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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40613?offset=160</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44292/gget</guid>
	<pubDate>Sat, 01 Apr 2023 09:42:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44292/gget</link>
	<title><![CDATA[gget]]></title>
	<description><![CDATA[<p><code>gget</code><span>&nbsp;is a free, open-source command-line tool and Python package that enables efficient querying of genomic databases.&nbsp;</span><code>gget</code><span>&nbsp;consists of a collection of separate but interoperable modules, each designed to facilitate one type of database querying in a single line of code.</span></p>
<p><span><img src="https://github.com/pachterlab/gget/raw/main/figures/gget_overview.png?raw=true" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/pachterlab/gget" rel="nofollow">https://github.com/pachterlab/gget</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44581/biokit-a-set-of-tools-dedicated-to-bioinformatics-data-visualisation</guid>
	<pubDate>Tue, 18 Jun 2024 02:04:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44581/biokit-a-set-of-tools-dedicated-to-bioinformatics-data-visualisation</link>
	<title><![CDATA[BioKit: a set of tools dedicated to bioinformatics, data visualisation]]></title>
	<description><![CDATA[<p><span>BioKit is a set of tools dedicated to bioinformatics, data visualisation (</span><a href="https://biokit.readthedocs.io/en/latest/references.html#module-biokit.viz" title="biokit.viz"><code><span>biokit.viz</span></code></a><span>), access to online biological data (e.g. UniProt, NCBI thanks to bioservices). It also contains more advanced tools related to data analysis (e.g.,&nbsp;</span><a href="https://biokit.readthedocs.io/en/latest/references.html#module-biokit.stats" title="biokit.stats"><code><span>biokit.stats</span></code></a><span>). Since R is quite common in bioinformatics, we also provide a convenient module to run R inside your Python scripts or shell (:mod:biokit.rtools module).</span></p><p>Address of the bookmark: <a href="https://biokit.readthedocs.io/en/latest/index.html" rel="nofollow">https://biokit.readthedocs.io/en/latest/index.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/10741/managing-and-analyzing-next-generation-sequence-data</guid>
	<pubDate>Sat, 10 May 2014 06:28:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/10741/managing-and-analyzing-next-generation-sequence-data</link>
	<title><![CDATA[Managing and Analyzing Next-Generation Sequence Data]]></title>
	<description><![CDATA[<p>Centralized Bioinformatics Core Facilities provide shared resources for the computational and IT requirements of the investigators in their department or institution. As such, they must be able to effectively react to new types of experimental technology. Recently faced with an unprecedented flood of data generated by the next generation of DNA sequencers, these groups found it necessary to respond quickly and efficiently to the informatics and infrastructure demands. Centralized Facilities newly facing this challenge need to anticipate time and design considerations of necessary components, including infrastructure upgrades, staffing, and tools for data analyses and management ...</p>
<p>More at http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369</p><p>Address of the bookmark: <a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369" rel="nofollow">http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/33869/import-r-data</guid>
	<pubDate>Wed, 12 Jul 2017 08:30:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/33869/import-r-data</link>
	<title><![CDATA[Import R Data]]></title>
	<description><![CDATA[<p>It is often necessary to import sample textbook data into R before you start working on your homework.</p><div id="node-69"><div><p><strong>Excel File</strong></p><p>Quite frequently, the sample data is in&nbsp;<span>Excel&nbsp;</span>format, and needs to be imported into R prior to use. For this, we can use the function&nbsp;<span>read.xls&nbsp;</span>from the&nbsp;<span>gdata&nbsp;</span>package. It reads from an Excel spreadsheet and returns a&nbsp;<a href="http://www.r-tutor.com/r-introduction/data-frame">data frame</a>. The following shows how to load an Excel spreadsheet named&nbsp;<span>"mydata.xls"</span>. This method requires Perl runtime to be present in the system.</p><blockquote><div id="listing-68"><span><a></a></span>&gt;&nbsp;library(gdata)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;load&nbsp;gdata&nbsp;package&nbsp;<br /><span><a></a></span>&gt;&nbsp;help(read.xls)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;documentation&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.xls("mydata.xls")&nbsp;&nbsp;#&nbsp;read&nbsp;from&nbsp;first&nbsp;sheet</div></blockquote><p>Alternatively, we can use the function&nbsp;<span>loadWorkbook&nbsp;</span>from the&nbsp;<span>XLConnect&nbsp;</span>package to read the entire workbook, and then load the worksheets with&nbsp;<span>readWorksheet</span>. The&nbsp;<span>XLConnect&nbsp;</span>package requires Java to be pre-installed.</p><blockquote><div id="listing-69"><span><a></a></span>&gt;&nbsp;library(XLConnect)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;load&nbsp;XLConnect&nbsp;package&nbsp;<br /><span><a></a></span>&gt;&nbsp;wk&nbsp;=&nbsp;loadWorkbook("mydata.xls")&nbsp;<br /><span><a></a></span>&gt;&nbsp;df&nbsp;=&nbsp;readWorksheet(wk,&nbsp;sheet="Sheet1")</div></blockquote><p>&nbsp;</p><h4><a></a>Minitab File</h4><p>If the data file is in&nbsp;<span>Minitab Portable Worksheet&nbsp;</span>format, it can be opened with the function&nbsp;<span>read.mtp&nbsp;</span>from the&nbsp;<span>foreign&nbsp;</span>package. It returns a&nbsp;<a href="http://www.r-tutor.com/r-introduction/list">list</a>&nbsp;of components in the Minitab worksheet.</p><blockquote><div id="listing-70"><span><a></a></span>&gt;&nbsp;library(foreign)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;load&nbsp;the&nbsp;foreign&nbsp;package&nbsp;<br /><span><a></a></span>&gt;&nbsp;help(read.mtp)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;documentation&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.mtp("mydata.mtp")&nbsp;&nbsp;#&nbsp;read&nbsp;from&nbsp;.mtp&nbsp;file</div></blockquote><p>&nbsp;</p><h4><a></a>SPSS File</h4><p>For the data files in&nbsp;<span>SPSS&nbsp;</span>format, it can be opened with the function&nbsp;<span>read.spss&nbsp;</span>also from the&nbsp;<span>foreign&nbsp;</span>package. There is a&nbsp;<span>"to.data.frame"&nbsp;</span>option for choosing whether a data frame is to be returned. By default, it returns a list of components instead.</p><blockquote><div id="listing-71"><span><a></a></span>&gt;&nbsp;library(foreign)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;load&nbsp;the&nbsp;foreign&nbsp;package&nbsp;<br /><span><a></a></span>&gt;&nbsp;help(read.spss)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;documentation&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.spss("myfile",&nbsp;to.data.frame=TRUE)</div></blockquote><p>&nbsp;</p><h4><a></a>Table File</h4><p>A data table can resides in a text file. The cells inside the table are separated by blank characters. Here is an example of a table with 4 rows and 3 columns.</p><blockquote><div id="listing-72"><span><a></a></span>100&nbsp;&nbsp;&nbsp;a1&nbsp;&nbsp;&nbsp;b1&nbsp;<br /><span><a></a></span>200&nbsp;&nbsp;&nbsp;a2&nbsp;&nbsp;&nbsp;b2&nbsp;<br /><span><a></a></span>300&nbsp;&nbsp;&nbsp;a3&nbsp;&nbsp;&nbsp;b3&nbsp;<br /><span><a></a></span>400&nbsp;&nbsp;&nbsp;a4&nbsp;&nbsp;&nbsp;b4</div></blockquote><p>Now copy and paste the table above in a file named&nbsp;<span>"mydata.txt"&nbsp;</span>with a text editor. Then load the data into the workspace with the function&nbsp;<span>read.table</span>.</p><blockquote><div id="listing-73"><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.table("mydata.txt")&nbsp;&nbsp;#&nbsp;read&nbsp;text&nbsp;file&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;print&nbsp;data&nbsp;frame&nbsp;<br /><span><a></a></span>&nbsp;&nbsp;&nbsp;V1&nbsp;V2&nbsp;V3&nbsp;<br /><span><a></a></span>1&nbsp;100&nbsp;a1&nbsp;b1&nbsp;<br /><span><a></a></span>2&nbsp;200&nbsp;a2&nbsp;b2&nbsp;<br /><span><a></a></span>3&nbsp;300&nbsp;a3&nbsp;b3&nbsp;<br /><span><a></a></span>4&nbsp;400&nbsp;a4&nbsp;b4</div></blockquote><p>For further detail of the function&nbsp;<span>read.table</span>, please consult the R documentation.</p><blockquote><div id="listing-74"><span><a></a></span>&gt;&nbsp;help(read.table)</div></blockquote><p>&nbsp;</p><h4><a></a>CSV File</h4><p>The sample data can also be in&nbsp;<span>comma separated values&nbsp;</span>(CSV) format. Each cell inside such data file is separated by a special character, which usually is a comma, although other characters can be used as well.</p><p>The first row of the data file should contain the column names instead of the actual data. Here is a sample of the expected format.</p><blockquote><div id="listing-75"><span><a></a></span>Col1,Col2,Col3&nbsp;<br /><span><a></a></span>100,a1,b1&nbsp;<br /><span><a></a></span>200,a2,b2&nbsp;<br /><span><a></a></span>300,a3,b3</div></blockquote><p>After we copy and paste the data above in a file named&nbsp;<span>"mydata.csv"&nbsp;</span>with a text editor, we can read the data with the function&nbsp;<span>read.csv</span>.</p><blockquote><div id="listing-76"><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.csv("mydata.csv")&nbsp;&nbsp;#&nbsp;read&nbsp;csv&nbsp;file&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;<br /><span><a></a></span>&nbsp;&nbsp;Col1&nbsp;Col2&nbsp;Col3&nbsp;<br /><span><a></a></span>1&nbsp;&nbsp;100&nbsp;&nbsp;&nbsp;a1&nbsp;&nbsp;&nbsp;b1&nbsp;<br /><span><a></a></span>2&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;a2&nbsp;&nbsp;&nbsp;b2&nbsp;<br /><span><a></a></span>3&nbsp;&nbsp;300&nbsp;&nbsp;&nbsp;a3&nbsp;&nbsp;&nbsp;b3</div></blockquote><p>In various European locales, as the comma character serves as the decimal point, the function&nbsp;<span>read.csv2&nbsp;</span>should be used instead. For further detail of the&nbsp;<span>read.csv&nbsp;</span>and&nbsp;<span>read.csv2&nbsp;</span>functions, please consult the R documentation.</p><blockquote><div id="listing-77"><span><a></a></span>&gt;&nbsp;help(read.csv)</div></blockquote><p>&nbsp;</p><h4><a></a>Working Directory</h4><p>Finally, the code samples above assume the data files are located in the R&nbsp;<span>working</span>&nbsp;<span>directory</span>, which can be found with the function&nbsp;<span>getwd</span>.</p><blockquote><div id="listing-78"><span><a></a></span>&gt;&nbsp;getwd()&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;get&nbsp;current&nbsp;working&nbsp;directory</div></blockquote><p>You can select a different working directory with the function&nbsp;<span>setwd()</span>, and thus avoid entering the full path of the data files.</p><blockquote><div id="listing-79"><span><a></a></span>&gt;&nbsp;setwd("")&nbsp;&nbsp;&nbsp;#&nbsp;set&nbsp;working&nbsp;directory</div></blockquote><p>Note that the forward slash should be used as the path separator even on Windows platform.</p><blockquote><div id="listing-80"><span><a></a></span>&gt;&nbsp;setwd("C:/MyDoc")</div></blockquote></div></div>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</guid>
	<pubDate>Tue, 27 Nov 2018 04:43:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</link>
	<title><![CDATA[lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data]]></title>
	<description><![CDATA[<p><span>lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.</span></p>
<p>lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/lordfast" rel="nofollow">https://github.com/vpc-ccg/lordfast</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42324/comparative-genomics-data-set-including-240-mammals-released</guid>
	<pubDate>Thu, 19 Nov 2020 06:45:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42324/comparative-genomics-data-set-including-240-mammals-released</link>
	<title><![CDATA[Comparative Genomics Data Set Including 240 Mammals Released !]]></title>
	<description><![CDATA[<p>The genome of 130 mammals was sequenced by a large international consortium and the data was analyzed together with 110 existing genomes to allow scientists to identify the important positions in the DNA. This report, published in Nature today will help advance research on human disease mutations and inform how best to protect endangered species.</p><p>In addition to the knowledge of the human genome, all these genomes, widely sampled across mammals, can be used to research how particular organisms respond to different conditions. Some otters, for example, have a thick, water-resistant shell, and some rodents, but not all, have adapted to hibernation. These animal traits will help us to understand human traits, such as metabolic diseases.</p><p><img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41586-020-2876-6/MediaObjects/41586_2020_2876_Fig1_HTML.png?as=webp" alt="image" style="border: 0px; border: 0px;"></p><p>With climate change and more animal ecosystems being threatened by human activity, the protection of endangered species is becoming increasingly important. Scientists have historically researched several people in various populations of a species to understand the genetic variation that occurs in that species. This is important for understanding how particular species can be protected. In this study, animals on the Red List of Endangered Species of the International Union for Conservation of Nature had fewer differences in their genomes, which is consistent with their endangered status.</p><p>Ref @&nbsp;A comparative genomics multitool for scientific discovery and conservation&nbsp;https://www.nature.com/articles/s41586-020-2876-6</p><p>&nbsp;Data at&nbsp;http://zoonomiaproject.org/</p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44557/fundamentals-of-data-visualization-by-claus-o-wilke</guid>
	<pubDate>Sat, 08 Jun 2024 16:07:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44557/fundamentals-of-data-visualization-by-claus-o-wilke</link>
	<title><![CDATA[Fundamentals of Data Visualization by Claus O. Wilke]]></title>
	<description><![CDATA[<p><span><span>The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.</span></span></p>
<p><span>The entire book is written in R Markdown, using RStudio as my text editor and the&nbsp;</span><span>bookdown</span><span>&nbsp;package to turn a collection of markdown documents into a coherent whole. The book&rsquo;s source code is hosted on GitHub, at&nbsp;</span><a href="https://github.com/clauswilke/dataviz">https://github.com/clauswilke/dataviz</a><span>.&nbsp;</span></p><p>Address of the bookmark: <a href="https://clauswilke.com/dataviz/" rel="nofollow">https://clauswilke.com/dataviz/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced</guid>
	<pubDate>Tue, 17 Sep 2013 16:48:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced</link>
	<title><![CDATA[Tigers genome sequenced]]></title>
	<description><![CDATA[<p>Fifteen scientists led by Dr Jong Bhak of Genome Research Foundation, South Korea, decoded as many as 3 billion nucleotides (organic molecules that form the basic building blocks of nucleic acids, such as DNA). They identified 20,000 genes related to various functions of the tiger.&nbsp;</p><p>The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.</p><p>The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.</p><p>Reference:</p><p><a href="http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690">http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690</a></p><p><a href="http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms">http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms</a></p><p>Paper:</p><p><a href="http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html">http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</guid>
	<pubDate>Mon, 27 Nov 2017 10:18:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</link>
	<title><![CDATA[Opera: An optimal genome scaffolding program]]></title>
	<description><![CDATA[<p><span>Opera (Optimal Paired-End Read Assembler) is a sequence assembly program (</span><a href="http://en.wikipedia.org/wiki/Sequence_assembly" target="_blank">http://en.wikipedia.org/wiki/Sequence_assembly&nbsp;<img src="https://a.fsdn.com/con/img/icons/external_asset.png" alt="image" style="border: 0px;"></a><span>). It uses information from paired-end or long reads to optimally order and orient contigs assembled from shotgun-sequencing reads.</span><br><br><span>An updated version called OPERA-LG has been re-engineered with features for the assembly of large and complex genomes.</span><br><br><span>Song Gao, Denis Bertrand, Burton K. H. Chia and Niranjan Nagarajan. OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees. Genome Biology, May 2016, doi: 10.1186/s13059-016-0951-y.</span><br><br><span>Song Gao, Wing-Kin Sung, Niranjan Nagarajan. Opera: reconstructing optimal genomic scaffolds with high-throughput paired-end sequences. Journal of Computational Biology, Sept. 2011, doi:10.1089/cmb.2011.0170.</span></p>
<p><span>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0951-y</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/operasf/" rel="nofollow">https://sourceforge.net/projects/operasf/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly</guid>
	<pubDate>Mon, 27 Nov 2017 08:05:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly</link>
	<title><![CDATA[SPAdes hybrid genome assembly]]></title>
	<description><![CDATA[<p>When you have both Illumina and Nanopore data, then SPAdes remains a good option for hybrid assembly - SPAdes was used to produce the&nbsp;<a href="https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0101-6">B fragilis assembly</a>&nbsp;by Mick Watson&rsquo;s group.</p><p>Again, running spades.py will show you the options:</p><div><pre><code>spades.py
</code></pre></div><p>This produces:</p><div><pre><code>SPAdes genome assembler v3.10.1

Usage: /usr/local/SPAdes-3.10.1-Linux/bin/spades.py [options] -o &lt;output_dir&gt;

Basic options:
-o      &lt;output_dir&gt;    directory to store all the resulting files (required)
--sc                    this flag is required for MDA (single-cell) data
--meta                  this flag is required for metagenomic sample data
--rna                   this flag is required for RNA-Seq data
--plasmid               runs plasmidSPAdes pipeline for plasmid detection
--iontorrent            this flag is required for IonTorrent data
--test                  runs SPAdes on toy dataset
-h/--help               prints this usage message
-v/--version            prints version

Input data:
--12    &lt;filename&gt;      file with interlaced forward and reverse paired-end reads
-1      &lt;filename&gt;      file with forward paired-end reads
-2      &lt;filename&gt;      file with reverse paired-end reads
-s      &lt;filename&gt;      file with unpaired reads
--pe&lt;#&gt;-12      &lt;filename&gt;      file with interlaced reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-1       &lt;filename&gt;      file with forward reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-2       &lt;filename&gt;      file with reverse reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-s       &lt;filename&gt;      file with unpaired reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-&lt;or&gt;    orientation of reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--s&lt;#&gt;          &lt;filename&gt;      file with unpaired reads for single reads library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-12      &lt;filename&gt;      file with interlaced reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-1       &lt;filename&gt;      file with forward reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-2       &lt;filename&gt;      file with reverse reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-s       &lt;filename&gt;      file with unpaired reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-&lt;or&gt;    orientation of reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--hqmp&lt;#&gt;-12    &lt;filename&gt;      file with interlaced reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-1     &lt;filename&gt;      file with forward reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-2     &lt;filename&gt;      file with reverse reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-s     &lt;filename&gt;      file with unpaired reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-&lt;or&gt;  orientation of reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--nxmate&lt;#&gt;-1   &lt;filename&gt;      file with forward reads for Lucigen NxMate library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--nxmate&lt;#&gt;-2   &lt;filename&gt;      file with reverse reads for Lucigen NxMate library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--sanger        &lt;filename&gt;      file with Sanger reads
--pacbio        &lt;filename&gt;      file with PacBio reads
--nanopore      &lt;filename&gt;      file with Nanopore reads
--tslr  &lt;filename&gt;      file with TSLR-contigs
--trusted-contigs       &lt;filename&gt;      file with trusted contigs
--untrusted-contigs     &lt;filename&gt;      file with untrusted contigs

Pipeline options:
--only-error-correction runs only read error correction (without assembling)
--only-assembler        runs only assembling (without read error correction)
--careful               tries to reduce number of mismatches and short indels
--continue              continue run from the last available check-point
--restart-from  &lt;cp&gt;    restart run with updated options and from the specified check-point ('ec', 'as', 'k&lt;int&gt;', 'mc')
--disable-gzip-output   forces error correction not to compress the corrected reads
--disable-rr            disables repeat resolution stage of assembling

Advanced options:
--dataset       &lt;filename&gt;      file with dataset description in YAML format
-t/--threads    &lt;int&gt;           number of threads
                                [default: 16]
-m/--memory     &lt;int&gt;           RAM limit for SPAdes in Gb (terminates if exceeded)
                                [default: 250]
--tmp-dir       &lt;dirname&gt;       directory for temporary files
                                [default: &lt;output_dir&gt;/tmp]
-k              &lt;int,int,...&gt;   comma-separated list of k-mer sizes (must be odd and
                                less than 128) [default: 'auto']
--cov-cutoff    &lt;float&gt;         coverage cutoff value (a positive float number, or 'auto', or 'off') [default: 'off']
--phred-offset  &lt;33 or 64&gt;      PHRED quality offset in the input reads (33 or 64)
                                [default: auto-detect]
</code></pre></div><p>As you can see this is also a &ldquo;pipeline&rdquo; of tools that can be switched on or off. SPAdes takes quite a long time, so for the purposes of this practical, something like this may suffice:</p><div><pre><code>spades.py -t 4 <span>\</span>
          -m 32 <span>\</span>
          -k 31,51,71 <span>\</span>
          --only-assembler <span>\</span>
          -1 miseq.1.fastq -2 miseq.2.fastq <span>\</span>
          --nanopore minion.fastq <span>\</span>
          -o hybrid_assembly
</code></pre></div><p>In turn, these parameters mean</p><ul>
<li>use 4 threads</li>
<li>max memory is 32Gb</li>
<li>use 3 kmer values to build the de bruijn graph(s) - 31, 51 and 71</li>
<li>only run the assembler, not the correction algorithm (for speed)</li>
<li>read 1 and read 2 of the MiSeq data</li>
<li>the nanopore data</li>
<li>put the output in folder &ldquo;hybrid_assembly&rdquo;</li>
</ul>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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