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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36398?offset=170</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44476/omark-software-for-proteome-protein-coding-gene-repertoire-quality-assessment</guid>
	<pubDate>Wed, 21 Feb 2024 15:01:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44476/omark-software-for-proteome-protein-coding-gene-repertoire-quality-assessment</link>
	<title><![CDATA[OMArk: software for proteome (protein-coding gene repertoire) quality assessment]]></title>
	<description><![CDATA[<p><span>OMArk is a software for proteome (protein-coding gene repertoire) quality assessment. It provides measures of proteome completeness, characterizes the consistency of all protein coding genes with regard to their homologs, and identifies the presence of contamination from other species. OMArk relies on the OMA orthology database, from which it exploits orthology relationships, and on the OMAmer software for fast placement of all proteins into gene families.</span></p><p>Address of the bookmark: <a href="https://github.com/DessimozLab/OMArk" rel="nofollow">https://github.com/DessimozLab/OMArk</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/7387/bioinformatics-software-for-biologists-in-the-genomics-era</guid>
	<pubDate>Sun, 22 Dec 2013 17:31:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/7387/bioinformatics-software-for-biologists-in-the-genomics-era</link>
	<title><![CDATA[Bioinformatics software for biologists in the genomics era]]></title>
	<description><![CDATA[<p>The genome sequencing revolution is approaching a landmark figure of 1000 completely sequenced genomes. Coupled with fast-declining, per-base sequencing costs, this influx of DNA sequence data has encouraged laboratory scientists to engage large datasets in comparative sequence analyses for making evolutionary, functional and translational inferences. However, the majority of the scientists at the forefront of experimental research are not bioinformaticians, so a gap exists between the user-friendly software needed and the scripting/programming infrastructure often employed for the analysis of large numbers of genes, long genomic segments and groups of sequences. We see an urgent need for the expansion of the fundamental paradigms under which biologist-friendly software tools are designed and developed to fulfill the needs of biologists to analyze large datasets by using sophisticated computational methods. We argue that the design principles need to be sensitive to the reality that comparatively small teams of biologists have historically developed some of the most popular biological software packages in molecular evolutionary analysis. Furthermore, biological intuitiveness and investigator empowerment need to take precedence over the current supposition that biologists should re-tool and become programmers when analyzing genome scale datasets.</p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/23/14/1713.full" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/23/14/1713.full</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/24297/bioinformatics-walkin-at-nii</guid>
  <pubDate>Fri, 04 Sep 2015 21:48:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics WalkIn at NII]]></title>
  <description><![CDATA[
<p>ADVERTISEMENT OF WALK-IN-INTERVIEW</p>

<p>NAME OF THE POST : Bioinformatician (Part time 3 days in a week) (One Position only)</p>

<p>DURATION : One Year</p>

<p>NAME OF THE PROJECT : Next generation sequencing facility</p>

<p>EDUCATIONAL QUALIFICATIONS : At least a Masters degree in Bioinformatics and Bachelors degree in any stream of life sciences</p>

<p>REQUIREMENTS :</p>

<p>Around 5 years of experience and proven track record in next generation sequence data analysis (supported by publications in peer-reviewed journals), ability to analyze transcriptomics, Chip-seq, and small RNA –seq data.</p>

<p>: Should have the ability to analyze raw primary data generated by Illumina next generation sequencing platforms and create / troubleshoot custom analysis Pipelines.</p>

<p>Should have ability to handle all downstream secondary and tertiary data analysis using commercially available as well as open source softwares (transcriptomics, ChIP-seq, small RNA-seq)</p>

<p>Apart from these, the applicant should have knowledge of the following: Programming: Perl and Python. Operating system:</p>

<p>Linux and Windows. NGS Analysis tools: Maq, BWA, Bowtie, SAM tools, BEDTools, MACS, Galaxy, FastQC, Bismark, MEDIPS, Tophat, Cufflinks, AvadisNGS, CLC Genomics Workbench, Galaxy, BaseSpace, Trinity Statistics: Microsoft Excel and R. Database: MySQL Genome Browser: UCSC, Ensemble, IGV, IGB Motif Analysis Tools: MEME Suite, Transfac and RSAT Functional Annotation Tools: DAVID, GeneCodis, Gene Cards Networking Tools: Cytoscape</p>

<p>EMOLUMENTS : The incumbent will be paid a fee of Rs. 2000/- per sitting/ per day.</p>

<p>SCIENTIST NAME : Dr. Arnab Mukhopadhyay,</p>

<p>Staff Scientific V Next generation sequencing facility</p>

<p>SCIENTIST’S E-MAIL ID : arnab@nii.ac.in</p>

<p>WALK IN INTERVIEW ON : 18th September, 2015</p>

<p>REGISTRATION OF CANDIDATES: 10.30 AM to 11.00 AM</p>

<p>PLEASE NOTE- 1. CANDIDATE MAY FILL UP APPLICATION IN THE PRECRIBED FORMAT ALONG WITH NECESSARY DOCUMENTS FOR VERIFICATION. 2. APPLICATIONS CONTAINING INCOMPLETE INFORMATION SHALL NOT BE ENTERTAINED. 3. DATE OF PASSING THE EXAMINATIONS MUST BE INDICATED CLEARLY. 4. ONLY REGISTERED CANDIDATES WILL BE INTERVIEWED. 5. NO TA/DA WILL BE PAID FOR ATTENDING THE INTERVIEW PRESCRIBED FORM 1. NAME 2. FATHER’S NAME 3. MOTHER’S NAME 4. DATE OF BIRTH 5. SEX (MALE/FEMALE) 6. CATEGORY (SC/ ST/ OBC/ PH) 7. ADDRESS a. (CORRSPONDENCE) b. (PERMANENT) 8. E MAIL, TELEPHONE NO. &amp; MOBILE No (if any) 9. ACADEMIC &amp; PROFESSIONAL QUALIFICATIONS NAME OF EXAMINATION PASSED WITH SUBJECTS YEAR OF PASSING BOARD/ UNIVERSITY PERCENTAGE/ DIVISION REMARKS 10. PAST EXPERIENCE &amp; PRESENT EMPLOYMENT, IF ANY 11. CANDIDATES SHOULD STATE CLEARLY WHETHER THEY HAVE BEEN AWARDED PH.D DEGREE OR THESIS HAS BEEN SUBMITTED. 12. HAVE YOU APPLIED FOR A POSITION EARLIER IN THE INSTITUTE? IF SO:- (1) THE DETAILS OF THE PROJECT AND PROJECT INVESTIGATOR (2) IF CALLED FOR INVERVIEW, RESULTS THEREOF</p>

<p>More at http://www1.nii.res.in/sites/default/files/walkininterview-18sept2015.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26852/awesome-bioinformatics-pipelines</guid>
	<pubDate>Wed, 30 Mar 2016 21:50:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26852/awesome-bioinformatics-pipelines</link>
	<title><![CDATA[Awesome bioinformatics pipelines !]]></title>
	<description><![CDATA[<p><span>A curated list of awesome pipeline toolkits ...</span></p>
<p><span>https://github.com/pditommaso/awesome-pipeline</span></p><p>Address of the bookmark: <a href="https://github.com/pditommaso/awesome-pipeline" rel="nofollow">https://github.com/pditommaso/awesome-pipeline</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</guid>
	<pubDate>Wed, 22 Jun 2016 05:37:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</link>
	<title><![CDATA[DarkHorse]]></title>
	<description><![CDATA[<p><em>DarkHorse</em>&nbsp;is a bioinformatic method for rapid, automated identification and ranking of phylogenetically atypical proteins on a genome-wide basis. It works by selecting potential ortholog matches from a reference database of amino acid sequences, then using these matches to calculate a lineage probability index (LPI) score for each genome protein.</p>
<p>LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scale<em>de novo</em>&nbsp;predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.</p>
<p>One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.</p>
<p><em>DarkHorse</em>&nbsp;is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the&nbsp;<a href="http://darkhorse.ucsd.edu/#references">references</a>&nbsp;cited below for a complete explanation of parameter selection and result interpretation. A brief&nbsp;<a href="http://darkhorse.ucsd.edu/tutorial.html">tutorial</a>&nbsp;page is also available on-line.</p><p>Address of the bookmark: <a href="http://darkhorse.ucsd.edu/download.html" rel="nofollow">http://darkhorse.ucsd.edu/download.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30336/finding-patterns-in-biological-sequences</guid>
	<pubDate>Thu, 22 Dec 2016 10:30:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30336/finding-patterns-in-biological-sequences</link>
	<title><![CDATA[Finding Patterns in Biological Sequences]]></title>
	<description><![CDATA[<p>In this report we provide an overview of known techniques for discovery of patterns of biological sequences (DNA and proteins). We also provide biological motivation, and methods of biological verification of such patterns. Finally we list publicly available tools and databases for pattern discovery. On-line supplement is available through http://genetics.uwaterloo.ca/&sim;tvinar/cs798g/motif.</p><p>Address of the bookmark: <a href="http://engr.case.edu/li_jing/papers/00798gpattern.pdf" rel="nofollow">http://engr.case.edu/li_jing/papers/00798gpattern.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32018/tmap-torrent-mapping-alignment-program-general-notes</guid>
	<pubDate>Sun, 02 Apr 2017 15:53:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32018/tmap-torrent-mapping-alignment-program-general-notes</link>
	<title><![CDATA[TMAP - torrent mapping alignment program General Notes]]></title>
	<description><![CDATA[<p>TMAP - torrent mapping alignment program <a href="https://github.com/iontorrent/TS/tree/master/Analysis/TMAP#general-notes"></a>General Notes</p>
<p>TMAP is a fast and accurate alignment software for short and long nucleotide sequences produced by next-generation sequencing technologies.</p>
<ul>
<li>
<p>The latest TMAP is unsupported. To use a supported version, please see the TMAP version associated with a Torrent Suite release below.</p>
</li>
<li>
<p>Get the latest source code:</p>
<div>
<pre>git clone git://github.com/iontorrent/TMAP.git
 <span>cd</span> TMAP
 git submodule init
 git submodule update</pre>
</div>
</li>
</ul>
<p>https://github.com/iontorrent/TS/tree/master/Analysis/TMAP</p><p>Address of the bookmark: <a href="https://github.com/iontorrent/TS/tree/master/Analysis/TMAP" rel="nofollow">https://github.com/iontorrent/TS/tree/master/Analysis/TMAP</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36111/d3networktools-for-creating-d3-javascript-network-tree-dendrogram-and-sankey-graphs-from-r</guid>
	<pubDate>Fri, 06 Apr 2018 12:10:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36111/d3networktools-for-creating-d3-javascript-network-tree-dendrogram-and-sankey-graphs-from-r</link>
	<title><![CDATA[d3Network:Tools for creating D3 JavaScript network, tree, dendrogram, and Sankey graphs from R.]]></title>
	<description><![CDATA[<p><a href="http://bost.ocks.org/mike/">Mike Bostock</a><span>&rsquo;s&nbsp;</span><a href="http://d3js.org/">D3.js</a><span>&nbsp;is great for creating&nbsp;</span><a href="http://bl.ocks.org/mbostock/4062045">interactive network graphs</a><span>&nbsp;with JavaScript. The&nbsp;</span><a href="https://github.com/christophergandrud/d3Network">d3Network</a><span>&nbsp;package makes it easy to create these network graphs from&nbsp;</span><a href="http://www.r-project.org/">R</a><span>. The main idea is that you should able to take an R data frame with information about the relationships between members of a network and create full network graphs with one command.</span></p><p>Address of the bookmark: <a href="http://christophergandrud.github.io/d3Network/" rel="nofollow">http://christophergandrud.github.io/d3Network/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36510/scallop-reference-based-transcriptome-assembler-for-rna-seq</guid>
	<pubDate>Tue, 08 May 2018 04:23:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36510/scallop-reference-based-transcriptome-assembler-for-rna-seq</link>
	<title><![CDATA[Scallop: reference-based transcriptome assembler for RNA-seq]]></title>
	<description><![CDATA[<p>Scallop is an accurate reference-based transcript assembler. Scallop features its high accuracy in assembling multi-exon transcripts as well as lowly expressed transcripts. Scallop achieves this improvement through a novel algorithm that can be proved preserving all phasing paths from reads and paired-end reads, while also achieves both transcripts parsimony and coverage deviation minimization.</p>
<p>Scallop paper has been published at&nbsp;<a href="https://www.nature.com/articles/nbt.4020"><span>Nature Biotechnology</span></a>. The datasets and scripts used in this paper to compare the performance of Scallop and other assemblers are available at&nbsp;<a href="https://github.com/Kingsford-Group/scalloptest"><span>scalloptest</span></a>.</p>
<p>Please also checkout the&nbsp;<span>podcast</span>&nbsp;about Scallop (thanks&nbsp;<a href="https://ro-che.info/">Roman Cheplyaka</a>&nbsp;for the interview). It is available at both&nbsp;<a href="https://bioinformatics.chat/scallop">the bioinformatics chat</a>&nbsp;and&nbsp;<a href="https://itunes.apple.com/us/podcast/the-bioinformatics-chat/id1227281398">iTunes</a>.</p>
<p>&nbsp;</p>
<p>https://github.com/Kingsford-Group/scallop</p><p>Address of the bookmark: <a href="https://github.com/Kingsford-Group/scallop" rel="nofollow">https://github.com/Kingsford-Group/scallop</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37520/mmgenome-tools-for-extracting-individual-genomes-from-metagneomes</guid>
	<pubDate>Thu, 09 Aug 2018 17:41:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37520/mmgenome-tools-for-extracting-individual-genomes-from-metagneomes</link>
	<title><![CDATA[mmgenome: Tools for extracting individual genomes from metagneomes]]></title>
	<description><![CDATA[<p>The mmgenome toolbox enables reproducible extraction of individual genomes from metagenomes. It builds on the&nbsp;<a href="http://madsalbertsen.github.io/multi-metagenome/">multi-metagenome</a>&nbsp;concept, but wraps most of the process of extracting genomes in simple R functions. Thereby making the whole process of binning easy and at the same time reproducible through the Rmarkdown format.</p>
<p>The mmgenome R package also facilitates effortless integration with additional data sources and hence should not be seen as "yet another binning method", but rather a package to integrate different binning strategies.</p>
<p>All functions in the mmgenome R package has associated documentation, check it out in R by e.g.&nbsp;<code>?mmplot</code>.</p><p>Address of the bookmark: <a href="https://github.com/MadsAlbertsen/mmgenome" rel="nofollow">https://github.com/MadsAlbertsen/mmgenome</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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