Evidence map›Paper›PMID 30544207›Full record

ArticleGigaScience2019

Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes.

Lisa K Johnson, Harriet Alexander, C Titus Brown

Abstract read
In one paragraph

Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed
–field-weighted citation impact
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

56 citing papers in PubMed.

  1. Article
  2. Identification and Transcriptome Resource of the MiteCurrent issues in molecular biology · 2026
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  6. Apusomonad rhodopsins: A new family of ultraviolet to blue light-absorbing rhodopsin channels.Proceedings of the National Academy of Sciences of the United States of America · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Lisa K JohnsonDepartment of Population Health, and Reproduction, School of Veterinary Medicine, University of California Davis, One Shields Ave, Davis, CA 95616, USA.
Harriet AlexanderDepartment of Population Health, and Reproduction, School of Veterinary Medicine, University of California Davis, One Shields Ave, Davis, CA 95616, USA.
C Titus BrownDepartment of Population Health, and Reproduction, School of Veterinary Medicine, University of California Davis, One Shields Ave, Davis, CA 95616, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDe novo transcriptome assemblies are required prior to analyzing RNA sequencing data from a species without an existing reference genome or transcriptome. Despite the prevalence of transcriptomic studies, the effects of using different workflows, or "pipelines," on the resulting assemblies are poorly understood. Here, a pipeline was programmatically automated and used to assemble and annotate raw transcriptomic short-read data collected as part of the Marine Microbial Eukaryotic Transcriptome Sequencing Project. The resulting transcriptome assemblies were evaluated and compared against assemblies that were previously generated with a different pipeline developed by the National Center for Genome Research.

resultsNew transcriptome assemblies contained the majority of previous contigs as well as new content. On average, 7.8% of the annotated contigs in the new assemblies were novel gene names not found in the previous assemblies. Taxonomic trends were observed in the assembly metrics. Assemblies from the Dinoflagellata showed a higher number of contigs and unique k-mers than transcriptomes from other phyla, while assemblies from Ciliophora had a lower percentage of open reading frames compared to other phyla.

conclusionsGiven current bioinformatics approaches, there is no single "best" reference transcriptome for a particular set of raw data. As the optimum transcriptome is a moving target, improving (or not) with new tools and approaches, automated and programmable pipelines are invaluable for managing the computationally intensive tasks required for re-processing large sets of samples with revised pipelines and ensuring a common evaluation workflow is applied to all samples. Thus, re-assembling existing data with new tools using automated and programmable pipelines may yield more accurate identification of taxon-specific trends across samples in addition to novel and useful products for the community.

Indexed as

Computational BiologyGene Expression ProfilingTranscriptomeDatabases, GeneticEukaryotaGenomeGenomicsHigh-Throughput Nucleotide SequencingWorkflowautomated pipelinemarine microbial eukaryotere-analysistranscriptome assembly

Identifiers

PMID30544207
PMCPMC6481552

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.