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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/39674?offset=170</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33506/bedops-v2426-high-performance-genomic-feature-operations</guid>
	<pubDate>Mon, 12 Jun 2017 10:11:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33506/bedops-v2426-high-performance-genomic-feature-operations</link>
	<title><![CDATA[BEDOPS v2.4.26: high-performance genomic feature operations]]></title>
	<description><![CDATA[<p><strong>BEDOPS v2.4.26</strong> is a suite of tools to address common questions raised in genomic studies &mdash; mostly with regard to overlap and proximity relationships between data sets. It aims to be scalable and flexible, facilitating the efficient and accurate analysis and management of large-scale genomic data.</p>
<p>The <a href="https://bedops.readthedocs.io/en/latest/content/overview.html#overview">overview</a> section of the <strong>BEDOPS v2.4.26</strong> documentation summarizes the toolkit, functionality and performance enhancements. The <a href="https://bedops.readthedocs.io/en/latest/index.html#reference">reference</a> table offers documentation for all applications and scripts.</p><p>Address of the bookmark: <a href="https://github.com/bedops/bedops" rel="nofollow">https://github.com/bedops/bedops</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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36884/halc-high-throughput-algorithm-for-long-read-error-correction</guid>
	<pubDate>Fri, 08 Jun 2018 10:47:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36884/halc-high-throughput-algorithm-for-long-read-error-correction</link>
	<title><![CDATA[HALC: High throughput algorithm for long read error correction]]></title>
	<description><![CDATA[HALC, a high throughput algorithm for long read error correction. HALC aligns the long reads to short read contigs from the same species with a relatively low identity requirement so that a long read region can be aligned to at least one contig region, including its true genome region’s repeats in the contigs sufficiently similar to it (similar repeat based alignment approach)

HALC was able to obtain 6.7-41.1% higher throughput than the existing algorithms while maintaining comparable accuracy. The HALC corrected long reads can thus result in 11.4-60.7% longer assembled contigs than the existing algorithms.<p>Address of the bookmark: <a href="https://github.com/lanl001/halc" rel="nofollow">https://github.com/lanl001/halc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37257/asar-advanced-metagenomic-sequence-analysis-in-r</guid>
	<pubDate>Mon, 09 Jul 2018 05:20:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37257/asar-advanced-metagenomic-sequence-analysis-in-r</link>
	<title><![CDATA[ASAR: Advanced metagenomic Sequence Analysis in R]]></title>
	<description><![CDATA[<p><span>An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.</span></p>
<p><span><span>To read the manual, please click the link&nbsp;</span><a href="https://askarbek-orakov.github.io/ASAR/">https://askarbek-orakov.github.io/ASAR/</a></span></p><p>Address of the bookmark: <a href="https://github.com/Askarbek-orakov/ASAR" rel="nofollow">https://github.com/Askarbek-orakov/ASAR</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/37514/list-of-non-commercial-ngs-genotype-calling-software</guid>
	<pubDate>Thu, 09 Aug 2018 04:21:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/37514/list-of-non-commercial-ngs-genotype-calling-software</link>
	<title><![CDATA[List of non-commercial NGS genotype-calling software]]></title>
	<description><![CDATA[<p><span>Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data.&nbsp;</span></p><p><span>A list of programs for genotype and SNP calling :</span></p><p><br />SOAP2&nbsp;http://soap.genomics.org.cn/index.html</p><p>Single-sample High-quality variant database (for example, dbSNP) Package for NGS data analysis, which includes a single individual genotype caller (SOAPsnp)</p><p>realSFS&nbsp;http://128.32.118.212/thorfinn/realSFS/</p><p>Single-sample Aligned reads Software for SNP and genotype calling using single individuals and allele frequencies. Site frequency spectrum (SFS) estimation</p><p>Samtools http://samtools.sourceforge.net/</p><p>Multi-sample Aligned reads Package for manipulation of NGS alignments, which includes a computation of genotype likelihoods (samtools) and SNP and genotype calling (bcftools)</p><p>GATK http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit Multi-sample Aligned reads Package for aligned NGS data analysis, which includes a SNP and genotype caller (Unifed Genotyper), SNP filtering (Variant Filtration) and SNP quality recalibration (Variant Recalibrator)</p><p>Beagle http://faculty.washington.edu/browning/beagle/beagle.html</p><p>Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation, phasing and association that includes a mode for genotype calling</p><p>IMPUTE2 http://mathgen.stats.ox.ac.uk/impute/impute_v2.html</p><p>Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation and phasing, including a mode for genotype calling. Requires fine-scale linkage map</p><p>QCall ftp://ftp.sanger.ac.uk/pub/rd/QCALL</p><p>Multi-sample LD &lsquo;Feasible&rsquo; genealogies at a dense set of loci, genotype likelihoods Software for SNP and genotype calling, including a method for generating candidate SNPs without LD information (NLDA) and a method for incorporating LD information (LDA). The &lsquo;feasible&rsquo; genealogies can be generated using Margarita (http://www.sanger.ac.uk/resources/software/margarita)</p><p>MaCH http://genome.sph.umich.edu/wiki/Thunder</p><p>Multi-sample LD Genotype likelihoods Software for SNP and genotype calling, including a method (GPT_Freq) for generating candidate SNPs without LD information and a method (thunder_glf_freq) for incorporating LD information</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</guid>
	<pubDate>Fri, 19 Oct 2018 07:25:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads</link>
	<title><![CDATA[BASE: a practical de novo assembler for large genomes using long NGS reads]]></title>
	<description><![CDATA[<p><span>new&nbsp;</span><em>de novo</em><span>&nbsp;assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.</span></p><p>Address of the bookmark: <a href="https://github.com/dhlbh/BASE" rel="nofollow">https://github.com/dhlbh/BASE</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/38618/canu-genome-assembly-parameters</guid>
	<pubDate>Mon, 07 Jan 2019 08:40:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/38618/canu-genome-assembly-parameters</link>
	<title><![CDATA[CANU genome assembly parameters !]]></title>
	<description><![CDATA[<p>Choose the appropriate parameters to run Canu and run it. The assembly will take about an hour. You can use two cores (parameter&nbsp;<code>-maxThreads=2</code>) and you would like to disable cluster option, since we compute on a single Amazon server set off the option to compute on cluster&nbsp;<code>useGrid=false</code>. This specifications should be for your project discussed with a local computing guru. The parameters that are in square brackets&nbsp;<code>[]</code>&nbsp;are optional, symbol&nbsp;<code>|</code>&nbsp;stands for "or".</p><pre><code>usage:   canu [-correct | -trim | -assemble | -trim-assemble] \
              [-s ] \
               -p  \
               -d  \
               genomeSize=[g|m|k] \
               -maxThreads=2 \
               useGrid=false \
              [other-options] \
               read_file.fastq.gz
</code></pre><p>A default&nbsp;<code>Canu</code>&nbsp;run produces usually high quality assembly, example of a command that was used for testing can be found below. However, there are still a lot of parameters that are possible to tweak. For example if we desire to assemble haplotypes separately of if we want to smash them together, we can alternate the error correction process.</p><pre><code>canu -p test_asmbl \
     -d asm_test3 \
     genomeSize=2m \
     -maxThreads=2 useGrid=false \
     -pacbio-raw \ ~/pacbio/dna/sample_reads.fastq.gz</code></pre><p>There is a brilliant&nbsp;<a href="http://canu.readthedocs.io/en/latest/faq.html#what-parameters-can-i-tweak">section in documentation</a>&nbsp;about parameter tweaking.</p><p>The output directory contains will contain many files. The most interesting ones are:</p><ul>
<li><code>*.correctedReads.fasta.gz</code>&nbsp;: file containing the input sequences after correction, trim and split based on consensus evidence.</li>
<li><code>*.trimmedReads.fastq</code>&nbsp;: file containing the sequences after correction and final trimming</li>
<li><code>*.layout</code>&nbsp;: file containing informations about read inclusion in the final assembly</li>
<li><code>*.gfa</code>&nbsp;: file containing the assembly graph by Canu</li>
<li><code>*.contigs.fasta</code>&nbsp;: file containing everything that could be assembled and is part of the primary assembly</li>
</ul><p>The basic stats of assembly can be read from reports generated by the assembler, or calculated using standard UNIX command line tools.</p><p>More at&nbsp;https://canu.readthedocs.io/en/latest/faq.html</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/39469/introduction-to-bioinformatics</guid>
	<pubDate>Wed, 05 Jun 2019 14:58:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/39469/introduction-to-bioinformatics</link>
	<title><![CDATA[Introduction to Bioinformatics]]></title>
	<description><![CDATA[<p><img src="https://edu.t-bio.info/wp-content/uploads/2017/07/Introduction-Course-Title-11.jpg" alt="Introduction to Bioinformatics Course" width="600" height="315.6" style="vertical-align: top; border: 0px; border: 0px;"></p><p>Introduction to bioinformatics is a course for biologists and clinicians that would like to learn more about the way bioinformatics is used in healthcare, biotech and pharmaceuitcal industry as well as basic research. The course covers many of the topics transformed by the emergence of big data and computational technologies. To learn more about the course, visit:&nbsp;<a href="https://edu.t-bio.info/course/introduction-bioinformatics/">https://edu.t-bio.info/course/introduction-bioinformatics/</a></p>]]></description>
	<dc:creator>eliabrodsky</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/40228/bioinformatics-services-cro-services</guid>
	<pubDate>Wed, 06 Nov 2019 00:33:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/40228/bioinformatics-services-cro-services</link>
	<title><![CDATA[Bioinformatics Services / CRO Services]]></title>
	<description><![CDATA[<p>RASA is set to provide premium technical and scientific services in a form of solutions, product development and training. .We are also very proficient in providing the high quality Research &amp; Development services in life science informatics field like Next Generation Sequencing (NGS) Data Analysis,Computational Drug Discovery, Bioinformatics, Chemo-informatics and BIO-IT.</p><p>RASA offers faster, better and cost effective cutting edge technology solutions to chemical and life science research and industry. We provide our customers with A seamless model of wide expertise and comprehensive platforms. Our Value is to take our customers</p>]]></description>
	<dc:creator>RASA Life Sciences</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40604/gapfinisher-a-reliable-gap-filling-pipeline-for-sspace-longread-scaffolder-output</guid>
	<pubDate>Fri, 24 Jan 2020 06:04:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40604/gapfinisher-a-reliable-gap-filling-pipeline-for-sspace-longread-scaffolder-output</link>
	<title><![CDATA[gapFinisher: A reliable gap filling pipeline for SSPACE-LongRead scaffolder output]]></title>
	<description><![CDATA[<p><span>gapFinisher is based on the controlled use of a previously published gap filling tool FGAP and works on all standard Linux/UNIX command lines. They compare the performance of gapFinisher against two other published gap filling tools PBJelly and GMcloser. </span></p>
<p><span>gapFinisher can fill gaps in draft genomes quickly and reliably.</span></p><p>Address of the bookmark: <a href="https://github.com/kammoji/gapFinisher" rel="nofollow">https://github.com/kammoji/gapFinisher</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

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