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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44497?offset=0</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29583/graph-genome-suite</guid>
	<pubDate>Fri, 28 Oct 2016 07:59:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29583/graph-genome-suite</link>
	<title><![CDATA[Graph Genome Suite]]></title>
	<description><![CDATA[<p><span>Seven Bridges is the biomedical data analysis company accelerating breakthroughs in genomics research for cancer, drug development and precision medicine. We build self-improving systems to analyze millions of genomes, including the&nbsp;</span><strong>Graph Genome Suite</strong><span>&nbsp;&mdash; the most advanced population genomics tools in the world.</span></p><p>Address of the bookmark: <a href="https://www.sbgenomics.com/graph/" rel="nofollow">https://www.sbgenomics.com/graph/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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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>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34925/rectangle-graph-for-repeat-resolution-in-genome-assembly</guid>
	<pubDate>Thu, 28 Dec 2017 09:43:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34925/rectangle-graph-for-repeat-resolution-in-genome-assembly</link>
	<title><![CDATA[Rectangle Graph for Repeat Resolution in Genome Assembly]]></title>
	<description><![CDATA[<p>Ultimate tool for resolving repeats in genome assemblies.</p>
<p>Though the specific implementation of the idea of the rectangle graph approach is already included into the&nbsp;<a href="http://bioinf.spbau.ru/spades">current SPAdes distribution</a>, we're also releasing the Rectangle Graph Module (RGM) as the separate code which can be run independently of SPAdes. Although RGM differs from the current implementation of the rectangle graph approach in SPAdes, in the future we plan to integrate RGM in SPAdes. RGM can be run with other genome assemblers if they use the graph format as SPAdes files.</p>
<p>For more details see: Nikolay Vyahhi, Son K. Pham, Pavel Pevzner.&nbsp;<a href="http://www.springerlink.com/content/e617788h25u36440/">From de Bruijn Graphs to Rectangle Graphs for Genome Assembly</a>,&nbsp;<em>Lecture Notes in Bioinformatics</em>&nbsp;7534 (2012), pp. 249-261.</p><p>Address of the bookmark: <a href="http://bioinf.spbau.ru/en/rectangles" rel="nofollow">http://bioinf.spbau.ru/en/rectangles</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40713/glia-a-graphsmith-waterman-partial-order-alignerrealigner</guid>
	<pubDate>Tue, 28 Jan 2020 04:02:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40713/glia-a-graphsmith-waterman-partial-order-alignerrealigner</link>
	<title><![CDATA[Glia: a Graph/Smith-Waterman (partial order) aligner/realigner]]></title>
	<description><![CDATA[<p><span>glia's main use is as a local realigner. It will realign reads to a set of known (or putative) variants in a VCF, both consuming and producing an ordered stream of BAM alignments.&nbsp;</span></p>
<p><span>More at&nbsp;<a href="https://github.com/ekg/glia">https://github.com/ekg/glia</a></span></p>
<pre><code>glia -f ~/human_g1k_v37.fasta -t 20:62900077-62902077 -v variants.vcf.gz \
     -s AAATGTAAACATTTTATAGGGGATTCCCCTAAAAACAAAAAAACTTTCTGGGAAAGATTTTTCAAAAAATAAAA</code></pre><p>Address of the bookmark: <a href="https://github.com/ekg/glia" rel="nofollow">https://github.com/ekg/glia</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38212/megahit-an-ultra-fast-single-node-solution-for-large-and-complex-metagenomics-assembly-via-succinct-de-bruijn-graph</guid>
	<pubDate>Wed, 14 Nov 2018 04:50:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38212/megahit-an-ultra-fast-single-node-solution-for-large-and-complex-metagenomics-assembly-via-succinct-de-bruijn-graph</link>
	<title><![CDATA[MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph]]></title>
	<description><![CDATA[<p><span>MEGAHIT is a single node assembler for large and complex metagenomics NGS reads, such as soil. It makes use of succinct&nbsp;</span><em>de Bruijn</em><span>&nbsp;graph (SdBG) to achieve low memory assembly. MEGAHIT can&nbsp;</span><span>optionally</span><span>&nbsp;utilize a CUDA-enabled GPU to accelerate its SdBG contstruction. The GPU-accelerated version of MEGAHIT has been tested on NVIDIA GTX680 (4G memory) and Tesla K40c (12G memory) with CUDA 5.5, 6.0 and 6.5. MEGAHIT v1.0 or greater also supports IBM Power PC and has been tested on IBM POWER8.</span></p>
<p><span>https://academic.oup.com/bioinformatics/article/31/10/1674/177884</span></p><p>Address of the bookmark: <a href="https://github.com/voutcn/megahit" rel="nofollow">https://github.com/voutcn/megahit</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42806/graphunzip-phases-an-assembly-graph-using-hi-c-data-andor-long-reads</guid>
	<pubDate>Fri, 05 Feb 2021 21:22:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42806/graphunzip-phases-an-assembly-graph-using-hi-c-data-andor-long-reads</link>
	<title><![CDATA[GraphUnzip: Phases an assembly graph using Hi-C data and/or long reads.]]></title>
	<description><![CDATA[<p>GraphUnzip, a fast, memory-efficient and accurate tool to unzip assembly graphs into their constituent haplotypes using long reads and/or Hi-C data. As GraphUnzip only connects sequences in the assembly graph that already had a potential link based on overlaps, it yields high-quality gap-less supercontigs. To demonstrate the efficiency of GraphUnzip, we tested it on a simulated diploid Escherichia coli genome, and on two real datasets for the genomes of the rotifer Adineta vaga and the potato Solanum tuberosum. In all cases, GraphUnzip yielded highly continuous phased assemblies.</p>
<p>https://www.biorxiv.org/content/biorxiv/early/2021/02/01/2021.01.29.428779.full.pdf</p><p>Address of the bookmark: <a href="https://github.com/nadegeguiglielmoni/GraphUnzip" rel="nofollow">https://github.com/nadegeguiglielmoni/GraphUnzip</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44887/alfapang-alignment-free-algorithm-for-pangenome-graph-construction</guid>
	<pubDate>Thu, 28 Aug 2025 02:56:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44887/alfapang-alignment-free-algorithm-for-pangenome-graph-construction</link>
	<title><![CDATA[AlfaPang: alignment free algorithm for pangenome graph construction]]></title>
	<description><![CDATA[<p><span>AlfaPang constructs variation graphs, leveraging its alignment-free and reference-free approach, based solely on intrinsic sequence properties. This design allows AlfaPang's runtime and memory usage to scale linearly with the size of input sequences, enabling it to handle significantly larger genome sets compared to other methods.</span></p><p>Address of the bookmark: <a href="https://github.com/AdamCicherski/AlfaPang" rel="nofollow">https://github.com/AdamCicherski/AlfaPang</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42661/3d-genome-browser-explore-chromatin-interaction-data-such-as-hi-c-chia-pet-capture-hi-c-plac-seq-and-more</guid>
	<pubDate>Fri, 22 Jan 2021 20:19:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42661/3d-genome-browser-explore-chromatin-interaction-data-such-as-hi-c-chia-pet-capture-hi-c-plac-seq-and-more</link>
	<title><![CDATA[3D Genome Browser: explore chromatin interaction data, such as Hi-C, ChIA-PET, Capture Hi-C, PLAC-Seq, and more]]></title>
	<description><![CDATA[<p><span>Beside visualizing chromatin interaction data, you can also seamlessly browse other omics data such as ChIP-Seq or RNA-Seq for the same genomic region, and gain a complete view of both regulatory landscape and 3D genome structure for any given gene. You can also check the expression of any queried gene across hundreds of tissue/cell types measured by the ENCODE consortium. Finally, please check out the virtual 4C page, where we provide multiple methods to link distal cis-regulatory elements with their potential target genes, including virtual 4C, ChIA-PET and cross-cell-type correlation of proximal and distal DHSs.</span></p><p>Address of the bookmark: <a href="http://3dgenome.fsm.northwestern.edu/index.html" rel="nofollow">http://3dgenome.fsm.northwestern.edu/index.html</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44882/fantasia</guid>
	<pubDate>Wed, 20 Aug 2025 02:48:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44882/fantasia</link>
	<title><![CDATA[FANTASIA]]></title>
	<description><![CDATA[<p dir="auto">FANTASIA is an advanced pipeline for the automatic functional annotation of protein sequences using state-of-the-art protein language models. It integrates deep learning embeddings and in-memory similarity searches, retrieving reference vectors from a PostgreSQL database with pgvector, to associate Gene Ontology (GO) terms with proteins.</p>
<p>https://www.nature.com/articles/s42003-025-08651-2</p><p>Address of the bookmark: <a href="https://github.com/CBBIO/FANTASIA" rel="nofollow">https://github.com/CBBIO/FANTASIA</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/34375/the-10th-north-east-bioinformatics-network-nebinet-annual-coordinators-meet</guid>
	<pubDate>Sat, 18 Nov 2017 15:02:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/34375/the-10th-north-east-bioinformatics-network-nebinet-annual-coordinators-meet</link>
	<title><![CDATA[The 10th North East Bioinformatics Network (NEBINet) Annual Coordinators' Meet]]></title>
	<description><![CDATA[<p>The 10th North East Bioinformatics Network (NEBINet) Annual Coordinators' Meet organised by the Bioinformatics Centre, St Edmund's College, Shillong and sponsored by the Department of Biotechnology, Government of India, was held at St Edmund's College Auditorium here on Thursday. Meghalaya Governor Ganga Prasad graced the inaugural programme as chief guest. <br />In his inaugural address, the Governor said the panorama of scientific scenario has greatly changed over the years, the thrust areas have undergone a metamorphosis but the conceptual underpinning of the basic sciences still continues. <br />"Of late, the activity of basic research has been intricately intertwined with technology. And we are determined to carry forward this change, for it is through technology that science can actually reach the masses in our country and afar, and the changing times have also inculcated a culture of cross-departmental and interdisciplinary research. Science and technology has always played a pivotal role in taking a nation towards greater heights by ways of innovations and inventions," he added. <br />Prasad also hoped that discussions, suggestions and sharing of innovative ideas during the two-day 10th NEBINet Annual Coordinators' Meet will open up new avenues to make substantial advancement in Biological Sciences which will provide a platform for proper and effective delivery mechanism for the common man. <br />During the inaugural function, Advisor of Department of Biotechnology Dr T Madhan Mohan gave an overview of the NEBINet and Bioinformatics programme. <br />President of Epygen Biotech FZ LLC, Dubai, UAE, Dr Debayan Ghosh, delivered the keynote address. <br />St Edmund's College governing body secretary Brother Simon Coelho and St Edmund's College Principal Dr Sylvanus Lamare also spoke during the function.</p>]]></description>
	<dc:creator>Jit</dc:creator>
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