An approach to improved microbial eukaryotic genome annotation
New sequencing technologies have considerably accelerated the rate at which genomic data is being generated. One ongoing challenge is the accurate structural annotation of those novel genomes once sequenced and assembled, in particular if the organism does not have close relatives with well-annotated genomes. Whole-transcriptome sequencing (RNA-Seq) and assembly—both of which share similarities to whole-genome sequencing and assembly, respectively—have been shown to dramatically increase the accuracy of gene annotation. Read coverage, inferred splice junctions and assembled transcripts can provide valuable information about gene structure. Several annotation pipelines have been developed to automate structural annotation by incorporating information from RNA-Seq, as well as protein sequence similarity data, with the goal of reaching the accuracy of an expert curator. Annotation pipelines follow a similar workflow. The first step is to identify repetitive regions to prevent misinformed sequence alignments and gene predictions. The next step is to construct a database of evidence from experimental data such as RNA-Seq mapping and assembly, and protein sequence alignments, which are used to inform the generalised Hidden Markov Models of gene prediction software. The final step is to consolidate sequence alignments and gene predictions into a high-confidence consensus set. Thus, automated pipelines are complex, and therefore susceptible to incomplete and erroneous use of information, which can poison gene predictions and consensus model building. Here, we present an improved approach to microbial eukaryotic genome annotation. Its conception was based on identifying and mitigating potential sources of error and bias that are present in available pipelines. Our approach has two main aspects. The first is to create a more complete and diverse set of extrinsic evidence to better inform gene predictions. The second is to use extrinsic evidence in tandem with predictions such that the influence of their respective biases in the consensus gene models is reduced. We benchmarked our new tool against three known pipelines, showing significant gains in gene, transcript, exon and intron sensitivity and specificity in the genome annotation of microbial eukaryotes.
Annotation Structurale Saccharomyces Neurospora Ustilago Plasmodium Nuclear Genome Structural Annotation Microbial Eukaryote Protists Fungi