VARUS: sampling complementary RNA reads from the sequence read archive

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.

BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database

“… The test sets were (All) all annotated multi-exon genes and (Reliable) all annotated complete multi-exon genes having all introns supported by mapped RNA-seq reads, the ones sampled by VARUS ( 36 ). …”

“…We used the OrthoDB database ( 34 ) as a source of protein data. RNA-seq data used in runs of BRAKER1 was sampled from the Sequence Read Archive ( 35 ) by VARUS ( 36 ). To determine to which degree both predicted and annotated genes covered the sets of universal single copy genes identified by the BUSCO protein families, we used the BUSCO database v4 ( 37 ).…”

“…BRAKER1 has been run on the genomes of A. thaliana, C. elegans and D. melanogaster with hints originating from RNA-seq reads sampled by VARUS ( 36 ) from the NCBI Sequence Read Archive ( 35 ). VARUS used HISAT2 ( 43 ) for mapping RNA-seq reads to genomic sequences ( Supplementary Materials section 1.10).…”

GeneMark-EP and -EP+: eukaryotic gene prediction with self-training in the space of genes and proteins

“…We observed that if annotated genes of S. lycopersicum genome were supported by RNA-Seq, they were significantly better predicted by GeneMark-EP+ (Table S4). To generate intron hints from RNA-Seq we used VARUS (29). We divided annotated tomato genes into two groups: a/ genes with all introns predicted by VARUS and b/ all other genes.…”

BRAKER2: Automatic Eukaryotic Genome Annotation with GeneMark-EP+ and AUGUSTUS Supported by a Protein Database

“…BRAKER1 has been run on the genomes of A. thaliana, C. elegans, and D. melanogaster with hints originating from RNA-Seq reads sampled by VARUS [34] from the NCBI Sequence Read Archive [33]. VARUS used HISAT2 [41] for mapping RNA-Seq reads to genomic sequences (Supplementary Materials, section 1.10).…”