Superstring Graph: A New Approach for Genome Assembly

  • Bastien Cazaux
  • Gustavo Sacomoto
  • Eric RivalsEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9778)


With the increasing impact of genomics in life sciences, the inference of high quality, reliable, and complete genome sequences is becoming critical. Genome assembly remains a major bottleneck in bioinformatics: indeed, high throughput sequencing apparatus yield millions of short sequencing reads that need to be merged based on their overlaps. Overlap graph based algorithms were used with the first generation of sequencers, while de Bruijn graph (DBG) based methods were preferred for the second generation. Because the sequencing coverage varies locally along the molecule, state-of-the-art assembly programs now follow an iterative process that requires the construction of de Bruijn graphs of distinct orders (i.e., sizes of the overlaps). The set of resulting sequences, termed unitigs, provide an important improvement compared to single DBG approaches. Here, we present a novel approach based on a digraph, the Superstring Graph, that captures all desired sizes of overlaps at once and allows to discard unreliable overlaps. With a simple algorithm, the Superstring Graph delivers sequences that includes all the unitigs obtained from multiple DBG as substrings. In linear time and space, it combines the efficiency of a greedy approach to the advantages of using a single graph. In summary, we present a first and formal comparison of the output of state-of-the-art genome assemblers.


Greedy Algorithm Suffix Tree Input Word Circular Permutation Cyclic Cover 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank the reviewers for their comments and suggestions.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bastien Cazaux
    • 1
    • 2
  • Gustavo Sacomoto
    • 3
  • Eric Rivals
    • 1
    • 2
    Email author
  1. 1.LIRMM, Université de Montpellier, CNRS UMR 5506MontpellierFrance
  2. 2.Institut Biologie ComputationnelleMontpellierFrance
  3. 3.INRIA Rhône-Alpes and Université Lyon 1, CNRS, UMR 5558LyonFrance

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