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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41969?offset=60</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly</guid>
	<pubDate>Mon, 27 Nov 2017 08:05:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly</link>
	<title><![CDATA[SPAdes hybrid genome assembly]]></title>
	<description><![CDATA[<p>When you have both Illumina and Nanopore data, then SPAdes remains a good option for hybrid assembly - SPAdes was used to produce the&nbsp;<a href="https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0101-6">B fragilis assembly</a>&nbsp;by Mick Watson&rsquo;s group.</p><p>Again, running spades.py will show you the options:</p><div><pre><code>spades.py
</code></pre></div><p>This produces:</p><div><pre><code>SPAdes genome assembler v3.10.1

Usage: /usr/local/SPAdes-3.10.1-Linux/bin/spades.py [options] -o &lt;output_dir&gt;

Basic options:
-o      &lt;output_dir&gt;    directory to store all the resulting files (required)
--sc                    this flag is required for MDA (single-cell) data
--meta                  this flag is required for metagenomic sample data
--rna                   this flag is required for RNA-Seq data
--plasmid               runs plasmidSPAdes pipeline for plasmid detection
--iontorrent            this flag is required for IonTorrent data
--test                  runs SPAdes on toy dataset
-h/--help               prints this usage message
-v/--version            prints version

Input data:
--12    &lt;filename&gt;      file with interlaced forward and reverse paired-end reads
-1      &lt;filename&gt;      file with forward paired-end reads
-2      &lt;filename&gt;      file with reverse paired-end reads
-s      &lt;filename&gt;      file with unpaired reads
--pe&lt;#&gt;-12      &lt;filename&gt;      file with interlaced reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-1       &lt;filename&gt;      file with forward reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-2       &lt;filename&gt;      file with reverse reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-s       &lt;filename&gt;      file with unpaired reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-&lt;or&gt;    orientation of reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--s&lt;#&gt;          &lt;filename&gt;      file with unpaired reads for single reads library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-12      &lt;filename&gt;      file with interlaced reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-1       &lt;filename&gt;      file with forward reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-2       &lt;filename&gt;      file with reverse reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-s       &lt;filename&gt;      file with unpaired reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-&lt;or&gt;    orientation of reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--hqmp&lt;#&gt;-12    &lt;filename&gt;      file with interlaced reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-1     &lt;filename&gt;      file with forward reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-2     &lt;filename&gt;      file with reverse reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-s     &lt;filename&gt;      file with unpaired reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-&lt;or&gt;  orientation of reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--nxmate&lt;#&gt;-1   &lt;filename&gt;      file with forward reads for Lucigen NxMate library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--nxmate&lt;#&gt;-2   &lt;filename&gt;      file with reverse reads for Lucigen NxMate library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--sanger        &lt;filename&gt;      file with Sanger reads
--pacbio        &lt;filename&gt;      file with PacBio reads
--nanopore      &lt;filename&gt;      file with Nanopore reads
--tslr  &lt;filename&gt;      file with TSLR-contigs
--trusted-contigs       &lt;filename&gt;      file with trusted contigs
--untrusted-contigs     &lt;filename&gt;      file with untrusted contigs

Pipeline options:
--only-error-correction runs only read error correction (without assembling)
--only-assembler        runs only assembling (without read error correction)
--careful               tries to reduce number of mismatches and short indels
--continue              continue run from the last available check-point
--restart-from  &lt;cp&gt;    restart run with updated options and from the specified check-point ('ec', 'as', 'k&lt;int&gt;', 'mc')
--disable-gzip-output   forces error correction not to compress the corrected reads
--disable-rr            disables repeat resolution stage of assembling

Advanced options:
--dataset       &lt;filename&gt;      file with dataset description in YAML format
-t/--threads    &lt;int&gt;           number of threads
                                [default: 16]
-m/--memory     &lt;int&gt;           RAM limit for SPAdes in Gb (terminates if exceeded)
                                [default: 250]
--tmp-dir       &lt;dirname&gt;       directory for temporary files
                                [default: &lt;output_dir&gt;/tmp]
-k              &lt;int,int,...&gt;   comma-separated list of k-mer sizes (must be odd and
                                less than 128) [default: 'auto']
--cov-cutoff    &lt;float&gt;         coverage cutoff value (a positive float number, or 'auto', or 'off') [default: 'off']
--phred-offset  &lt;33 or 64&gt;      PHRED quality offset in the input reads (33 or 64)
                                [default: auto-detect]
</code></pre></div><p>As you can see this is also a &ldquo;pipeline&rdquo; of tools that can be switched on or off. SPAdes takes quite a long time, so for the purposes of this practical, something like this may suffice:</p><div><pre><code>spades.py -t 4 <span>\</span>
          -m 32 <span>\</span>
          -k 31,51,71 <span>\</span>
          --only-assembler <span>\</span>
          -1 miseq.1.fastq -2 miseq.2.fastq <span>\</span>
          --nanopore minion.fastq <span>\</span>
          -o hybrid_assembly
</code></pre></div><p>In turn, these parameters mean</p><ul>
<li>use 4 threads</li>
<li>max memory is 32Gb</li>
<li>use 3 kmer values to build the de bruijn graph(s) - 31, 51 and 71</li>
<li>only run the assembler, not the correction algorithm (for speed)</li>
<li>read 1 and read 2 of the MiSeq data</li>
<li>the nanopore data</li>
<li>put the output in folder &ldquo;hybrid_assembly&rdquo;</li>
</ul>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</guid>
	<pubDate>Mon, 04 May 2020 02:04:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</link>
	<title><![CDATA[HASLR: a hybrid assembler which uses both second and third generation sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32485/bacterial-genome-assembly</guid>
	<pubDate>Fri, 05 May 2017 06:11:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32485/bacterial-genome-assembly</link>
	<title><![CDATA[Bacterial genome assembly !!]]></title>
	<description><![CDATA[<p>This tutorial will serve as an example of how to use free and open-source genome assembly and secondary scaffolding tools to generate high quality assemblies of&nbsp;bacterial sequence data. The bacterial sample used in this tutorial will be referred&nbsp;to simply&nbsp;as &ldquo;Species&rdquo; since it is&nbsp;live data. This data is paired-end data, meaning that there are forward and reverse reads, which we will designate as Sample_R1.fastq and Sample_R2.fastq, respectively.</p>
<p>https://github.com/jennomics/WorkflowPaper/blob/master/Genome%20Assembly%20and%20Annotation.md</p><p>Address of the bookmark: <a href="http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/" rel="nofollow">http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43013/deg-50-a-database-of-essential-genes-in-both-prokaryotes-and-eukaryotes</guid>
	<pubDate>Tue, 30 Mar 2021 11:47:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43013/deg-50-a-database-of-essential-genes-in-both-prokaryotes-and-eukaryotes</link>
	<title><![CDATA[DEG 5.0: a database of essential genes in both prokaryotes and eukaryotes]]></title>
	<description><![CDATA[<p><span>Essential genes are those indispensable for the survival of an organism, and their functions are therefore considered a foundation of life. Determination of a minimal gene set needed to sustain a life form, a fundamental question in biology, plays a key role in the emerging field, synthetic biology. </span></p>
<p><span></span><span>DEG is freely available at the website&nbsp;</span><a href="http://tubic.tju.edu.cn/deg" target="_blank">http://tubic.tju.edu.cn/deg</a><span>&nbsp;or&nbsp;</span><a href="http://www.essentialgene.org/" target="_blank">http://www.essentialgene.org</a><span>.</span></p><p>Address of the bookmark: <a href="http://www.essentialgene.org/" rel="nofollow">http://www.essentialgene.org/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36865/perga-a-paired-end-read-guided-de-novo-assembler-for-extending-contigs-using-svm-and-look-ahead-approach</guid>
	<pubDate>Tue, 05 Jun 2018 09:57:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36865/perga-a-paired-end-read-guided-de-novo-assembler-for-extending-contigs-using-svm-and-look-ahead-approach</link>
	<title><![CDATA[PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach]]></title>
	<description><![CDATA[PERGA - Paired End Reads Guided Assembler

PERGA is a novel sequence reads guided de novo assembly approach which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds. Instead of using single-end reads to construct contig, PERGA uses paired-end reads and different read overlap sizes from O ≥ Omax to Omin to resolve the gaps and branches. Moreover, by constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. PERGA will try to extend the contigs by all feasible nucleotides and determine if these multiple extensions due to sequencing errors or repeats by using looking ahead technology, and it also try to separate the different repeats of nearby genomic regions to make the assembly result more longer and accurate.

The simulated E.coli paired-end reads data are generated using GemSim (KE McElroy, F Luciani, T Thomas. Gemsim: General, Error-Model Based Simulator of Next-Generation Sequencing Data. BMC Genomics 2012, 13:74), with coverage 50x, 60x, 100x, read lengths 100-bp, and can be downloaded from https://github.com/zhuxiao/data_PERGA.<p>Address of the bookmark: <a href="https://github.com/hitbio/PERGA" rel="nofollow">https://github.com/hitbio/PERGA</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42267/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding</guid>
	<pubDate>Mon, 26 Oct 2020 21:23:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42267/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding</link>
	<title><![CDATA[HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding.]]></title>
	<description><![CDATA[<p><span>Despite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding.</span></p>
<p><span>More at&nbsp;https://github.com/esolares/HapSolo</span></p><p>Address of the bookmark: <a href="https://github.com/esolares/HapSolo" rel="nofollow">https://github.com/esolares/HapSolo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44513/mike-an-ultrafast-assembly-and-alignment-free-approach-for-phylogenetic-tree-construction</guid>
	<pubDate>Mon, 08 Apr 2024 06:19:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44513/mike-an-ultrafast-assembly-and-alignment-free-approach-for-phylogenetic-tree-construction</link>
	<title><![CDATA[MIKE: an ultrafast, assembly-, and alignment-free approach for phylogenetic tree construction]]></title>
	<description><![CDATA[<p><span>MIKE (MinHash-based&nbsp;</span><em>k</em><span>-mer algorithm). This algorithm is designed for the swift calculation of the Jaccard coefficient directly from raw sequencing reads and enables the construction of phylogenetic trees based on the resultant Jaccard coefficient. Simulation results highlight the superior speed of MIKE compared to existing state-of-the-art methods. We used MIKE to reconstruct a phylogenetic tree, incorporating 238 yeast, 303&nbsp;</span><em>Zea</em><span>, 141&nbsp;</span><em>Ficus</em><span>, 67&nbsp;</span><em>Oryza</em><span>, and 43&nbsp;</span><em>Saccharum spontaneum</em><span>&nbsp;samples. MIKE demonstrated accurate performance across varying evolutionary scales, reproductive modes, and ploidy levels, proving itself as a powerful tool for phylogenetic tree construction.</span></p><p>Address of the bookmark: <a href="https://github.com/Argonum-Clever2/mike" rel="nofollow">https://github.com/Argonum-Clever2/mike</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34562/harvest-a-suite-of-core-genome-alignment-and-visualization-tools</guid>
	<pubDate>Fri, 08 Dec 2017 07:16:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34562/harvest-a-suite-of-core-genome-alignment-and-visualization-tools</link>
	<title><![CDATA[Harvest: a suite of core-genome alignment and visualization tools]]></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="https://harvest.readthedocs.io/en/latest/_images/screen.png"><img src="https://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="https://harvest.readthedocs.io/en/latest/content/parsnp.html">Parsnp</a>&nbsp;- Core-genome alignment and analysis</li>
<li><a href="https://harvest.readthedocs.io/en/latest/content/gingr.html">Gingr</a>&nbsp;- Interactive visualization of alignments, trees and variants</li>
<li><a href="https://harvest.readthedocs.io/en/latest/content/harvest-tools.html">HarvestTools</a>&nbsp;- Archiving and postprocessing</li>
<li></li>
</ul><p>Address of the bookmark: <a href="https://harvest.readthedocs.io/en/latest/" rel="nofollow">https://harvest.readthedocs.io/en/latest/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41144/seqmule-automated-human-exomegenome-variants-detection</guid>
	<pubDate>Tue, 18 Feb 2020 03:22:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41144/seqmule-automated-human-exomegenome-variants-detection</link>
	<title><![CDATA[SeqMule: Automated human exome/genome variants detection]]></title>
	<description><![CDATA[<p>SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file.</p><p>Address of the bookmark: <a href="https://doc-openbio.readthedocs.io/projects/seqmule/en/latest/" rel="nofollow">https://doc-openbio.readthedocs.io/projects/seqmule/en/latest/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44641/heliano-a-fast-and-accurate-tool-for-detection-of-helitron-like-elements</guid>
	<pubDate>Tue, 13 Aug 2024 07:16:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44641/heliano-a-fast-and-accurate-tool-for-detection-of-helitron-like-elements</link>
	<title><![CDATA[HELIANO: A fast and accurate tool for detection of Helitron-like elements]]></title>
	<description><![CDATA[<p><span>Helitron-like elements (HLE1 and HLE2) are DNA transposons. They have been found in diverse species and seem to play significant roles in the evolution of host genomes. Although known for over twenty years, Helitron sequences are still challenging to identify. Here, we propose HELIANO (Helitron-like elements annotator) as an efficient solution for detecting Helitron-like elements.</span></p>
<p>https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkae679/7730539?login=true</p><p>Address of the bookmark: <a href="https://github.com/Zhenlisme/heliano/" rel="nofollow">https://github.com/Zhenlisme/heliano/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

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