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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30242?offset=10</link>
	<atom:link href="https://bioinformaticsonline.com/related/30242?offset=10" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28855/vcfr</guid>
	<pubDate>Fri, 19 Aug 2016 07:38:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28855/vcfr</link>
	<title><![CDATA[vcfR]]></title>
	<description><![CDATA[<p><span>Most variant calling pipelines result in files containing large quantities of variant information. The&nbsp;</span><a href="http://samtools.github.io/hts-specs/" title="VCF format at hts-specs">variant call format (vcf)</a><span>&nbsp;is an increasingly popular format for this data. The format of these files and their content is discussed in the vignette &lsquo;vcf data.&rsquo; These files are typically intended to be post-processed (i.e., filtered) as an attempt to remove false positives or otherwise problematic sites. The R package vcfR provides tools to facilitate this filtering as well as to visualize the effects of choices made during this process.</span></p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/vcfR/vignettes/visualization_1.html" rel="nofollow">https://cran.r-project.org/web/packages/vcfR/vignettes/visualization_1.html</a></p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28915/useful-bioinformatics-tools</guid>
	<pubDate>Mon, 29 Aug 2016 04:08:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28915/useful-bioinformatics-tools</link>
	<title><![CDATA[Useful Bioinformatics Tools]]></title>
	<description><![CDATA[<p>Collections of few handy tools for bioinformatician</p>
<p>http://molbiol-tools.ca/Convert.htm</p><p>Address of the bookmark: <a href="http://molbiol-tools.ca/Convert.htm" rel="nofollow">http://molbiol-tools.ca/Convert.htm</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29485/ribbon</guid>
	<pubDate>Fri, 21 Oct 2016 04:54:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29485/ribbon</link>
	<title><![CDATA[Ribbon !!]]></title>
	<description><![CDATA[<p><span>Visualization has played an extremely important role in the current genomic revolution to inspect and understand variants, expression patterns, evolutionary changes, and a number of other relationships. However, most of the information in read-to-reference or genome-genome alignments is lost for structural variations in the one-dimensional views of most genome browsers showing only reference coordinates. Instead, structural variations captured by long reads or assembled contigs often need more context to understand, including alignments and other genomic information from multiple chromosomes. We have addressed this problem by creating Ribbon (genomeribbon.com) an interactive online visualization tool that displays alignments along both reference and query sequences, along with any associated variant calls in the sample. This way Ribbon shows patterns in alignments of many reads across multiple chromosomes, while allowing detailed inspection of individual reads (Supplementary Note 1). For example, here we show a gene fusion in the SK-BR-3 breast cancer cell line linking the genes CYTH1 and EIF3H. While it has been found in the transcriptome previously, genome sequencing did not identify a direct chromosomal fusion between these two genes. After SMRT sequencing, Ribbon shows that there are indeed long reads that span from one gene to the other, going through not one but two variants, for the first time showing the genomic link between these two genes (Figure 1a). More gene fusions of this cancer cell line are investigated in Supplementary Note 2. Figure 1b shows another complex event in this sample made simple in Ribbon: the translocation of a 4.4 kb sequence deleted from chr19 and inserted into chr16 (Figure 1b). Thus, Ribbon enables understanding of complex variants, and it may also help in the detection of sequencing and sample preparation issues, testing of aligners and variant-callers, and rapid curation of structural variant candidates (Supplementary Note 3). In addition to SAM and BAM files with long, short, or paired-end reads, Ribbon can also load coordinate files from whole genome aligners such as MUMmer. Therefore, Ribbon can be used to test assembly algorithms or inspect the similarity between species. Supplementary Note 4 shows a comparison of gorilla and human genomes using Ribbon, highlighting major structural differences. In conclusion, Ribbon is a powerful interactive web tool for viewing complex genomic alignments.</span></p>
<p>Script at&nbsp;https://github.com/MariaNattestad/ribbon</p><p>Address of the bookmark: <a href="http://genomeribbon.com/" rel="nofollow">http://genomeribbon.com/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26306/busco</guid>
	<pubDate>Sun, 07 Feb 2016 16:02:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26306/busco</link>
	<title><![CDATA[BUSCO]]></title>
	<description><![CDATA[<p>Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs</p>
<p>More at http://busco.ezlab.org/</p><p>Address of the bookmark: <a href="http://busco.ezlab.org/" rel="nofollow">http://busco.ezlab.org/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30076/sga-string-graph-assembler</guid>
	<pubDate>Thu, 08 Dec 2016 05:08:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30076/sga-string-graph-assembler</link>
	<title><![CDATA[SGA: String Graph Assembler]]></title>
	<description><![CDATA[<p><span>SGA is a de novo genome assembler based on the concept of string graphs. The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads.</span></p>
<p><span>More at</span></p>
<p><span>https://github.com/jts/sga</span></p>
<p>SGA dependencies:<br> -google sparse hash library (http://code.google.com/p/google-sparsehash/)<br> -the bamtools library (https://github.com/pezmaster31/bamtools)<br> -zlib (http://www.zlib.net/)<br> -(optional but suggested) the jemalloc memory allocator (http://www.canonware.com/jemalloc/download.html)</p><p>Address of the bookmark: <a href="https://github.com/jts/sga" rel="nofollow">https://github.com/jts/sga</a></p>]]></description>
	<dc:creator>Jit</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/30701/harvest</guid>
	<pubDate>Tue, 31 Jan 2017 10:57:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30701/harvest</link>
	<title><![CDATA[Harvest]]></title>
	<description><![CDATA[<p>Harvest is a suite of core-genome alignment and visualization tools for quickly analyzing thousands of intraspecific microbial genomes, including variant calls, recombination detection, and phylogenetic trees.</p>
<p><a href="http://harvest.readthedocs.io/en/latest/_images/screen.png"><img src="http://harvest.readthedocs.io/en/latest/_images/screen.png" alt="_images/screen.png" style="border: 0px;"></a><span></span></p>
<p><strong>Tools</strong></p>
<ul>
<li><a href="http://harvest.readthedocs.io/en/latest/content/parsnp.html">Parsnp</a>&nbsp;- Core-genome alignment and analysis</li>
<li><a href="http://harvest.readthedocs.io/en/latest/content/gingr.html">Gingr</a>&nbsp;- Interactive visualization of alignments, trees and variants</li>
<li><a href="http://harvest.readthedocs.io/en/latest/content/harvest-tools.html">HarvestTools</a>&nbsp;- Archiving and postprocessing</li>
</ul>
<p><strong>Citation</strong></p>
<blockquote>
<div>Treangen TJ, Ondov BD, Koren S, Phillippy AM. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biology, 15 (11), 1-15 [<a href="http://www.biomedcentral.com/content/pdf/s13059-014-0524-x.pdf">PDF</a>]</div>
</blockquote><p>Address of the bookmark: <a href="http://harvest.readthedocs.io/en/latest/index.html" rel="nofollow">http://harvest.readthedocs.io/en/latest/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<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/36592/lachesis-genome-assembly-with-hi-c-based-contact-probability-maps-lachesis</guid>
	<pubDate>Mon, 14 May 2018 04:26:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36592/lachesis-genome-assembly-with-hi-c-based-contact-probability-maps-lachesis</link>
	<title><![CDATA[LACHESIS: Genome Assembly with Hi-C-based Contact Probability Maps (LACHESIS)]]></title>
	<description><![CDATA[<p>LACHESIS is method that exploits contact probability map data (e.g. from Hi-C) for chromosome-scale&nbsp;<em>de novo</em>&nbsp;genome assembly.</p>
<p>Further information about LACHESIS, including source code, documentation and a user's guide are available at:&nbsp;<a href="http://shendurelab.github.io/LACHESIS/">http://shendurelab.github.io/LACHESIS</a>.</p>
<p>Manuscript describing LACHESIS was published as: Burton JN#, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J#.&nbsp;<em>Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions.</em>&nbsp;Nature Biotechnology 2013 Dec;31(12):1119-25. doi:&nbsp;<a href="http://dx.doi.org/10.1038/nbt.2727">10.1038/nbt.272</a>. PubMed PMID:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/24185095">24185095</a>.</p>
<p>&nbsp;</p>
<p>http://shendurelab.github.io/LACHESIS/</p><p>Address of the bookmark: <a href="http://shendurelab.github.io/LACHESIS/" rel="nofollow">http://shendurelab.github.io/LACHESIS/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35429/list-of-visualization-tools-for-genome-alignments</guid>
	<pubDate>Fri, 02 Feb 2018 13:25:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35429/list-of-visualization-tools-for-genome-alignments</link>
	<title><![CDATA[List of visualization tools for genome alignments]]></title>
	<description><![CDATA[<p><span>Genome</span><span>&nbsp;browsers are useful not only for showing final results but also for improving analysis protocols, testing data quality, and generating result drafts. Its integration in analysis pipelines allows the optimization of parameters, which leads to better results. But sometime, we need publication ready figure of genomes. Following are the list of genome alignment visualization tools, which could be useful for analysis and&nbsp;interpretation of results:</span></p><p>ABySS Explorer</p><p>Interactive Java application that uses a novel graph-based representation to display a sequence assembly and associated metadata</p><p>http://www.bcgsc.ca/platform/bioinfo/software/abyss-explorer</p><p>BamView</p><p>Genome browser and annotation tool that allows visualization of sequence features, next-generation sequencing (NGS) data and the results of analyses within the context of the sequence, and also its six-frame translation</p><p>http://www.sanger.ac.uk/resources/software/artemis/</p><p>DNannotator&nbsp;</p><p>Annotation web toolkit for regional genomic sequences</p><p>http://bioapp.psych.uic.edu/DNannotator.htm</p><p>JVM&nbsp;</p><p>Java Visual Mapping tool for NGS reads</p><p>http://www.springer.com/cda/content/document/cda_downloaddocument/9789401792448-c2.pdf?SGWID=0-0-45-1487072-p176815501</p><p>LookSeq&nbsp;</p><p>Web-based visualization of sequences derived from multiple sequencing technologies. Low- or high-depth read pileups and easy visualization of putative single nucleotide and structural variation</p><p>http://lookseq.sourceforge.net</p><p>MagicViewer&nbsp;</p><p>Visualization of short read alignment, identification of genetic variation and association with annotation information of a reference genome</p><p>http://bioinformatics.zj.cn/magicviewer/</p><p>MapView&nbsp;</p><p>Alignments of huge-scale single-end and pair-end short reads</p><p>http://omictools.com/mapview-s1367.html</p><p>MultiPipMaker</p><p>Computes alignments of similar regions in two DNA sequences. The resulting alignments are summarized with a &lsquo;percent identity plot&rsquo; (pip)</p><p>http://pipmaker.bx.psu.edu/pipmaker/</p><p>PileLineGUI&nbsp;</p><p>Handling genome position files in NGS studies</p><p>http://sing.ei.uvigo.es/pileline/pilelinegui.html</p><p>SAMtools tview&nbsp;</p><p>Simple and fast text alignment viewer; NGS compatible</p><p>http://www.htslib.org/</p><p>SEWAL</p><p>Uses a locality-sensitive hashing algorithm to enumerate all unique sequences in an entire Illumina sequencing run</p><p>http://www.sourceforge.net/projects/sewal</p><p>STAR&nbsp;</p><p>A web-based integrated solution to management and visualization of sequencing data</p><p>http://wanglab.ucsd.edu/star/browser</p><p>SVA&nbsp;</p><p>Software for annotating and visualizing sequenced human genomes</p><p>http://www.svaproject.org</p><p>Viewer (IGV)&nbsp;</p><p>Visualization of large heterogeneous datasets, providing a smooth and intuitive user experience at all levels of genome resolution</p><p>https://www.broadinstitute.org/igv/</p><p>ZOOM Lite&nbsp;</p><p>NGS data mapping and visualization software</p><p>http://bioinfor.com/zoom/lite/</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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