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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35286?offset=10</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27113/picard</guid>
	<pubDate>Fri, 29 Apr 2016 08:21:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27113/picard</link>
	<title><![CDATA[Picard]]></title>
	<description><![CDATA[<p>Picard is a set of command line tools for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF. These file formats are defined in the <a href="http://samtools.github.io/hts-specs/">Hts-specs</a> repository. See especially the <a href="http://samtools.github.io/hts-specs/SAMv1.pdf">SAM specification</a> and the <a href="http://samtools.github.io/hts-specs/VCFv4.3.pdf">VCF specification</a>.</p>
<p>Note that the information on this page is targeted at end-users. For developers, the source code, building instructions and implementation/development resources are available on <a href="https://github.com/broadinstitute/picard">GitHub</a>.</p>
<p>The Picard toolkit is open-source under the <a href="https://tldrlegal.com/license/mit-license">MIT license</a> and free for all uses.</p>
<p>Enjoy!</p><p>Address of the bookmark: <a href="http://broadinstitute.github.io/picard/" rel="nofollow">http://broadinstitute.github.io/picard/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38004/vcfr-a-package-to-manipulate-and-visualize-vcf-data-in-r</guid>
	<pubDate>Thu, 25 Oct 2018 09:05:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38004/vcfr-a-package-to-manipulate-and-visualize-vcf-data-in-r</link>
	<title><![CDATA[vcfR:  a package to manipulate and visualize VCF data in R]]></title>
	<description><![CDATA[<p><span>VcfR is an R package intended to allow easy manipulation and visualization of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices from the VCF data for use with typical R functions. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file or converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and the R environment connecting familiar software with genomic data.</span></p><p>Address of the bookmark: <a href="https://github.com/knausb/vcfR" rel="nofollow">https://github.com/knausb/vcfR</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41487/tinycov-standalone-command-line-utility-written-in-python-to-plot-coverage-from-a-bam-file</guid>
	<pubDate>Mon, 23 Mar 2020 06:22:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41487/tinycov-standalone-command-line-utility-written-in-python-to-plot-coverage-from-a-bam-file</link>
	<title><![CDATA[tinycov: standalone command line utility written in python to plot coverage from a BAM file]]></title>
	<description><![CDATA[<p>Tinycov is a small standalone command line utility written in python to plot the coverage of a BAM file quickly. This software was inspired by&nbsp;<a href="https://github.com/matted/genome_coverage_plotter">Matt Edwards' genome coverage plotter</a>.</p>
<p>To install the stable version:&nbsp;<code>pip3 install --user tinycov</code></p>
<p>To install the development version:</p>
<pre><code>git clone https://github.com/cmdoret/tinycov.git
cd tinycov
pip install .</code></pre><p>Address of the bookmark: <a href="https://github.com/cmdoret/tinycov" rel="nofollow">https://github.com/cmdoret/tinycov</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35351/sam-to-bam-conversion</guid>
	<pubDate>Fri, 26 Jan 2018 02:36:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35351/sam-to-bam-conversion</link>
	<title><![CDATA[sam to bam conversion !!]]></title>
	<description><![CDATA[<p>To do sam to bam conversion, follow the following commands :-&nbsp;</p><div><div>Code:</div><pre dir="ltr">$ samtools view -b -S file.sam &gt; file.bam</pre></div><p>Then you will need to use&nbsp;<br /><br /></p><div><div>Code:</div><pre dir="ltr">$ samtools sort file.bam file-sorted</pre></div><p>followed by<br /><br /></p><div><div>Code:</div><pre dir="ltr">$ samtools index  file-sorted.bam</pre></div><p>in order to get an indexed file.<br /><br />If you just type&nbsp;<br /><br /></p><div><div>Code:</div><pre dir="ltr">$ samtools</pre></div><p>or samtools followed by the name of one of the samtools commands, you will get a few lines of help giving the correct syntax for that command,</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43273/understanding-kmer</guid>
	<pubDate>Wed, 18 Aug 2021 04:27:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43273/understanding-kmer</link>
	<title><![CDATA[Understanding kmer !]]></title>
	<description><![CDATA[<p><a href="https://en.wikipedia.org/wiki/k-mer">What is a&nbsp;<em>k-mer</em>&nbsp;anyway?</a><span>&nbsp;A&nbsp;</span><em>k-mer</em><span>&nbsp;is just a sequence of&nbsp;</span><em>k</em><span>&nbsp;characters in a string (or nucleotides in a DNA sequence). Now, it is important to remember that to get&nbsp;</span><em>all k-mers</em><span>&nbsp;from a sequence you need to get the first&nbsp;</span><em>k</em><span>&nbsp;characters, then move just a single character for the start of the next&nbsp;</span><em>k-mer</em><span>&nbsp;and so on. Effectively, this will create sequences that overlap in&nbsp;</span><code>k-1</code><span>&nbsp;positions.</span></p><p>Address of the bookmark: <a href="https://bioinfologics.github.io/post/2018/09/17/k-mer-counting-part-i-introduction/" rel="nofollow">https://bioinfologics.github.io/post/2018/09/17/k-mer-counting-part-i-introduction/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/41586/primer-blast</guid>
	<pubDate>Tue, 28 Apr 2020 00:28:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/41586/primer-blast</link>
	<title><![CDATA[Primer BLAST !]]></title>
	<description><![CDATA[<p>BLAST team added a new feature (Max 3' match), shown in Figure 1, to Primer-BLAST that limits the length of 3' exon matches when designing exon-exon spanning primers. This makes it less likely that primers specifically designed to amplify transcripts will also amplify genomic DNA contamination in expression assays. See the NCBI Insights post (<a href="https://go.usa.gov/xvUT4" target="_blank"><span>https://go.usa.gov/xvUT4</span></a>) for more details.</p><p>&nbsp;</p><p><span>If you have any questions or concerns, please contact&nbsp;<a href="mailto:blast-help@ncbi.nlm.nih.gov" target="_blank" title="Follow link">blast-help@ncbi.nlm.nih.gov<sup><span><img src="https://mail.google.com/mail/u/0?ui=2&amp;ik=024a8aa0b9&amp;attid=0.1&amp;permmsgid=msg-f:1665129030912557674&amp;th=171bba0808bbc26a&amp;view=fimg&amp;sz=s0-l75-ft&amp;attbid=ANGjdJ-yC7WlxAuBOITc1ND1AN0YIdrtaQ3utEJuH_vnvOTM3uh8Wwn652wjlqDQ6HJOKApVPRJNpBRVd3H_AisXJXRWtzl0Y9alARMC05_yINEwa2lkBGoA7Q93-GU&amp;disp=emb" width="13" height="12" alt="image" style="border: 0px;"></span></sup></a></span></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/24178/essentials-of-statistics-and-data-analysis-using-r</guid>
  <pubDate>Mon, 31 Aug 2015 01:32:12 -0500</pubDate>
  <link></link>
  <title><![CDATA[Essentials of Statistics and Data Analysis using R]]></title>
  <description><![CDATA[
<p>Clinical Development Services Agency (CDSA) is an extramural unit of Translational Health Science and Technology Institute (THSTI), Department of Biotechnology, Ministry of Science &amp; Technology, Government of India. CDSA has a national mandate of strengthening capacity and capability building in the area of Clinical development and Translational Research.</p>

<p>CDSA is pleased to announce a 4 days hands-on training program on “Essentials of Statistics and Data Analysis using R” at ICGEB, Aruna Asaf Ali Road, New Delhi on December 1 – 4, 2015. This will involve developing and enhancing skills to understand basic principles of statistics for summarizing data and use of appropriate statistical tests as well as providing an understanding of data analysis using R. Didactic lectures with practical sessions will be delivered by experienced faculties from AIIMS and Novartis. Live classroom with power point presentations, case studies, mock exercise, practical sessions on R, group work with time for discussion and Q&amp;A sessions are added advantages of this workshop.</p>

<p>Please contact gayatrivishwakarma.cdsa@thsti.res.in or vineetabaloni.cdsa@thsti.res.in for program and registration details.</p>

<p>Please nominate personage or register yourself on or before November 6, 2015 along with the electronic transfer of registration fee.</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35059/lrcstats-long-read-correction-statistics</guid>
	<pubDate>Fri, 05 Jan 2018 04:04:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35059/lrcstats-long-read-correction-statistics</link>
	<title><![CDATA[LRCstats: Long Read Correction Statistics]]></title>
	<description><![CDATA[<p>LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.</p>
<p>Of course, some long read correction algorithms are better than others, and developers of long read correction algorithms will wish to compare their algorithm with others currently available. LRCstats benchmarks long read correction algorithms using long reads produced by simulators (such as SimLoRD or PBSim) where the two-way alignments between the uncorrected long reads (uLR) and the corresponding sequences in the reference genome (Ref) are given in some sort of alignment file and then aligning the corrected long reads (cLR) to the Ref-uLR two-way alignments to create three-way alignments using a dynamic programming algorithm. Statistics on these three-way alignments are then collected, such as the overall error rates of the corrected long reads.</p>
<p>https://www.healthcare.uiowa.edu/labs/au/LSC/</p><p>Address of the bookmark: <a href="https://github.com/cchauve/lrcstats" rel="nofollow">https://github.com/cchauve/lrcstats</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/35868/simpson-lab</guid>
  <pubDate>Tue, 06 Mar 2018 08:59:09 -0600</pubDate>
  <link></link>
  <title><![CDATA[Simpson Lab]]></title>
  <description><![CDATA[
<p>We are the Statistical Bioinformatics group in the Institute for Adaptive and Neural Computation in the School of Informatics at the University of Edinburgh. The group is led by Dr. Ian Simpson who is a Lecturer in Biological Informatics in the School of Informatics at Edinburgh University. Details to follow....</p>

<p>http://statbio.github.io</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44508/a-web-based-tool-for-sequence-alignment-statistics-and-innovative-visualization</guid>
	<pubDate>Thu, 04 Apr 2024 01:44:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44508/a-web-based-tool-for-sequence-alignment-statistics-and-innovative-visualization</link>
	<title><![CDATA[A web-based tool for sequence alignment statistics and innovative visualization]]></title>
	<description><![CDATA[<p>AlignStatPlot, a new R package and online tool that is well-documented and easy-to usefor MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analy-ses on sequencing data and generates new visualisation methods for MSA results. Whencompared to currently available tools, AlignStatPlot provides a robust ability to handle andvisualise diversity data, while the online version will save time and encourage researchersto focus on explaining their findings. It is a simple tool that can be used in conjunction withpopulation genetics software (PDF) AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data.</p><p>Address of the bookmark: <a href="https://bioinformatics.um6p.ma/AlignStatPlot/" rel="nofollow">https://bioinformatics.um6p.ma/AlignStatPlot/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

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