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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/20015?offset=620</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/4099/sequencing-solutions-to-world-health</guid>
	<pubDate>Thu, 29 Aug 2013 15:05:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/4099/sequencing-solutions-to-world-health</link>
	<title><![CDATA[Sequencing Solutions to World Health]]></title>
	<description><![CDATA[<p>"<em>New technology that quickly, easily and economically reveals the genomes of viruses and pathogens transforms public health and medicine."</em></p>
<p><strong>Source</strong>: Life technologies</p><p>Address of the bookmark: <a href="http://www.lifetechnologies.com/global/en/home/communities-social/blog/blogs/sequencing-solutions-to-world-health.html?cid=social_blogseries_20130829_11098264" rel="nofollow">http://www.lifetechnologies.com/global/en/home/communities-social/blog/blogs/sequencing-solutions-to-world-health.html?cid=social_blogseries_20130829_11098264</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/6700/tedmed-great-challenges-genomics-and-medicine-where-promise-meets-clinical-practice</guid>
	<pubDate>Fri, 22 Nov 2013 12:05:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/6700/tedmed-great-challenges-genomics-and-medicine-where-promise-meets-clinical-practice</link>
	<title><![CDATA[TEDMED Great Challenges: Genomics and Medicine: Where promise meets clinical practice]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/-VdRMFuB5vo" frameborder="0" allowfullscreen></iframe>November 21, 2013 - NHGRI Director Eric Green, M.D., Ph.D, hosted the TEDMED Google+ Hangout to discuss genomic medicine with an all-star cast that includes Carlos Bustamante, James Evans, Amy McGuire and Sharon Terry.

More: http://www.tedmed.com/greatchallenges]]></description>
	
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/35026/junior-research-fellow-position-at-translational-health-group-icgeb-new-delhi</guid>
  <pubDate>Tue, 02 Jan 2018 19:47:28 -0600</pubDate>
  <link></link>
  <title><![CDATA[Junior Research Fellow position at Translational Health Group, ICGEB, New Delhi]]></title>
  <description><![CDATA[
<p>One Junior Research Fellow position, in a DBT funded project, is available in the Translational Health Group, ICGEB, New Delhi</p>

<p>Qualifications: MSc (preferably in Biotechnology, Life Sciences or Zoology, Chemistry, Bioinformatics). Candidates with hands-on experience on GC-MS data acquisition and analysis will be given preference. Bioinformatics expertise required.</p>

<p>Fellowship: As per DBT guidelines.</p>

<p>Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project. </p>

<p>Application Deadline: Applications will be accepted until 07/01/2018</p>

<p>Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the date indicated.</p>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/3030/illuminating-next-generation-sequencing-data-with-go</guid>
	<pubDate>Fri, 23 Aug 2013 07:13:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/3030/illuminating-next-generation-sequencing-data-with-go</link>
	<title><![CDATA[Illuminating next generation sequencing data with Go]]></title>
	<description><![CDATA[<p>Another good lecture for Illumina sequencing data analysis from&nbsp;</p>
<p>Dan Kortschak,&nbsp;Bioinformatics Group,&nbsp;School of Molecular and Biomedical Science ,The University of Adelaide</p><p>Address of the bookmark: <a href="http://talks.biogo.googlecode.com/git/illumination/illumination.pdf" rel="nofollow">http://talks.biogo.googlecode.com/git/illumination/illumination.pdf</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</guid>
	<pubDate>Thu, 31 May 2018 09:35:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</link>
	<title><![CDATA[BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data]]></title>
	<description><![CDATA[BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes.

The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC.

https://github.com/lh3/bfc<p>Address of the bookmark: <a href="https://github.com/lh3/bfc" rel="nofollow">https://github.com/lh3/bfc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40573/de-novo-genome-assembly-for-illumina-data</guid>
	<pubDate>Mon, 20 Jan 2020 05:13:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40573/de-novo-genome-assembly-for-illumina-data</link>
	<title><![CDATA[De novo Genome Assembly for Illumina Data]]></title>
	<description><![CDATA[<p>Written and maintained by <a href="mailto:simon.gladman@unimelb.edu.au">Simon Gladman</a> - Melbourne Bioinformatics (formerly VLSCI)</p>
<p>Protocol Overview / Introduction</p>
<p>In this protocol we discuss and outline the process of de novo assembly for small to medium sized genomes.</p>
<p>https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/</p><p>Address of the bookmark: <a href="https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/" rel="nofollow">https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42633/protocol-for-de-novo-genome-assembly-using-illumina-reads</guid>
	<pubDate>Sat, 16 Jan 2021 21:42:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42633/protocol-for-de-novo-genome-assembly-using-illumina-reads</link>
	<title><![CDATA[Protocol for De novo Genome Assembly using Illumina Reads]]></title>
	<description><![CDATA[<p>In this protocol, we address and describe the de novo assembly method for small to medium-sized genomes.</p><p><strong>What is de novo genome assembly?<br /></strong>The method of taking a large number of short DNA sequences and placing them back together to create a reflection of the original chromosomes from which the DNA originated relates to genome assembly. No previous knowledge of the source DNA sequence length, structure or composition is inferred by De novo genome assemblies. The DNA of the target organism is split up into millions of tiny parts and read on a sequencing computer in a genome sequencing experiment. Depending on the sequencing system used, these "reads" range from 20 to 1000 nucleotide base pairs (bp) in length. Usually, length reads of 36 - 150 bp are produced for Illumina style short read sequencing. These reads can be either &ldquo;single ended&rdquo; as described above or &ldquo;paired end.&rdquo;</p><p><strong>Why genome assembly?</strong><br />In basic research into why and how they live, as well as in applied topics, identifying the DNA sequence of an organism is useful. Awareness of a DNA sequence may be useful in virtually any biological research because of the relevance of DNA to living things. For example, it may be used in medicine to classify, diagnose and eventually improve genetic disorder therapies. Similarly, pathogens study can lead to treatments for infectious diseases.</p><p><strong>Raw NGS data</strong><br />Reads can be saved as a Fasta file as text or in a FastQ file with their attributes.&nbsp;FastQ is the most common read file format since this is what the Illumina sequencing pipeline creates. This will henceforth be the subject of our conversation.</p><p><strong>In a nutshell the protocol:</strong> <br />Get the sequence file(s) read from the sequencing machine (s). <br />Look at the readings - have an idea of what you have and what the standard is like. <br />If required, raw data cleanup/quality trimming. <br />Choose an adequate parameter set for assembly. <br />Assemble the data into scaffolds/contigs. <br />Examine the assembly performance and determine the efficiency of the assembly.</p><p><strong>Read Quality Control:</strong><br />Check the qualiy with fastQC.<br />Script<br />https://bioinformaticsonline.com/snippets/view/42540/install-fastqc-using-conda</p><p>Quality trimming/cleanup of read files.<br />This function trims adapters, barcodes and other contaminants from the reads.<br />Script<br />https://bioinformaticsonline.com/snippets/view/42542/trimmomatic-command</p><p><strong>Genome Assembly:</strong><br />The object of this portion of the protocol is to explain the method of assembling the reads trimmed by quality into draft contigs.</p><blockquote><p>spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o result_of_spades_assembly_all_illumina</p></blockquote><p>A significant range of short-read assemblers are available. Everyone with strengths and disadvantages of their own. <br /><em>Some of the assemblers available include:</em><br />Velvet<br />SOAP-denovo<br />MIRA<br />ALLPATHS</p><p>Next step is to assess the suitability and what to do with a draft package of contiguous details for the remainder of the study now.&nbsp;Few stuff you can note about the contigs you just created:&nbsp;They're the draft Contigs. Any mis-assemblies can occur.</p><p><strong>Mis-assembly checking and assembly metric tools:</strong><br />QUAST - Quality assessment tool for genome assembly http://bioinf.spbau.ru/quast<br />Mauve assembly metrics - http://code.google.com/p/ngopt/wiki/How_To_Score_Genome_Assemblies_with_Mauve<br />InGAP-SV - https://sites.google.com/site/nextgengenomics/ingap and http://ingap.sourceforge.net/<br />inGAP is also useful for finding structural variants between genomes from read mappings.</p><p><strong>Genome finishing tools:</strong><br />Semi-automated gap fillers:<br />Gap filler - http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/gapfiller/</p><p>IMAGE (V2) - http://sourceforge.net/apps/mediawiki/image2/index.php?title=Main_Page</p><p><strong>Genome visualisers and editors:</strong><br />Artemis - http://www.sanger.ac.uk/resources/software/artemis/<br />IGV - http://www.broadinstitute.org/igv/</p><p><strong>Automated and semi automated annotation tools:</strong><br />Prokka - https://github.com/tseemann/prokka<br />RAST - http://www.nmpdr.org/FIG/wiki/view.cgi/FIG/RapidAnnotationServer<br />JCVI Annotation Service - http://www.jcvi.org/cms/research/projects/annotation-service/</p><p><strong>Frequent command use for the analysis are at:</strong></p><p>https://bioinformaticsonline.com/blog/view/38765/list-of-tools-frequently-used-while-genome-assembly<br />https://bioinformaticsonline.com/pages/view/42275/frequent-parameters-for-bioinformatics-tools</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/12883/breaking-chromosomes-to-study-cancer</guid>
	<pubDate>Fri, 18 Jul 2014 05:42:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/12883/breaking-chromosomes-to-study-cancer</link>
	<title><![CDATA[Breaking chromosomes to study cancer !!!]]></title>
	<description><![CDATA[<p>Chromosomes are present in every cell of our body and they contain the information the body needs to develop and function properly. This information is carried in genes that are arranged along the chromosomes. There are usually 46 chromosomes in every cell. These chromosomes come in pairs, one from our mother and one from our father. The chromosomes can be sorted into 23 pairs by looking at them down a microscope.</p><p>Most people who have a balanced translocation have the right amount of chromosome material but it has been rearranged in some way. This may happen if two chromosomes swap pieces (a reciprocal translocation). In other cases two whole chromosomes may become stuck together (a Robertsonian translocation). This page describes what happens when someone has a reciprocal translocation. <br /><br />Reciprocal chromosomal translocations occur following double-strand breaks (DSBs) in DNA when a section of one chromosome is exchanged with that of another, non-homologous chromosome. These exchanges may produce a dysfunctional fusion gene that disrupts cell growth and survival pathways, such as the translocations seen in leukemia and childhood sarcomas. <br /><br />Chromosomal translocations have been well studied in cancer cell lines which are associated with two types of cancer, acute myeloid leukemia and Ewing's sarcoma, but determining how they contribute to cancer development is complicated by additional mutations and altered gene expression profiles in these cultured cells. Now, Juan Carlos Ramirez, head of the Viral Vector Facility at the Fundacion Centro Nacional de Investigaciones Cardiovasculares (CNIC) and his colleagues Raul Torres at CNIC and Sandra Rodriguez-Peralez at the Spanish National Cancer Center (CNIO) in Madrid, Spain have used a new genome editing tool, CRISPR-Cas9, to induce chromosomal translocations for the first time in a human cell line and in primary cells. The study's authors conclude by stating that the use of this technology will allow for the clarification of how and why chromosomal translocation occurs, which without doubt will allow new anti-cancer therapeutic strategies to be tackled.</p><p>Using RNA-Guided Endonuclease (RGEN) technology or CRISPR/Cas9 genome engineering technology, CNIO and CNIC researchers have shown that it is possible to obtain such chromosomal translocations. The CRISPR-Cas9 system is extremely simple to introduce a cut at the desired locus, easier to design, and cheaper than many other systems. Using the CRISPR-Cas9 system, Ramirez and his colleagues reproduced the translocations observed in Ewing&rsquo;s Sarcoma (ES) and Acute Myeloid Leukemia (AML) patient cell lines in HEK293 cells and also generated the ES translocation in human mesenchymal stem cells and the AML translocation in umbilical cord blood cells.</p><p>By focusing on chromosomal translocation without the confounding characteristics of established cell lines, these new cells lines should help answer the fundamental question of what causes a cell to become cancerous. Ramirez and his team now look forward to modeling other chromosome translocations in a variety of cell types.</p><p>Reference:</p><p>http://en.wikipedia.org/wiki/Chromosomal_translocation</p><p>http://www.nature.com/ncomms/2014/140603/ncomms4964/abs/ncomms4964.html<br /><br /></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17505/kau-thrissur-biotechbioinformatics-rasrfjrftraineestudentships</guid>
  <pubDate>Fri, 26 Sep 2014 20:07:28 -0500</pubDate>
  <link></link>
  <title><![CDATA[KAU Thrissur Biotech/Bioinformatics RA/SRF/JRF/Trainee/Studentships]]></title>
  <description><![CDATA[
<p>Applications are invited from eligible candidates for the following posts at Bioinformatics Centre (DIC), IT- BT Complex, College of Horticulture, Kerala Agricultural University, Vellanikkara, Thrissur.</p>

<p>1. Research Associate <br />Emoluments*: 14880/- + HRA 	<br />Qualification needed: Ph.D/M.Sc in Bioinformatics or Ph.D/M.Sc in Agriculture or Biotechnology with advanced Diploma in Bioinformatics <br />Desirable: 2 year experience in Bioinformatics.</p>

<p>2 Senior Research Fellow <br />Emoluments*: 10230/- 	<br />Qualification needed: M.Sc/ M.tech in Bioinformatics or M.Sc in Agriculture/ Biotechnology with Diploma in Bioinformatics. <br />Desirable: One year experience in Bioinformatics</p>

<p>3 Junior Research Fellow <br />Emoluments*: 9300/- 	<br />Qualification needed: M.Sc/ M.tech in Bioinformatics or M.Sc in Agriculture/Biotechnology/Plant Sciences with Diploma in Bioinformatics.</p>

<p>4 .Trainee/Studentship Bioinformatics <br />Emoluments*: 5000/- 	<br />Qualification needed: M.Sc in Bioinformatics with good knowledge of Bioinformatics softwares and tools.</p>

<p>5 Trainee/ Studentship Biotechnology <br />Emoluments*: 5000/- 	<br />Qualification needed: M.Sc in Biotechnology, with working knowledge in tissue culture, molecular markers and cloning of genes.</p>

<p>Candidates with the required qualifications and experience may give an application in the prescribed format with attested copies of certificates to prove eligibility on or before 30th November 2014. The applications are to be addressed to The Associate Dean, College of Horticulture and send to "Professor &amp; Coordinator, Bioinformatics Centre (DIC), IT-BT Complex, Kerala Agricultural University, Vellanikkara, Thrissur, Kerala 680 656”. The envelope may be superscribed “Application for the post at Bioinformatics Centre”.</p>

<p>*Emoluments are likely to be revised in 2014-2015</p>

<p>More at http://www.kaubic.in/downloads/Notification_bic.pdf<br />http://www.kaubic.in/downloads/Application%20form.pdf</p>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/17924/software-developed-in-pevsner-lab</guid>
	<pubDate>Mon, 06 Oct 2014 12:41:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/17924/software-developed-in-pevsner-lab</link>
	<title><![CDATA[Software developed in pevsner lab]]></title>
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<p><a href="http://pevsnerlab.kennedykrieger.org/dragon.htm">DRAGON</a>: Database Referencing of Array Genes Online</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/96">SNOMAD</a>: Standardization and Normalization of Microarray Data</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/70">SNPduo</a>: SNP Analysis Between Two Individuals</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/71">SNPtrio</a>: Analyzing and Visualizing and Inheritance Patterns in Trios</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/64">SNPscan</a>: Data Analysis and Visualization of SNP Data</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/64">pediSNP</a>: Analyze SNP Data From a Pedigree of Two Generations</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/73">kcoeff</a>: Calculate Cotterman Coefficients of SNP Genotype Data</p>
<p><a href="http://pevsnerlab.kennedykrieger.org/php/node/113">triPOD:</a> Detects chromosomal abnormalities in parent-child trio-based microarray data</p>
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</div><p>Address of the bookmark: <a href="http://pevsnerlab.kennedykrieger.org/php/?q=software" rel="nofollow">http://pevsnerlab.kennedykrieger.org/php/?q=software</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
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