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Sense from sequence reads: methods for alignment and assembly

A Corrigendum to this article was published on 01 June 2010

This article has been updated


The most important first step in understanding next-generation sequencing data is the initial alignment or assembly that determines whether an experiment has succeeded and provides a first glimpse into the results. In parallel with the growth of new sequencing technologies, several algorithms that align or assemble the large data output of today's sequencing machines have been developed. We discuss the current algorithmic approaches and future directions of these fundamental tools and provide specific examples for some commonly used tools.

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Figure 1: Schematic of a hash table–based alignment strategy.
Figure 2: The Burrows-Wheeler transform for genomic sequence data.
Figure 3: Constructing and visualizing a de Bruijn graph of a DNA sequence.

Change history

  • 06 May 2010

    In the version of this article initially published online, the caption to Figure 3b was mislabeled. It shows a de Bruijn graph of two plasmids partially overlapping in sequence. The error has been corrected online in the HTML and PDF versions of the article.


  1. Medvedev, P., Stanciu, M. & Brudno, M. Computational methods for discovering structural variation with next-generation sequencing. Nat. Methods 6, S13–S20 (2009).

    CAS  Article  Google Scholar 

  2. Pepke, S., Wold, B. & Mortazavi, A. Computational approaches to the analysis of ChIP-seq and RNA-seq data. Nat. Methods 6, S22–S32 (2009).

    CAS  Article  Google Scholar 

  3. Boyle, A.P. et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311–322 (2008).

    CAS  Article  Google Scholar 

  4. Margulies, M. et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–380 (2005).

    CAS  Article  Google Scholar 

  5. McKernan, K.J. et al. Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Res. 19, 1527–1541 (2009)

    CAS  Article  Google Scholar 

  6. Bentley, D.R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).

    CAS  Article  Google Scholar 

  7. Batzoglou, S. The many faces of sequence alignment. Brief Bioinform. 6, 6–22 (2005).

    CAS  Article  Google Scholar 

  8. Li, H., Ruan, J. & Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18, 1851–1858 (2008).

    CAS  Article  Google Scholar 

  9. Li, R., Li, Y., Kristiansen, K. & Wang, J. SOAP: short oligonucleotide alignment program. Bioinformatics 24, 713–714 (2008).

    CAS  Article  Google Scholar 

  10. Rumble, S.M. et al. SHRiMP: accurate mapping of short color-space reads. PLOS Comput. Biol. 5, e1000386 (2009).

    Article  Google Scholar 

  11. Lin, H., Zhang, Z., Zhang, M.Q., Ma, B. & Li, M. ZOOM! Zillions of oligos mapped. Bioinformatics 24, 2431–2437 (2008).

    CAS  Article  Google Scholar 

  12. Ma, B., Tromp, J. & Li, M. PatternHunter: faster and more sensitive homology search. Bioinformatics 18, 440–445 (2002). PatternHunter was the first alignment program to implement the method of finding alignments by scanning with 'spaced seeds' that require exact matching positions to seed the alignments but do not require these seeds to be consecutive. This method is extremely effective for the mapping short sequencing reads and has been adopted by most hash-based alignment methods.

    CAS  Article  Google Scholar 

  13. Rasmussen, K.R., Stoye, J. & Myers, E.W. Efficient q-gram filters for finding all epsilon-matches over a given length. J. Comput. Biol. 13, 296–308 (2006).

    CAS  Article  Google Scholar 

  14. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  15. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  Google Scholar 

  16. Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009).

    CAS  Article  Google Scholar 

  17. Burrows, M. & Wheeler, D.J. A block-sorting lossless data compression algorithm. Technical report 124, Digital Equipment Corporation (1994).

  18. Ferragina, P. & Manzini, G. Opportunistic data structures with applications; doi:10.1109/SFCS.2000.892127 in Proceedings of the 41st Symposium on Foundation of Computer Science (FOCS 2000) 390–398 (IEEE Computer Society, 2000). The FMindex of the BWT sequence first described in this paper is the fundamental result that has been leveraged by each of BWT-based alignment programs. The sequencing matching algorithm described here has been incorporated into each of the methods, with extensions to handle the specific problems of mismatches, gaps and paired reads.

    Chapter  Google Scholar 

  19. Gräf, S. et al. Optimized design and assessment of whole genome tiling arrays. Bioinformatics 23, i195–i204 (2007).

    Article  Google Scholar 

  20. Kärkkäinen, J. Fast BWT in small space by blockwise suffix sorting. Theor. Comput. Sci. 387, 249–257 (2007).

    Article  Google Scholar 

  21. Flicek, P. The need for speed. Genome Biol. 10, 212 (2009).

    Article  Google Scholar 

  22. Staden, R. A strategy of DNA sequencing employing computer programs. Nucleic Acids Res. 6, 2601–2610 (1979).

    CAS  Article  Google Scholar 

  23. Staden, R., Beal, K.F. & Bonfield, J.K. in Computer methods in molecular biology. in Bioinformatics Methods and Protocols vol. 132 (eds. Misener, S. & Krawetz, S.A.) 115–130 (Humana, Totowa, New Jersey, USA, 1998).

    Google Scholar 

  24. Pevzner, P.A., Borodovsky, M.Y. & Mironov, A.A. Linguistics of nucleotide sequences. II: Stationary words in genetic texts and the zonal structure of DNA. J. Biomol. Struct. Dyn. 6, 1027–1038 (1989).

    CAS  Article  Google Scholar 

  25. Idury, R.M. & Waterman, M.S. A new algorithm for DNA sequence assembly. J. Comput. Biol. 2, 291–306 (1995). Idury and Waterman first presented the fundamental algorithm for sequence assembly by k-mer extension. The representation of algorithm with the de Bruijn graph data structure is at the heart of the assembly method described here.

    CAS  Article  Google Scholar 

  26. Pevzner, P.A. & Tang, H. Fragment assembly with double-barreled data. Bioinformatics 17 (suppl. 1), S225–S233 (2001).

    Article  Google Scholar 

  27. Dohm, J.C., Lottaz, C., Borodina, T. & Himmelbauer, H. SHARCGS, a fast and highly accurate short-read assembly algorithm for de novo genomic sequencing. Genome Res. 17, 1697–1706 (2007).

    CAS  Article  Google Scholar 

  28. Jeck, W.R. et al. Extending assembly of short DNA sequences to handle error. Bioinformatics 23, 2942–2944 (2007).

    CAS  Article  Google Scholar 

  29. Zerbino, D.R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).

    CAS  Article  Google Scholar 

  30. Chaisson, M.J. & Pevzner, P.A. Short read fragment assembly of bacterial genomes. Genome Res. 18, 324–330 (2008).

    CAS  Article  Google Scholar 

  31. Hernandez, D., François, P., Farinelli, L., Osterås, M. & Schrenzel, J. De novo bacterial genome sequencing: millions of very short reads assembled on a desktop computer. Genome Res. 18, 802–809 (2008).

    CAS  Article  Google Scholar 

  32. Simpson, J.T. et al. ABySS: a parallel assembler for short read sequence data. Genome Res. 19, 1117–1123 (2009).

    CAS  Article  Google Scholar 

  33. Butler, J. et al. ALLPATHS: de novo assembly of whole-genome shotgun microreads. Genome Res. 18, 810–820 (2008).

    CAS  Article  Google Scholar 

  34. Korf, I. Serial BLAST searching. Bioinformatics 19, 1492–1496 (2003).

    CAS  Article  Google Scholar 

  35. Li, H. et al. The Sequence Alignment/Map (SAM) format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

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The authors acknowledge D. Zerbino and support by the Wellcome Trust and the European Molecular Biology Laboratory.

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Correspondence to Paul Flicek.

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Flicek, P., Birney, E. Sense from sequence reads: methods for alignment and assembly. Nat Methods 6 (Suppl 11), S6–S12 (2009).

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