bioRxiv Science⌕ Search

Biology subjects

Kenobi, K.

Publications and source records attributed to Kenobi, K..

2 recordsLinked to original sources

StORF-Reporter: Finding Genes between Genes

Large regions of prokaryotic genomes are currently without any annotation, in part due to well-established limitations of annotation tools. For example, it is routine for annotation tools to misreport or completely omit genes using alternative start codons. Therefore, we present StORF-Reporter, a tool that takes an annotated genome and returns missing CDS genes from unannotated regions. StORF-Reporter consists of two parts. The first begins with the extraction of unannotated regions from an annotated genome. Next, Stop-ORFs (StORFs) are identified in these unannotated regions. StORFs are open reading frames that are delimited by stop codons and thus can capture those genes most often missing in genome annotations. We show that this methodology recovers genes missing from canonical genome annotations. We inspected the results of the genomes of model organisms, the pangenome of Escherichia coli, and a further 6,223 prokaryotic genomes of 179 genera from the Ensembl Bacteria database. StORF-Reporter was able to extend the core, soft-core and accessory gene-collections, identify novel gene families and extend families into additional genera. The high levels of sequence conservation observed between genera suggest that many of these StORF sequences are likely to be functional genes that must now be added to the canonical annotations.

bioinformatics↗

No one tool to rule them all: Prokaryotic gene prediction tool performance is highly dependent on the organism of study

MotivationThe biases in Open Reading Frame (ORF) prediction tools, which have been based on historic genomic annotations from model organisms, impact our understanding of novel genomes and metagenomes. This hinders the discovery of new genomic information as it results in predictions being biased towards existing knowledge. To date users have lacked a systematic and replicable approach to identify the strengths and weaknesses of any ORF prediction tool and allow them to choose the right tool for their analysis. ResultsWe present an evaluation framework (ORForise) based on a comprehensive set of 12 primary and 60 secondary metrics that facilitate the assessment of the performance of ORF prediction tools. This makes it possible to identify which performs better for specific use-cases. We use this to assess 15 ab initio and model-based tools representing those most widely used (historically and currently) to generate the knowledge in genomic databases. We find that the performance of any tool is dependent on the genome being analysed, and no individual tool ranked as the most accurate across all genomes or metrics analysed. Even the top-ranked tools produced conflicting gene collections which could not be resolved by aggregation. The ORForise evaluation framework provides users with a replicable, data-led approach to make informed tool choices for novel genome annotations and for refining historical annotations. Availabilityhttps://github.com/NickJD/ORForise Contactnicholas@dimonaco.co.uk Supplementary informationSupplementary data are available at bioRxiv online.

bioinformatics↗