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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/7674?offset=600</link>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17186/urdip-pune-bioinformatics-srfpa-openings</guid>
  <pubDate>Sat, 20 Sep 2014 20:48:50 -0500</pubDate>
  <link></link>
  <title><![CDATA[URDIP Pune Bioinformatics SRF/PA Openings]]></title>
  <description><![CDATA[
<p>CSIR UNIT FOR RESEARCH AND DEVELOPMENT OF INFORMATION PRODUCTS<br />NCL Campus, S.No.113,114, Pashan, Pune 411 008</p>

<p>ADVERTISEMENT NO. - URDIP/ 5/2014</p>

<p>Learning opportunity for young Science and Engineering professionals to make a career in Information Science Industry CSIR has set up a Unit for Research and Development of Information Products (CSIR-URDIP) at Pune to work in the area of Scientific Informatics (ChemBioinformatics/Patent Informatics/Phytoinformatics/Toxinformatics) and related<br />software development projects.</p>

<p>Applications are invited from CSIR - UGC NET Qualified Candidates for consideration as Project Fellow (PF) and/or Senior Project Fellow (SPF) based on the experience to work on existing and new projects at CSIRURDIP.</p>

<p>Project Fellow</p>

<p>    Remuneration - (Rs. 16,000.00 + 20% HRA)</p>

<p>    M. Sc. In Biochemistry/Microbiology/Bioinformatics [Post-code A02] only with minimum of 55% marks</p>

<p>Senior Project Fellow</p>

<p>    Remuneration - (Rs. 18,000.00 + 20% HRA)</p>

<p>    M. Sc. in Biochemistry/Microbiology/Bioinformatics [Post-code A05] only with minimum of 55% marks plus two years research or relevant informatics experience</p>

<p>Please visit www.urdip.res.in/career.htm to apply online by 30th September, 2014.</p>

<p>Successful candidates who have appeared for NET exam in 2012 and 2013 are only eligible to apply.</p>

<p>Advertisement: http://115.112.95.114/urhr/download/Advt5_2014.pdf</p>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42713/gggenomes-a-grammar-of-graphics-for-comparative-genomics</guid>
	<pubDate>Mon, 01 Feb 2021 14:47:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42713/gggenomes-a-grammar-of-graphics-for-comparative-genomics</link>
	<title><![CDATA[gggenomes: A grammar of graphics for comparative genomics]]></title>
	<description><![CDATA[<p><span>gggenomes is a versatile graphics package for comparative genomics. It extends the popular R visualization package</span><a href="https://ggplot2.tidyverse.org/">ggplot2</a><span>&nbsp;by adding dedicated plot functions for genes, syntenic regions, etc. and verbs to manipulate the plot to, for example, quickly zoom in into gene neighborhoods.</span></p><p>Address of the bookmark: <a href="https://github.com/thackl/gggenomes" rel="nofollow">https://github.com/thackl/gggenomes</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17189/bioinformatics-svims-project-assistant-walk-in</guid>
  <pubDate>Sat, 20 Sep 2014 21:02:29 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics SVIMS Project Assistant Walk IN]]></title>
  <description><![CDATA[
<p>SRI VENKATESWARA INSTITUTE OF MEDICAL SCIENCES<br />TIRUPATI, ANDHRA PRADESH, INDIA- 517 507<br />BIOINFORMATICS CENTRE, DEPARTMENT OF BIOINFORMATICS</p>

<p>Eligible candidates are invited for a walk-in-interview for recruitment of Project Assistant in SVIMS Bioinformatics centre under the BTISnet Project entitled “Creation of Bioinformatics Infrastructure Facility for promotion of Biology teaching through Bioinformatics” on 25.09.2014 at 11 AM in SVIMS, Tirupati. The engagement will be made purely on temporary basis for a period of one year and it can be terminated at any time without notice or without assigning any reason thereof by the Coordinator of the Project. The person engaged shall not be entitled for any claim implicit or explicit for absorption in the University.</p>

<p>1. Name of the post : Project Assistant</p>

<p>2. Qualification :<br />i) Essential : MSc Bioinformatics/MTech (Biotechnology/Bioinformatics)</p>

<p>ii) Desirable : Experience in Bioinformatics research work (Preference will be given to candidates  qualified in BINC/UGC/CSIR/NET/GATE)</p>

<p>3. Remuneration : 16000 + 10% HRA for NET/GATE candidates 14000 + 10% HRA for M. Tech / M.Sc. Candidates</p>

<p>4. Place of posting : Tirupati</p>

<p>5. Duration of the Project : One year</p>

<p>Terms and conditions:</p>

<p>1. Candidates are required to submit the Biodata relevant certificates in support of their age and educational qualification etc., before the interview committee, SVIMS University, Tirupati.</p>

<p>2. Candidates called for interview will attend the interview at their own cost.<br />3. Interim enquiries will not be entertained.<br />4. The maximum age limit for Project Assistant is 28 years for general category and 33 years for SC and ST category candidates as on 25th September, 2014.</p>

<p>Advertisement:</p>

<p>http://svr98.ehostpros.com/~svimsb98/Project%20Assistant_notification.pdf</p>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43683/genview-a-phylogeny-based-comparative-genomics-software-to-analyze-the-genetic-environment-of-genes</guid>
	<pubDate>Tue, 28 Dec 2021 01:49:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43683/genview-a-phylogeny-based-comparative-genomics-software-to-analyze-the-genetic-environment-of-genes</link>
	<title><![CDATA[GEnView: A phylogeny based comparative genomics software to analyze the genetic environment of genes]]></title>
	<description><![CDATA[<p><span>A phylogeny based comparative genomics software to analyze the genetic environment of genes. The user can select one or several taxa and provide one or several reference protein(s). Genomes and plasmids (based on user choice) will be downloaded from the NCBI Assembly/NR database and searched for the respective gene. Alternatively, custom genomes can be provided. User selected stretches (20kbp by default) of the genes genetic environment are extracted, annotated and aligned between all genomes. The sequences are then visualized, enabling comparison of synteny and gene content.</span></p>
<p><span>More at&nbsp;https://pubmed.ncbi.nlm.nih.gov/34951622/</span></p><p>Address of the bookmark: <a href="https://github.com/EbmeyerSt/GEnView" rel="nofollow">https://github.com/EbmeyerSt/GEnView</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/17501/nieduszynski-group</guid>
  <pubDate>Fri, 26 Sep 2014 19:35:06 -0500</pubDate>
  <link></link>
  <title><![CDATA[Nieduszynski Group]]></title>
  <description><![CDATA[
<p>Complete, accurate replication of the genome is essential for life. All chromosomes in eukaryotic cells must be duplicated and then segregated to daughter cells to ensure genetic integrity and produce the large number of cells that make up a multicellular organism. We are using genetic, genomic and computational methods to understand how chromosome replication is regulated to ensure genome stability. By focusing on the basic biology that underpins cell growth and division we aim to provide new insights that may help our understanding of diseases such as cancer and congenital disorders. </p>

<p>More http://www.nieduszynski.org/index.php<br />http://www.path.ox.ac.uk/research/cell-biology-and-pathology/conrad-nieduszynski-group</p>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44799/unlocking-evolutionary-secrets-a-dive-into-comparative-genomics-methods</guid>
	<pubDate>Tue, 20 May 2025 00:25:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44799/unlocking-evolutionary-secrets-a-dive-into-comparative-genomics-methods</link>
	<title><![CDATA[Unlocking Evolutionary Secrets: A Dive into Comparative Genomics Methods]]></title>
	<description><![CDATA[<p>Comparative genomics is the art and science of comparing genomes&mdash;across species, within species, or even among individuals&mdash;to unravel evolutionary relationships, functional elements, and genetic adaptations. As sequencing technologies have advanced and genome databases have expanded, comparative genomics has become a cornerstone of modern biology, shedding light on everything from antibiotic resistance in bacteria to human disease genetics.</p><p>In this post, we&rsquo;ll explore the core methods used in comparative genomics, the questions they help answer, and how they&rsquo;re shaping our understanding of life.</p><p><strong>1. Whole-Genome Alignment</strong><br />Whole-genome alignment involves mapping the entire genome of one species to another. Tools like MUMmer, MAUVE, and LASTZ perform large-scale sequence alignments to detect conserved regions, rearrangements, insertions, and deletions.</p><p>Use Case:<br />Comparing human and chimpanzee genomes to identify evolutionary conserved sequences (ECS) and regions of divergence.</p><p>Key Challenges:<br />Handling repetitive sequences and genome rearrangements.</p><p>Computational complexity in large genomes.</p><p><strong>2. Synteny and Collinearity Analysis</strong><br />Synteny refers to conserved blocks of gene order across species. Tools like MCScanX, SynMap, or CHITRA (for visualizing synteny interactively) detect these blocks to understand chromosomal evolution.</p><p>Use Case:<br />Studying ancient genome duplications in plants.</p><p>Investigating chromosomal rearrangements in cancer genomes.</p><p><strong>3. Ortholog and Paralog Detection</strong><br />Orthologs are genes in different species that evolved from a common ancestor, while paralogs are genes duplicated within a genome. Identifying them is crucial for functional annotation and evolutionary studies.</p><p>Popular Tools:<br />OrthoFinder, Orthologous MAtrix (OMA), InParanoid, and EggNOG.</p><p>Use Case:<br />Functional prediction of uncharacterized genes based on orthologs in model organisms.</p><p>Tracing gene family evolution.</p><p><strong>4. Phylogenomic Analysis</strong><br />Phylogenomic methods combine phylogenetics and genomics to infer evolutionary trees based on genome-wide data. These methods can handle dozens to hundreds of genomes, using concatenated alignments or gene trees.</p><p>Tools:<br />RAxML, IQ-TREE, ASTRAL, Phylip, BEAST.</p><p>Use Case:<br />Resolving the evolutionary relationships between microbial species.</p><p>Studying speciation events.</p><p><strong>5. Pan-Genome Analysis</strong><br />The pan-genome consists of the core genome (shared by all strains) and the accessory genome (strain-specific genes). This is especially popular in microbial genomics.</p><p>Tools:<br />Roary, Panaroo, BPGA, PGAP.</p><p>Use Case:<br />Understanding virulence factor diversity in E. coli.</p><p>Designing broad-spectrum vaccines.</p><p><strong>6. Comparative Transcriptomics</strong><br />Comparing transcriptomes across species or conditions reveals conserved and unique expression patterns. RNA-seq data can be mapped to reference genomes to identify orthologous expression profiles.</p><p>Use Case:<br />Comparing stress response in extremophiles and model species.</p><p>Studying conserved regulatory networks.</p><p><strong>7. Functional Element Comparison</strong><br />Beyond genes, comparative genomics also targets non-coding regions&mdash;enhancers, promoters, miRNAs. Conservation across species often implies functional importance.</p><p>Tools:<br />PhastCons, GERP, phyloP (based on multiple alignments).</p><p>Use Case:<br />Detecting conserved non-coding elements in vertebrates.</p><p>Studying regulatory divergence in human evolution.</p><p><strong>8. Horizontal Gene Transfer (HGT) Detection</strong><br />In microbes, genes often jump across species boundaries. Comparative genomics can detect HGT by identifying genes that defy the expected phylogenetic pattern.</p><p>Tools:<br />HGTector, DarkHorse, AlienHunter, SIGI-HMM.</p><p>Use Case:<br />Tracing antibiotic resistance genes.</p><p>Exploring microbial adaptability in extreme environments.</p><p><strong>Final Thoughts</strong><br />Comparative genomics is a powerful lens to observe the diversity and unity of life. With a broad toolkit&mdash;from aligners to orthology pipelines, phylogenetic engines to visualization tools&mdash;it allows scientists to ask big questions: How did genomes evolve? What makes species unique? Where do new genes come from?</p><p>Whether you're studying extremophiles, building better crops, or exploring human ancestry, comparative genomics offers the methods to connect the dots across the tree of life.</p><p>&nbsp;</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/11175/next-generation-sequencingngs-books</guid>
	<pubDate>Fri, 30 May 2014 04:48:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/11175/next-generation-sequencingngs-books</link>
	<title><![CDATA[Next generation sequencing(NGS) books]]></title>
	<description><![CDATA[<p>Employing different technologies, the purpose of NGS platform is to decode the identity or modification on the nucleotides. NGS platforms evolve quickly and capture the main stream.</p>
<p>This bookmark is created to provide NGS online books links.</p><p>Address of the bookmark: <a href="http://en.wikibooks.org/wiki/Next_Generation_Sequencing_%28NGS%29/Print_version" rel="nofollow">http://en.wikibooks.org/wiki/Next_Generation_Sequencing_%28NGS%29/Print_version</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26179/alignment-of-closely-related-whole-genomesscaffolds</guid>
	<pubDate>Fri, 29 Jan 2016 10:37:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26179/alignment-of-closely-related-whole-genomesscaffolds</link>
	<title><![CDATA[Alignment of closely related whole genomes/scaffolds]]></title>
	<description><![CDATA[<p>With the relative ease and low cost of current generation sequencing technologies has led to a dramatic increase in the number of sequenced genomes for species across the tree of life. This increasing volume of data requires tools that can quickly compare multiple whole-genome sequences, millions of base pairs in length, to aid in the study of populations, pan-genomes, and genome evolution.This bookmaks have been created to report new tools for whole genome alignments.</p>
<p>Please report new whole genome alignment tools under comment sections.</p><p>Address of the bookmark: <a href="http://www.cs.utoronto.ca/~brudno/721.full.pdf" rel="nofollow">http://www.cs.utoronto.ca/~brudno/721.full.pdf</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32875/finishing</guid>
	<pubDate>Sat, 20 May 2017 15:50:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32875/finishing</link>
	<title><![CDATA[Finishing !!]]></title>
	<description><![CDATA[<p>The process of&nbsp;<em>finishing</em>&nbsp;a genome and moving it from a&nbsp;<em>draft</em>&nbsp;stage (the result of sequencing and initial assembly) to a complete genome is typically a time and resource intensive task. The advent of new sequencing technologies has come with its own set of opportunities and pitfalls in the finishing process. While genomes can now be sequenced to high redundancy in a cost-effective manner, the process of assembling the genomes is more challenging and often draft genomes are fragmented into hundreds of contigs. Correspondingly, the task of producing the complete genome can involve months of lab work and thousands of finishing experiments and is usually done in large genome centers.</p>
<p>The work in our lab has focussed on computational approaches to speed-up the finishing process. Specifically, we have explored the use of optical mapping and mate-pair data to augment assemblies and direct finishing experiments. The tools developed in our lab have been used in several finishing projects, producing complete genomes (and near-complete ones) with surprisingly little computational and experimental effort (Nagarajan et al., in submission). The executables (as well as source code) for these tools are freely available here:</p>
<ul>
<li><strong>Scaffolding using Optical Restriction Mapping</strong><br>Optical Maps are global, ordered maps of restriction site locations in a genome. This information can be quite useful in scaffolding contigs from a shotgun assembly to guide the finishing process. A set of programs to exploit optical maps for assembly can be found here:&nbsp;<a href="http://www.cbcb.umd.edu/finishing/soma-v2.tar.gz">SOMA v2.0 (63 MB tar.gz file)</a>. This version of SOMA contains several improvements to programs in v1.0 as well as new scripts for working with multiple maps, contig graphs and scaffolds.&nbsp;<br><br></li>
<li><strong>Augmenting assemblies with mate-pair data</strong><br>Mate-pair information can be valuable in augmenting short-read assemblies and reconstructing the genome as larger scaffolds. AMOS-Hybrid is a pipeline written in the AMOS framework (open-source assembly tools) to merge arbitrary mated reads into an existing assembly and merge contigs and create scaffolds where possible. Source code and executables for AMOS-Hybrid are available here:&nbsp;<a href="http://www.cbcb.umd.edu/finishing/AMOS-Hybrid-v1.tar.gz">AMOS-Hybrid v1.0 (142 MB tar.gz file)</a>.&nbsp;<br><br></li>
<li><strong>Assembly and sequence-composition guided finishing</strong><br>Contigs from a shotgun assembly are typically linked together in a graph structure that can serve to guide finishing and in some case close gaps&nbsp;<em>in-silico</em>. Also, in many cases, sequence composition of contigs can provide clues to fill gaps in scaffolds. A set of scripts to automate some of these tasks can be found here:&nbsp;<a href="http://www.cbcb.umd.edu/finishing/finishing-v1.tar.gz">Finishing Scripts v1.0 (63 MB tar.gz file)</a>.&nbsp;</li>
</ul>
<p>http://www.cbcb.umd.edu/finishing/</p><p>Address of the bookmark: <a href="http://www.cbcb.umd.edu/finishing/" rel="nofollow">http://www.cbcb.umd.edu/finishing/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/18381/how-far-can-bioinformatics-go-creating-organisms-used-for-testing</guid>
	<pubDate>Fri, 17 Oct 2014 02:08:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/18381/how-far-can-bioinformatics-go-creating-organisms-used-for-testing</link>
	<title><![CDATA[How far can bioinformatics go creating organisms used for testing?]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/FojhDef2NW4" frameborder="0" allowfullscreen></iframe>"I think you can get very far on a technical level. The problem is that a human body is more complex than just one cell." ... "At some point we still need clinical tests on animals and humans before we use it for real treatment. But we will likely be able to remove 99 % of animal tests in the future."

Erik Lindahl, Professor of Theoretical and Computational Biophysics at KTH Royal Institute of Technology is telling us about his work.

From the episode "Science for life – mapping the building blocks of the human body". Watch the rest of the talk, and other talks at www.crosstalks.tv

Crosstalks is an academic talkshow produced by KTH Royal Institute of Technology and Stockholm University.]]></description>
	
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