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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31345?offset=1310</link>
	<atom:link href="https://bioinformaticsonline.com/related/31345?offset=1310" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial</guid>
	<pubDate>Mon, 27 Nov 2017 16:24:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial</link>
	<title><![CDATA[Single Cell RNAseq data analysis tutorial !!]]></title>
	<description><![CDATA[<ul>
<li>A major breakthrough (replaced microarrays) in the late 00&rsquo;s and has been widely used since</li>
<li>Measures the&nbsp;average expression level&nbsp;for each gene across a large population of input cells</li>
<li>Useful for comparative transcriptomics, e.g.&nbsp;samples of the same tissue from different species</li>
<li>Useful for quantifying expression signatures from ensembles, e.g.&nbsp;in disease studies</li>
<li>Insufficient&nbsp;for studying heterogeneous systems, e.g.&nbsp;early development studies, complex tissues (brain)</li>
<li>Does&nbsp;not&nbsp;provide insights into the stochastic nature of gene expression</li>
</ul><p>Following are the useful links:</p><p><a href="http://hemberg-lab.github.io/scRNA.seq.course/scRNA-seq-course.pdf" target="_blank">Single Cell RNAseq data analysis Tutorial</a></p><p><a href="https://f1000research.com/articles/5-2122/v2" target="_blank">A step-by-step workflow for low-level analysis of single-cell RNA-seq data</a></p><p><a href="https://www.bioconductor.org/help/workflows/simpleSingleCell/" target="_blank">A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor</a></p><p>SCell: single-cell RNA-seq analysis software</p><p><a href="https://github.com/diazlab/SCell">https://github.com/diazlab/SCell</a></p><p>Beta-Poisson model for single-cell RNA-seq data analyses</p><p><a href="https://github.com/nghiavtr/BPSC">https://github.com/nghiavtr/BPSC</a></p><p>Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis</p><p><a href="https://research.cchmc.org/pbge/sincera.html">https://research.cchmc.org/pbge/sincera.html</a></p><p>SC3 &ndash; consensus clustering of single-cell RNA-Seq data</p><p><a href="http://biorxiv.org/content/early/2016/09/02/036558">http://biorxiv.org/content/early/2016/09/02/036558</a></p><p>Citrus: A toolkit for single cell sequencing analysis</p><p><a href="http://biorxiv.org/content/early/2016/09/14/045070">http://biorxiv.org/content/early/2016/09/14/045070</a></p><p>Single-Cell Resolution of Temporal Gene Expression during Heart Development</p><p><a href="http://www.cell.com/developmental-cell/fulltext/S1534-5807%2816%2930682-7">http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7</a></p><p>Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects</p><p><a href="http://biorxiv.org/content/early/2016/11/15/087775">http://biorxiv.org/content/early/2016/11/15/087775</a></p><p>Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes</p><p><a href="http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract">http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract</a></p><p>SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation</p><p><a href="http://biorxiv.org/content/early/2016/11/21/088856">http://biorxiv.org/content/early/2016/11/21/088856</a></p><p>SCOUP is a probabilistic model to analyze single-cell expression data during differentiation</p><p><a href="https://github.com/hmatsu1226/SCOUP">https://github.com/hmatsu1226/SCOUP</a></p><p>scLVM is a modelling framework for single-cell RNA-seq data</p><p><a href="https://github.com/PMBio/scLVM">https://github.com/PMBio/scLVM</a></p><p>Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories</p><p><a href="https://github.com/jw156605/SLICER">https://github.com/jw156605/SLICER</a></p><p>SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality</p><p><a href="http://www.morgridge.net/SinQC.html">http://www.morgridge.net/SinQC.html</a></p><p>TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis</p><p><a href="https://github.com/zji90/TSCAN">https://github.com/zji90/TSCAN</a></p><p>Visualization and cellular hierarchy inference of single-cell data using SPADE</p><p><a href="http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html">http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html</a></p><p>OEFinder: Identify ordering effect genes in single cell RNA-seq data</p><p><a href="https://github.com/lengning/OEFinder">https://github.com/lengning/OEFinder</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28121/kaiju</guid>
	<pubDate>Mon, 27 Jun 2016 11:23:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28121/kaiju</link>
	<title><![CDATA[Kaiju]]></title>
	<description><![CDATA[<p>Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.</p>
<p>By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database <em>nr</em> used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.</p>
<p>Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.</p>
<p>Kaiju is described in <a href="http://www.nature.com/ncomms/2016/160413/ncomms11257/full/ncomms11257.html">Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. <em>Nat. Commun.</em> 7:11257</a> (open access).</p><p>Address of the bookmark: <a href="http://kaiju.binf.ku.dk/" rel="nofollow">http://kaiju.binf.ku.dk/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/28425/advertisement-for-junior-research-fellowjrf-at-school-of-computational-and-integrative-sciences-jawaharlal-nehru-university</guid>
  <pubDate>Thu, 14 Jul 2016 07:24:53 -0500</pubDate>
  <link></link>
  <title><![CDATA[Advertisement for Junior Research Fellow(JRF)  at School of Computational and Integrative Sciences  Jawaharlal Nehru University]]></title>
  <description><![CDATA[
<p>Advertisement for Junior Research Fellow(JRF) - (1)</p>

<p>Applications are invited for a post in DST, India funded Project entitled: "Positive and negative impacts of macromolecular crowding agents during target site location by DNA binding proteins – origin of optimal search at physiological ionic concentration (Reference Number: ECR/2016/000188) ''. The selected candidate will be appointed purely on temporary basis, initially for two years as a JRF that may be extended to one year of SRF based on the performance.</p>

<p>Position: Junior Research Fellow (1)</p>

<p>Qualifications &amp; Experience: Candidate must have a consistently good academic record with at least 60% marks in all throughout and must have qualified NET/GATE.</p>

<p>Desirable: Basic knowledge in the field of biophysics, molecular simulations and computational biology are desirable.</p>

<p>Salary: Consolidated Rs. 25,000 per month.</p>

<p>Tenure: The project duration is for three years and the selected candidate would be appointed after an interview. Appointment will be purely on temporary basis as stipulated by the existing rules of the University.</p>

<p>Interested candidates need to send an application to the address mentioned below mentioning the name of the project and post applied for (on the cover of the envelope).</p>

<p>The applications along with CV should be mailed at the address given below. Name, address, contact number and e. mail address of two referees must be enclosed with the application. The last date for the application is July 31st 2016.</p>

<p>Dr. Arnab Bhattacharjee (Principal Investigator) <br />Assistant Professor <br />School of Computational and Integrative Sciences <br />Jawaharlal Nehru University <br />New Delhi-110067 <br />E-mail: arnab@jnu.ac.in</p>

<p>Note: 1. Only shortlisted candidates will be communicated to appear in the interview at SCIS, JNU and no other communications in this regard will be entertained.</p>

<p>2. No TA/DA will be paid for appearing in interview.</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/28547/jrf-bioinformatics-at-iit-delhi</guid>
  <pubDate>Mon, 25 Jul 2016 03:26:20 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF Bioinformatics at IIT, Delhi]]></title>
  <description><![CDATA[
<p>No. IITD/IRD/RP03017/4254/Advertisement No.: IITD/IRD/093/2016<br />JRF Bioinformatics  job vacancies in Indian Institute of Technology Delhi (IIT Delhi)<br />Title : Elucidation of Pathologically Relevant miRNAs Responsible for Disease Progression and Resistance to Chemotherapy in Chronic Lymphocytic Leukemia (CLL) (RP03017)<br />Qualification : Candidates having first class B. Tech. / M.Sc. Degree or equivalent in Bioinformatics or Biotechnology with NET qualification. Desirable: Candidates having computer programming skills (C++, Python, Java, Web designing using Materialize frameworks, database management, offline software GUI development) with knowledge of Linux server environment and / or experience in next generation sequencing (NGS) data analysis, MD simulations will be preferred.<br />No. of Post : 01<br />Pay Scale : Rs.25,000/-<br />How to apply<br />Walk-in test / interview will be held on 04/08/2016, 03.00 p.m. at Committee Room No. 230, Block-I, Department of Biochemical Engineering &amp; Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi-110016.</p>

<p>More at http://ird.iitd.ac.in/sites/default/files/jobs/project/IITD-IRD-093-2016.pdf</p>
]]></description>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/28577/research-associate-computer-sciences-recruitment-in-national-bureau-of-plant-genetic-resources</guid>
  <pubDate>Thu, 28 Jul 2016 04:39:13 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate (Computer Sciences) recruitment in National Bureau of Plant Genetic Resources]]></title>
  <description><![CDATA[
<p>Research Associate (Computer Sciences) recruitment in National Bureau of Plant Genetic Resources</p>

<p>Project: Indo-UK Centre for improvement of Nitrogen use efficiency in wheat Dr. Soma S. Marla, Pr. Scientist (Bioinformatics), Division of Genomic Resources, ICAR, NBPGR, ND.</p>

<p>Qualification: Ph.D. Degree in Computer Sciences/Bioinformatics OR 1. First class Master’s degree in any discipline of Plant Sciences with specialization in Computer Sciences/ Bioinformatics having 1st division or 60% marks or equivalent overall grade point average with at least two years of research experience as evidenced from Fellowship/ Associate ship. 2. NET qualification is essential for the candidates with 3+2 years B.Sc.+ M.Sc. Desirable: Demonstrated experience &amp; skills in database design, management, UNIX OS, in NGS data analysis. Experience substantiated by publications of high quality will be preferred.</p>

<p>No.of Post: 1</p>

<p>Pay Scale: Rs. 40,000 (Ph.D)/ Rs + 30 % HRA; Rs.38,000 ( Masters Degree 0 + 30 % HRA).</p>

<p>Age Limit : below 40 years for RA position<br />How to apply<br />Applicants for RA post should send their complete CV (Advance copy of the application may be sent by email to :soma.marla@icar.gov.in or ssmarl@yahoo.com, should enclose the copy of the research publications; one page summary of their achievement relevant to the post applied for; and should enclose two reference letters (one must be from the person with whom worked latest). Shortlisted candidate will be intimated for interview by email.</p>

<p>The candidates who wish to attend the walk-in interview are requested to bring with them five copies of the CV (one copy with photograph) as per the format given below. Also, the candidates should bring the original documents such as degree certificates, marks sheets, publications, thesis, experience certificate etc. for verification. </p>

<p>Date of Interview: 17.8.2016.</p>

<p>More at http://www.nbpgr.ernet.in/Downloadfile.aspx?EntryId=7133</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40611/deepvariant-an-analysis-pipeline-that-uses-a-deep-neural-network-to-call-genetic-variants-from-next-generation-dna-sequencing-data</guid>
	<pubDate>Sat, 25 Jan 2020 13:28:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40611/deepvariant-an-analysis-pipeline-that-uses-a-deep-neural-network-to-call-genetic-variants-from-next-generation-dna-sequencing-data</link>
	<title><![CDATA[DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.]]></title>
	<description><![CDATA[<p><span>DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.</span></p>
<p><span><span>DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on&nbsp;</span><a href="https://github.com/google/nucleus">Nucleus</a><span>, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the&nbsp;</span><a href="https://www.tensorflow.org/">TensorFlow</a><span>&nbsp;machine learning framework.</span></span></p>
<p><span><a href="https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html">https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html</a></span></p>
<p><span><a href="https://www.biorxiv.org/content/10.1101/092890v6">https://www.biorxiv.org/content/10.1101/092890v6</a></span></p>
<p><span><img src="https://4.bp.blogspot.com/-2KlXZO60sWE/WiGc8qlZfxI/AAAAAAAACOs/s1pNiKI8jsAvJLr1E_po5udDO8eObm_awCLcBGAs/s640/image3.png" width="640" height="427" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/google/deepvariant" rel="nofollow">https://github.com/google/deepvariant</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/28618/jrf-bioinformatics-at-dpu-india</guid>
  <pubDate>Fri, 05 Aug 2016 03:06:26 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF Bioinformatics at DPU, India]]></title>
  <description><![CDATA[
<p>Advertisement for position of “JRF (Junior Research Fellow)” on DST research project “Molecular modeling and docking studies on Deguelin and its derivatives with cell cycle arrest, apoptosis and anti-angiogenesis pathway proteins in cancer cell signaling pathway”</p>

<p>Applications are invited on plain paper from eligible candidates along with biodata and copies of certificates in support of age, qualification and experience for the following position:</p>

<p>Particulars Description</p>

<p>1. Position &amp; No. JRF (Junior Research Fellow) 01</p>

<p>2. Title of the Project Molecular modeling and docking studies on Deguelin and its derivatives with cell cycle arrest, apoptosis and anti-angiogenesis pathway proteins in cancer cell signaling pathway</p>

<p>3. Tenure 3 years</p>

<p>4. Investigator Dr. K. V.Swamy</p>

<p>5. Institute Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Tathawade, Pune 411033.</p>

<p>5. Qualifications/Eligibility</p>

<p>Essential: NET (National Eligibility Test) qualified M. Sc Bioinformatics/ M. Tech Bioinformatics/M. Sc Biotechnology/M. Tech Biotechnology or Graduate degree in Professional course with NET qualification or Post graduation degree in professional course</p>

<p>The following examinations conducted by various Central Government Departments/Agencies are considered as National Eligibility Test (NET).</p>

<p>1. CSIR-UGC-LS <br />2. GATE (Graduate Aptitude Test in Engineering) <br />3. JAM (Joint Admission Test) <br />4. GPAT (Graduate Pharmacy Aptitude Test) <br />5. BET(Biotechnology Eligibility Test) <br />6. BINC(Bioinformatics National Consortium) <br />7. JEST( Joint Entrance Screening Test) <br />8. JGEEBILS(Joint Graduate Entrance Examination for Biology &amp; Interdisciplinary Life Sciences) <br />9. NBHM Ph.D scholarship Screening Test <br />10. ICMR- JRF Entrance Examination <br />11. AICE (ICAR-All India competitive Examination ) <br />(For all above examinations valid score considered at the time of interview)</p>

<p>Desirable: Knowledge and skills in Bioinformatics tools/ softwares</p>

<p>6. Monthly Emoluments Rs.25,000/ (As per DST-SERB rules)</p>

<p>7. Last date for submission of prescribed application 20/08/2016</p>

<p>Kindly send your applications to “Dr. K. V. Swamy, Asst.Professor, Dr. D. Y. Patil Biotechnology &amp; Bioinformatics Institute, Survey No. 87/88, Mumbai-Pune Express Way, Tathawade, Pune - 411033, Maharashtra, India”. Highlight the envelope with “Application for post of JRF (Junior Research Fellow)”.</p>

<p>Note: No TA/DA will be paid for attending the interview.</p>

<p>Advertisement: http://careers.dpu.edu.in/Biotech.aspx</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41209/juicebox-visualization-and-analysis-software-for-hi-c-data</guid>
	<pubDate>Fri, 21 Feb 2020 00:33:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41209/juicebox-visualization-and-analysis-software-for-hi-c-data</link>
	<title><![CDATA[Juicebox: Visualization and analysis software for Hi-C data]]></title>
	<description><![CDATA[<p>Juicebox is visualization software for Hi-C data. This distribution includes the source code for Juicebox,&nbsp;<a href="https://github.com/theaidenlab/juicer/wiki/Download">Juicer Tools</a>, and&nbsp;<a href="https://aidenlab.org/assembly/">Assembly Tools</a>.&nbsp;<a href="https://github.com/theaidenlab/juicebox/wiki/Download">Download Juicebox here</a>, or use&nbsp;<a href="https://aidenlab.org/juicebox">Juicebox on the web</a>. Detailed documentation is available&nbsp;<a href="https://github.com/theaidenlab/juicebox/wiki">on the wiki</a>. Instructions below pertain primarily to usage of command line tools and the Juicebox jar files.</p>
<p>Juicebox can now be used to visualize and interactively (re)assemble genomes. Check out the Juicebox Assembly Tools Module website&nbsp;<a href="https://aidenlab.org/assembly">https://aidenlab.org/assembly</a>&nbsp;for more details on how to use Juicebox for assembly.</p>
<p>GUI at&nbsp;<a href="https://aidenlab.org/juicebox/">https://aidenlab.org/juicebox/</a></p><p>Address of the bookmark: <a href="https://github.com/aidenlab/Juicebox" rel="nofollow">https://github.com/aidenlab/Juicebox</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/28802/research-associate-bioinformatics-recruitment-in-icgeb-new-delhi</guid>
  <pubDate>Tue, 16 Aug 2016 03:38:05 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate Bioinformatics recruitment in ICGEB, New Delhi]]></title>
  <description><![CDATA[
<p>Research Associate Bioinformatics recruitment in ICGEB, New Delhi </p>

<p>Project :“Genetic Transformation and Development of Elite Transgenic Maize (Zea mays L.) for Biotic and Abiotic Stresses Tolerance”.</p>

<p>Qualification: Ph.D. degree in:Biotechnology/Bioinformatics/Biochemistry/Plant Molecular Biology/Plant Physiology/Botany or any related area with evidence of prior experience in maize transformation.</p>

<p>Additional experience in plant transformation of any cereal crop would be preferable.</p>

<p>The appointment would initially be for one year.</p>

<p>How to apply<br />Interested applicants should send their detailed CV including brief synopsis regarding the previous research experience (along withcontact email address by email) to: Dr. Tanushri Kaul (tanushri@icgeb.res.in). Group Leader, Nutritional Improvement of Crops Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi-110 067.</p>

<p>Closing date for applications: 22 August 2016.</p>

<p>More at http://www.icgeb.org/vacancies.html</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications</guid>
	<pubDate>Thu, 23 Jul 2020 05:49:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications</link>
	<title><![CDATA[wgd—simple command line tools for the analysis of ancient whole-genome duplications]]></title>
	<description><![CDATA[<p><span>wgd is a easy to use command-line tool for<span>&nbsp;</span></span><em>K</em><sub>S</sub><span><span>&nbsp;</span>distribution construction named wgd. The wgd suite provides commonly used<span>&nbsp;</span></span><em>K</em><sub>S</sub><span><span>&nbsp;</span>and colinearity analysis workflows together with tools for modeling and visualization, rendering these analyses accessible to genomics researchers in a convenient manner.</span></p>
<p><a href="https://academic.oup.com/bioinformatics/article/35/12/2153/5162749">https://academic.oup.com/bioinformatics/article/35/12/2153/5162749</a></p><p>Address of the bookmark: <a href="https://github.com/arzwa/wgd" rel="nofollow">https://github.com/arzwa/wgd</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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