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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36837?offset=400</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</guid>
	<pubDate>Thu, 24 Jan 2019 10:35:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</link>
	<title><![CDATA[NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads]]></title>
	<description><![CDATA[<p>NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.</p>
<p>https://nxrepair.readthedocs.io/en/latest/tutorial.html</p>
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<pre>nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta</pre>
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<div>&nbsp;</div><p>Address of the bookmark: <a href="https://github.com/rebeccaroisin/nxrepair" rel="nofollow">https://github.com/rebeccaroisin/nxrepair</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39098/sda-long-read-sequence-and-assembly-of-segmental-duplications</guid>
	<pubDate>Tue, 05 Mar 2019 10:00:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39098/sda-long-read-sequence-and-assembly-of-segmental-duplications</link>
	<title><![CDATA[SDA: Long-read sequence and assembly of segmental duplications]]></title>
	<description><![CDATA[<p><span><span>Segmental Duplication Assembler (SDA; https://github.com/mvollger/SDA) constructs graphs in which paralogous sequence variants define the nodes and long-read sequences provide attraction and repulsion edges, enabling the partition and assembly of long reads corresponding to distinct paralogs.<br></span></span></p>
<p><span><span>https://github.com/mvollger/SDA</span></span></p><p>Address of the bookmark: <a href="https://www.nature.com/articles/s41592-018-0236-3" rel="nofollow">https://www.nature.com/articles/s41592-018-0236-3</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41843/stringtie-transcript-assembly-and-quantification-for-rna-seq</guid>
	<pubDate>Tue, 09 Jun 2020 05:21:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41843/stringtie-transcript-assembly-and-quantification-for-rna-seq</link>
	<title><![CDATA[StringTie Transcript assembly and quantification for RNA-Seq]]></title>
	<description><![CDATA[<p><strong>StringTie</strong><span>&nbsp;is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional&nbsp;</span><em>de novo</em><span>&nbsp;assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only alignments of short reads that can also be used by other transcript assemblers, but also alignments of longer sequences that have been assembled from those reads. In order to identify differentially expressed genes between experiments, StringTie's output can be processed by specialized software like&nbsp;</span><a href="https://github.com/alyssafrazee/ballgown">Ballgown</a><span>,&nbsp;</span><a href="http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html">Cuffdiff</a><span>&nbsp;or other programs (DESeq2, edgeR, etc.).</span></p><p>Address of the bookmark: <a href="https://ccb.jhu.edu/software/stringtie/" rel="nofollow">https://ccb.jhu.edu/software/stringtie/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40897/mec-contig-misassembly-correction</guid>
	<pubDate>Tue, 04 Feb 2020 23:40:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40897/mec-contig-misassembly-correction</link>
	<title><![CDATA[MEC: Contig Misassembly Correction]]></title>
	<description><![CDATA[<p><span>MEC, to identify and correct misassemblies in contigs. Firstly, MEC takes fragment coverage as the feature to detect the candidate misassemblies. Then, it can distinguish a large number of false positives from the candidate misassemblies based on the distribution of paired-end reads and the statistical analysis of GC-contents. We apply MEC to four real contig datasets, and carry out experiments to analyze the influence of MEC on scaffolding results, which shows that MEC can reduce misassemblies effectively and result in quantitative improvements in scaffolding quality. MEC is publicly available for download at https://github.com/bioinfomaticsCSU/MEC.</span></p><p>Address of the bookmark: <a href="https://github.com/bioinfomaticsCSU/MEC" rel="nofollow">https://github.com/bioinfomaticsCSU/MEC</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41397/svaba-structural-variation-and-indel-detection-by-local-assembly</guid>
	<pubDate>Tue, 10 Mar 2020 07:52:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41397/svaba-structural-variation-and-indel-detection-by-local-assembly</link>
	<title><![CDATA[SvABA: Structural variation and indel detection by local assembly]]></title>
	<description><![CDATA[<p><span>SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of&nbsp;</span><a href="https://github.com/jts/sga">SGA</a><span>&nbsp;(String Graph Assembler) by Jared Simpson, and&nbsp;</span><a href="https://github.com/lh3/bwa">BWA-MEM</a><span>&nbsp;by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using&nbsp;</span><a href="https://github.com/walaj/VariantBam">VariantBam</a><span>&nbsp;rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.</span></p><p>Address of the bookmark: <a href="https://github.com/walaj/svaba" rel="nofollow">https://github.com/walaj/svaba</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42166/software-for-genome-assembly</guid>
	<pubDate>Sun, 30 Aug 2020 09:51:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42166/software-for-genome-assembly</link>
	<title><![CDATA[Software for genome assembly !]]></title>
	<description><![CDATA[<p>List of bioinformatics tools/Software Website References for genome assembly:</p><p>1 Falcon&nbsp;https://github.com/PacificBiosciences/pb-assembly</p><p>2 Canu assembler http://canu.readthedocs.io/en/latest/index.html</p><p>3 Miniasm assembler https://github.com/lh3/miniasm</p><p>4 PBJelly scaffolding tool https://sourceforge.net/projects/pb-jelly/</p><p>5 ARCS scaffolding tool https://github.com/bcgsc/arcs</p><p>6 Redundans reduction and scaffolding tool https://github.com/Gabaldonlab/redundans</p><p>7 Arrow error correction https://github.com/PacificBiosciences/ GenomicConsensus</p><p>8 PILON error correction https://github.com/broadinstitute/pilon/wiki</p><p>9 BUSCO single copy gene markers http://busco.ezlab.org/</p><p>10 Bandage graph assembly viewer https://rrwick.github.io/Bandage/</p><p>11 Gepard dotter http://cube.univie.ac.at/gepard</p><p>12 MUMmer aligner and plotter http://mummer.sourceforge.net/</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43614/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</guid>
	<pubDate>Tue, 30 Nov 2021 23:23:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43614/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</link>
	<title><![CDATA[MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization]]></title>
	<description><![CDATA[<p>MitoZ, consisting of independent modules of <em>de novo</em> assembly, findMitoScaf (find Mitochondrial Scaffolds), annotation and visualization, that can generate mitogenome assembly together with annotation and visualization results from HTS raw reads.</p>
<p>https://academic.oup.com/nar/article/47/11/e63/5377471</p><p>Address of the bookmark: <a href="https://github.com/linzhi2013/MitoZ" rel="nofollow">https://github.com/linzhi2013/MitoZ</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44352/bioinformatics-tools-for-genome-assembly</guid>
	<pubDate>Mon, 24 Jul 2023 07:04:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44352/bioinformatics-tools-for-genome-assembly</link>
	<title><![CDATA[Bioinformatics tools for genome assembly !]]></title>
	<description><![CDATA[<p>There are numerous genome assembly tools available, each with its strengths and weaknesses. Here is a list of some widely used genome assembly tools as of my last update in September 2021:</p><ol>
<li>
<p><span>SPAdes:</span> An assembler specifically designed for single-cell and multi-cell bacterial genomes, as well as small eukaryotic genomes.</p>
</li>
<li>
<p><span>ABySS:</span> A parallelized assembler for large genomes that uses de Bruijn graphs.</p>
</li>
<li>
<p><span>Velvet:</span> Another de Bruijn graph-based assembler optimized for short-read sequencing data.</p>
</li>
<li>
<p><span>SOAPdenovo:</span> A de Bruijn graph-based assembler designed for short reads, widely used for assembling large and complex genomes.</p>
</li>
<li>
<p><span>MaSuRCA:</span> A hybrid assembler that combines data from multiple sequencing technologies, such as Illumina and PacBio.</p>
</li>
<li>
<p><span>Canu:</span> A long-read assembler optimized for PacBio and Oxford Nanopore sequencing data.</p>
</li>
<li>
<p><span>Flye:</span> A long-read assembler suitable for bacterial and small eukaryotic genomes.</p>
</li>
<li>
<p><span>SMARTdenovo:</span> An assembler designed for long reads, particularly suited for PacBio data.</p>
</li>
<li>
<p><span>SPAdes Long Read (SPAdesLR):</span> An extension of SPAdes for long-read data, such as those from PacBio or Nanopore.</p>
</li>
<li>
<p><span>Minia:</span> An assembler optimized for low memory consumption, suitable for small and medium-sized genomes.</p>
</li>
<li>
<p><span>Unicycler:</span> A hybrid assembler that combines short and long reads for circular bacterial genome assembly.</p>
</li>
<li>
<p><span>wtdbg2:</span> A de Bruijn graph assembler for long reads, efficient for very large genomes.</p>
</li>
<li>
<p><span>Shasta:</span> A long-read assembler that uses the Overlap-Layout-Consensus approach, suitable for PacBio and Nanopore data.</p>
</li>
<li>
<p><span>Sparc:</span> An assembler designed to handle noisy long reads from Nanopore sequencing.</p>
</li>
<li>
<p><span>CANA:</span> An assembler for metagenomic data, particularly for complex and diverse microbial communities.</p>
</li>
<li>
<p><span>Ra</span> Assembler: A metagenome assembler for long reads, designed for highly complex metagenomic samples.</p>
</li>
</ol><p>Please note that the field of bioinformatics is constantly evolving, and new assembly tools may have emerged since my last update. Additionally, the performance of these tools can vary depending on the characteristics of the sequencing data and the genome being assembled. When selecting an assembly tool, consider the specific requirements of your project, the available data types, and the computational resources at your disposal. Always refer to the respective tool's documentation and publications for the most up-to-date information and recommendations.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44549/quartet-a-telomere-to-telomere-toolkit-for-gap-free-genome-assembly-and-centromeric-repeat-identification</guid>
	<pubDate>Sat, 08 Jun 2024 15:54:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44549/quartet-a-telomere-to-telomere-toolkit-for-gap-free-genome-assembly-and-centromeric-repeat-identification</link>
	<title><![CDATA[quarTeT: a telomere-to-telomere toolkit for gap-free genome assembly and centromeric repeat identification.]]></title>
	<description><![CDATA[<p><span>quarTeT is a collection of tools for T2T genome assembly and basic analysis in automatic workflow.</span><br><br><span>Task include:</span></p>
<ul>
<li><a href="http://www.atcgn.com:8080/quarTeT/docuWeb.html#AssemblyMapper">AssemblyMapper</a>&nbsp;: reference-guided genome assembly</li>
<li><a href="http://www.atcgn.com:8080/quarTeT/docuWeb.html#GapFiller">GapFiller</a>&nbsp;: long-reads based gap filling</li>
<li><a href="http://www.atcgn.com:8080/quarTeT/docuWeb.html#TeloExplorer">TeloExplorer</a>&nbsp;: telomere identification</li>
<li><a href="http://www.atcgn.com:8080/quarTeT/docuWeb.html#CentroMiner">CentroMiner</a>&nbsp;: centromere candidate prediction</li>
</ul>
<p>https://academic.oup.com/hr/article/10/8/uhad127/7197191?login=false&nbsp;</p><p>Address of the bookmark: <a href="http://www.atcgn.com:8080/quarTeT/home.html" rel="nofollow">http://www.atcgn.com:8080/quarTeT/home.html</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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