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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26309?offset=1580</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29656/statistics-and-probability</guid>
	<pubDate>Tue, 08 Nov 2016 07:34:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29656/statistics-and-probability</link>
	<title><![CDATA[Statistics and probability]]></title>
	<description><![CDATA[<h3><span>Topics</span></h3>
<div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/displaying-describing-data">Displaying and describing data</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data">Modeling distributions of data</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data">Describing relationships in quantitative data</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/designing-studies">Designing studies</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/probability-library">Probability</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/random-variables-stats-library">Random variables</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library">Sampling distributions</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/confidence-intervals-one-sample">Confidence intervals (one sample)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample">Significance tests (one sample)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/significance-tests-confidence-intervals-two-samples">Significance tests and confidence intervals (two samples)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/inference-categorical-data-chi-square-tests">Inference for categorical data (chi-square tests)</a></div>
<div><a href="https://www.khanacademy.org/math/statistics-probability/advanced-regression-inference-transforming">Advanced regression (inference and tran</a></div>
</div><p>Address of the bookmark: <a href="https://www.khanacademy.org/math/statistics-probability" rel="nofollow">https://www.khanacademy.org/math/statistics-probability</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29693/bioistats-online-course</guid>
	<pubDate>Thu, 10 Nov 2016 04:22:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29693/bioistats-online-course</link>
	<title><![CDATA[Bioistats Online course]]></title>
	<description><![CDATA[<p><span>One of our primary focuses will be to develop an understanding of the various ways in which we can assign a probability to some chance event. We'll also learn the&nbsp;</span><strong>fundamental&nbsp;</strong><span><strong>properties of probability</strong>, investigate how probability behaves, and learn how to calculate the probability of a new chance event.</span></p>
<p><span>This book is handy understanding basic concepts.</span></p><p>Address of the bookmark: <a href="https://onlinecourses.science.psu.edu/stat414/node/287" rel="nofollow">https://onlinecourses.science.psu.edu/stat414/node/287</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29886/research-officer-at-national-tea-research-foundation</guid>
  <pubDate>Fri, 18 Nov 2016 04:19:24 -0600</pubDate>
  <link></link>
  <title><![CDATA[Research Officer at National Tea Research Foundation]]></title>
  <description><![CDATA[
<p>National Tea Research Foundation (NTRF) a registered body, requires qualified and experienced agricultural research scientist for contractual appointment as per details below :</p>

<p>Post Research Officer, NTRF, Kolkata</p>

<p>Essential Post Graduate degree in Life Science having special paper in Bioinformatics. Post Graduate degree in Bioinformatics.</p>

<p>Desirable Ph. D. in the area of computational biology or bioinformatics.</p>

<p>Job Experience Work experience on database development, capable to work independently and efficient in bioinformatics related project.</p>

<p>Job Responsibility</p>

<p>To develop database on various aspects of tea research.</p>

<p>Screening of the In-house projects of NTRF at the preliminary level.</p>

<p>Review and evaluation of In-house projects of NTRF. Technical monitoring of the In-house projects of NTRF.</p>

<p>Reviewing the final technical report of the In-house projects of NTRF.</p>

<p>Putting-up of same proposals for taking approval and sanction of the competent authority.</p>

<p>Physical and technical verification of the In-house projects of NTRF.</p>

<p>To assist in organizing seminars / workshops / meetings etc.</p>

<p>Interested candidate may appear for walk-in Interview on 17 th November, 2016</p>

<p>More Info :</p>

<p>http://www.teaboard.gov.in/pdf/Recruitment_for_the_post_of_Research_Officer_pdf7538.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42160/vicuna-a-software-tool-that-enables-consensus-assembly-of-ultra-deep-sequence-derived-from-diverse-viral-or-other-heterogeneous-populations</guid>
	<pubDate>Tue, 25 Aug 2020 03:40:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42160/vicuna-a-software-tool-that-enables-consensus-assembly-of-ultra-deep-sequence-derived-from-diverse-viral-or-other-heterogeneous-populations</link>
	<title><![CDATA[VICUNA: a software tool that enables consensus assembly of ultra-deep sequence derived from diverse viral or other heterogeneous populations.]]></title>
	<description><![CDATA[<p><span>VICUNA</span><span>&nbsp;is a&nbsp;</span><em>de novo</em><span>&nbsp;assembly program targeting populations with high mutation rates. It creates a single linear representation of the mixed population on which intra-host variants can be mapped. For clinical samples rich in contamination (e.g., &gt;95%), VICUNA can leverage existing genomes, if available, to assemble only target-alike reads. After initial assembly, it can also use existing genomes to perform guided merging of contigs. For each data set (e.g., Illumina paired read, 454), VICUNA outputs consensus sequence(s) and the corresponding multiple sequence alignment of constituent reads. VICUNA efficiently handles ultra-deep sequence data with tens of thousands fold coverage.</span></p>
<p><a href="http://software.broadinstitute.org/viral/docs/vicuna_v1.0.pdf">http://software.broadinstitute.org/viral/docs/vicuna_v1.0.pdf</a></p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/viral-genomics/vicuna" rel="nofollow">https://www.broadinstitute.org/viral-genomics/vicuna</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29992/spines</guid>
	<pubDate>Mon, 28 Nov 2016 05:33:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29992/spines</link>
	<title><![CDATA[Spines]]></title>
	<description><![CDATA[<p><a href="https://www.broadinstitute.org/ftp/distribution/software/spines/"><em>Spines</em></a>&nbsp;is a collection of software tools, developed and used by the Vertebrate Genome Biology Group at the Broad Institute. It provides basic data structures for efficient data manipulation (mostly genomic sequences, alignments, variation etc.), as well as specialized tool sets for various analyses. It also features three sequence alignment packages:&nbsp;<em>Satsuma,</em>&nbsp;a highly parallelized program for high-sensitivity, genome-wide synteny;&nbsp;<em>Papaya,</em>&nbsp;an all-purpose alignment tool for less diverged sequences; and&nbsp;<em>SLAP,</em>&nbsp;a context-sensitive local aligner for diverged sequences with large gaps.</p>
<p>Access&nbsp;<em>Spines</em>&nbsp;<a href="https://www.broadinstitute.org/ftp/distribution/software/spines/">here</a>.</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/genome-sequencing-and-analysis/spines" rel="nofollow">https://www.broadinstitute.org/genome-sequencing-and-analysis/spines</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42806/graphunzip-phases-an-assembly-graph-using-hi-c-data-andor-long-reads</guid>
	<pubDate>Fri, 05 Feb 2021 21:22:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42806/graphunzip-phases-an-assembly-graph-using-hi-c-data-andor-long-reads</link>
	<title><![CDATA[GraphUnzip: Phases an assembly graph using Hi-C data and/or long reads.]]></title>
	<description><![CDATA[<p>GraphUnzip, a fast, memory-efficient and accurate tool to unzip assembly graphs into their constituent haplotypes using long reads and/or Hi-C data. As GraphUnzip only connects sequences in the assembly graph that already had a potential link based on overlaps, it yields high-quality gap-less supercontigs. To demonstrate the efficiency of GraphUnzip, we tested it on a simulated diploid Escherichia coli genome, and on two real datasets for the genomes of the rotifer Adineta vaga and the potato Solanum tuberosum. In all cases, GraphUnzip yielded highly continuous phased assemblies.</p>
<p>https://www.biorxiv.org/content/biorxiv/early/2021/02/01/2021.01.29.428779.full.pdf</p><p>Address of the bookmark: <a href="https://github.com/nadegeguiglielmoni/GraphUnzip" rel="nofollow">https://github.com/nadegeguiglielmoni/GraphUnzip</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30018/bipype</guid>
	<pubDate>Thu, 01 Dec 2016 08:47:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30018/bipype</link>
	<title><![CDATA[bipype]]></title>
	<description><![CDATA[<p><span>Bipype is a very useful program, which prepare a lot of types of bioinformatics analyses. There are three input options: amplicons, WGS (whole genome sequences) and metatranscriptomic data. If amplicons are input data, then bipype does reconstruction and pairs merging. After that biodiversity is searching. There are two types of searching depending on the amplicons types (ITS or 16S). If WGS are chosen, then bipype finds the SA coordinates of the input reads and generates alignments in the SAM format given single-end reads, aligns reads to reference sequence(s). All of these analyses will be shown with Krona program, which allows to show hierarchical data with pie charts.</span></p><p>Address of the bookmark: <a href="https://readthedocs.org/projects/bipype/" rel="nofollow">https://readthedocs.org/projects/bipype/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43090/loretta-a-user-friendly-tool-for-assembling-viral-genomes-from-pacbio-sequence-data</guid>
	<pubDate>Wed, 23 Jun 2021 07:54:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43090/loretta-a-user-friendly-tool-for-assembling-viral-genomes-from-pacbio-sequence-data</link>
	<title><![CDATA[LoReTTA, a user-friendly tool for assembling viral genomes from PacBio sequence data]]></title>
	<description><![CDATA[<p>LoReTTA (Long Read Template-Targeted Assembler), a tool designed for performing <em>de novo</em> assembly of long reads generated from viral genomes on the PacBio platform. LoReTTA exploits a reference genome to guide the assembly process, an approach that has been successful with short reads.</p>
<p>https://academic.oup.com/ve/article/7/1/veab042/6248116</p><p>Address of the bookmark: <a href="https://academic.oup.com/ve/article/7/1/veab042/6248116" rel="nofollow">https://academic.oup.com/ve/article/7/1/veab042/6248116</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/30334/postdoc-at-ubritishcolumbia-in-troutgenomics</guid>
  <pubDate>Thu, 22 Dec 2016 08:18:46 -0600</pubDate>
  <link></link>
  <title><![CDATA[Postdoc at UBritishColumbia in TroutGenomics]]></title>
  <description><![CDATA[
<p>Landscape Genomics Postdoc at UBC A research team at the University of British Columbia’s Department of Zoology and Biodiversity Research Centre is seeking a postdoctoral researcher in landscape genetics of native rainbow trout (Oncorhynchus mykiss). </p>

<p>This project is part of a larger Genome Canada project on genetics and physiology of adaptation to climate change in rainbow trout, and the population genomics component is in the labs of Eric Taylor and Michael Whitlock. The landscape genomics component primarily involves whole genome sequencing approaches to understanding the genomic basis of adaptation to features of habitat, but also to provide insights into phylogeography and the influence of watershed structure on population subdivision in rainbow trout. A PhD in a related field with expertise in basic theory and bioinformatic analysis of population genomics data is required. </p>

<p>The position is available for one year with renewal for up to three additional years. Salary is $55,000 per year plus benefits. To apply, please send a brief cover letter summarizing your qualifications for the position, a CV, and the names, addresses, phone numbers and emails of three references. </p>

<p>Review of applications will begin January 16, 2017. Address application materials to etaylor@zoology.ubc.ca to whom any questions can also be addressed. etaylor@zoology.ubc.ca</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30375/mauve-a-system-for-constructing-multiple-genome-alignments-in-the-presence-of-large-scale-evolutionary-events-such-as-rearrangement-and-inversion</guid>
	<pubDate>Sat, 24 Dec 2016 09:20:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30375/mauve-a-system-for-constructing-multiple-genome-alignments-in-the-presence-of-large-scale-evolutionary-events-such-as-rearrangement-and-inversion</link>
	<title><![CDATA[Mauve: a system for constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion]]></title>
	<description><![CDATA[<p>Mauve is a system for constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion. Multiple genome alignments provide a basis for research into comparative genomics and the study of genome-wide evolutionary dynamics.</p>
<p>Mauve has been developed with the idea that a multiple genome aligner should require only modest computational resources. It employs algorithmic techniques that scale well in the lengths of sequences being aligned. For example, a pair of&nbsp;<em>Y. pestis</em>&nbsp;genomes can be aligned in under a minute, while a group of 9 divergent Enterobacterial genomes can be aligned in a few hours. However, the current algorithm&rsquo;s compute time (progressiveMauve) scales cubically in the number of genomes to align, making it unsuitable for datasets containing more than 50-100 bacterial genomes.</p><p>Address of the bookmark: <a href="http://darlinglab.org/mauve/mauve.html" rel="nofollow">http://darlinglab.org/mauve/mauve.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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