Content sensors exploit the coding versus non-coding sequence features, such as exon or intron lengths or nucleotide composition. Signal sensors exploit specific sites and patterns such as splicing sites, promotor and terminator sequences, polyadenylation signals or branch points. Ab initio methods typically use statistical models, such as Support Vector Machines (SVMs) or hidden Markov models (HMMs), to combine two types of sensors: signal and content sensors. Therefore, similarity-based approaches are generally combined with ab initio methods that predict protein coding potential based on the target genome alone. Furthermore, such approaches encourage the propagation of erroneous annotations across genomes and cannot be used to discover novelty. The main limitation of similarity-based approaches is in cases where transcriptome sequences or closely related genomes are not available.
Numerous automated gene prediction methods have been developed that incorporate similarity information, either from transcriptome data or known gene models, including GenomeScan, GeneWise, FGENESH, Augustus, Splign, CodingQuarry, and LoReAN. In addition, information from closely related genomes can be exploited, in order to transfer known gene models to the target genome. Įxperimental data from high-throughput expression profiling experiments, such as RNA-seq or direct RNA sequencing technologies, have been applied to complement the genome sequencing and provide direct evidence of expressed genes. However, identifying genes in a newly assembled genome is challenging, especially in eukaryotes where the aim is to establish accurate gene models with precise exon-intron structures of all genes. The first essential step in the analysis process is to identify the functional elements, and in particular the protein-coding genes. The major bottleneck is now the high-throughput analysis and exploitation of the resulting sequence data. The plunging costs of DNA sequencing have made de novo genome sequencing widely accessible for an increasingly broad range of study systems with important applications in agriculture, ecology, and biotechnologies amongst others. We believe that the baseline results associated with the complex gene test sets in G3PO provide useful guidelines for future studies. The experiments showed that ab initio gene structure prediction is a very challenging task, which should be further investigated. More importantly, we highlight a number of features that could be exploited in order to improve the accuracy of current prediction tools. We used the benchmark to perform an independent comparative analysis of the most widely used ab initio gene prediction programs and identified the main strengths and weaknesses of the programs. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms, and a number of test sets are defined to evaluate the effects of different features, including genome sequence quality, gene structure complexity, protein length, etc. We describe the construction of a new benchmark, called G3PO (benchmark for Gene and Protein Prediction PrOgrams), designed to represent many of the typical challenges faced by current genome annotation projects. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality, complex gene structures, or a lack of suitable sequences for evidence-based annotations. The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models.
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