Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Next-generation transcriptome assembly

Key Points

  • The protocols used for library construction, sequencing and data pre-processing can have a great impact on the quality of an assembled transcriptome and the accuracy of gene expression quantification.

  • Before starting an RNA sequencing (RNA-seq) experiment, one should carefully consider using protocols that are strand-specific, that remove ribosomal RNA and that do not require PCR amplification of the template.

  • Strand-specific RNA-seq protocols are important for correctly assembling overlapping transcripts, especially for compact genomes.

  • The reference-based, or ab initio, assembly strategy requires a reference genome and uses much fewer computing resources than the de novo strategy. However, the quality of the genome and the ability of the short-read aligner to align reads across introns will directly influence the accuracy of the assembled transcripts when using the reference-based strategy.

  • The de novo assembly strategy does not use a reference genome but instead uses a De Bruijn graph to represent overlaps between sequences and assemble transcripts. Most de novo approaches require significant computing resources: random access memory (RAM) is the typical limitation. However, de novo assemblers can assemble trans-spliced genes and novel transcripts that are not present in the genome assembly.

  • To take full advantage of the current assembly strategies, a combined assembly approach should be considered that leverages the strengths of reference-based and de novo assembly strategies.

  • Most transcriptome assemblers are still being developed, and the results from these programs should be evaluated using unbiased quantitative metrics.

  • Transcriptome assembly involves an informatics approach to solve an experimental limitation. As sequencing strategies continually improve, it may no longer be necessary in the near future to assemble transcriptomes, as the read length will be longer than any individual transcript.

Abstract

Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalogue of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches — reference-based, de novo and combined strategies — along with some perspectives on transcriptome assembly in the near future.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: The data generation and analysis steps of a typical RNA-seq experiment.
Figure 2: Overview of the reference-based transcriptome assembly strategy.
Figure 3: Overview of the de novo transcriptome assembly strategy.
Figure 4: Alternative approaches for combined transcriptome assembly.

References

  1. Ozsolak, F. & Milos, P. M. RNA sequencing: advances, challenges and opportunities. Nature Rev. Genet. 12, 87–98 (2011). This Review provides a good, up-to-date summary of the RNA-seq experimental protocol and its usefulness in addressing important biological questions.

    CAS  Article  PubMed  Google Scholar 

  2. Wang, Z., Gerstein, M. & Snyder, M. RNA-seq: a revolutionary tool for transcriptomics. Nature Rev. Genet. 10, 57–63 (2009).

    CAS  Article  PubMed  Google Scholar 

  3. Marguerat, S. & Bahler, J. RNA-seq: from technology to biology. Cell. Mol. Life Sci. 67, 569–579 (2010).

    CAS  Article  PubMed  Google Scholar 

  4. Wilhelm, B. T. & Landry, J. R. RNA-seq-quantitative measurement of expression through massively parallel RNA-sequencing. Methods 48, 249–257 (2009).

    CAS  Article  PubMed  Google Scholar 

  5. Metzker, M. L. Sequencing technologies — the next generation. Nature Rev. Genet. 11, 31–46 (2010). This Review provides a good introduction to NGS technologies and the analysis challenges that they pose.

    CAS  PubMed  Google Scholar 

  6. 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  PubMed  PubMed Central  Google Scholar 

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. Paszkiewicz, K. & Studholme, D. J. De novo assembly of short sequence reads. Brief. Bioinform. 11, 457–472 (2010).

    CAS  Article  PubMed  Google Scholar 

  10. Miller, J. R., Koren, S. & Sutton, G. Assembly algorithms for next-generation sequencing data. Genomics 95, 315–327 (2010). This paper provides a good introduction to the current algorithms used in next-generation genome assembly and the challenges posed by these approaches.

    CAS  Article  PubMed  Google Scholar 

  11. Makalowska, I., Lin, C. F. & Makalowski, W. Overlapping genes in vertebrate genomes. Comput. Biol. Chem. 29, 1–12 (2005).

    CAS  Article  PubMed  Google Scholar 

  12. Normark, S. et al. Overlapping genes. Annu. Rev. Genet. 17, 499–525 (1983).

    CAS  Article  PubMed  Google Scholar 

  13. Johnson, Z. I. & Chisholm, S. W. Properties of overlapping genes are conserved across microbial genomes. Genome Res. 14, 2268–2272 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Fukuda, Y., Washio, T. & Tomita, M. Comparative study of overlapping genes in the genomes of Mycoplasma genitalium and Mycoplasma pneumoniae. Nucleic Acids Res. 27, 1847–1853 (1999).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Martin, J. et al. Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-seq reads. BMC Genomics 11, 663 (2010). This paper describes the first de novo transcriptome assembler to automate the use of several k-mers for assembly. It also provides a good overview of methods used for the pre- and post-processing of de novo transcriptome assemblies.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Guttman, M. et al. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nature Biotech. 28, 503–510 (2010). This paper introduces the Scripture algorithm, which was one of the first reference-based assemblers that effectively tackled the assembly of alternative isoforms using NGS data.

    CAS  Article  Google Scholar 

  17. Denoeud, F. et al. Annotating genomes with massive-scale RNA sequencing. Genome Biol. 9, R175 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Robertson, G. et al. De novo assembly and analysis of RNA-seq data. Nature Methods 7, 909–912 (2010).

    CAS  Article  PubMed  Google Scholar 

  19. Surget-Groba, Y. & Montoya-Burgos, J. I. Optimization of de novo transcriptome assembly from next-generation sequencing data. Genome Res. 20, 1432–1440 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotech. 28, 511–515 (2010). The Cufflinks algorithm is introduced in this paper, which, like the Scripture algorithm described in reference 16, was one of the first reference-based assemblers that effectively tackled the assembly of alternative isoforms using NGS data.

    CAS  Article  Google Scholar 

  21. Birol, I. et al. De novo transcriptome assembly with ABySS. Bioinformatics 25, 2872–2877 (2009).

    CAS  Article  PubMed  Google Scholar 

  22. Crawford, J. E. et al. De novo transcriptome sequencing in Anopheles funestus using Illumina RNA-seq technology. PLoS ONE 5, e14202 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Garg, R., Patel, R. K., Tyagi, A. K. & Jain, M. De novo assembly of chickpea transcriptome using short reads for gene discovery and marker identification. DNA Res. 18, 53–63 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Yassour, M. et al. Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing. Proc. Natl Acad. Sci. USA 106, 3264–3269 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Adamidi, C. et al. De novo assembly and validation of planaria transcriptome by massive parallel sequencing and shotgun proteomics. Genome Res. 21, 1193–1200 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Katz, Y., Wang, E. T., Airoldi, E. M. & Burge, C. B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nature Methods 7, 1009–1015 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Levin, J. Z. et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nature Methods 7, 709–715 (2010). This paper provides an excellent comparison of different RNA-seq protocols and how they affect the quantification of expression levels.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. He, S. et al. Validation of two ribosomal RNA removal methods for microbial metatranscriptomics. Nature Methods 7, 807–812 (2010).

    CAS  Article  PubMed  Google Scholar 

  29. Chen, Z. & Duan, X. Ribosomal RNA depletion for massively parallel bacterial RNA-sequencing applications. Methods Mol. Biol. 733, 93–103 (2011).

    CAS  Article  PubMed  Google Scholar 

  30. Christodoulou, D. C., Gorham, J. M., Herman, D. S. & Seidman, J. G. Construction of normalized RNA-seq libraries for next-generation sequencing using the crab duplex-specific nuclease. Curr. Protoc. Mol. Biol. 1 Apr 2011 (doi:10.1002/0471142727.mb0412s94).

  31. Kozarewa, I. et al. Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G+C)-biased genomes. Nature Methods 6, 291–295 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. Mamanova, L. et al. FRT-seq: amplification-free, strand-specific transcriptome sequencing. Nature Methods 7, 130–132 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Sam, L. T. et al. A comparison of single molecule and amplification based sequencing of cancer transcriptomes. PLoS ONE 6, e17305 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Ozsolak, F. et al. Amplification-free digital gene expression profiling from minute cell quantities. Nature Methods 7, 619–621 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Chen, S. et al. De novo analysis of transcriptome dynamics in the migratory locust during the development of phase traits. PLoS ONE 5, e15633 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. Schwartz, T. S. et al. A garter snake transcriptome: pyrosequencing, de novo assembly, and sex-specific differences. BMC Genomics 11, 694 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Passalacqua, K. D. et al. Structure and complexity of a bacterial transcriptome. J. Bacteriol. 191, 3203–3211 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Dalloul, R. A. et al. Multi-platform next-generation sequencing of the domestic turkey (Meleagris gallopavo): genome assembly and analysis. PLoS Biol. 8, e1000475 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Jackman, S. D. & Birol, I. Assembling genomes using short-read sequencing technology. Genome Biol. 11, 202 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Rodrigue, S. et al. Unlocking short read sequencing for metagenomics. PLoS ONE 5, e11840 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Shi, H., Schmidt, B., Liu, W. & Muller-Wittig, W. A parallel algorithm for error correction in high-throughput short-read data on CUDA-enabled graphics hardware. J. Comput. Biol. 17, 603–615 (2010).

    CAS  Article  PubMed  Google Scholar 

  42. Kelley, D. R., Schatz, M. C. & Salzberg, S. L. Quake: quality-aware detection and correction of sequencing errors. Genome Biol. 11, R116 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. Falgueras, J. et al. SeqTrim: a high-throughput pipeline for pre-processing any type of sequence read. BMC Bioinformatics 11, 38 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lassmann, T., Hayashizaki, Y. & Daub, C. O. TagDust—a program to eliminate artifacts from next generation sequencing data. Bioinformatics 25, 2839–2840 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. Kent, W. J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

    CAS  PubMed Central  PubMed  Google Scholar 

  47. Au, K. F., Jiang, H., Lin, L., Xing, Y. & Wong, W. H. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Res. 38, 4570–4578 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wu, T. D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nature Methods 5, 621–628 (2008).

    CAS  Article  PubMed  Google Scholar 

  51. Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Perkins, T. T. et al. A strand-specific RNA-seq analysis of the transcriptome of the typhoid bacillus Salmonella typhi. PLoS Genet. 5, e1000569 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Ozsolak, F. et al. Comprehensive polyadenylation site maps in yeast and human reveal pervasive alternative polyadenylation. Cell 143, 1018–1029 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Salzberg, S. L. & Yorke, J. A. Beware of mis-assembled genomes. Bioinformatics 21, 4320–4321 (2005). This study highlights the importance of having standardized metrics to assess the quality of NGS assemblies.

    CAS  Article  PubMed  Google Scholar 

  55. Kinsella, M., Harismendy, O., Nakano, M., Frazer, K. A. & Bafna, V. Sensitive gene fusion detection using ambiguously mapping RNA-seq read pairs. Bioinformatics 27, 1068–1075 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. McPherson, A. et al. deFuse: an algorithm for gene fusion discovery in tumor RNA-seq data. PLoS Comput. Biol. 7, e1001138 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. Tomlins, S. A. et al. Distinct classes of chromosomal rearrangements create oncogenic ETS gene fusions in prostate cancer. Nature 448, 595–599 (2007).

    CAS  Article  PubMed  Google Scholar 

  58. Pevzner, P. A., Tang, H. & Waterman, M. S. An Eulerian path approach to DNA fragment assembly. Proc. Natl Acad. Sci. USA 98, 9748–9753 (2001). This paper introduces the idea of using a De Bruijn graph for the purposes of assembly.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nature Biotech. 29, 644–652 (2011). The Trinity de novo assembly program is introduced in this paper. This was the first NGS transcriptome assembly strategy not to rely on a genome assembler while also addressing the assembly of alternative isoforms.

    CAS  Article  Google Scholar 

  60. Burset, M., Seledtsov, I. A. & Solovyev, V. V. Analysis of canonical and non-canonical splice sites in mammalian genomes. Nucleic Acids Res. 28, 4364–4375 (2000).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. Jager, M. et al. Composite transcriptome assembly of RNA-seq data in a sheep model for delayed bone healing. BMC Genomics 12, 158 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Cocquet, J., Chong, A., Zhang, G. & Veitia, R. A. Reverse transcriptase template switching and false alternative transcripts. Genomics 88, 127–131 (2006).

    CAS  Article  PubMed  Google Scholar 

  63. Haas, B. J. & Zody, M. C. Advancing RNA-seq analysis. Nature Biotech. 28, 421–423 (2010).

    CAS  Article  Google Scholar 

  64. Greninger, A. L. et al. A metagenomic analysis of pandemic influenza A (2009 H1N1) infection in patients from North America. PLoS ONE 5, e13381 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Mizuno, H. et al. Massive parallel sequencing of mRNA in identification of unannotated salinity stress-inducible transcripts in rice (Oryza sativa L.). BMC Genomics 11, 683 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  66. Twine, N. A., Janitz, K., Wilkins, M. R. & Janitz, M. Whole transcriptome sequencing reveals gene expression and splicing differences in brain regions affected by Alzheimer's disease. PLoS ONE 6, e16266 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. Meader, S., Hillier, L. W., Locke, D., Ponting, C. P. & Lunter, G. Genome assembly quality: assessment and improvement using the neutral indel model. Genome Res. 20, 675–84 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  68. Schaefer, B. C. Revolutions in rapid amplification of cDNA ends: new strategies for polymerase chain reaction cloning of full-length cDNA ends. Anal. Biochem. 227, 255–273 (1995).

    CAS  Article  PubMed  Google Scholar 

  69. Taylor, R. C. An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. BMC Bioinformatics 11 (Suppl. 12), S1 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Eid, J. et al. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009).

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

The work conducted by the US Department of Energy (DOE) Joint Genome Institute is supported by the Office of Science of the DOE under contract number DE-AC02-05CH11231. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the United States government, or any agency thereof, or the Regents of the University of California.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jeffrey A. Martin or Zhong Wang.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

Related links

FURTHER INFORMATION

Zhong Wang's homepage

Nature Reviews Genetics series on Applications of Next-Generation Sequencing

Nature Reviews Genetics series on Study Designs

US Department of Energy Joint Genome Institute

Glossary

RNA sequencing

(RNA-seq). An experimental protocol that uses next-generation sequencing technologies to sequence the RNA molecules within a biological sample in an effort to determine the primary sequence and relative abundance of each RNA.

Sequencing depth

The average number of reads representing a given nucleotide in the reconstructed sequence. A 10× sequence depth means that each nucleotide of the transcript was sequenced, on average, ten times.

Paired-end protocol

A library construction and sequencing strategy in which both ends of a DNA fragment are sequenced to produce pairs of reads (mate pairs).

Contigs

An abbreviation for contiguous sequences that is used to indicate a contiguous piece of DNA assembled from shorter overlapping sequence reads.

Low-complexity reads

Short DNA sequences composed of stretches of homopolymer nucleotides or simple sequence repeats.

Quality scores

An integer representing the probability that a given base in a nucleic acid sequence is correct.

k-mer frequency

The number of times that each k-mer (that is, a short oligonucleotide of length k) appears in a set of DNA sequences.

Splice-aware aligner

A program that is designed to align cDNA reads to a genome.

Traversing

A method for systematically visiting all nodes in a mathematical graph.

Seed-and-extend aligners

An alignment strategy that first builds a hash table containing the location of each k-mer (seed) within the reference genome. These algorithms then extend these seeds in both directions to find the best alignment (or alignments) for each read.

Burrows–Wheeler transform

(BWT). This reorders the characters within a sequence, which allows for better data compression. Many short-read aligners implement this transform in order to use less memory when aligning reads to a genome.

Parallel computing

A computer programming model for distributing data processing across multiple processors, so that multiple tasks can be carried out simultaneously.

Trans-spliced genes

Genes whose transcripts are created by the splicing together of two precursor mRNAs to form a single mature mRNA.

De Bruijn graph

A directed mathematical graph that uses a sequence of letters of length k to represent nodes. Pairs of nodes are connected if shifting a sequence by one character creates an exact k–1 overlap between the two sequences.

Greedily assembling

The use of an algorithm that joins overlapping reads together by making a series of locally optimal solutions. This strategy usually leads to a globally suboptimal solution.

N50 size

The size at which half of all assembled bases reside in contigs of this size or longer.

RACE

An experimental protocol termed Rapid Amplification of cDNA Ends, which is used to determine the start and end points of gene transcription.

Cloud computing

The abstraction of underlying hardware architectures (for example, servers, storage and networking) to a shared pool of computing resources that can be readily provisioned and released.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Martin, J., Wang, Z. Next-generation transcriptome assembly. Nat Rev Genet 12, 671–682 (2011). https://doi.org/10.1038/nrg3068

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg3068

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing