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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27333?offset=300</link>
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	<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/blog/view/30104/structural-variation-the-hidden-genomic-treasure</guid>
	<pubDate>Sat, 10 Dec 2016 16:19:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/30104/structural-variation-the-hidden-genomic-treasure</link>
	<title><![CDATA[Structural variation: the hidden genomic treasure]]></title>
	<description><![CDATA[<p>Genome re-sequencing projects have revealed substantial amounts of genetic variation between individuals extending beyond single nucleotide polymorphisms (SNPs) and short indels. Structural Variations (SVs) and Copy Number Variations (CNVs) are a major source of genomic variation. However, compared to SNPs, accurate detection, genotyping and understanding of CNVs is lagging behind due to much greater analytical challenges related to SV/CNV detection and analysis. In our lab we analyse SVs/CNVs using high-throughput sequencing and different analytical approaches.&nbsp;The most‐studied structural variants are copy number variations (CNVs) which can be generated by several different mechanisms including non‐allelic homologous recombination, non‐homologous end‐joining and deoxyribonucleic acid (DNA) replication‐related fork stalling and template switching. CNVs are closely related to segmental duplications (SDs): SDs can stimulate the formation of CNVs and themselves started out as CNVs, but became fixed in a species. Structural variation can be neutral but has also influenced our phenotypic evolution, for example our susceptibility to disease and our ability to digest certain types of food. Our understanding of the extent of structural variation is increasing rapidly, but it will be much more difficult to understand its phenotypic consequences.&nbsp;</p><p><img src="http://www.nature.com/nmeth/journal/v9/n2/images/nmeth.1858-F3.jpg" alt="image" width="946" height="603" style="border: 0px; border: 0px;"></p><p>Structural variants (SVs) such as deletions, insertions, duplications, inversions and translocations litter genomes and are often associated with gene expression changes and severe phenotypes (ie. genetic diseases in humans). Recent studies on the functional aspects of different types of SVs have unveiled several cases of adaptive evolution. For example, inversions have been associated with ecological adaptations and may facilitate speciation. Due to their prevalent nature, SVs arguably have a large impact on genome evolution and should not be neglected when studying the genetics of adaptation and speciation.&nbsp;SVs were classically defined as chromosomal rearrangements larger than 1kb, but due to a higher resolution of new detection methods, smaller variants (between 50 and 1000 base pairs) can now be accurately assessed. Besides various methods of detection in next generation sequencing data (paired end mapping, split reads, and depth of coverage), array-based approaches have proven to be particularly useful for detecting copy number variations (CNVs). These technologies have enabled researchers to catalog a wide spectrum of SVs in many organisms and infer the effects of selection shaping their evolutionary trajectories.</p><p><strong>Structure variation sequencing signature (Source: NatRev Genetics)</strong></p><p><img src="http://www.nature.com/nrg/journal/v12/n5/images/nrg2958-f2.jpg" alt="image" width="800" height="824" style="border: 0px; border: 0px;"></p><p>Related tools, databases and publications are listed below. If you know any interesing papers, please let us know in comment section:</p><p><br /><strong>Key concepts</strong></p><p>Structural variation includes balanced variants such as inversions and translocations, and unbalanced ones such as duplications and deletions (copy number variations or CNVs).</p><p>Structural variants can arise by several mechanisms, including nonallelic homologous recombination (NAHR), nonhomologous end‐joining (NHEJ) and DNA replication‐based fork stalling and template switching (FoSTeS).</p><p>CNV is closely linked to segmental duplication, but is not exactly the same. Segmental duplications can stimulate CNV formation by NAHR, and themselves arise from CNVs that have become fixed.</p><p>Segmental duplications did not appear uniformly during the evolution of the Great Ape species, but rather during a burst of activity around the time of the divergence of gorilla from the human/chimpanzee ancestor.</p><p>Duplicated genes play a critical role in the evolution of a genome as they act as &lsquo;spare parts&rsquo; than can evolve to perform new or more specialized functions.</p><p>Effects of structural variation on gene expression can be identified but only a few examples of the consequences for species biology have been documented.</p><p><strong style="font-size: 12.8px;">Tools</strong></p><p><a href="http://sv.gersteinlab.org/cnvnator">CNVnator</a>a tool for CNV discovery and genotyping from depth of read mapping.<a href="http://www.ncbi.nlm.nih.gov/pubmed/21293372">2011a</a>,<a href="http://www.ncbi.nlm.nih.gov/pubmed/21324876">2011b</a></p><p><a href="http://sv.gersteinlab.org/age">AGE</a>a tools that implements an algorithm for optimal alignment of sequences with SVs.<a href="http://www.ncbi.nlm.nih.gov/pubmed/21233167">2011</a></p><p><a href="http://sv.gersteinlab.org/breakseq">BreakSeq</a>a pipeline for annotation, classification and analysis of SVs at single nucleotide resolution.<a href="http://www.ncbi.nlm.nih.gov/pubmed/20037582">2010</a></p><p><a href="http://sv.gersteinlab.org/pemer">PEMer</a>a computational and simulation framework for discovering SVs by paired-end read mapping.<a href="http://www.ncbi.nlm.nih.gov/pubmed/19236709">2009</a>,<a href="http://www.ncbi.nlm.nih.gov/pubmed/17901297">2007</a></p><p>GASV https://code.google.com/archive/p/gasv/</p><p>PAIROSCOPE http://pairoscope.sourceforge.net/</p><p>SVDetect&nbsp;http://svdetect.sourceforge.net/Site/Home.html</p><p>BreakPtr, discovery of unbalanced structural variants (copy-number variants) with tiling microarrays&nbsp;<a href="http://tiling.mbb.yale.edu/BreakPtr/" target="_top">Link</a>&nbsp;</p><p>R Package&nbsp;https://www.bioconductor.org/help/course-materials/2010/EMBL2010/Practical-4-StructuralVariants.pdf<br /><br />BreakSeq, structural variant genotyping using split reads&nbsp;<a href="http://sv.gersteinlab.org/breakseq/" target="_top">Link</a>&nbsp;<br /><br />CopySeq, genotyping of unbalanced structural variants (copy-number variants) using read-depth&nbsp;<a href="http://www.korbel.embl.de/CopySeq/" target="_top">Link</a>&nbsp;<br /><br />DELLY2, integrated structural variant discovery, genotyping and visualization in deep sequencing data&nbsp;<a href="https://github.com/dellytools/delly" target="_top">Link</a>&nbsp;<br /><br />PEMer, structural variant discovery in 454 sequencing data by paired-end mapping&nbsp;<a href="http://www.korbel.embl.de/PEMer/" target="_top">Link</a>&nbsp;<br /><br />TIGER, transduction inference in germline genomes using short read data&nbsp;<a href="https://github.com/jelena-tica/TIGER" target="_top">Link</a>&nbsp;</p><p>MANTA&nbsp;https://github.com/Illumina/manta</p><p>SV-Bay&nbsp;https://github.com/InstitutCurie/SV-Bay</p><p>BreakDancer&nbsp;http://breakdancer.sourceforge.net/</p><p>Variation Hunter&nbsp;http://compbio.cs.sfu.ca/software-variation-hunter</p><p>Lumpy&nbsp;https://github.com/arq5x/lumpy-sv</p><p>ForestSV&nbsp;http://sebatlab.ucsd.edu/index.php/software-data&nbsp;</p><p>PBSuites for long reads&nbsp;https://sourceforge.net/projects/pb-jelly/</p><p><strong>Visualization</strong></p><p>The SV visualization tool:&nbsp;<a href="http://genomesavant.com/savant/">http://genomesavant.com/savant/</a></p><p>InGAP-SV (<a href="http://ingap.sourceforge.net/">http://ingap.sourceforge.net/</a>) that is nice tools for both detection and visualisation of severals kind of structural variations (Large insertions, translocation, deletion, inversions....)&nbsp;</p><p>Tools table: http://www.nature.com/nbt/journal/v29/n8/fig_tab/nbt.1904_T2.html</p><p>Variation Viewer https://www.ncbi.nlm.nih.gov/variation/view/</p><p><strong style="font-size: 12.8px;">Papers</strong></p><p>http://www.nature.com/nmeth/journal/v9/n2/full/nmeth.1858.html</p><p>http://journal.frontiersin.org/researchtopic/1412/structural-variations-in-genomes-ecological-and-evolutionary-implications</p><p>http://www.mi.fu-berlin.de/wiki/pub/ABI/GenomicsLecture10Materials/structural-variation.pdf</p><p>http://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-015-1479-3</p><p>https://www.ncbi.nlm.nih.gov/dbvar/content/overview/</p><p>http://www.nature.com/subjects/structural-variation</p><p>https://eichlerlab.gs.washington.edu/news/NatMeth_Feb2012.pdf</p><p>https://www.ncbi.nlm.nih.gov/pubmed/19477992 ***</p><p>https://www.ncbi.nlm.nih.gov/pubmed/22452995</p><p>http://biorxiv.org/content/early/2016/09/06/073833</p><p>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479793/</p><p>http://www.nature.com/articles/srep18501</p><p>http://www.genetics.org/content/202/1/351</p><p>http://www.cs.cmu.edu/~sssykim/teaching/s13/slides/Lecture_SVI.pdf</p><p>https://www.omicsonline.org/open-access/structural-variation-detection-from-next-generation-sequencing-2469-9853-S1-007.php?aid=69055</p><p>http://schatzlab.cshl.edu/presentations/2016/2016.01.12.PAG.Structural%20Variations.pdf</p><p>&nbsp;</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30168/gene-synteny-database</guid>
	<pubDate>Fri, 16 Dec 2016 11:09:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30168/gene-synteny-database</link>
	<title><![CDATA[Gene Synteny Database]]></title>
	<description><![CDATA[<p>Comparative genomics remains a pivotal strategy to study the evolution of gene organization, and this primacy is reinforced by the growing number of full genome sequences available in public repositories. Despite this growth, bioinformatic tools available to visualize and compare genomes and to infer evolutionary events remain restricted to two or three genomes at a time, thus limiting the breadth and the nature of the question that can be investigated. Here we present Genomicus, a new synteny browser that can represent and compare unlimited numbers of genomes in a broad phylogenetic view. In addition, Genomicus includes reconstructed ancestral gene organization, thus greatly facilitating the interpretation of the data.</p>
<p><strong>Availability:</strong>&nbsp;Genomicus is freely available for online use at&nbsp;<a href="http://www.dyogen.ens.fr/genomicus" target="pmc_ext">http://www.dyogen.ens.fr/genomicus</a>&nbsp;while data can be downloaded at&nbsp;<a href="ftp://ftp.biologie.ens.fr/pub/dyogen/genomicus" target="pmc_ext">ftp://ftp.biologie.ens.fr/pub/dyogen/genomicus</a></p>
<p><strong>Contact:</strong>&nbsp;<a href="mailto:dev@null">rf.sne.eigoloib@crh</a></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853686/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853686/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30214/megamerge-a-tool-to-merge-assembled-contigs-long-reads-from-metagenomic-sequencing-runs</guid>
	<pubDate>Mon, 19 Dec 2016 09:42:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30214/megamerge-a-tool-to-merge-assembled-contigs-long-reads-from-metagenomic-sequencing-runs</link>
	<title><![CDATA[MeGAMerge: A tool to merge assembled contigs, long reads from metagenomic sequencing runs]]></title>
	<description><![CDATA[<p>MeGAMerge</p>
<p>MeGAMerge (A tool to merge assembled contigs, long reads from metagenomic sequencing runs)</p>
<p>Description</p>
<p>MeGAMerge is a perl based wrapper/tool that can accept any number of sequence (FASTA) files containing assembled contigs of any length in Multi-FASTA format to produce an improved contig set based on OLC based assembly. All overlap parameters (Minimum Overlap Length, Identity, etc) are user-declarable at runtime. It is written to run on Linux.</p>
<p>Requirements:</p>
<p>You will need to have the following tools installed and in $PATH, or added to $binpath in the tool:</p>
<p>Newbler (specifically runAssembly)<br>Minimus2 (part of AMOS, also requires MUMmer)</p><p>Address of the bookmark: <a href="https://github.com/LANL-Bioinformatics/MeGAMerge" rel="nofollow">https://github.com/LANL-Bioinformatics/MeGAMerge</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/bookmarks/view/32376/diamond</guid>
	<pubDate>Thu, 27 Apr 2017 04:21:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32376/diamond</link>
	<title><![CDATA[DIAMOND]]></title>
	<description><![CDATA[<p><span>DIAMOND is a sequence aligner for protein and translated DNA searches and functions as a drop-in replacement for the NCBI BLAST software tools. It is suitable for protein-protein search as well as DNA-protein search on short reads and longer sequences including contigs and assemblies, providing a speedup of BLAST ranging up to x20,000.</span></p>
<p><span>More at&nbsp;file:///home/urbe/Downloads/diamond_manual.pdf</span></p>
<p><span>http://www.nature.com/nmeth/journal/v12/n1/full/nmeth.3176.html</span></p><p>Address of the bookmark: <a href="https://github.com/bbuchfink/diamond" rel="nofollow">https://github.com/bbuchfink/diamond</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34038/quota-synteny-alignment</guid>
	<pubDate>Mon, 31 Jul 2017 04:11:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34038/quota-synteny-alignment</link>
	<title><![CDATA[Quota synteny alignment]]></title>
	<description><![CDATA[<p><span>Typically in comparative genomics, we can identify anchors, chain them into syntenic blocks and interpret these blocks as derived from a common descent. However, when comparing two genomes undergone ancient genome duplications (plant genomes in particular), we have large number of blocks that are not orthologous, but are paralogous. This has forced us sometimes to use&nbsp;</span><em>ad-hoc</em><span>&nbsp;rules to screen these blocks. So the question is:&nbsp;</span><span>given the expected depth (quota) along both x- and y-axis, select a subset of the anchors with maximized total score</span><span>.</span></p><p>Address of the bookmark: <a href="https://github.com/tanghaibao/quota-alignment" rel="nofollow">https://github.com/tanghaibao/quota-alignment</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39689/msaprobs-parallel-and-accurate-multiple-sequence-alignment</guid>
	<pubDate>Tue, 09 Jul 2019 23:58:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39689/msaprobs-parallel-and-accurate-multiple-sequence-alignment</link>
	<title><![CDATA[MSAProbs - Parallel and accurate multiple sequence alignment]]></title>
	<description><![CDATA[<p><strong>MSAProbs</strong><span>&nbsp;is a well-established state-of-the-art multiple sequence alignment algorithm for protein sequences. The design of MSAProbs is based on a combination of pair hidden Markov models and partition functions to calculate posterior probabilities. Assessed using the popular benchmarks: BAliBASE, PREFAB, SABmark and OXBENCH, MSAProbs achieves statistically significant accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons and Probalign. In addition, MSAProbs is optimized for shared-memory CPUs by employing a multi-threaded design, and further parallelized for distributed-memory systems using MPI to overcome high memory overhead barrier and achieve good parallel and data-size scalability.</span></p><p>Address of the bookmark: <a href="http://msaprobs.sourceforge.net/homepage.htm#latest" rel="nofollow">http://msaprobs.sourceforge.net/homepage.htm#latest</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31714/krona</guid>
	<pubDate>Wed, 22 Mar 2017 04:47:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31714/krona</link>
	<title><![CDATA[Krona]]></title>
	<description><![CDATA[<p>Krona allows hierarchical data to be explored with zooming, multi-layered pie charts. Krona charts can be created using an <a href="https://github.com/marbl/Krona/wiki/ExcelTemplate">Excel template</a> or <a href="https://github.com/marbl/Krona/wiki/KronaTools">KronaTools</a>, which includes support for several bioinformatics tools and raw data formats. The interactive charts are self-contained and can be viewed with any modern web browser (see <a href="https://github.com/marbl/Krona/wiki/Browser%20support">Browser support</a>).</p>
<p><a href="http://marbl.github.io/Krona/img/screen_mgrast.png"><img src="https://camo.githubusercontent.com/27b71b1f1832523723c3d14dec764e7ad098438c/687474703a2f2f6d6172626c2e6769746875622e696f2f4b726f6e612f696d672f7468756d625f6d67726173742e706e67" width="210" height="167" alt="image" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/marbl/Krona/wiki" rel="nofollow">https://github.com/marbl/Krona/wiki</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44481/unialigner-a-parameter-free-framework-for-fast-sequence-alignment</guid>
	<pubDate>Fri, 08 Mar 2024 23:36:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44481/unialigner-a-parameter-free-framework-for-fast-sequence-alignment</link>
	<title><![CDATA[UniAligner: a parameter-free framework for fast sequence alignment]]></title>
	<description><![CDATA[<p>UniAligner (formerly, TandemAligner) is the first parameter-free algorithm for sequence alignment that introduces a sequence-dependent alignment scoring that automatically changes for any pair of compared sequences. Classical alignment approaches, such as the Smith-Waterman algorithm, that work well for most sequences, fail to construct biologically adequate alignments of extra-long tandem repeats (ETRs), such as human centromeres and immunoglobulin loci. This limitation was overlooked in the previous studies since the sequences of the centromeres and other ETRs across multiple genomes only became available recently.</p>
<p>More at https://www.nature.com/articles/s41592-023-01970-4</p><p>Address of the bookmark: <a href="https://github.com/seryrzu/unialigner" rel="nofollow">https://github.com/seryrzu/unialigner</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

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