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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35400?offset=90</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33955/crocoblast-optimized-parallel-implementation-of-local-sequence-alignment-algorithms</guid>
	<pubDate>Tue, 25 Jul 2017 05:03:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33955/crocoblast-optimized-parallel-implementation-of-local-sequence-alignment-algorithms</link>
	<title><![CDATA[CrocoBLAST: Optimized parallel implementation of local sequence alignment algorithms]]></title>
	<description><![CDATA[<p><span>Local sequence alignment is a cornerstone of bioinformatics, allowing to compare the amino-acid sequences of different proteins, or the nucleotide sequences of different pieces of DNA. The Basic Local Alignment Search Tool (BLAST) has revolutionized the field of bioinformatics, and is currently implemented in all free and commercial bioinformatics packages. However, with the advent of Next Generation Sequencing (NGS) and the development of new sequencing techniques, the utility of traditional BLAST implementations is limited. CrocoBLAST combines the accuracy and general applicability of BLAST with computational efficiency, accessibility, and user experience, so that NGS data can be analyzed efficiently even when only modest computational resources are available.</span></p>
<p>https://webchem.ncbr.muni.cz/Platform/App/CrocoBLAST</p><p>Address of the bookmark: <a href="https://webchem.ncbr.muni.cz/Platform/App/CrocoBLAST" rel="nofollow">https://webchem.ncbr.muni.cz/Platform/App/CrocoBLAST</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34569/ksnp30-snp-detection-and-phylogenetic-analysis-of-genomes-without-genome-alignment-or-reference-genome</guid>
	<pubDate>Fri, 08 Dec 2017 16:48:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34569/ksnp30-snp-detection-and-phylogenetic-analysis-of-genomes-without-genome-alignment-or-reference-genome</link>
	<title><![CDATA[kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome]]></title>
	<description><![CDATA[<p><span>Sept. 20, 2017 Version 3.1 released. Major upgrade. Version 3.1 fixes the problems with SNP annotation that arose when NCBI discontinued use of GI numbers. Please read carefully the Preface (page 3) and the File of annotated genomes section (pages 9-10) in the version 3.1 User Guide. Thanks to Tom Slezak for revsing the get_genbank_file3 script and to Tod Stuber (USDA) for testing version 3.1 even though he doesn't need the annotation feature. All users are encouraged to upgrade to version 3.1.&nbsp;<br></span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/ksnp/files/" rel="nofollow">https://sourceforge.net/projects/ksnp/files/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads</guid>
	<pubDate>Tue, 08 May 2018 04:27:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads</link>
	<title><![CDATA[HISAT2: a fast and sensitive alignment program for mapping next-generation sequencing reads]]></title>
	<description><![CDATA[<p><strong>HISAT2</strong><span>&nbsp;is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs&nbsp;</span><a href="http://dl.acm.org/citation.cfm?id=2674828">[Sir&eacute;n et al. 2014]</a><span>, we designed and implemented a graph FM index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).&nbsp;</span></p>
<p><span>more at&nbsp;https://ccb.jhu.edu/software/hisat2/index.shtml</span></p><p>Address of the bookmark: <a href="https://github.com/infphilo/hisat2" rel="nofollow">https://github.com/infphilo/hisat2</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36846/gblocks-eliminates-poorly-aligned-positions-and-divergent-regions-of-a-dna-or-protein-alignment</guid>
	<pubDate>Sat, 02 Jun 2018 07:36:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36846/gblocks-eliminates-poorly-aligned-positions-and-divergent-regions-of-a-dna-or-protein-alignment</link>
	<title><![CDATA[Gblocks: eliminates poorly aligned positions and divergent regions of a DNA or protein alignment]]></title>
	<description><![CDATA[<p><a href="http://molevol.cmima.csic.es/castresana/Gblocks.html">Gblocks</a><span>&nbsp;eliminates poorly aligned positions and divergent regions of a DNA or protein alignment so that it becomes more suitable for phylogenetic analysis. This server implements the most important features of the Gblocks program to make its use as simple as possible without loosing the functionality that it is necessary in most of the cases. Other options can be changed in the stand-alone program. You can see here an&nbsp;</span><a href="http://molevol.cmima.csic.es/castresana/Gblocks_server/nad3.pir-gb.htm">example output file</a><span>&nbsp;showing the blocks selected from a protein alignment. Further information can be found in the&nbsp;</span><a href="http://molevol.cmima.csic.es/castresana/Gblocks/Gblocks_documentation.html">online documentation</a><span>.&nbsp;</span></p><p>Address of the bookmark: <a href="http://molevol.cmima.csic.es/castresana/Gblocks_server.html" rel="nofollow">http://molevol.cmima.csic.es/castresana/Gblocks_server.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</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/40711/vg-variation-graph-data-structures-interchange-formats-alignment-genotyping-and-variant-calling-methods</guid>
	<pubDate>Tue, 28 Jan 2020 03:53:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40711/vg-variation-graph-data-structures-interchange-formats-alignment-genotyping-and-variant-calling-methods</link>
	<title><![CDATA[VG: variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods]]></title>
	<description><![CDATA[<p><em>Variation graphs</em>&nbsp;provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:</p>
<ul>
<li><em>nodes</em>, which are labeled by sequences and ids</li>
<li><em>edges</em>, which connect two nodes via either of their respective ends</li>
<li><em>paths</em>, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges</li>
</ul><p>Address of the bookmark: <a href="https://github.com/vgteam/vg" rel="nofollow">https://github.com/vgteam/vg</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite</guid>
	<pubDate>Wed, 03 Jan 2018 05:05:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite</link>
	<title><![CDATA[Seal: SEquence ALignment evaluation suite]]></title>
	<description><![CDATA[<p><span>Seal</span>&nbsp;is a comprehensive sequencing simulation and alignment tool evaluation suite. This software (implemented in Java) provides several utilities that can be used to evaluate alignment algorithms, including:</p>
<ul>
<li>Reading a pre-existing reference genome from one or more FASTA files.</li>
<li>Alternatively, generating an artificial reference genome based on input parameters (length, repeat count, repeat length, repeat variability rate).</li>
<li>Simulating reads from random locations in the genome based on input parameters of read length, coverage, sequencing error rate, and indel rate.</li>
<li>Applying alignment tools to the genome and the reads through a standardized interface.</li>
<li>Parsing the output of the alignment tool and calculating the number of reads that were correctly or incorrectly mapped.</li>
<li>Computing run times and measures of accuracy.</li>
</ul>
<p><span>Seal</span>&nbsp;has interfaces to evaluate the following software packages:</p>
<ul>
<li>Bowtie</li>
<li>BWA</li>
<li>MAQ</li>
<li>mrFAST</li>
<li>mrsFAST</li>
<li>Novoalign</li>
<li>SHRiMP</li>
<li>SOAPv2</li>
</ul><p>Address of the bookmark: <a href="http://compbio.case.edu/seal/" rel="nofollow">http://compbio.case.edu/seal/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43736/odgi-optimized-dynamic-genomegraph-implementation</guid>
	<pubDate>Tue, 01 Feb 2022 23:42:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43736/odgi-optimized-dynamic-genomegraph-implementation</link>
	<title><![CDATA[odgi: optimized dynamic genome/graph implementation]]></title>
	<description><![CDATA[<p dir="auto"><code>odgi</code>&nbsp;provides an efficient and succinct dynamic DNA sequence graph model, as well as a host of algorithms that allow the use of such graphs in bioinformatic analyses.</p>
<p dir="auto">Careful encoding of graph entities allows&nbsp;<code>odgi</code>&nbsp;to efficiently compute and transform&nbsp;<a href="https://pangenome.github.io/">pangenomes</a>&nbsp;with minimal overheads.&nbsp;<code>odgi</code>&nbsp;implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.</p>
<p dir="auto">The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.</p>
<p dir="auto">The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.</p><p>Address of the bookmark: <a href="https://github.com/pangenome/odgi" rel="nofollow">https://github.com/pangenome/odgi</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/12944/orione-%E2%80%93-a-web-based-framework-for-ngs-analysis-in-microbiology</guid>
	<pubDate>Wed, 23 Jul 2014 06:43:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/12944/orione-%E2%80%93-a-web-based-framework-for-ngs-analysis-in-microbiology</link>
	<title><![CDATA[Orione – a web-based framework for NGS analysis in microbiology]]></title>
	<description><![CDATA[<p>End-to-end NGS microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult due to a lack of interoperability, reproducibility, and transparency. To overcome these limitations researchers at <a href="http://www.crs4.it/" target="_blank">CRS4</a>, Italy have developed Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for NGS microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.</p>
<p>Reference</p>
<p>Cuccuru G1, Orsini M, Pinna A, Sbardellati A, Soranzo N, Travaglione A, Uva P, Zanetti G, Fotia G. (2014)<strong> Orione, a web-based framework for NGS analysis in microbiology.</strong> <em>Bioinformatics</em> [Epub ahead of print]. [<a href="http://bioinformatics.oxfordjournals.org/content/early/2014/03/10/bioinformatics.btu135.long" target="_blank">article</a>]</p><p>Address of the bookmark: <a href="http://orione.crs4.it/" rel="nofollow">http://orione.crs4.it/</a></p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
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