ArticleBMC bioinformatics2019
VARUS: sampling complementary RNA reads from the sequence read archive.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- AccuratebioRxiv : the preprint server for biology · 2026Article
- Annotation of 200 Insect Genomes with BRAKER for Consistent Comparisons across Species.Scientific data · 2026Article
- Cell type-specific immune regulation under symbiosis in a facultatively symbiotic coral.The ISME journal · 2025Article
- The chromosomal genome sequence of the kidney sponge,Wellcome open research · 2025Article
- The chromosomal genome sequence of the stone spongeWellcome open research · 2025Article
- BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA.Genome research · 2024Article
- Galba: genome annotation with miniprot and AUGUSTUS.BMC bioinformatics · 2023Article
- GALBA: Genome Annotation with Miniprot and AUGUSTUS.bioRxiv : the preprint server for biology · 2023Article
- TSEBRA: transcript selector for BRAKER.BMC bioinformatics · 2021Article
- BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database.NAR genomics and bioinformatics · 2021Article
- GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins.NAR genomics and bioinformatics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundVast amounts of next generation sequencing RNA data has been deposited in archives, accompanying very diverse original studies. The data is readily available also for other purposes such as genome annotation or transcriptome assembly. However, selecting a subset of available experiments, sequencing runs and reads for this purpose is a nontrivial task and complicated by the inhomogeneity of the data.
resultsThis article presents the software VARUS that selects, downloads and aligns reads from NCBI's Sequence Read Archive, given only the species' binomial name and genome. VARUS automatically chooses runs from among all archived runs to randomly select subsets of reads. The objective of its online algorithm is to cover a large number of transcripts adequately when network bandwidth and computing resources are limited. For most tested species VARUS achieved both a higher sensitivity and specificity with a lower number of downloaded reads than when runs were manually selected. At the example of twelve eukaryotic genomes, we show that RNA-Seq that was sampled with VARUS is well-suited for fully-automatic genome annotation with BRAKER.
conclusionsWith VARUS, genome annotation can be automatized to the extent that not even the selection and quality control of RNA-Seq has to be done manually. This introduces the possibility to have fully automatized genome annotation loops over potentially many species without incurring a loss of accuracy over a manually supervised annotation process.
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