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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36918?offset=180</link>
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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/40604/gapfinisher-a-reliable-gap-filling-pipeline-for-sspace-longread-scaffolder-output</guid>
	<pubDate>Fri, 24 Jan 2020 06:04:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40604/gapfinisher-a-reliable-gap-filling-pipeline-for-sspace-longread-scaffolder-output</link>
	<title><![CDATA[gapFinisher: A reliable gap filling pipeline for SSPACE-LongRead scaffolder output]]></title>
	<description><![CDATA[<p><span>gapFinisher is based on the controlled use of a previously published gap filling tool FGAP and works on all standard Linux/UNIX command lines. They compare the performance of gapFinisher against two other published gap filling tools PBJelly and GMcloser. </span></p>
<p><span>gapFinisher can fill gaps in draft genomes quickly and reliably.</span></p><p>Address of the bookmark: <a href="https://github.com/kammoji/gapFinisher" rel="nofollow">https://github.com/kammoji/gapFinisher</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36271/heap-a-highly-sensitive-and-accurate-snp-detection-tool-for-low-coverage-high-throughput-sequencing-data</guid>
	<pubDate>Thu, 19 Apr 2018 08:06:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36271/heap-a-highly-sensitive-and-accurate-snp-detection-tool-for-low-coverage-high-throughput-sequencing-data</link>
	<title><![CDATA[Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data]]></title>
	<description><![CDATA[<p><span>Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both end of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from&nbsp;</span><a href="https://github.com/meiji-bioinf/heap">https://github.com/meiji-bioinf/heap</a><span>&nbsp;and our web site (</span><a href="http://bioinf.mind.meiji.ac.jp/lab/en/tools.html">http://bioinf.mind.meiji.ac.jp/lab/en/tools.html</a><span>).</span></p><p>Address of the bookmark: <a href="https://github.com/meiji-bioinf/heap" rel="nofollow">https://github.com/meiji-bioinf/heap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/40497/artificial-intelligence-is-more-accurate-than-doctors-in-diagnosing-breast-cancer</guid>
	<pubDate>Wed, 01 Jan 2020 22:12:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/40497/artificial-intelligence-is-more-accurate-than-doctors-in-diagnosing-breast-cancer</link>
	<title><![CDATA[Artificial intelligence is more accurate than doctors in diagnosing breast cancer]]></title>
	<description><![CDATA[<p>Artificial intelligence is more accurate than doctors in diagnosing breast cancer from mammograms, a study in the journal Nature suggests.</p><p>An international team, including researchers from&nbsp;<a href="https://health.google/" target="_blank">Google Health</a>&nbsp;and&nbsp;<a href="https://www.imperial.ac.uk/news/183293/research-collaboration-aims-improve-breast-cancer/" target="_blank">Imperial College London</a>, designed and trained a computer model on X-ray images from nearly 29,000 women.</p><p>The algorithm&nbsp;<a href="https://nature.com/articles/s41586-019-1799-6" target="_blank">outperformed six radiologists</a>&nbsp;in reading mammograms.</p><p>AI was still as good as two doctors working together.</p><p>Unlike humans, AI is tireless. Experts say it could improve detection. Read More:&nbsp;<a href="https://www.bbc.com/news/health-50857759" target="_blank">https://www.bbc.com/news/health-50857759</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44894/dna2bit-an-ultra-fast-and-accurate-genomic-distance-estimation-software</guid>
	<pubDate>Sun, 31 Aug 2025 06:24:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44894/dna2bit-an-ultra-fast-and-accurate-genomic-distance-estimation-software</link>
	<title><![CDATA[dna2bit: an ultra-fast and accurate genomic distance estimation software]]></title>
	<description><![CDATA[<p><span>dna2bit is a software tool developed in C++11, leveraging the capabilities of OpenMP for parallel computing and the popcount technique for efficient bit manipulation. It has been thoroughly tested using the g++ and clang compilers on both Linux and MacOS platforms.</span></p><p>Address of the bookmark: <a href="https://github.com/lijuzeng/dna2bit" rel="nofollow">https://github.com/lijuzeng/dna2bit</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37842/rapclust-accurate-lightweight-clustering-of-de-novo-transcriptomes-using-fragment-equivalence-classes</guid>
	<pubDate>Thu, 04 Oct 2018 17:57:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37842/rapclust-accurate-lightweight-clustering-of-de-novo-transcriptomes-using-fragment-equivalence-classes</link>
	<title><![CDATA[RapClust: Accurate, Lightweight Clustering of de novo Transcriptomes using Fragment Equivalence Classes]]></title>
	<description><![CDATA[<p><span>RapClust is a tool for clustering contigs from&nbsp;</span><em>de novo</em><span>&nbsp;transcriptome assemblies. RapClust is designed to be run downstream of the&nbsp;</span><a href="https://github.com/kingsfordgroup/sailfish">Sailfish</a><span>&nbsp;or&nbsp;</span><a href="https://github.com/COMBINE-lab/salmon">Salmon</a><span>&nbsp;tools for rapid transcript-level quantification. Specifically, RapClust relies on the&nbsp;</span><em>fragment equivalence classes</em><span>&nbsp;computed by these tools in order to determine how seqeunce is shared across the transcriptome, and how reads map to potentially-related contigs across different conditions.</span></p><p>Address of the bookmark: <a href="https://github.com/COMBINE-lab/RapClust" rel="nofollow">https://github.com/COMBINE-lab/RapClust</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/44515/cleaner-blast-databases-for-more-accurate-results</guid>
	<pubDate>Tue, 23 Apr 2024 01:23:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/44515/cleaner-blast-databases-for-more-accurate-results</link>
	<title><![CDATA[Cleaner BLAST Databases for More Accurate Results]]></title>
	<description><![CDATA[<p>Do you use&nbsp;<a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=blast-cleaner-20240422">BLAST</a><span style="font-size: 12.8px; font-weight: normal;">&nbsp;to identify a sequence or the evolutionary scope of a gene? That can be challenging if contaminated and misclassified sequences are in the BLAST databases and show up in your search results. To address</span><span style="font-size: 12.8px; font-weight: normal;">&nbsp;this problem</span><span style="font-size: 12.8px; font-weight: normal;">, we now use the NCBI quality assurance tools listed below to systematically remove these misleading sequences from the default nucleotide (nt) and protein (nr) BLAST databases.</span><span style="font-size: 12.8px; font-weight: normal;">&nbsp;</span></p><div><ul>
<li><a href="https://github.com/ncbi/fcs">Foreign Contamination Screen tool for genome cross-species screening (FCS-GX)</a>&nbsp;detects contamination from foreign organisms in genomes and other sequences using the genome cross-species aligner (GX)&nbsp;</li>
<li><a href="https://ncbiinsights.ncbi.nlm.nih.gov/2022/05/27/ani-for-assembly-validation?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=blast-cleaner-20240422">Average Nucleotide Identity (ANI)</a>&nbsp;evaluates the taxonomic classification of prokaryotic genome assemblies. Sequences from genomes marked up as &lsquo;unverified source organism&rsquo; are considered suspect and removed.&nbsp;</li>
</ul><p>Ref&nbsp;https://ncbiinsights.ncbi.nlm.nih.gov/2024/04/22/cleaner-blast-databases-more-accurate-results/</p></div>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33960/mgra-breakpoint-graphs-and-ancestral-genome-reconstructions</guid>
	<pubDate>Tue, 25 Jul 2017 08:48:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33960/mgra-breakpoint-graphs-and-ancestral-genome-reconstructions</link>
	<title><![CDATA[MGRA: Breakpoint graphs and ancestral genome reconstructions]]></title>
	<description><![CDATA[<p>MGRA (Multiple Genome Rearrangements and Ancestors) is a tool for reconstruction of ancestor genomes and evolutionary history of extant genomes.</p>
<p>It takes as an input a set of genomes represented as sequences of genes (or synteny blocks) and produces such sequences for ancestral genomes at the internal nodes of the phylogenetic tree.</p>
<p>The phylogenetic tree may be also specified completely or partially, in the latter case MGRA can reconstruct conserved ancestral regions (CARs) of the ancestral genome of interest.</p>
<p>Since version 2 MGRA supports gene insertion and deletions in addition to genome rearrangements and allows the input genomes to have different gene content.</p>
<p>It also can reconstruct most plausible phylogenetic tree based on the rearrangement characters.</p><p>Address of the bookmark: <a href="http://mgra.cblab.org/" rel="nofollow">http://mgra.cblab.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34377/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</guid>
	<pubDate>Sat, 18 Nov 2017 16:10:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34377/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</link>
	<title><![CDATA[Genomicus: genome browser that enables users to navigate in genomes in several dimensions]]></title>
	<description><![CDATA[<p>Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.</p>
<p>Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.</p>
<p>All the data on extant species displayed in this browser are from&nbsp;<a href="http://www.ensembl.org/">Ensembl</a>.</p><p>Address of the bookmark: <a href="http://genomicus.biologie.ens.fr/genomicus-90.01/cgi-bin/search.pl" rel="nofollow">http://genomicus.biologie.ens.fr/genomicus-90.01/cgi-bin/search.pl</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34488/scripts-for-the-analysis-of-hgt-in-genome-sequence-data</guid>
	<pubDate>Wed, 29 Nov 2017 16:44:10 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34488/scripts-for-the-analysis-of-hgt-in-genome-sequence-data</link>
	<title><![CDATA[Scripts for the analysis of HGT in genome sequence data.]]></title>
	<description><![CDATA[<p><span>Scripts for the analysis of HGT in genome sequence data</span></p><p>Address of the bookmark: <a href="https://github.com/reubwn/hgt" rel="nofollow">https://github.com/reubwn/hgt</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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