SPAdes (version 2.5.1)
K-mers used in the de novo assemlbly

What is SPAdes

SPAdes (St. Petersburg genome assembler) is also a de Bruijn graph based de novo assembler, which is provided here as an alternative to the default velvet assembler. Other than standard NGS datasets, SPAdes specializes in single-cell multiple displacement amplification (MDA) bacterial assemblies, which are more akin to viral genome assemblies than to large eukaryotic genome assemblies. At times, SPAdes performs better than other method for de novo assembly of viral genomes. We encourage users to try all of the de novo assemblers provided here and to compare the results. The assemblers could perform differently depending on the dataset and genome under study.

For more details about SPAdes, please go to http://bioinf.spbau.ru/spades


What to input

Here we require that you combine datasets of a similar kind into one file and submit at most one single-end file and one paired-end file. This means if you have the paired-end reads separated into two different files (e.g forward and reverse, as outputted by Illumina sequencers), you will need to combine them together into one interleaved file. We provide a tool for this task in the UTILITY section.

Please fill one or more odd numbers as the k-mer size, separated by commas(no spaces). By default the k-mers are set to be "21,33,55", as recommended by the SPAdes manual.

Notice k-mer size is very critical in de novo assembly. No single k-mer is a best choice, the performance depends on various factor including read length and coverage. If the run fail or the result is not what you expected, we advise the user might try different k-mers.


Reference

Anton Bankevich, Sergey Nurk, Dmitry Antipov, Alexey A. Gurevich, Mikhail Dvorkin, Alexander S. Kulikov, Valery M. Lesin, Sergey I. Nikolenko, Son Pham, Andrey D. Prjibelski, Alexey V. Pyshkin, Alexander V. Sirotkin, Nikolay Vyahhi, Glenn Tesler, Max A. Alekseyev, and Pavel A. Pevzner. SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. Journal of Computational Biology 19(5) (2012), 455-477. doi:10.1089/cmb.2012.0021