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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30701?offset=690</link>
	<atom:link href="https://bioinformaticsonline.com/related/30701?offset=690" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29574/beagle</guid>
	<pubDate>Thu, 27 Oct 2016 11:19:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29574/beagle</link>
	<title><![CDATA[Beagle]]></title>
	<description><![CDATA[<p>Beagle is a software package that performs genotype calling, genotype phasing, imputation of ungenotyped markers, and identity-by-descent segment detection.</p>
<p>Beagle version 4.1 has a more accurate genotype phasing algorithm and a very fast and accurate genotype imputation algorithm. Version 4.1 also has several changes to the command line arguments which are described in the&nbsp;<a href="http://faculty.washington.edu/browning/beagle/release_notes" target="_blank">release notes</a>. The "ped" argument has no effect in version 4.1. If your data contains nuclear families and you want to model the parent-offspring relationships when phasing genotypes, please use&nbsp;<a href="https://faculty.washington.edu/browning/beagle/b4_0.html">version 4.0</a>.</p>
<p>If you use Beagle 4.1 in a published analysis, please report the program version and cite the appropriate article.</p>
<p>The citation for Beagle's phasing algorithm is:</p>
<p>S R Browning and B L Browning (2007) Rapid and accurate haplotype phasing and missing data inference for whole genome association studies by use of localized haplotype clustering. Am J Hum Genet 81:1084-1097.<a href="http://dx.doi.org/doi:10.1086/521987" target="_blank">doi:10.1086/521987</a></p>
<p>The citation for Beagle's genotype imputation algorithm is:</p>
<p>B L Browning and S R Browning (2016). Genotype imputation with millions of reference samples. Am J Hum Genet 98:116-126.<a href="http://dx.doi.org/doi:10.1016/j.ajhg.2015.11.020" target="_blank">doi:10.1016/j.ajhg.2015.11.020</a></p>
<p>The citation for Beagle's IBD detection algorithm is:</p>
<p>B L Browning and S R Browning (2013). Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194(2):459-71.<a href="http://dx.doi.org/doi:10.1534/genetics.113.150029" target="_blank">doi:10.1534/genetics.113.150029</a></p><p>Address of the bookmark: <a href="http://faculty.washington.edu/browning/beagle/beagle.html" rel="nofollow">http://faculty.washington.edu/browning/beagle/beagle.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/29601/statistics-using-r-with-biological-examples</guid>
	<pubDate>Thu, 03 Nov 2016 04:55:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29601/statistics-using-r-with-biological-examples</link>
	<title><![CDATA[Statistics Using R   with Biological Examples]]></title>
	<description><![CDATA[<p>This book is a manifestation of my desire to teach researchers in biology a bit more about statistics than an ordinary introductory course covers and to introduce the utilization of R as a tool for analyzing their data. My goal is to reach those with little or no training in higher level statistics so that they can do more of their own data analysis, communicate more with statisticians, and appreciate the great potential statistics has to offer as a tool to answer biological questions. </p><p>This is necessary in light of the increasing use of higher level statistics in biomedical research. I hope it accomplishes this mission and encourage its free distribution and use as a course text or supplement.</p><p>K Seefeld, May 2007</p>]]></description>
	<dc:creator>Neel</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/29601" length="4581031" type="application/pdf" />
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29644/junior-research-fellow-at-rajiv-gandhi-centre-for-biotechnology-thiruvananthapuram</guid>
  <pubDate>Mon, 07 Nov 2016 10:27:06 -0600</pubDate>
  <link></link>
  <title><![CDATA[Junior Research Fellow at Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram]]></title>
  <description><![CDATA[
<p>Adv. # 22/ 2016<br />Applications are invited from suitable candidates for one position of Junior Research Fellow in a DST funded bioinformatics research project entitled "Major gene influxes in microbial genome evolution" in the Laboratory of Dr. Shijulal Nelson-Sathi at Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram.</p>

<p>ESSENTIAL QUALIFICATIONS:<br />We are looking for a motivated candidate with keen interest in bioinformatics and microbial genome evolution. The candidate must have a Master’s Degree in Bioinformatics, Computational Biology, Computer Science, Microbiology, Biology or a related field with good academic record.</p>

<p>DESIRABLE QUALIFICATIONS<br />Hands on research experience on handling next generation sequencing data and phylogenetic reconstruction methods. Excellent programming skills (Perl/Python/Java/Php) and experience in working on Unix/Linux platform is preferred. Furthermore; good knowledge is required in statistics (R/Matlab) and the application of bioinformatics analysis tools.</p>

<p>AGE:<br />Below 28 years as on 15th November, 2016.</p>

<p>EMOLUMENTS:<br />Rs. 25,000 + 20% HRA for NET/GATE qualified and Post Graduate in Professional Degree course qualified candidates and <br />Rs. 12,000/- + 20% HRA for others.</p>

<p>DURATION:<br />Initial appointment will be given for one year and further extension will be based on the performance till termination of the project.<br />Only those fulfilling the above criteria need apply and will be called for interview. In the event of more than 10 candidates being short-listed by screening the applications, a written test will be conducted before the selection interview and only those who are successful in the written test will be interviewed. No TA/ DA will be given for appearing in the interview.</p>

<p>Suitably qualified candidates may send applications in the prescribed format (Download here) with a photograph, a copy of full resume indicating the percentage of Marks obtained and attested photocopies of credentials &amp; experience to reach the undersigned on or before 15th November, 2016. Envelopes must be superscripted with abbreviated title of the project, advertisement number and job title. Selection to the position will not entitle the candidate to any future positions at RGCB (permanent or otherwise). As with all project positions at RGCB, the position will be co terminus with end of the project.</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29883/ra-bioinformatics-at-school-of-computational-integrative-sciences-jnu-india</guid>
  <pubDate>Fri, 18 Nov 2016 03:57:56 -0600</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at School of Computational &amp; Integrative Sciences, JNU, India]]></title>
  <description><![CDATA[
<p>School of Computational &amp; Integrative Sciences<br />Jawaharlal Nehru University<br />New Delhi – 110067</p>

<p>Date: Nov 11th. 2016                                                            Last Date:  Nov 25th. 2016</p>

<p>PROJECT ID: 632</p>

<p>The following posts are urgently required to be filled for the Department of Biotechnology, Government of India funded project entitled "Computational Core for Plant Metabolomics" administrated by Prof Indira Ghosh,  School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110 067</p>

<p>NB:For all Bioinformatics posts, preference will be given to candidates with a good knowledge of Python and/or R. Knowledge of JAVA will also get a special consideration.</p>

<p>RA / Research Associate (Metabolic engineering/Computational Biologist)</p>

<p>Salary: Rs. 36000/- + HRA<br />Vacancy: 1<br />Essential Qualifications: PhD in  Bioinformatics /Mathematics/Computer Science with experience in analyzing high throughput omics-based data/ system Biology/ Analysis of Network Biology. Published paper in the field is a must to prove the experience.<br />Desired Skills: Prior experience in handling and guiding bioinformatics, metabolomics data, planning of new research area in metabolic driven network , managing the project portal, preparing and filing reports etc. Will be expected to communicate with user groups and coordinate with LIMS group in Hyderabad and the Cheminformatics group in Delhi.</p>

<p>RA / Research Associate (Chemo-informatics/Computational Biologist)</p>

<p>Salary: Rs. 36000/- + HRA<br />Vacancy: 1<br />Essential Qualifications: PhD in Bioinformatics/ computational biology/ Biophysics/Computer Science. Computational and Chemical structure related experience is a necessary qualification proven by paper published and program developed. <br />Desired Skills:  Research experience in Chemical scaffold mapping, in silico Spectral analysis, Biological Database Designing &amp; Integration is required. Individual is responsible to develop methods related to metabolite identification, Testing and refining and integrate LIMS with IIIT Hyderabad and will be expected to communicate with user groups.</p>

<p>Project SRF (Bioinformatics/Programming)</p>

<p>Salary: As per DBT rules<br />Vacancy: 1<br />Essential Qualifications: Masters/B Tech in Basic Sciences with at least 2yrs of research experience in Bioinformatics/Computational Biology related to Database /portal building &amp; maintenance ,high throughput data handling and analysis etc. For M.Sc/B.Tec, Published paper  in peer-reviewed Journal and for M.Tech, thesis submission in computational biology is a must.</p>

<p>More at http://www.jnu.ac.in/Career/currentjobs.htm</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38462/egad-ultra-fast-functional-analysis-of-gene-networks</guid>
	<pubDate>Fri, 14 Dec 2018 04:10:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38462/egad-ultra-fast-functional-analysis-of-gene-networks</link>
	<title><![CDATA[EGAD: Ultra-fast functional analysis of gene networks]]></title>
	<description><![CDATA[<p><span>With the EGAD (Extending &lsquo;Guilt-by-Association&rsquo; by Degree) package, we present a series of highly efficient tools to calculate functional properties in networks based on the guilt-by-association principle. These allow rapid controlled comparisons and analyses. Two of the core features are: a function prediction algorithm which is fully vectorized (neighbor_voting), allowing network characterization across even thousands of functional groups to be accomplished in minutes in cross-validation and an analytic determination of the optimal prior to guess candidates genes across multiple functional sets (calculate_multifunc, auc_multifunc).</span></p><p>Address of the bookmark: <a href="https://github.com/sarbal/EGAD" rel="nofollow">https://github.com/sarbal/EGAD</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29998/csir-nehru-science-postdoctoral-research-fellowship</guid>
  <pubDate>Tue, 29 Nov 2016 12:34:59 -0600</pubDate>
  <link></link>
  <title><![CDATA[CSIR Nehru Science Postdoctoral Research Fellowship]]></title>
  <description><![CDATA[
<p>CSIR Nehru Science Postdoctoral Research Fellowship</p>

<p>About Fellowship: <br />CSIR Nehru Science Postdoctoral Research Fellowship Scheme is an Research Fellowship awarded/given by HRD Ministry, Govt. of India every year to more than 100 fellows.</p>

<p>It was started to identify promising and young researchers with novel ideas and provide them research opportunities in the areas of basic science, engineering, medicine &amp; agriculture.</p>

<p>The fellowship aims at facilitating their transition from mentored to independent research career.</p>

<p>In addition, check these ICTS Research Fellowships:<br />1.) Max-Planck Partner Group Fellowships 2017-18<br />2.) ICTS-Simons Postdoctoral Fellowships 2017<br />3.) ICTS Post Doctoral Fellowships 2017<br />4.) Airbus Prize Postdoctoral Fellowship 2017-18</p>

<p>Eligibility: To be eligible for this fellowship, you:<br />1.) PhD degree (within 3 years of award of PhD degree), OR<br />2.) Those who have submitted PhD theses.<br />3.) Applicants should have research publications in good impact factor SCI journals.<br />4.) Indian nationals, Persons of Indian Origin (PIO) &amp; Overseas Citizen of India (OCI), a can also apply.<br />5.) Maximum Age Limit: 32 years.</p>

<p>Duration: <br />– 2 Years.<br />– extendable for a maximum of 1 more year based on performance.</p>

<p>Remuneration: <br />– Rs. 50,000/- per month plus House Rent Allowance (HRA)<br />– A contingency grant of Rs. 3.0 lakh per annum.<br />– 25% of the contingency grant can be used for domestic and international travel.</p>

<p>Mode of Selection: You can apply throughout the year, but selection will be made twice a year, in June and December.</p>

<p>How to Apply: <br />– Read the instructions, given at Annexure-I in this PDF file.<br />– And, application form is given as, Annexure-II.<br />– Fill the form &amp; send it to the given address in the PDF file.</p>

<p>Deadline: Rolling Deadline (Applications accepted throughout the year)</p>

<p>Also See: Research Internship/ Fellowship in India:<br />1.) IIT Bombay Research Internship Awards Programme 2016-17<br />2.) IIT Delhi Internship Program 2016-17<br />3.) DAAD WISE – International Internship in Germany<br />4.) Summer Research Fellowship Programme| JNCASR Bangalore<br />5.) Indian Academy of Sciences Summer Internships 2017<br />6.) Winter Internship – IIT Bombay NPDE-TCA<br />7.) Viterbi – India Program 2017 | Research Internship in US<br />8.) Internship – Centre for Stem Cell Research, Vellore</p>

<p>Accommodation &amp; other benefits: <br />– Accommodation may be provided by CSIR, if available.<br />– Medical benefits as per CSIR norms.</p>

<p>For more details: <br />– Check this PDF Notification of Fellowship.<br />– List of CSIR Labs &amp; their work/activities can be seen at www.csir.res.in.</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30027/dbt-india</guid>
	<pubDate>Sun, 04 Dec 2016 22:30:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30027/dbt-india</link>
	<title><![CDATA[DBT India]]></title>
	<description><![CDATA[<p>Latest announcement on DBT India.&nbsp;</p>
<p>Calls</p>
<p>Events</p>
<p>Projects</p>
<p>Jobs</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://www.dbtindia.nic.in/out-reach/latest-announcements/" rel="nofollow">http://www.dbtindia.nic.in/out-reach/latest-announcements/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41475/proteoclade-a-taxonomic-toolkit-for-multi-species-and-metaproteomic-analysis</guid>
	<pubDate>Wed, 18 Mar 2020 14:27:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41475/proteoclade-a-taxonomic-toolkit-for-multi-species-and-metaproteomic-analysis</link>
	<title><![CDATA[ProteoClade: A taxonomic toolkit for multi-species and metaproteomic analysis]]></title>
	<description><![CDATA[<p>ProteoClade is a Python library for&nbsp;<span>taxonomic-based annotation and quantification of bottom-up proteomics data</span>. It is designed to be user-friendly, and has been optimized for speed and storage requirements.</p>
<p>ProteoClade helps you analyze two general categories of experiments:</p>
<ol>
<li>
<p><span><em>Targeted Database</em>&nbsp;Searches:</span>&nbsp;Experiments in which a limited number of species are defined ahead of time, such as those involving Patient-Derived Xenografts (PDXs) or host-pathogen interactions. Reference protein sequence databases are used for targeted searches (ex: using Mascot, MaxQuant).</p>
</li>
<li>
<p><span><em>De Novo</em>&nbsp;Searches:</span>&nbsp;Experiments in which the organisms are unspecified ahead of time or involve samples of high taxonomic complexity. Mass spectra are analyzed in the absence of a reference database (ex: using PEAKS, PepNovo).</p>
</li>
</ol>
<p>ProteoClade scales from two organisms to every organism in UniProt. Please&nbsp;<a href="https://proteoclade.readthedocs.io/">refer to the complete documentation at proteoclade.readthedocs.io</a>&nbsp;for installation, a user's guide, and examples.</p>
<p><a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007741">https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007741</a></p><p>Address of the bookmark: <a href="https://github.com/HeldLab/ProteoClade" rel="nofollow">https://github.com/HeldLab/ProteoClade</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/30331/16th-congress-of-the-european-society-for-evolutionary-biology-eseb</guid>
  <pubDate>Thu, 22 Dec 2016 08:08:37 -0600</pubDate>
  <link></link>
  <title><![CDATA[16th Congress of the European Society for Evolutionary Biology (ESEB)]]></title>
  <description><![CDATA[
<p>Abstract submissions for our upcoming symposium on the Genomics of Adaptation that will take place as part of the 16th Congress of the European Society for Evolutionary Biology (ESEB). The conference will take place from August 20th - August 25th, 2017 in Groningen, the Netherlands. </p>

<p>SYMPOSIUM DESCRIPTION: Genomics of Adaptation [S16] Model organisms for life-history research are mainly studied in the lab where functional genetics is assessable. In general, however, knowledge about their eco-evolutionary dynamics, such as biotic interactions, is rare. By contrast, in organisms for which the ecology and adaptation strategies in the field are well known, we typically lack the appropriate genetic tools to investigate functionality. Advances in genomics and statistics as well as investments in evolutionary model organisms are now providing access to putatively adaptive genome-wide variation within species from across the tree of life. In this symposium, we focus on integrating life-history biology, genetics and evolutionary ecology in the genomics era. </p>

<p>We wish to (1) highlight the role of genetic architecture of complex traits, such as adaptations to biotic interactions or life-history traits; (2) contrast this to morphological traits which are generally thought to have a less complex genetic architecture; and (3) discuss the opportunities and drawbacks of specific model systems. </p>

<p>INVITED SPEAKERS: Josephine Pemberton, University of Cambridge (http://bit.ly/2hJWytJ ) Peter Tiffin, University of Minnesota (http://bit.ly/2hK7HuS ) </p>

<p>ABSTRACT SUBMISSION The deadline for abstract submission is January 10, 2017. For more information and to submit abstracts online, please visit: http://bit.ly/2fBXlvN We look forward to an exciting symposium and seeing you all in Groningen! Sincerely, Ben Blackman, UC Berkeley Maaike de Jong, University of Bristol Bart Pannebakker, Wageningen University Noah Whiteman, UC Berkeley Jelle Zandveld, Wageningen University</p>
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