Using the Velvet de novo assembler for short-read sequencing technologies

By Daniel R. Zerbino. Current Protocols in Bioinformatics, 2010

540 citations recorded by the source corpus.

The Velvet de novo assembler was designed to build contigs and eventually scaffolds from short‐read sequencing data. This protocol describes how to use Velvet, interpret its output, and tune its parameters for optimal results. It also covers practical issues such as configuration, using the VelvetOptimiser routine, and processing colorspace data. Curr. Protoc. Bioinform. 31:11.5.1‐11.5.12. © 2010 by John Wiley & Sons, Inc.

Subfields: Artificial Intelligence, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/93f3263b0a7041757d6683f6272d5b733538230f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sergey Koren
2. Jillian F. Banfield
3. Brian C. Thomas
4. Todd J. Treangen
5. Adam M. Phillippy
6. Brian D. Ondov

In Molecular Biology

1. Keith A. Jolley
2. Adam M. Phillippy
3. Sergey Koren
4. Jillian F. Banfield
5. C. Castelle
6. Karthik Anantharaman
7. Todd J. Treangen
8. Brian D. Ondov

In Plant Science

1. Sergey Koren
2. Adam M. Phillippy
3. Karthik Anantharaman
4. C. Bliss
5. Martin C. J. Maiden
6. Jillian F. Banfield
7. S. Sheppard
8. Brian C. Thomas

Citations in the corpus

• Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system (2016, methodology): “Reads mapped to a 1kb region flanking the misassembly were collected and reassembled with Velvet 56 to attempt to correct the error.”
• Ribosomal Multi-Locus Sequence Typing: universal characterisation of bacteria from domain to strain (2012, methodology): “The VELVET de novo assembler (Zerbino, 2010; Zerbino & Birney, 2008), shuffle and optimization scripts were used to create contigs using optimal parameters, with kmer lengths between 21 and 51 bp, with no scaffolding.”
• The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes (2014, methodology): “Genome assembly [4,68-75], followed by whole-genome alignment [38,76,77], is the original method for variant detection between closely related bacterial genomes [78] and has been shown to perform well across multiple sequencing platforms [79].”