ArticleGigaScience2019
Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes.
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.
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Who cites it
56 citing papers in PubMed.
- Eukaryotic metagenome-assembled genomes recovered from deep metagenomic sequencing of the seagrass,Microbial genomics · 2026Article
- Identification and Transcriptome Resource of the MiteCurrent issues in molecular biology · 2026Article
- Review
- Bioluminescence of the heterotrophic dinoflagellate Polykrikos kofoidii Chatton 1914 (Dinophyceae).Journal of phycology · 2026Article
- Diel and eddy driven changes in microbial gene expression and biogeochemistry in the oceanic chlorophyll maximum.Nature communications · 2026Article
- 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 · 2025Article
- Genome-resolved biogeography of Phaeocystales, cosmopolitan bloom-forming algae.Nature communications · 2025Article
- A multifunctional organelle coordinates phagocytosis and chlorophagy in a marine eukaryote phytoplankton Scyphosphaera apsteinii.The New phytologist · 2025Article
- Heat shock transcription factor-mediated thermal tolerance and cell size plasticity in marine diatoms.Nature communications · 2025Article
- Unveiling the functional nature of retrogenes in dinoflagellates.Open biology · 2025Article
- Phylogenetic proximity is a key driver of temporal succession of marine giant viruses in a five-year metagenomic time-series.ISME communications · 2025Article
- Latitude- and depth-driven divergence in protist trophic strategies revealed by a machine learning model.Frontiers in microbiology · 2025Article
- Article
- Missing microbial eukaryotes and misleading meta-omic conclusions.Nature communications · 2024Article
- Microeukaryote metabolism across the western North Atlantic Ocean revealed through autonomous underwater profiling.Nature communications · 2024Article
- Article
- First regional reference database of northern Adriatic diatom transcriptomes.Scientific reports · 2024Article
- DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and viruses.NAR genomics and bioinformatics · 2024Article
- Noncanonical usage of stop codons in ciliates expands proteins with structurally flexible Q-rich motifs.eLife · 2024Article
- Diurnal Rhythms in the Red Seaweed Gracilariopsis chorda are Characterized by Unique Regulatory Networks of Carbon Metabolism.Molecular biology and evolution · 2024Article
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3 authors.
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No grant is acknowledged in the PubMed record.
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.
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