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  • Review Article
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Next-generation transcriptome assembly

Nature Reviews Genetics volume 12, pages 671–682 (2011)Cite this article

Subjects

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.

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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.

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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.

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  1. Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, 2800 Mitchell Drive, MS100 Walnut Creek, California, 94598, USA

    Jeffrey A. Martin & Zhong Wang

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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.

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

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