks, Construction of Synteny Blocks and Generating Signed Permutations.</li>
<li>Step 4. Generate pairwise genome rearrangement scenarios and find repeats at the breakpoints of each rearrangement events.</li>
<li></li>
<li></li>
</ul>
<p>https://github.com/DanwangJessica/GRSR</p><p>Address of the bookmark: <a href="https://github.com/DanwangJessica/GRSR" rel="nofollow">https://github.com/DanwangJessica/GRSR</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40699/kevler-reference-free-variant-discovery-in-large-eukaryotic-genomes</guid>
	<pubDate>Tue, 28 Jan 2020 03:21:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40699/kevler-reference-free-variant-discovery-in-large-eukaryotic-genomes</link>
	<title><![CDATA[Kevler: Reference-free variant discovery in large eukaryotic genomes]]></title>
	<description><![CDATA[<p><span>Welcome to&nbsp;</span><span>kevlar</span><span>, software for predicting&nbsp;</span><em>de novo</em><span>&nbsp;genetic variants without mapping reads to a reference genome! kevlar's&nbsp;</span><em>k</em><span>-mer abundance based method calls single nucleotide variants (SNVs), multinucleotide variants (MNVs), insertion/deletion variants (indels), and structural variants (SVs) simultaneously with a single simple model.&nbsp;</span></p>
<p><span>More at&nbsp;<a href="https://kevlar.readthedocs.io/en/latest/">https://kevlar.readthedocs.io/en/latest/</a></span></p>
<p><span><a href="https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf">https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf</a></span></p><p>Address of the bookmark: <a href="https://github.com/kevlar-dev/kevlar" rel="nofollow">https://github.com/kevlar-dev/kevlar</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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