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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42166?offset=570</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36865/perga-a-paired-end-read-guided-de-novo-assembler-for-extending-contigs-using-svm-and-look-ahead-approach</guid>
	<pubDate>Tue, 05 Jun 2018 09:57:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36865/perga-a-paired-end-read-guided-de-novo-assembler-for-extending-contigs-using-svm-and-look-ahead-approach</link>
	<title><![CDATA[PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach]]></title>
	<description><![CDATA[PERGA - Paired End Reads Guided Assembler

PERGA is a novel sequence reads guided de novo assembly approach which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds. Instead of using single-end reads to construct contig, PERGA uses paired-end reads and different read overlap sizes from O ≥ Omax to Omin to resolve the gaps and branches. Moreover, by constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. PERGA will try to extend the contigs by all feasible nucleotides and determine if these multiple extensions due to sequencing errors or repeats by using looking ahead technology, and it also try to separate the different repeats of nearby genomic regions to make the assembly result more longer and accurate.

The simulated E.coli paired-end reads data are generated using GemSim (KE McElroy, F Luciani, T Thomas. Gemsim: General, Error-Model Based Simulator of Next-Generation Sequencing Data. BMC Genomics 2012, 13:74), with coverage 50x, 60x, 100x, read lengths 100-bp, and can be downloaded from https://github.com/zhuxiao/data_PERGA.<p>Address of the bookmark: <a href="https://github.com/hitbio/PERGA" rel="nofollow">https://github.com/hitbio/PERGA</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37221/asplice-a-scalable-and-memory-efficient-algorithm-for-de-novo-transcriptome-assembly</guid>
	<pubDate>Tue, 03 Jul 2018 04:09:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37221/asplice-a-scalable-and-memory-efficient-algorithm-for-de-novo-transcriptome-assembly</link>
	<title><![CDATA[ASplice: a scalable and memory-efficient algorithm for de novo transcriptome assembly]]></title>
	<description><![CDATA[With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries.

Texas A&amp;M University researchers develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory.

Availability – A software program that implements the algorithm is available at: http://faculty.cse.tamu.edu/shsze/asplice.

Sze SH, Pimsler ML, Tomberlin JK, Jones CD, Tarone AM. (2017) A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms. BMC Genomics 18(Suppl 4):387.<p>Address of the bookmark: <a href="http://faculty.cse.tamu.edu/shsze/asplice/" rel="nofollow">http://faculty.cse.tamu.edu/shsze/asplice/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38212/megahit-an-ultra-fast-single-node-solution-for-large-and-complex-metagenomics-assembly-via-succinct-de-bruijn-graph</guid>
	<pubDate>Wed, 14 Nov 2018 04:50:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38212/megahit-an-ultra-fast-single-node-solution-for-large-and-complex-metagenomics-assembly-via-succinct-de-bruijn-graph</link>
	<title><![CDATA[MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph]]></title>
	<description><![CDATA[<p><span>MEGAHIT is a single node assembler for large and complex metagenomics NGS reads, such as soil. It makes use of succinct&nbsp;</span><em>de Bruijn</em><span>&nbsp;graph (SdBG) to achieve low memory assembly. MEGAHIT can&nbsp;</span><span>optionally</span><span>&nbsp;utilize a CUDA-enabled GPU to accelerate its SdBG contstruction. The GPU-accelerated version of MEGAHIT has been tested on NVIDIA GTX680 (4G memory) and Tesla K40c (12G memory) with CUDA 5.5, 6.0 and 6.5. MEGAHIT v1.0 or greater also supports IBM Power PC and has been tested on IBM POWER8.</span></p>
<p><span>https://academic.oup.com/bioinformatics/article/31/10/1674/177884</span></p><p>Address of the bookmark: <a href="https://github.com/voutcn/megahit" rel="nofollow">https://github.com/voutcn/megahit</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29262/bioinformatics-jobs-at-chittaranjan-national-cancer-institute</guid>
  <pubDate>Thu, 29 Sep 2016 09:36:33 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics jobs at Chittaranjan National Cancer Institute]]></title>
  <description><![CDATA[
<p>Chittaranjan National Cancer Institute Advertisement No.497/2016 Invites Applications For Senior Scientific Officer, Gr. II </p>

<p>Note: Experience in the following field required: Molecular cancer cytogenetic and genetic toxicology Molecular drug Designing and targeted therapy Cancer genomics, proteomics, bioinformatics and next generation sequencing Therapeutic stem cell research and gene therapy Molecular cancer immunology and immunotherapy Molecular epidemiology Tumor endocrinology Translation research Ultra structural/tissue engg/development biology research Virus and cancer Molecular pathology No. of Posts: 11 (Eleven), (SC-1, OBC-3, UR-7) </p>

<p>Location: Kolkata (Calcutta) Salary: Rs.15600-39100 + Grade, Pay Rs.5400/- </p>

<p>For details kindly refer to the Employment News dated 24-30 September, 2016 and in the Institute’s Website: http://www.cnci.org.in </p>

<p>Last date for receipt of applications is 30 days from the date of notification in the Employment News. Director Chittaranjan National Cancer Institute 378, S.P. </p>

<p>Institute’s Website: http://www.cnci.org.in</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</guid>
	<pubDate>Thu, 24 Jan 2019 10:35:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</link>
	<title><![CDATA[NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads]]></title>
	<description><![CDATA[<p>NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.</p>
<p>https://nxrepair.readthedocs.io/en/latest/tutorial.html</p>
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<pre>nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta</pre>
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<div>&nbsp;</div><p>Address of the bookmark: <a href="https://github.com/rebeccaroisin/nxrepair" rel="nofollow">https://github.com/rebeccaroisin/nxrepair</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39098/sda-long-read-sequence-and-assembly-of-segmental-duplications</guid>
	<pubDate>Tue, 05 Mar 2019 10:00:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39098/sda-long-read-sequence-and-assembly-of-segmental-duplications</link>
	<title><![CDATA[SDA: Long-read sequence and assembly of segmental duplications]]></title>
	<description><![CDATA[<p><span><span>Segmental Duplication Assembler (SDA; https://github.com/mvollger/SDA) constructs graphs in which paralogous sequence variants define the nodes and long-read sequences provide attraction and repulsion edges, enabling the partition and assembly of long reads corresponding to distinct paralogs.<br></span></span></p>
<p><span><span>https://github.com/mvollger/SDA</span></span></p><p>Address of the bookmark: <a href="https://www.nature.com/articles/s41592-018-0236-3" rel="nofollow">https://www.nature.com/articles/s41592-018-0236-3</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40099/contiguator</guid>
	<pubDate>Fri, 04 Oct 2019 01:27:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40099/contiguator</link>
	<title><![CDATA[CONTIGuator !]]></title>
	<description><![CDATA[<p><span>CONTIGuator is a Python script for Linux environments whose purpose is to speed-up the bacterial genome assembly process and to obtain a first insight of the genome structure using the well-known artemis comparison tool (ACT).</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/contiguator/" rel="nofollow">https://sourceforge.net/projects/contiguator/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29576/impute2</guid>
	<pubDate>Thu, 27 Oct 2016 11:21:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29576/impute2</link>
	<title><![CDATA[IMPUTE2]]></title>
	<description><![CDATA[<p><strong>IMPUTE2</strong>&nbsp;is a computer program for phasing observed genotypes and imputing missing genotypes. Most people use just a couple of the program's basic functions, but we have also built up a collection of specialized and powerful options. If you are new to&nbsp;<strong>IMPUTE2</strong>, or indeed to phasing and imputation in general, we suggest that you start by learning the basics.</p>
<p>You should begin by downloading the program from&nbsp;<a href="https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#download">here</a>. You will need to choose the link that matches your computing platform and then follow the instructions for opening the download package.</p>
<p>Once you have done this, you will be ready to try some example analyses on the test data that are provided with the download. The section on&nbsp;<a href="https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#examples">Examples</a>&nbsp;shows how to use the most common&nbsp;<strong>IMPUTE2</strong>&nbsp;functions. We suggest that you work through these examples and try to understand what the elements of each command are doing. If you don't understand something or would like to know if the program can perform a function that isn't listed, you can read our&nbsp;<a href="https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#faq">FAQ</a>&nbsp;or submit a question to our&nbsp;<a href="https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#mail_list">mail list</a>.</p>
<p>When you have learned the basic functionality of the program, you can use several features of this website to prepare your own analysis:</p>
<ul>
<li>Learn about&nbsp;<a href="https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#best_practices">best practices</a>&nbsp;for imputation.</li>
<li>Download&nbsp;<a href="https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#reference">reference data</a>&nbsp;that you can use to impute genotypes in your study.</li>
<li>Look through a complete list of&nbsp;<a href="https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#options">program options</a>.</li>
</ul><p>Address of the bookmark: <a href="https://mathgen.stats.ox.ac.uk/impute/impute_v2.html" rel="nofollow">https://mathgen.stats.ox.ac.uk/impute/impute_v2.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29588/research-associate-and-junior-research-fellow-at-north-eastern-hill-university-tura-meghalaya</guid>
  <pubDate>Fri, 28 Oct 2016 09:54:43 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate and Junior Research Fellow at North-Eastern Hill University - Tura, Meghalaya]]></title>
  <description><![CDATA[
<p>Research Associate and Junior Research Fellow <br />North-Eastern Hill University - Tura, Meghalaya <br />₹18,000 a month<br />Applications are invited for the post of Research Associate and JRF in the DBT sponsored Bioinformatics Infrastructure Facility (BIF), posts are purely temporary and terminable at anytime without prior notice or assigning any reason thereof. </p>

<p>Research Associate : <br />Essential Qualification: Ph.D in Bioinformatics/Biotechnology/Life Science from a reocngised univeristy/institute <br />Pay: Rs.36000-/- + Admissible 10% HRA per month <br />Age: Below 35 years </p>

<p>Junior Research Fellow <br />Essential Qualification: M.Sc in Bioinformatics/Biotechnology/Life Science from a reocngised univeristy/institute <br />Pay: Rs.18000-/- + per month <br />Age: Below 35 years </p>

<p>Last date for receving application by mail or post is 08.11.2016 </p>

<p>Company Info. <br />North-Eastern Hill University </p>

<p>Bioinformatics Infrastructure Facility (BIF) Department of RDAP North-Eastern Hill University, Tura Campus Tura-794002, Meghalaya</p>

<p>More at http://www.nehu.ac.in/Advertisements/BIFTuraManpowerAdvt_25102016.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/29638/r-graphical-cookbook-by-winston-chang</guid>
	<pubDate>Fri, 04 Nov 2016 12:50:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29638/r-graphical-cookbook-by-winston-chang</link>
	<title><![CDATA[R Graphical Cookbook by Winston Chang]]></title>
	<description><![CDATA[<p>R Graphical Cookbook by Winston Chang</p><p>A very nice book by Winston Chang for R ethusiast. The R code presented in these pages is the R code actually used to produce the Figures in the book. There will be differences compared to the code chunks shown in the text of the book, but in most cases the differences will be that these pages contain additional code to lay out multiple plots on a single "page".</p><p>The code presented for each figure is self-contained, i.e., all code required to produce the figure is included. This means that there is sometimes considerable overlap of code between several figures  In some cases, it may be necessary to install an add-on package from CRAN to get the code to run.</p><p>More books at http://www.e-reading.club/bookreader.php/137370/C486x_APPb.pdf</p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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