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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27257?offset=1200</link>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/8385/peng-lab</guid>
  <pubDate>Tue, 18 Feb 2014 13:53:46 -0600</pubDate>
  <link></link>
  <title><![CDATA[Peng Lab]]></title>
  <description><![CDATA[
<p>Peng Lab at Janelia Farm Research Campus, Howard Hughes Medical Institute focuses on data mining for bioinformatics and computational molecular biology, particularly, bioimage data mining and informatics. These bioimages include cellular and molecular images and related medical images. </p>

<p>* Analysis of Gene Expression Pattern Images: high-performance image analysis and mining for different model organisms, such as fruitfly, C. elegans, and mouse;<br />* Feature/Model Learning: developing algorithms and software</p>

<p>Location :Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.</p>

<p>http://research.janelia.org/peng/</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/37514/list-of-non-commercial-ngs-genotype-calling-software</guid>
	<pubDate>Thu, 09 Aug 2018 04:21:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/37514/list-of-non-commercial-ngs-genotype-calling-software</link>
	<title><![CDATA[List of non-commercial NGS genotype-calling software]]></title>
	<description><![CDATA[<p><span>Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data.&nbsp;</span></p><p><span>A list of programs for genotype and SNP calling :</span></p><p><br />SOAP2&nbsp;http://soap.genomics.org.cn/index.html</p><p>Single-sample High-quality variant database (for example, dbSNP) Package for NGS data analysis, which includes a single individual genotype caller (SOAPsnp)</p><p>realSFS&nbsp;http://128.32.118.212/thorfinn/realSFS/</p><p>Single-sample Aligned reads Software for SNP and genotype calling using single individuals and allele frequencies. Site frequency spectrum (SFS) estimation</p><p>Samtools http://samtools.sourceforge.net/</p><p>Multi-sample Aligned reads Package for manipulation of NGS alignments, which includes a computation of genotype likelihoods (samtools) and SNP and genotype calling (bcftools)</p><p>GATK http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit Multi-sample Aligned reads Package for aligned NGS data analysis, which includes a SNP and genotype caller (Unifed Genotyper), SNP filtering (Variant Filtration) and SNP quality recalibration (Variant Recalibrator)</p><p>Beagle http://faculty.washington.edu/browning/beagle/beagle.html</p><p>Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation, phasing and association that includes a mode for genotype calling</p><p>IMPUTE2 http://mathgen.stats.ox.ac.uk/impute/impute_v2.html</p><p>Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation and phasing, including a mode for genotype calling. Requires fine-scale linkage map</p><p>QCall ftp://ftp.sanger.ac.uk/pub/rd/QCALL</p><p>Multi-sample LD &lsquo;Feasible&rsquo; genealogies at a dense set of loci, genotype likelihoods Software for SNP and genotype calling, including a method for generating candidate SNPs without LD information (NLDA) and a method for incorporating LD information (LDA). The &lsquo;feasible&rsquo; genealogies can be generated using Margarita (http://www.sanger.ac.uk/resources/software/margarita)</p><p>MaCH http://genome.sph.umich.edu/wiki/Thunder</p><p>Multi-sample LD Genotype likelihoods Software for SNP and genotype calling, including a method (GPT_Freq) for generating candidate SNPs without LD information and a method (thunder_glf_freq) for incorporating LD information</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/8442/assistant-professor-king-saud-university-riyadh</guid>
  <pubDate>Fri, 21 Feb 2014 05:57:18 -0600</pubDate>
  <link></link>
  <title><![CDATA[Assistant Professor @ King Saud University Riyadh]]></title>
  <description><![CDATA[
<p>Qualifications: Candidates must have a Ph.D. and a strong background in Molecular and Cellular Biology, protein expression, FACS, or computational biology, and ability to work collaboratively.</p>

<p>This position will have a significant focus on providing analytical support for next generation sequencing data analysis – Exome-sequencing, Targetted sequencing as well as high-throughput genotyping on Illumina platform.</p>

<p>Job location:</p>

<p>Genome Research Chair<br />King Saud University, Riyadh-11451<br />KSA</p>

<p>Interested candidate may forward their CV to grcksu@gmail.com</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</guid>
	<pubDate>Fri, 19 Oct 2018 07:25:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</link>
	<title><![CDATA[BASE: a practical de novo assembler for large genomes using long NGS reads]]></title>
	<description><![CDATA[<p><span>new&nbsp;</span><em>de novo</em><span>&nbsp;assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.</span></p><p>Address of the bookmark: <a href="https://github.com/dhlbh/BASE" rel="nofollow">https://github.com/dhlbh/BASE</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/8466/srf-position-in-computational-systems-biology-computational-biology-group-iiit-delhi</guid>
  <pubDate>Sun, 23 Feb 2014 20:56:08 -0600</pubDate>
  <link></link>
  <title><![CDATA[SRF position in Computational Systems Biology Computational biology Group, IIIT-Delhi]]></title>
  <description><![CDATA[
<p>An opportunity to perform research in DST supported project that involves building of mathematical models to understand the functional relationship between circadian rhythms and memory formation under stressful condition.  In this project, mathematical model of circadian rhythms based on gene regulatory mechanisms will be unified with the mathematical model of calcium signal transduction pathway to understand and predict the formation of fear memory under stressful conditions. The research scholar will spend full time on this project to build new models and expected to contribute significantly to prepare the results for publication and presentation, and to contribute to grant proposals. </p>

<p>Required Qualifications: Masters in physics/chemistry/mathematics (or) MTech in bioengineering, chemical (or) Masters in any traditional field of science with outstanding performance throughout the program. Candidate should have cleared GATE/UGC-CSIR examinations. Applicant should have done basic mathematics courses like calculus, differential equations, numerical analysis etc in their degree program and have obtained good grades in those courses. Knowledge of MATLAB and C or at least one traditional programming language is absolutely necessary. Strong inclination to understand biological concepts is a must for this research work as this project is about modeling biological systems.     </p>

<p>Salary: A fixed salary of Rs 18000 PM including HRA will be paid. </p>

<p>Last date for application: This advertisement is open until suitable candidate is found for the project. </p>

<p>Preferred Qualifications:  - Expertise in dynamical systems theory, bifurcation theory, numerical simulations, parameter estimation. </p>

<p>Independence and high motivation for carrying out interdisciplinary research. - Excellent communication skills and ability to work independently. - Good working habits. </p>

<p>Interested candidates should submit both curriculum vitae and statement of interest in PDF format to sriramk@iiitd.ac.in and should clearly mention in the subject "Application for SRF".</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38829/nquire-a-statistical-framework-for-ploidy-estimation-using-ngs-short-read-data</guid>
	<pubDate>Thu, 31 Jan 2019 05:12:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38829/nquire-a-statistical-framework-for-ploidy-estimation-using-ngs-short-read-data</link>
	<title><![CDATA[nQuire: A statistical framework for ploidy estimation using NGS short-read data]]></title>
	<description><![CDATA[<p>nQuire implements a set of commands to estimate ploidy level of individuals from species, where recent polyploidization occurred and intraspecific ploidy variation is observed. Specifically, nQuire uses next-generation sequencing data to distinguish between diploids, triploids and tetraploids, on the basis of frequency distributions at variant sites where only two bases are segregating.</p>
<p>For more background see also the publication at&nbsp;<a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2128-z">BMC Bioinformatics</a>.</p>
<p>https://github.com/clwgg/nQuire</p><p>Address of the bookmark: <a href="https://github.com/clwgg/nQuire" rel="nofollow">https://github.com/clwgg/nQuire</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/12868/landry-lab</guid>
  <pubDate>Thu, 17 Jul 2014 14:33:57 -0500</pubDate>
  <link></link>
  <title><![CDATA[Landry Lab]]></title>
  <description><![CDATA[
<p>EVOLUTIONARY AND INTEGRATIVE CELL BIOLOGY</p>

<p>Our research is at the crossroad between cell biology, ecological genomics, systems biology, molecular evolution and population genetics. We study the architecture and evolution of protein and signalling networks.</p>

<p>More at http://landrylab.ibis.ulaval.ca/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40546/clincnv-detection-of-copy-number-changes-in-germlinetriosomatic-contexts-in-ngs-data</guid>
	<pubDate>Thu, 16 Jan 2020 23:16:02 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40546/clincnv-detection-of-copy-number-changes-in-germlinetriosomatic-contexts-in-ngs-data</link>
	<title><![CDATA[ClinCNV: Detection of copy number changes in Germline/Trio/Somatic contexts in NGS data]]></title>
	<description><![CDATA[<p><span>ClinCNV detects CNVs in germline and somatic context in NGS data (targeted and whole-genome). We work in cohorts, so it makes sense to try&nbsp;</span><code>ClinCNV</code><span>&nbsp;if you have more than 10 samples (recommended amount - 40 since we estimate variances from the data). By "cohort" we mean samples sequenced with the same enrichment kit with approximately the same depth (ie 1x WGS and 30x WGS better be analysed in separate runs of ClinCNV). Of course it is better if your samples were sequenced within the same sequencing facility.</span></p><p>Address of the bookmark: <a href="https://github.com/imgag/ClinCNV" rel="nofollow">https://github.com/imgag/ClinCNV</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/8970/j-aires-de-sousa-research-group</guid>
  <pubDate>Wed, 12 Mar 2014 09:57:25 -0500</pubDate>
  <link></link>
  <title><![CDATA[J. Aires de Sousa Research Group]]></title>
  <description><![CDATA[
<p>We are involved in the development of methods and software in chemoinformatics. Current main projects are:</p>

<p>1.automatic learning of chemical reactivity and metabolism,<br />2.simulation of NMR spectra,<br />3.modelling of properties of ionic liquids, and<br />4.representation of molecular chirality.</p>

<p>More at http://joao.airesdesousa.com/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41146/lofreq-a-sequence-quality-aware-ultra-sensitive-variant-caller-for-ngs-data</guid>
	<pubDate>Tue, 18 Feb 2020 03:24:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41146/lofreq-a-sequence-quality-aware-ultra-sensitive-variant-caller-for-ngs-data</link>
	<title><![CDATA[LoFreq*: A sequence-quality aware, ultra-sensitive variant caller for NGS data]]></title>
	<description><![CDATA[<p>LoFreq* (i.e. LoFreq version 2) is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. It makes full use of base-call qualities and other sources of errors inherent in sequencing (e.g. mapping or base/indel alignment uncertainty), which are usually ignored by other methods or only used for filtering.</p>
<p>https://github.com/CSB5/lofreq</p>
<p>http://csb5.github.io/lofreq/installation/</p>
<p>https://github.com/CSB5/lofreq/tree/master/dist</p><p>Address of the bookmark: <a href="http://csb5.github.io/lofreq/" rel="nofollow">http://csb5.github.io/lofreq/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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