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Cited by 255Genomics and Phylogenetic StudiesRNA and protein synthesis mechanismsSingle-cell and spatial transcriptomics

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Comparing de novo assemblers for 454 transcriptome data

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Kumar, Sujai, Blaxter, Mark L·2010·BMC Genomics
DOI 10.1186/1471-2164-11-571PMC PMC3091720
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Abstract

BACKGROUND: Roche 454 pyrosequencing has become a method of choice for generating transcriptome data from non-model organisms. Once the tens to hundreds of thousands of short (250-450 base) reads have been produced, it is important to correctly assemble these to estimate the sequence of all the transcripts. Most transcriptome assembly projects use only one program for assembling 454 pyrosequencing reads, but there is no evidence that the programs used to date are optimal. We have carried out a systematic comparison of five assemblers (CAP3, MIRA, Newbler, SeqMan and CLC) to establish best practices for transcriptome assemblies, using a new dataset from the parasitic nematode Litomosoides sigmodontis. RESULTS: Although no single assembler performed best on all our criteria, Newbler 2.5 gave longer contigs, better alignments to some reference sequences, and was fast and easy to use. SeqMan assemblies performed best on the criterion of recapitulating known transcripts, and had more novel sequence than the other assemblers, but generated an excess of small, redundant contigs. The remaining assemblers all performed almost as well, with the exception of Newbler 2.3 (the version currently used by most assembly projects), which generated assemblies that had significantly lower total length. As different assemblers use different underlying algorithms to generate contigs, we also explored merging of assemblies and found that the merged datasets not only aligned better to reference sequences than individual assemblies, but were also more consistent in the number and size of contigs. CONCLUSIONS: Transcriptome assemblies are smaller than genome assemblies and thus should be more computationally tractable, but are often harder because individual contigs can have highly variable read coverage. Comparing single assemblers, Newbler 2.5 performed best on our trial data set, but other assemblers were closely comparable. Combining differently optimal assemblies from different programs however gave a more credible final product, and this strategy is recommended.

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Figure 1 Cumulative contig lengths generated by different assembly programs. For each of six assemblies, contigs longer than 100 bases were ordered by length, and the cumulative length of all contigs shorter than or equal to a given contig was plotted. The total length of the assembly and the number of contigs present in the assembly define the end point of each curve, while the initial slope of each curve reflects the proportion of longer contigs.
Figure 1 Cumulative contig lengths generated by different assembly programs. For each of six assemblies, contigs longer than 100 bases were ordered by length, and the cumulative length of all contigs shorter than or equal to a given contig was plotted. The total length of the assembly and the number of contigs present in the assembly define the end point of each curve, while the initial slope of each curve reflects the proportion of longer contigs.CC BY · PubMed Central
Figure 2 Novel sequence in pair-wise comparisons between assemblies produced by different assemblers. For each assembly, we calculated the number of bases in the other assemblies that were not present in the focal assembly.
Figure 2 Novel sequence in pair-wise comparisons between assemblies produced by different assemblers. For each assembly, we calculated the number of bases in the other assemblies that were not present in the focal assembly.CC BY · PubMed Central

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Metadata

Authors
Kumar, S., & Blaxter, M. L.
Affiliations
University of Edinburgh (GB)
Corresponding author
Blaxter, Mark L
ORCID
Venue
BMC Genomics
Publisher
Springer Science and Business Media LLC
Published
2010-10-16
Volume
11
Issue
1
Pages
571-571
ISSN
1471-2164 (ISSN-L)
DOI
10.1186/1471-2164-11-571
PMC
PMC3091720
OpenAlex
W2100703845
Cited by
255
References
55
Citations by year
2026: 1 · 2025: 3 · 2022: 2 · 2021: 3 · 2020: 11 · 2019: 4 · 2018: 7 · 2017: 12 · 2016: 22 · 2015: 22 · 2014: 44 · 2013: 38 · 2012: 52
FWCI
16.14
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Genomics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders
Directorate for Biological Sciences
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
transcriptome assembly, de novo assembly, 454 pyrosequencing, assembler comparison, CAP3, Mira, CLC, parasitic nematodes, Litomosoides sigmodontis, non-model organisms
MeSH terms
Algorithms, Animals, Base Sequence, Female, Filarioidea, Gene Expression Regulation, Male, Reference Standards, Temperature, Reproducibility of Results, Sequence Alignment, Sequence Analysis, DNA, Expressed Sequence Tags, Contig Mapping, Gene Expression Profiling, Databases, Genetic

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