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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/29130?offset=30</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26303/maker</guid>
	<pubDate>Sun, 07 Feb 2016 15:59:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26303/maker</link>
	<title><![CDATA[MAKER]]></title>
	<description><![CDATA[<p>MAKER is a portable and easily configurable genome annotation pipeline.Its purpose is to allow smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values.</p>
<p>More at http://www.yandell-lab.org/software/maker.html</p><p>Address of the bookmark: <a href="http://www.yandell-lab.org/software/maker.html" rel="nofollow">http://www.yandell-lab.org/software/maker.html</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30124/understanding-greedy-algorithms</guid>
	<pubDate>Mon, 12 Dec 2016 04:37:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30124/understanding-greedy-algorithms</link>
	<title><![CDATA[Understanding Greedy Algorithms]]></title>
	<description><![CDATA[<p>Learning greedy algo for biologist.&nbsp;</p>
<p>https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/</p>
<p>This webpage is also useful for the same:</p>
<p>http://learninglover.com/examples.php?id=59</p>
<p>http://www.cs.rpi.edu/~magdon/ps/conference/super_biokdd.pdf</p>
<p>https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/lecture-slides/MIT7_91JS14_Lecture6.pdf</p>
<p>http://schatzlab.cshl.edu/teaching/AssemblyClass/01.%20Assembly%20Intro.pdf</p>
<p>http://lsl.sinica.edu.tw/Services/Class/files/20150612449.pdf</p>
<p>http://www.cs.jhu.edu/~langmea/resources/lecture_notes/assembly_scs.pdf</p>
<p>https://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-43.pdf</p><p>Address of the bookmark: <a href="https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/" rel="nofollow">https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30625/pandaseq</guid>
	<pubDate>Mon, 23 Jan 2017 04:54:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30625/pandaseq</link>
	<title><![CDATA[PANDASEQ]]></title>
	<description><![CDATA[<p>PANDASEQ assembles paired-end Illumina reads into sequences, trying to correct for errors and uncalled bases. The assembler reads two files in FASTQ format with quality information. If amplification primers were used (e.g., to isolate a variable region of the 16S gene, or the constant regions around zinc finger binding residues), they can be removed from the sequence during assembly. The final sequence will correct any uncalled bases in the overlapping region using the complementary strand. When mismatches occur in the overlapping region, the base with the better quality score is chosen.<br>The algorithm is as follows:<br><br>1.Find the positions where the forward and reverse primers match best above the threshold and discard the ends of the sequence, including the primer.<br>2.Pick and overlap to maximise the probability of the forward and reverse reads having come from a single piece of DNA.<br>3.Identify the masking of the end of the read with the quality score B or # as done by CASAVA and adjust the probabilities in this region.<br>4.Construct an assembled sequence between the primers and calculate the quality.<br>5.Check for various constraints, including quality, length, uncalled bases, and user-supplied modules.</p>
<p>http://neufeldserver.uwaterloo.ca/~apmasell/pandaseq_man1.html</p><p>Address of the bookmark: <a href="http://neufeldserver.uwaterloo.ca/~apmasell/pandaseq_man1.html" rel="nofollow">http://neufeldserver.uwaterloo.ca/~apmasell/pandaseq_man1.html</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/32713/salzberg-lab</guid>
  <pubDate>Mon, 15 May 2017 05:14:01 -0500</pubDate>
  <link></link>
  <title><![CDATA[Salzberg lab]]></title>
  <description><![CDATA[
<p>We are a computational biology lab that develops novel methods for analysis of DNA and RNA sequences. Our research includes software for aligning and assembling RNA-seq data, whole-genome assembly, and microbiome analysis. We work closely with biomedical scientists to apply these methods to current problems arising in a broad spectrum of biological and medical research areas. We’re also part of the Center for Computational Biology, a group of 20+ faculty members and their labs at Johns Hopkins working on computational, statistical, and mathematical methods that can turn massive genomic data sets into biologically and clinically useful information.</p>

<p>https://salzberg-lab.org/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27696/methylkit</guid>
	<pubDate>Fri, 03 Jun 2016 10:09:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27696/methylkit</link>
	<title><![CDATA[methylKit]]></title>
	<description><![CDATA[<p><em>methylKit</em> is an <a href="http://en.wikipedia.org/wiki/R_%28programming_language%29">R</a> package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from <a href="http://www.nature.com/nprot/journal/v6/n4/abs/nprot.2010.190.html">RRBS</a> and its variants, but also target-capture methods such as <a href="http://www.halogenomics.com/sureselect/methyl-seq">Agilent SureSelect methyl-seq</a>. In addition, methylKit can deal with base-pair resolution data for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome bisulfite sequencing data if proper input format is provided.</p><p>Address of the bookmark: <a href="https://github.com/al2na/methylKit" rel="nofollow">https://github.com/al2na/methylKit</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29103/genome-strip</guid>
	<pubDate>Tue, 06 Sep 2016 03:58:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29103/genome-strip</link>
	<title><![CDATA[Genome STRiP]]></title>
	<description><![CDATA[<p><strong>Genome STRiP</strong><span>&nbsp;(Genome STRucture In Populations) is a suite of tools for discovering and genotyping structural variations using sequencing data. The methods are designed to detect shared variation using data from multiple individuals.</span><br><br><span>Genome STRiP looks both across and within a set of sequenced genomes to detect variation. The methods are adaptive and support heterogeneous data sets, including variations in sequencing depth, read lengths and mixtures of paired and single-end reads. A minimum of 20 to 30 genomes are required to get acceptable results, but the method gains power across genomes and processing more genomes provide better results.</span><br><br><span>To run discovery or genotyping on a single sequenced genome or a small set of genomes, you need to call your data against a background population, such as a set of genomes from the 1000 Genomes Project.&nbsp; The background population does not need to be matched to the target individuals.</span></p><p>Address of the bookmark: <a href="http://software.broadinstitute.org/software/genomestrip/" rel="nofollow">http://software.broadinstitute.org/software/genomestrip/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28807/organellargenomedraw</guid>
	<pubDate>Tue, 16 Aug 2016 08:13:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28807/organellargenomedraw</link>
	<title><![CDATA[OrganellarGenomeDRAW]]></title>
	<description><![CDATA[<p><span>O</span><span>rganellar</span><span>G</span><span>enome</span><span>DRAW</span><span>&nbsp;is dedicated to convert genetic information stored in GenBank entries to graphical maps. The input text file has to be in GenBank flat file format, whereas the output format can be chosen among several formats. The application is especially optimized and adapted for the creation of high-quality, detailed circular maps of organellar genomes like the plastid genome (plastome) or the mitochondrial genome (chondriome). Nevertheless, you can upload any GenBank entry. The workflow is devided into three steps.&nbsp;</span></p>
<p><span>More at&nbsp;http://ogdraw.mpimp-golm.mpg.de/cgi-bin/ogdraw.pl</span></p><p>Address of the bookmark: <a href="http://ogdraw.mpimp-golm.mpg.de/index.shtml" rel="nofollow">http://ogdraw.mpimp-golm.mpg.de/index.shtml</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28870/genemania</guid>
	<pubDate>Mon, 22 Aug 2016 09:55:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28870/genemania</link>
	<title><![CDATA[GeneMANIA]]></title>
	<description><![CDATA[<p>Faster, more accurate algorithms function prediction "GeneMANIA (Multiple Association Network Integration Algorithm)" have however been developed in recent years and are publicly available on the web, indicating the future direction of function prediction.</p><p>Address of the bookmark: <a href="http://genemania.org/" rel="nofollow">http://genemania.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28906/gene-finding-and-predictions</guid>
	<pubDate>Fri, 26 Aug 2016 07:26:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28906/gene-finding-and-predictions</link>
	<title><![CDATA[Gene Finding and Predictions]]></title>
	<description><![CDATA[<p><span>In this exercise, a previously annotated gene will be used to measure the accuracy of different gene finding approaches. GRAIL, GENSCAN,&nbsp;</span><tt>geneid</tt><span>, FGENESH, GenomeScan, GrailEXP and GENEWISE will be used to annotate the sequence. Both search by signal, content and homology (protein and cDNA sequences) methods will be employed in order to improve the ab initio results. Weak conservation of Start codons will lead to wrong prediction of initial exons in most cases.</span></p>
<p>http://genome.crg.es/courses/Bioinformatics2003_genefinding/</p><p>Address of the bookmark: <a href="http://genome.crg.es/courses/Bioinformatics2003_genefinding/" rel="nofollow">http://genome.crg.es/courses/Bioinformatics2003_genefinding/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29123/artemis-comparison-tool-act</guid>
	<pubDate>Wed, 07 Sep 2016 03:54:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29123/artemis-comparison-tool-act</link>
	<title><![CDATA[Artemis Comparison Tool (ACT)]]></title>
	<description><![CDATA[<p><span>ACT is a Java application for displaying pairwise comparisons between two or more DNA sequences. It can be used to identify and analyse regions of similarity and difference between genomes and to explore conservation of synteny, in the context of the entire sequences and their annotation.&nbsp;It can read complete EMBL,&nbsp;GENBANK and GFF entries or sequences in FASTA or raw format.&nbsp;</span></p><p>Address of the bookmark: <a href="http://www.sanger.ac.uk/science/tools/artemis-comparison-tool-act" rel="nofollow">http://www.sanger.ac.uk/science/tools/artemis-comparison-tool-act</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>

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