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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43806?offset=220</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36830/crossmap-a-program-for-convenient-conversion-of-genome-coordinates</guid>
	<pubDate>Thu, 31 May 2018 06:00:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36830/crossmap-a-program-for-convenient-conversion-of-genome-coordinates</link>
	<title><![CDATA[CrossMap: a program for convenient conversion of genome coordinates]]></title>
	<description><![CDATA[CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) &lt;&gt; hg19 (GRCh37), Mouse mm9 (MGSCv37) &lt;&gt; mm10 (GRCm38)).

It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.

CrossMap is designed to liftover genome coordinates between assemblies. 

It’s not a program for aligning sequences to reference genome.

We do not recommend using CrossMap to convert genome coordinates between species.<p>Address of the bookmark: <a href="http://crossmap.sourceforge.net" rel="nofollow">http://crossmap.sourceforge.net</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36935/assemblytics-delta-file-to-analyze-alignments-of-an-assembly-to-another-assembly-or-a-reference-genome</guid>
	<pubDate>Thu, 14 Jun 2018 07:31:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36935/assemblytics-delta-file-to-analyze-alignments-of-an-assembly-to-another-assembly-or-a-reference-genome</link>
	<title><![CDATA[assemblytics: delta file to analyze alignments of an assembly to another assembly or a reference genome]]></title>
	<description><![CDATA[Download and install MUMmer
Align your assembly to a reference genome using nucmer (from MUMmer package)
$ nucmer -maxmatch -l 100 -c 500 REFERENCE.fa ASSEMBLY.fa -prefix OUT
Consult the MUMmer manual if you encounter problems

Optional: Gzip the delta file to speed up upload (usually 2-4X faster)
$ gzip OUT.delta
Then use the OUT.delta.gz file for upload.
Upload the .delta or delta.gz file (view example) to Assemblytics
Important: Use only contigs rather than scaffolds from the assembly. This will prevent false positives when the number of Ns in the scaffolded sequence does not match perfectly to the distance in the reference.

The unique sequence length required represents an anchor for determining if a sequence is unique enough to safely call variants from, which is an alternative to the mapping quality filter for read alignment.

http://assemblytics.com/<p>Address of the bookmark: <a href="http://assemblytics.com/" rel="nofollow">http://assemblytics.com/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36960/links-scaffolder-bloomfilter-setting</guid>
	<pubDate>Fri, 15 Jun 2018 10:39:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36960/links-scaffolder-bloomfilter-setting</link>
	<title><![CDATA[LINKS scaffolder bloomfilter setting !]]></title>
	<description><![CDATA[
<p>➜  bin git:(master) ✗ ls -l<br />total 68<br />drwxrwxr-x 3 urbe urbe  4096 Jun 15 12:15 lib<br />-rwxrwxrwx 1 urbe urbe 65141 Jun 15 17:13 LINKS<br />➜  bin git:(master) ✗ pwd<br />/home/urbe/Tools/LINKS_1.8.6/bin</p>

<p>➜  bloomfilter git:(master) ✗ swig -Wall -c++ -perl5 BloomFilter.i<br />➜  bloomfilter git:(master) ✗ g++ -c BloomFilter_wrap.cxx -I/home/urbe/anaconda3/lib/perl5/5.22.0/x86_64-linux-thread-multi/CORE/ -fPIC -Dbool=char -O3<br />BloomFilter_wrap.cxx:1892:30: fatal error: ../BloomFilter.hpp: No such file or directory<br />compilation terminated.<br />➜  bloomfilter git:(master) ✗ cd swig <br />➜  swig git:(master) ✗ g++ -c BloomFilter_wrap.cxx -I/home/urbe/anaconda3/lib/perl5/5.22.0/x86_64-linux-thread-multi/CORE/ -fPIC -Dbool=char -O3<br />In file included from BloomFilter_wrap.cxx:1877:0:<br />../BloomFilter.hpp: In member function ‘void BloomFilter::loadHeader(FILE*)’:<br />../BloomFilter.hpp:141:59: warning: ignoring return value of ‘size_t fread(void*, size_t, size_t, FILE*)’, declared with attribute warn_unused_result [-Wunused-result]<br />         fread(&amp;header, sizeof(struct FileHeader), 1, file);<br />                                                           ^<br />➜  swig git:(master) ✗ g++ -Wall -shared BloomFilter_wrap.o -o BloomFilter.so -O3<br />➜  swig git:(master) ✗ cd ..<br />➜  bloomfilter git:(master) ✗ cd ..<br />➜  lib git:(master) ✗ cd ..<br />➜  bin git:(master) ✗ ./LINKS  <br />Usage: ./LINKS [v1.8.6]<br />-f  sequences to scaffold (Multi-FASTA format, required)<br />-s  file-of-filenames, full path to long sequence reads or MPET pairs [see below] (Multi-FASTA/fastq format, required)<br />-m  MPET reads (default -m 1 = yes, default = no, optional)<br />	! DO NOT SET IF NOT USING MPET. WHEN SET, LINKS WILL EXPECT A SPECIAL FORMAT UNDER -s<br />	! Paired MPET reads in their original outward orientation &lt;- -&gt; must be separated by ":"<br />	  &gt;template_name<br />	  ACGACACTATGCATAAGCAGACGAGCAGCGACGCAGCACG:ATATATAGCGCACGACGCAGCACAGCAGCAGACGAC<br />-d  distance between k-mer pairs (ie. target distances to re-scaffold on. default -d 4000, optional)<br />	Multiple distances are separated by comma. eg. -d 500,1000,2000,3000<br />-k  k-mer value (default -k 15, optional)<br />-t  step of sliding window when extracting k-mer pairs from long reads (default -t 2, optional)<br />	Multiple steps are separated by comma. eg. -t 10,5<br />-o  offset position for extracting k-mer pairs (default -o 0, optional)<br />-e  error (%) allowed on -d distance   e.g. -e 0.1  == distance +/- 10% (default -e 0.1, optional)<br />-l  minimum number of links (k-mer pairs) to compute scaffold (default -l 5, optional)<br />-a  maximum link ratio between two best contig pairs (default -a 0.3, optional)<br />	 *higher values lead to least accurate scaffolding*<br />-z  minimum contig length to consider for scaffolding (default -z 500, optional)<br />-b  base name for your output files (optional)<br />-r  Bloom filter input file for sequences supplied in -s (optional, if none provided will output to .bloom)<br />	 NOTE: BLOOM FILTER MUST BE DERIVED FROM THE SAME FILE SUPPLIED IN -f WITH SAME -k VALUE<br />	 IF YOU DO NOT SUPPLY A BLOOM FILTER, ONE WILL BE CREATED (.bloom)<br />-p  Bloom filter false positive rate (default -p 0.001, optional; increase to prevent memory allocation errors)<br />-x  Turn off Bloom filter functionality (-x 1 = yes, default = no, optional)<br />-v  Runs in verbose mode (-v 1 = yes, default = no, optional)</p>

<p>Error: Missing mandatory options -f and -s.</p>

<p>ERROR fixed</p>

<p>perl: symbol lookup error: /home/urbe/Tools/LINKS_new/bin/./lib/bloomfilter/swig/BloomFilter.so: undefined symbol: Perl_Gthr_key_ptr</p>
]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/37396/converting-a-vcf-into-a-fasta-given-some-reference</guid>
	<pubDate>Fri, 20 Jul 2018 10:03:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/37396/converting-a-vcf-into-a-fasta-given-some-reference</link>
	<title><![CDATA[Converting a VCF into a FASTA given some reference !]]></title>
	<description><![CDATA[<p>Samtools/BCFtools (Heng Li) provides a Perl script&nbsp;<a href="https://github.com/lh3/samtools/blob/master/bcftools/vcfutils.pl"><code>vcfutils.pl</code></a>&nbsp;which does this, the function&nbsp;<code>vcf2fq</code>&nbsp;(lines 469-528)</p><p>This script has been modified by others to convert InDels as well, e.g.&nbsp;<a href="https://github.com/gringer/bioinfscripts/blob/master/vcf2fq.pl">this</a>&nbsp;by David Eccles</p><pre><code><span>./</span><span>vcf2fq</span><span>.</span><span>pl </span><span>-</span><span>f </span><span>&lt;</span><span>input</span><span>.</span><span>fasta</span><span>&gt;</span><span> </span><span>&lt;</span><span>all</span><span>-</span><span>site</span><span>.</span><span>vcf</span><span>&gt;</span><span> </span><span>&gt;</span><span> </span><span>&lt;</span><span>output</span><span>.</span><span>fastq</span><span>&gt;</span></code></pre><p>https://github.com/gringer/bioinfscripts/blob/master/vcf2fq.pl</p><p>https://github.com/lh3/samtools/blob/master/bcftools/vcfutils.pl</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41673/lr-gapcloser-a-tiling-path-based-gap-closer-that-uses-long-reads-to-complete-genome-assembly</guid>
	<pubDate>Thu, 14 May 2020 15:09:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41673/lr-gapcloser-a-tiling-path-based-gap-closer-that-uses-long-reads-to-complete-genome-assembly</link>
	<title><![CDATA[LR_Gapcloser: a tiling path-based gap closer that uses long reads to complete genome assembly]]></title>
	<description><![CDATA[<p>LR_Gapcloser is a gap closing tool using long reads from studied species. The long reads could be downloaed from public read archive database (for instance, NCBI SRA database ) or be your own data. Then they are fragmented and aligned to scaffolds using BWA mem algorithm in BWA package. In the package, we provided a compiled bwa, so the user needn't to install bwa. LR_Gapcloser uses the alignments to find the bridging that cross the gap, and then fills the long read original sequence into the genomic gaps.</p><p>Address of the bookmark: <a href="https://github.com/CAFS-bioinformatics/LR_Gapcloser" rel="nofollow">https://github.com/CAFS-bioinformatics/LR_Gapcloser</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37840/long-read-assembly-workshop</guid>
	<pubDate>Thu, 04 Oct 2018 17:23:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37840/long-read-assembly-workshop</link>
	<title><![CDATA[Long read assembly workshop !]]></title>
	<description><![CDATA[<p>This is a tutorial for a workshop on long-read (PacBio) genome assembly.</p>
<p>It demonstrates how to use long PacBio sequencing reads to assemble a bacterial genome, and includes additional steps for circularising, trimming, finding plasmids, and correcting the assembly with short-read Illumina data.</p>
<p>&nbsp;Please comment if you know any other long read addembly tutorial.</p><p>Address of the bookmark: <a href="http://sepsis-omics.github.io/tutorials/modules/cmdline_assembly_v2/" rel="nofollow">http://sepsis-omics.github.io/tutorials/modules/cmdline_assembly_v2/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38041/synima-a-synteny-imaging-tool-for-annotated-genome-assemblies</guid>
	<pubDate>Tue, 30 Oct 2018 10:49:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38041/synima-a-synteny-imaging-tool-for-annotated-genome-assemblies</link>
	<title><![CDATA[Synima: a Synteny imaging tool for annotated genome assemblies]]></title>
	<description><![CDATA[<p><span>Synima written in Perl, which uses the graphical features of R. Synima takes orthologues computed from reciprocal best BLAST hits or OrthoMCL, and DAGchainer, and outputs an overview of genome-wide synteny in PDF. Each of these programs are included with the Synima package, and a pipeline for their use. Synima has a range of graphical parameters including size, colours, order, and labels, which are specified in a config file generated by the first run of Synima &ndash; and can be subsequently edited. Synima runs quickly on a command line to generate informative and publication quality figures. Synima is open source and freely available from&nbsp;</span><a href="https://github.com/rhysf/Synima" target="_blank">https://github.com/rhysf/Synima</a><span>&nbsp;under the MIT License.</span></p><p>Address of the bookmark: <a href="https://github.com/rhysf/Synima" rel="nofollow">https://github.com/rhysf/Synima</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38224/novograph-building-whole-genome-graphs-from-long-read-based-de-novo-assemblies</guid>
	<pubDate>Thu, 15 Nov 2018 12:48:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38224/novograph-building-whole-genome-graphs-from-long-read-based-de-novo-assemblies</link>
	<title><![CDATA[NovoGraph: building whole genome graphs from long-read-based de novo assemblies]]></title>
	<description><![CDATA[<p><span>NovoGraph: building whole genome graphs from long-read-based de novo assemblies</span></p>
<p><span><span>An algorithmically novel approach to construct a genome graph representation of long-read-based&nbsp;</span><em>de novo</em><span>&nbsp;sequence assemblies. We then provide a proof of principle by creating a genome graph of seven ethnically-diverse human genomes.</span></span></p>
<p>&nbsp;</p>
<p>https://f1000research.com/articles/7-1391/v1</p><p>Address of the bookmark: <a href="https://github.com/NCBI-Hackathons/NovoGraph" rel="nofollow">https://github.com/NCBI-Hackathons/NovoGraph</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38475/purge-haplotigs-pipeline-to-help-with-curating-heterozygous-diploid-genome-assemblies</guid>
	<pubDate>Mon, 17 Dec 2018 03:17:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38475/purge-haplotigs-pipeline-to-help-with-curating-heterozygous-diploid-genome-assemblies</link>
	<title><![CDATA[Purge Haplotigs: Pipeline to help with curating heterozygous diploid genome assemblies]]></title>
	<description><![CDATA[<p>Some parts of a genome may have a very high degree of heterozygosity. This causes contigs for both haplotypes of that part of the genome to be assembled as separate primary contigs, rather than as a contig and an associated haplotig. This can be an issue for downstream analysis whether you're working on the haploid or phased-diploid assembly.</p>
<p><span>Identify pairs of contigs that are syntenic and move one of them to the haplotig 'pool'. The pipeline uses mapped read coverage and Minimap2 alignments to determine which contigs to keep for the haploid assembly. Dotplots are optionally produced for all flagged contig matches, juxtaposed with read-coverage, to help the user determine the proper assignment of any remaining ambiguous contigs. The pipeline will run on either a haploid assembly (i.e. Canu, FALCON or FALCON-Unzip primary contigs) or on a phased-diploid assembly (i.e. FALCON-Unzip primary contigs + haplotigs). Here are&nbsp;</span><a href="https://bitbucket.org/mroachawri/purge_haplotigs/wiki/Examples">two examples</a><span>&nbsp;of how Purge Haplotigs can improve a haploid and diploid assembly.</span></p><p>Address of the bookmark: <a href="https://bitbucket.org/mroachawri/purge_haplotigs" rel="nofollow">https://bitbucket.org/mroachawri/purge_haplotigs</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>

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