This article is part of the supplement: Proceedings of the Tenth Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics
TIGER: tiled iterative genome assembler
1 Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
2 Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
3 Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
BMC Bioinformatics 2012, 13(Suppl 19):S18 doi:10.1186/1471-2105-13-S19-S18Published: 19 December 2012
With the cost reduction of the next-generation sequencing (NGS) technologies, genomics has provided us with an unprecedented opportunity to understand fundamental questions in biology and elucidate human diseases. De novo genome assembly is one of the most important steps to reconstruct the sequenced genome. However, most de novo assemblers require enormous amount of computational resource, which is not accessible for most research groups and medical personnel.
We have developed a novel de novo assembly framework, called Tiger, which adapts to available computing resources by iteratively decomposing the assembly problem into sub-problems. Our method is also flexible to embed different assemblers for various types of target genomes. Using the sequence data from a human chromosome, our results show that Tiger can achieve much better NG50s, better genome coverage, and slightly higher errors, as compared to Velvet and SOAPdenovo, using modest amount of memory that are available in commodity computers today.
Most state-of-the-art assemblers that can achieve relatively high assembly quality need excessive amount of computing resource (in particular, memory) that is not available to most researchers to achieve high quality results. Tiger provides the only known viable path to utilize NGS de novo assemblers that require more memory than that is present in available computers. Evaluation results demonstrate the feasibility of getting better quality results with low memory footprint and the scalability of using distributed commodity computers.