PT  - JOURNAL ARTICLE
AU  - Hongzhe Guo
AU  - Yilei Fu
AU  - Yan Gao
AU  - Junyi Li
AU  - Yadong Wang
AU  - Bo Liu
TI  - deGSM: memory scalable construction of large scale de Bruijn Graph
AID  - 10.1101/388454
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 388454
4099  - http://biorxiv.org/content/early/2018/08/10/388454.short
4100  - http://biorxiv.org/content/early/2018/08/10/388454.full
AB  - Motivation De Bruijn graph, a fundamental data structure to represent and organize genome sequence, plays important roles in various kinds of sequence analysis tasks such as de novo assembly, high-throughput sequencing (HTS) read alignment, pan-genome analysis, metagenomics analysis, HTS read correction, etc. With the rapid development of HTS data and ever-increasing number of assembled genomes, there is a high demand to construct de Bruijn graph for sequences up to Tera-base-pair level. It is non-trivial since the size of the graph to be constructed could be very large and each graph consists of hundreds of billions of vertices and edges. Current existing approaches may have unaffordable memory footprints to handle such a large de Bruijn graph. Moreover, it also requires the construction approach to handle very large dataset efficiently, even if in a relatively small RAM space.Results We propose a lightweight parallel de Bruijn graph construction approach, de Bruijn Graph Constructor in Scalable Memory (deGSM). The main idea of deGSM is to efficiently construct the Bur-rows-Wheeler Transformation (BWT) of the unipaths of de Bruijn graph in constant RAM space and transform the BWT into the original unitigs. It is mainly implemented by a fast parallel external sorting of k-mers, which allows only a part of k-mers kept in RAM by a novel organization of the k-mers. The experimental results demonstrate that, just with a commonly used machine, deGSM is able to handle very large genome sequence(s), e.g., the contigs (305 Gbp) and scaffolds (1.1 Tbp) recorded in Gen-Bank database and Picea abies HTS dataset (9.7 Tbp). Moreover, deGSM also has faster or comparable construction speed compared with state-of-the-art approaches. With its high scalability and efficiency, deGSM has enormous potentials in many large scale genomics studies.Availability https://github.com/hitbc/deGSM.Contact ydwang{at}hit.edu.cn (YW) and bo.liu{at}hit.edu.cn (BL)Supplementary information Supplementary data are available online.