<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43583?offset=160</link>
	<atom:link href="https://bioinformaticsonline.com/related/43583?offset=160" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43916/understanding-dump-files-from-ncbi-taxonomy-database</guid>
	<pubDate>Fri, 15 Jul 2022 04:29:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43916/understanding-dump-files-from-ncbi-taxonomy-database</link>
	<title><![CDATA[Understanding DUMP files from NCBI Taxonomy database !]]></title>
	<description><![CDATA[<p>*.dmp files are bcp-like dump from GenBank taxonomy database</p><p>General information.</p><p>Field terminator is "\t|\t"</p><p>Row terminator is "\t|\n"</p><p>&nbsp;</p><p>nodes.dmp file consists of taxonomy nodes. The description for each node includes the following</p><p>fields:</p><p>tax_id -- node id in GenBank taxonomy database</p><p>&nbsp; parent tax_id -- parent node id in GenBank taxonomy database</p><p>&nbsp; rank -- rank of this node (superkingdom, kingdom, ...)&nbsp;</p><p>&nbsp; embl code -- locus-name prefix; not unique</p><p>&nbsp; division id -- see division.dmp file</p><p>&nbsp; inherited div flag&nbsp; (1 or 0) -- 1 if node inherits division from parent</p><p>&nbsp; genetic code id -- see gencode.dmp file</p><p>&nbsp; inherited GC&nbsp; flag&nbsp; (1 or 0) -- 1 if node inherits genetic code from parent</p><p>&nbsp; mitochondrial genetic code id -- see gencode.dmp file</p><p>&nbsp; inherited MGC flag&nbsp; (1 or 0) -- 1 if node inherits mitochondrial gencode from parent</p><p>&nbsp; GenBank hidden flag (1 or 0)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; -- 1 if name is suppressed in GenBank entry lineage</p><p>&nbsp; hidden subtree root flag (1 or 0) &nbsp; &nbsp; &nbsp; -- 1 if this subtree has no sequence data yet</p><p>&nbsp; comments -- free-text comments and citations</p><p>&nbsp;</p><p>Taxonomy names file (names.dmp):</p><p>tax_id -- the id of node associated with this name</p><p>name_txt -- name itself</p><p>unique name -- the unique variant of this name if name not unique</p><p>name class -- (synonym, common name, ...)</p><p>&nbsp;</p><p>Divisions file (division.dmp):</p><p>division id -- taxonomy database division id</p><p>division cde -- GenBank division code (three characters)</p><p>division name -- e.g. BCT, PLN, VRT, MAM, PRI...</p><p>comments</p><p>&nbsp;</p><p>Genetic codes file (gencode.dmp):</p><p>genetic code id -- GenBank genetic code id</p><p>abbreviation -- genetic code name abbreviation</p><p>name -- genetic code name</p><p>cde -- translation table for this genetic code</p><p>starts -- start codons for this genetic code</p><p>&nbsp;</p><p>Deleted nodes file (delnodes.dmp):</p><p>tax_id -- deleted node id</p><p>&nbsp;</p><p>Merged nodes file (merged.dmp):</p><p>old_tax_id&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; -- id of nodes which has been merged</p><p>new_tax_id&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; -- id of nodes which is result of merging</p><p>Citations file (citations.dmp):</p><p>cit_id -- the unique id of citation</p><p>cit_key -- citation key</p><p>pubmed_id -- unique id in PubMed database (0 if not in PubMed)</p><p>medline_id -- unique id in MedLine database (0 if not in MedLine)</p><p>url -- URL associated with citation</p><p>text -- any text (usually article name and authors).</p><p>-- The following characters are escaped in this text by a backslash:</p><p>-- newline (appear as "\n"),</p><p>-- tab character ("\t"),</p><p>-- double quotes ('\"'),</p><p>-- backslash character ("\\").</p><p>taxid_list -- list of node ids separated by a single space</p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44644/dengue-lineages</guid>
	<pubDate>Fri, 16 Aug 2024 04:40:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44644/dengue-lineages</link>
	<title><![CDATA[Dengue Lineages !]]></title>
	<description><![CDATA[<p><span>Our dengue virus lineage system splits up the current genotypes into major and minor lineages to provide additional spatiotemporal resolution and a common language to discuss important genomic diversity. A full description of the lineage system can be found&nbsp;</span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11118645/">here.</a></p>
<p>https://dengue-lineages.org/</p><p>Address of the bookmark: <a href="https://dengue-lineages.org/" rel="nofollow">https://dengue-lineages.org/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34600/converting-blast-output-into-csv</guid>
	<pubDate>Mon, 11 Dec 2017 04:17:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34600/converting-blast-output-into-csv</link>
	<title><![CDATA[Converting BLAST output into CSV]]></title>
	<description><![CDATA[<p>Suppose we wanted to do something with all this BLAST output. Generally, that&rsquo;s the case - you want to retrieve all matches, or do a reciprocal BLAST, or something.</p><p>As with most programs that run on UNIX, the text output is in some specific format. If the program is popular enough, there will be one or more parsers written for that format &ndash; these are just utilities written to help you retrieve whatever information you are interested in from the output.</p><p>Let&rsquo;s conclude this tutorial by converting the BLAST output in out.txt into a spreadsheet format, using a Python script.&nbsp;</p><p>First, we need to get the script. We&rsquo;ll do that using the &lsquo;git&rsquo; program:</p><div><div><pre>git clone <a href="https://github.com/ngs-docs/ngs-scripts.git">https://github.com/ngs-docs/ngs-scripts.git</a> /root/ngs-scripts
</pre></div></div><p>We&rsquo;ll discuss &lsquo;git&rsquo; more later; for now, just think of it as a way to get ahold of a particular set of files. In this case, we&rsquo;ve placed the files in /root/ngs-scripts/, and you&rsquo;re looking to run the script blast/blast-to-csv.py using Python:</p><div><div><pre>python /root/ngs-scripts/blast/blast-to-csv.py out.txt
</pre></div></div><p>This outputs a spread-sheet like list of names and e-values. To save this to a file, do:</p><div><div><pre>python /root/ngs-scripts/blast/blast-to-csv.py out.txt &gt; ~out.csv
</pre></div></div><p>If you have Excel installed, try double clicking on it.</p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42370/ncbi-blast-have-added-new-columns-to-the-descriptions</guid>
	<pubDate>Tue, 01 Dec 2020 09:56:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42370/ncbi-blast-have-added-new-columns-to-the-descriptions</link>
	<title><![CDATA[NCBI BLAST have added new columns to the Descriptions]]></title>
	<description><![CDATA[<p><span>NCBI BLAST have added new columns to the Descriptions Table for web BLAST output. The new columns are&nbsp; Scientific Name, Common Name, Taxid, and Accession Length. Common Name and Accession Length are now part of the default display. You can click 'Select columns' or 'Manage columns' to add or remove columns from the display Your preferences will be saved for your next visit to BLAST, and when you download your results, whatever columns you have displayed will be saved. See the NCBI Insights post (</span><a href="https://go.usa.gov/x7fPE" target="_blank">https://go.usa.gov/x7fPE</a><span>) for more details.</span></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26380/hicdat</guid>
	<pubDate>Fri, 12 Feb 2016 05:23:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26380/hicdat</link>
	<title><![CDATA[HiCdat]]></title>
	<description><![CDATA[<p>HiCdat: a fast and easy-to-use Hi-C data analysis tool</p>
<p>HiCdat is easy-to-use and provides solutions starting from aligned reads up to in-depth analyses. Importantly, HiCdat is focussed on the analysis of larger structural features of chromosomes, their correlation to genomic and epigenomic features, and on comparative studies. It uses simple input and output formats and can therefore easily be integrated into existing workflows or combined with alternative tools.</p>
<p>More at http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0678-x</p><p>Address of the bookmark: <a href="https://github.com/MWSchmid/HiCdat" rel="nofollow">https://github.com/MWSchmid/HiCdat</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28809/kissplice</guid>
	<pubDate>Tue, 16 Aug 2016 08:34:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28809/kissplice</link>
	<title><![CDATA[KisSplice]]></title>
	<description><![CDATA[<p>KisSplice is a software that enables to analyse RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition. It has been tested on Illumina datasets of up to 1G reads. Its memory consumption is around 5Gb for 100M reads.</p>
<p>KisSplice is not a full-length transcriptome assembler. This means that it will output the variable regions of the transcripts, not reconstruct them entirely.</p>
<p>KisSplice comes as a workflow, with several possible post-treatments meant to facilitate the analysis of the results. The choice of the post-treatment depends on the availability of a reference genome/transcriptome and on the need to perform a differential analysis, as summarised in the following table.</p><p>Address of the bookmark: <a href="http://kissplice.prabi.fr/" rel="nofollow">http://kissplice.prabi.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28200/machine-learning</guid>
	<pubDate>Fri, 01 Jul 2016 12:57:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28200/machine-learning</link>
	<title><![CDATA[Machine Learning !!!]]></title>
	<description><![CDATA[<p>In machine learning, computers apply&nbsp;<strong>statistical learning</strong>&nbsp;techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.</p>
<p><em>Keep scrolling.</em>&nbsp;Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.</p><p>Address of the bookmark: <a href="http://www.r2d3.us/visual-intro-to-machine-learning-part-1/" rel="nofollow">http://www.r2d3.us/visual-intro-to-machine-learning-part-1/</a></p>]]></description>
	<dc:creator>Gudiya Pal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28417/wisescaffolder</guid>
	<pubDate>Wed, 13 Jul 2016 08:08:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28417/wisescaffolder</link>
	<title><![CDATA[WiseScaffolder]]></title>
	<description><![CDATA[<p>Function</p>
<p>WiseScaffolder is a stand-alone semi-automatic application for genome scaffolding of pre-assembled contigs using mate-pair data. It also produces editable scaffold maps, allowing either to build gapped scaffolds or usable as a common thread for the manual improvement of scaffolds.</p>
<p>Description&nbsp;</p>
<p>WiseScaffolder includes 4 subcommands: dumpconfig generates a configuration file that notably specifies the average insert size of the mate-pair library preprocess allows the detection and correction of chimerae, the estimation of contigs copy number and produces valuable outputs for the manual improvement of scaffolds scaffold constitutes the central scaffold-builder and comprises two modules:</p>
<p>i) the interative_scaffold_extender, which works with big, unambiguous contigs, or when they run out, single copy contigs, and</p>
<p>ii) the small_contig_inserter, which inserts the small contigs within scaffolds buildfasta converts the scaffold(s) map(s) into Fasta sequences.</p><p>Address of the bookmark: <a href="http://abims.sb-roscoff.fr/wisescaffolder" rel="nofollow">http://abims.sb-roscoff.fr/wisescaffolder</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</guid>
	<pubDate>Mon, 05 Sep 2016 04:07:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</link>
	<title><![CDATA[CrossMap]]></title>
	<description><![CDATA[<ul>
<li>CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between&nbsp;<em>different assemblies</em>&nbsp;(such as Human&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2928/">hg18 (NCBI36)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2758/">hg19 (GRCh37)</a>, Mouse&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/165668/">mm9 (MGSCv37)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/327618/">mm10 (GRCm38)</a>).</li>
<li>It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.</li>
<li>CrossMap is designed to liftover genome coordinates between assemblies. It&rsquo;s&nbsp;<em>not</em>&nbsp;a program for aligning sequences to reference genome.</li>
<li>We&nbsp;<em>do not</em>&nbsp;recommend using CrossMap to convert genome coordinates between species.</li>
</ul><p>Address of the bookmark: <a href="http://crossmap.sourceforge.net/" rel="nofollow">http://crossmap.sourceforge.net/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28844/teannot</guid>
	<pubDate>Thu, 18 Aug 2016 10:02:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28844/teannot</link>
	<title><![CDATA[TEannot]]></title>
	<description><![CDATA[<p>We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the project name is "DmelChr4".</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto" rel="nofollow">https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>