Evidence map›Paper›PMID 20950480›Full record

ArticleBMC genomics2010

Comparing de novo assemblers for 454 transcriptome data.

Sujai Kumar, Mark L Blaxter

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 papers.

0numbers the graph read from it
0cells of the map it votes in
146citing papers in PubMed
16.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

146 citing papers in PubMed, 260 citations in OpenAlex.

  1. Article
  2. Insights into the global freshwater virome.Frontiers in microbiology · 2022
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  6. Transcriptome Analysis of Maternal Gene Transcripts in Unfertilized Eggs ofInternational journal of molecular sciences · 2020
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Comparative De Novo Transcriptome Assembly ofCalifornian journal of health promotion · 2018
    Article
  12. Article
  13. Article
  14. Article
  15. Draft Genome Sequence ofGenome announcements · 2017
    Article
  16. Perusal of parasitic nematode 'omics in the post-genomic era.Molecular and biochemical parasitology · 2017
    Review
  17. Transcriptomics technologies.PLoS computational biology · 2017
    Article
  18. Article
  19. Draft Genome Sequence ofGenome announcements · 2017
    Article
  20. Article

86 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Sujai KumarInstitute of Evolutionary Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK.
Mark L Blaxter
University of Edinburgh · GB

Funding

Medical Research Council G0900740
6 · The paper itself

Abstract

backgroundRoche 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.

resultsAlthough 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.

conclusionsTranscriptome 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.

Indexed as

Databases, GeneticTemperatureAlgorithmsAnimalsBase SequenceContig MappingExpressed Sequence TagsFemaleFilarioideaGene Expression ProfilingGene Expression RegulationMaleReference StandardsReproducibility of ResultsSequence AlignmentSequence Analysis, DNA

Identifiers

PMID20950480
PMCPMC3091720
OpenAlexW2100703845

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.