Velvet

Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. We have developed a new set of algorithms, collectively called ”Velvet,” to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.


References in zbMATH (referenced in 27 articles )

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  1. Acuña, V.; Grossi, R.; Italiano, G. F.; Lima, L.; Rizzi, R.; Sacomoto, G.; Sagot, M.-F.; Sinaimeri, B.: On bubble generators in directed graphs (2020)
  2. Pellegrina, Leonardo; Pizzi, Cinzia; Vandin, Fabio: Fast approximation of frequent (k)-mers and applications to metagenomics (2019)
  3. Ryšavý, Petr; Železný, Filip: Estimating sequence similarity from read sets for clustering next-generation sequencing data (2019)
  4. Wright, Christopher; Krishnamoorty, Sriram; Kulkarni, Milind: MULKSG: \textitMULtiple\textitK\textitSimultaneous\textitGraphassembly (2019)
  5. Blazewicz, Jacek; Kasprzak, Marta; Kierzynka, Michal; Frohmberg, Wojciech; Swiercz, Aleksandra; Wojciechowski, Pawel; Zurkowski, Piotr: Graph algorithms for DNA sequencing -- origins, current models and the future (2018)
  6. Alipanahi, Bahar; Salmela, Leena; Puglisi, Simon J.; Muggli, Martin; Boucher, Christina: Disentangled long-read de Bruijn graphs via optical maps (2017)
  7. Eugene Goltsman, Isaac Ho, Daniel Rokhsar: Meraculous-2D: Haplotype-sensitive Assembly of Highly Heterozygous genomes (2017) arXiv
  8. Jean, Géraldine; Radulescu, Andreea; Rusu, Irena: The contig assembly problem and its algorithmic solutions (2017)
  9. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  10. Liu, Yongchao; Schmidt, Bertil: CUSHAW suite: parallel and efficient algorithms for NGS read alignment (2017)
  11. Quiroz-Ibarra, J. Emilio; Mallén-Fullerton, Guillermo M.; Fernández-Anaya, Guillermo: DNA paired fragment assembly using graph theory (2017)
  12. Rosen, Yohei; Eizenga, Jordan; Paten, Benedict: Describing the local structure of sequence graphs (2017)
  13. Brankovic, Ljiljana; Iliopoulos, Costas S.; Kundu, Ritu; Mohamed, Manal; Pissis, Solon P.; Vayani, Fatima: Linear-time superbubble identification algorithm for genome assembly (2016)
  14. Iliopoulos, Costas S.; Kundu, Ritu; Mohamed, Manal; Vayani, Fatima: Popping superbubbles and discovering clumps: recent developments in biological sequence analysis (2016)
  15. Tomescu, Alexandru I.; Medvedev, Paul: Safe and complete contig assembly via omnitigs (2016)
  16. Nimmy, Sonia Farhana; Kamal, M. S.: Next generation sequencing under de novo genome assembly (2015)
  17. Blazewicz, Jacek; Frohmberg, Wojciech; Gawron, Piotr; Kasprzak, Marta; Kierzynka, Michal; Swiercz, Aleksandra; Wojciechowski, Pawel: DNA sequence assembly involving an acyclic graph model (2013)
  18. Sergushichev, A. A.; Aleksandrov, A. V.; Kazakov, S. V.; Tsarev, F. N.; Shalyto, A. A.: Combining de Bruijn graphs, overlap graphs and microassembly for \textitdenovo genome assembly (2013)
  19. Wendl, Michael C.; Kota, Karthik; Weinstock, George M.; Mitreva, Makedonka: Coverage theories for metagenomic DNA sequencing based on a generalization of Stevens’ theorem (2013)
  20. Rodríguez-Ezpeleta, Naiara (ed.); Hackenberg, Michael (ed.); Aransay, Ana M. (ed.): Bioinformatics for high throughput sequencing (2012)

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