<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37493?offset=10</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35144/converting-fastq-to-fasta</guid>
	<pubDate>Fri, 12 Jan 2018 03:49:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35144/converting-fastq-to-fasta</link>
	<title><![CDATA[Converting FASTQ to FASTA]]></title>
	<description><![CDATA[<div id="block-system-main"><div><div><div><div><div><div><p>There are several ways you can convert fastq to fasta sequences. Some methods are listed below.</p><h3>Using SED</h3><p><span><code><span>sed</span></code></span>&nbsp;can be used to selectively print the desired lines from a file, so if you print the first and 2rd line of every 4 lines, you get the sequence header and sequence needed for fasta format.</p><pre>sed -n '1~4s/^@/&gt;/p;2~4p' INFILE.fastq &gt; OUTFILE.fasta
</pre><h3>Using PASTE</h3><p>You can linerize every 4 lines in a tabular format and print first and second field using&nbsp;<span><code>paste</code></span></p><pre>cat INFILE.fastq | paste - - - - |cut -f 1, 2| sed 's/@/&gt;/'g | tr -s "/t" "/n" &gt; OUTFILE.fasta
</pre><h3>EMBOSS:seqret</h3><p>Standard script that can be used for many purposes. One such use is fastq-fasta conversion</p><pre>seqret -sequence reads.fastq -outseq reads.fasta
</pre><p><span><code><span>awk</span></code></span>&nbsp;can be used for conversion as follows:</p><h3>Using AWK</h3><pre>cat infile.fq | awk '{if(NR%4==1) {printf("&gt;%s\n",substr($0,2));} else if(NR%4==2) print;}' &gt; file.fa
</pre><h3>FASTX-toolkit</h3><p><span><code>fastq_to_fasta</code></span>&nbsp;is available in the FASTX-toolkit that scales really well with the huge datasets</p><pre>fastq_to_fasta -h
usage: fastq_to_fasta [-h] [-r] [-n] [-v] [-z] [-i INFILE] [-o OUTFILE]
# Remember to use -Q33 for illumina reads!
version 0.0.6
       [-h]         = This helpful help screen.
       [-r]         = Rename sequence identifiers to numbers.
       [-n]         = keep sequences with unknown (N) nucleotides.
                   Default is to discard such sequences.
       [-v]         = Verbose - report number of sequences.
                   If [-o] is specified,  report will be printed to STDOUT.
                   If [-o] is not specified (and output goes to STDOUT),
                   report will be printed to STDERR.
       [-z]         = Compress output with GZIP.
       [-i INFILE]  = FASTA/Q input file. default is STDIN.
       [-o OUTFILE] = FASTA output file. default is STDOUT.
</pre><h3>Bioawk</h3><p>Another option to convert fastq to fasta format using&nbsp;<span><code>bioawk</code></span></p><pre>bioawk -c fastx '{print "&gt;"$name"\n"$seq}' input.fastq &gt; output.fasta
</pre><h3>Seqtk</h3><p>From the same developer, there is another option using a tool called&nbsp;<span><code>seqtk</code></span></p><pre>seqtk seq -a input.fastq &gt; output.fasta
</pre><p>Note that you can use either compressed or uncompressed files for this tool</p></div></div></div></div></div></div></div>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27818/gaemr</guid>
	<pubDate>Tue, 14 Jun 2016 06:18:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27818/gaemr</link>
	<title><![CDATA[GAEMR]]></title>
	<description><![CDATA[<p>The&nbsp;<span>G</span>enome&nbsp;<span>A</span>ssembly&nbsp;<span>E</span>valuation&nbsp;<span>M</span>etrics and&nbsp;<span>R</span>eporting (GAEMR) package is an assembly analysis framework composed a number of integrated modules. These modules can be executed as a single program to generate a complete analysis report, or executed individually to generate specific charts and tables. GAEMR standardizes input by converting a variety of read types to Binary Alignment Map (BAM) format, allowing a single input format to be entered into GAEMR&rsquo;s analysis pipeline, hence enabling the generation of standard reports.</p>
<p>GAEMR&rsquo;s analysis philosophy is centered on contiguity, correctness, and completeness -- how many pieces in an assembly composed of, how well those pieces accurately represent the genome sequenced, and how much of that genome is represented by those pieces. By performing over twenty different analyses based on these principles, GAEMR gives a clear picture of the condition of a genome assembly.&nbsp;</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/software/gaemr/" rel="nofollow">https://www.broadinstitute.org/software/gaemr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29603/statistical-for-biological-research</guid>
	<pubDate>Thu, 03 Nov 2016 04:59:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29603/statistical-for-biological-research</link>
	<title><![CDATA[Statistical for biological research]]></title>
	<description><![CDATA[<p>There is no disputing the importance of statistical analysis in biological research, but too often it is considered only after an experiment is completed, when it may be too late.</p>
<p>This collection highlights important statistical issues that biologists should be aware of and provides practical advice to help them improve the rigor of their work.</p>
<p><em>Nature Methods</em>' <strong><a href="http://www.nature.com/collections/qghhqm/pointsofsignificance">Points of Significance</a></strong> column on statistics explains many key statistical and experimental design concepts. <strong><a href="http://www.nature.com/collections/qghhqm/resources">Other resources</a></strong> include an online plotting tool and links to statistics guides from other publishers.</p><p>Address of the bookmark: <a href="http://www.nature.com/collections/qghhqm" rel="nofollow">http://www.nature.com/collections/qghhqm</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/42206/pollard-lab</guid>
  <pubDate>Fri, 25 Sep 2020 20:20:50 -0500</pubDate>
  <link></link>
  <title><![CDATA[Pollard Lab]]></title>
  <description><![CDATA[
<p>We are a bioinformatics research lab focused on developing novel methods and using them to study genome evolution, organization, and regulation. Our mission is to decode biomedical knowledge that is missed without rigorous statistical approaches.</p>

<p>http://docpollard.org/</p>

<p>Tools</p>

<p>http://docpollard.org/resources/software/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27080/mrfast-micro-read-fast-alignment-search-tool</guid>
	<pubDate>Tue, 26 Apr 2016 03:50:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27080/mrfast-micro-read-fast-alignment-search-tool</link>
	<title><![CDATA[mrFAST:  Micro Read Fast Alignment Search Tool]]></title>
	<description><![CDATA[<p><span>mrFAST is a read mapper that is designed to map short reads to reference genome with a special emphasis on the discovery of structural variation and segmental duplications. mrFAST maps short reads with respect to user defined error threshold, including indels up to 4+4 bp. This manual, describes how to choose the parameters and tune mrFAST with respect to the library settings. mrFAST is designed to find&nbsp;</span><strong><span style="text-decoration: underline;">'all'</span></strong><span>&nbsp; mappings for a given set of reads, however it can return one "best" map location if the relevant parameter is invoked.</span></p>
<p><span>More at&nbsp;http://mrfast.sourceforge.net/manual.html</span></p><p>Address of the bookmark: <a href="http://mrfast.sourceforge.net/manual.html" rel="nofollow">http://mrfast.sourceforge.net/manual.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30085/fqtools</guid>
	<pubDate>Thu, 08 Dec 2016 09:31:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30085/fqtools</link>
	<title><![CDATA[fqtools]]></title>
	<description><![CDATA[<p><code>fqtools</code><span>&nbsp;is a software suite for fast processing of&nbsp;</span><code>FASTQ</code><span>&nbsp;files. Various file manipulations are supported. See below for a full list of the subcommands available and a brief description of their purpose. Most of the individual subcommands will take either a single file or a pair of files as input. If no input file is specified, fqtools will attempt to read data from&nbsp;</span><code>stdin</code><span>. In this case, it is advisabe to specify the format of the data provided. For subcommands that generate FASTQ data, either a single file or a pair of files will be generated. If no&nbsp;</span><code>-o</code><span>&nbsp;argument is provided, single files will be writted to&nbsp;</span><code>stdout</code><span>.</span></p><p>Address of the bookmark: <a href="https://github.com/alastair-droop/fqtools" rel="nofollow">https://github.com/alastair-droop/fqtools</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32420/fastq-format</guid>
	<pubDate>Wed, 03 May 2017 04:23:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32420/fastq-format</link>
	<title><![CDATA[Fastq format]]></title>
	<description><![CDATA[<p><strong>FASTQ format</strong>&nbsp;is a text-based&nbsp;<a href="https://en.wikipedia.org/wiki/File_format" title="File format">format</a>&nbsp;for storing both a biological sequence (usually&nbsp;<a href="https://en.wikipedia.org/wiki/Nucleotide_sequence" title="Nucleotide sequence">nucleotide sequence</a>) and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single&nbsp;<a href="https://en.wikipedia.org/wiki/ASCII" title="ASCII">ASCII</a>&nbsp;character for brevity.</p>
<p>It was originally developed at the&nbsp;<a href="https://en.wikipedia.org/wiki/Wellcome_Trust_Sanger_Institute" title="Wellcome Trust Sanger Institute">Wellcome Trust Sanger Institute</a>&nbsp;to bundle a&nbsp;<a href="https://en.wikipedia.org/wiki/FASTA_format" title="FASTA format">FASTA</a>&nbsp;sequence and its quality data, but has recently become the&nbsp;<em>de facto</em>&nbsp;standard for storing the output of high-throughput sequencing instruments such as the&nbsp;<a href="https://en.wikipedia.org/wiki/Illumina_(company)" title="Illumina (company)">Illumina</a>&nbsp;Genome Analyzer.<sup id="cite_ref-Cock2009_1-0"><a href="https://en.wikipedia.org/wiki/FASTQ_format#cite_note-Cock2009-1">[1]</a></sup></p><p>Address of the bookmark: <a href="https://en.wikipedia.org/wiki/FASTQ_format" rel="nofollow">https://en.wikipedia.org/wiki/FASTQ_format</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37205/afterqc-automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data</guid>
	<pubDate>Fri, 29 Jun 2018 03:26:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37205/afterqc-automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data</link>
	<title><![CDATA[AfterQC: Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data]]></title>
	<description><![CDATA[Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data
AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair.
Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats

The author has reimplemented this tool in C++ with multithreading support to make it much faster. The new tool is called fastp and can be found at: https://github.com/OpenGene/fastp . If you prefer a C++ based tool, please use fastp instead.

https://github.com/OpenGene/AfterQC<p>Address of the bookmark: <a href="https://github.com/OpenGene/AfterQC" rel="nofollow">https://github.com/OpenGene/AfterQC</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28884/tgnet</guid>
	<pubDate>Wed, 24 Aug 2016 05:36:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28884/tgnet</link>
	<title><![CDATA[TGNet]]></title>
	<description><![CDATA[<p><span>Recent technological progress has greatly facilitated&nbsp;</span><em>de novo</em><span>&nbsp;genome sequencing. However,&nbsp;</span><em>de novo</em><span>&nbsp;assemblies consist in many pieces of contiguous sequence (contigs) arranged in thousands of scaffolds instead of small numbers of chromosomes. Confirming and improving the quality of such assemblies is critical for subsequent analysis.&nbsp;</span></p>
<p>Visualization and quality assessment of de novo genome assemblies</p>
<p>Citation</p>
<p>This software is fully described in the paper:<br>Riba-Grognuz, Keller, Falquet, Xenarios &amp; Wurm (2011) Visualization and quality assessment of de novo genome assemblies.</p>
<p>In brief, our scripts create Cytoscape files to visualize transcript evidence that suggests adjacency between scaffolds and contigs.</p>
<p>Software requirements</p>
<p>BLAT (tested with Standalone BLAT v. 32&times;1). Source Binaries .<br>Cytoscape (tested with versions 2.7.0, 2.8.2)<br>a UNIX machine (tested on Mac OS X 10.6 and CentOS 4.6)</p><p>Address of the bookmark: <a href="https://github.com/ksanao/TGNet" rel="nofollow">https://github.com/ksanao/TGNet</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29614/art-set-of-simulation-tools</guid>
	<pubDate>Thu, 03 Nov 2016 08:28:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29614/art-set-of-simulation-tools</link>
	<title><![CDATA[ART: Set of Simulation Tools]]></title>
	<description><![CDATA[<p>ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the <span><a href="http://www.1000genomes.org/" target="_blank">1000 Genomes Project<span></span></a></span> . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. <span><a href="http://bioinform.github.io/varsim/" target="_blank">VarSim<span></span></a></span>) for evaluating variant calling tools or methods.</p><p>Address of the bookmark: <a href="http://www.niehs.nih.gov/research/resources/software/biostatistics/art/" rel="nofollow">http://www.niehs.nih.gov/research/resources/software/biostatistics/art/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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