An empirical analysis of training protocols for probabilistic gene finders

BACKGROUND: Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters. RESULTS: We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improvement in prediction accuracy can be achieved by this method. CONCLUSIONS: We conclude that training of GHMM-based gene finders is best performed using some form of discriminative training rather than simple maximum likelihood estimation at the submodel level, and that generalized gradient ascent methods are suitable for this task. We also conclude that partitioning of training data for the twin purposes of maximum likelihood initialization and gradient ascent optimization appears to be unnecessary, but that strict segregation of test data must be enforced during final gene finder evaluation to avoid artificially inflated accuracy measurements

JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions

BACKGROUND: Predicting complete protein-coding genes in human DNA remains a significant challenge. Though a number of promising approaches have been investigated, an ideal suite of tools has yet to emerge that can provide near perfect levels of sensitivity and specificity at the level of whole genes. As an incremental step in this direction, it is hoped that controlled gene finding experiments in the ENCODE regions will provide a more accurate view of the relative benefits of different strategies for modeling and predicting gene structures. RESULTS: Here we describe our general-purpose eukaryotic gene finding pipeline and its major components, as well as the methodological adaptations that we found necessary in accommodating human DNA in our pipeline, noting that a similar level of effort may be necessary by ourselves and others with similar pipelines whenever a new class of genomes is presented to the community for analysis. We also describe a number of controlled experiments involving the differential inclusion of various types of evidence and feature states into our models and the resulting impact these variations have had on predictive accuracy. CONCLUSION: While in the case of the non-comparative gene finders we found that adding model states to represent specific biological features did little to enhance predictive accuracy, for our evidence-based 'combiner' program the incorporation of additional evidence tracks tended to produce significant gains in accuracy for most evidence types, suggesting that improved modeling efforts at the hidden Markov model level are of relatively little value. We relate these findings to our current plans for future research

Genome bioinformatics of tomato and potato

In the past two decades genome sequencing has developed from a laborious and costly technology employed by large international consortia to a widely used, automated and affordable tool used worldwide by many individual research groups. Genome sequences of many food animals and crop plants have been deciphered and are being exploited for fundamental research and applied to improve their breeding programs. The developments in sequencing technologies have also impacted the associated bioinformatics strategies and tools, both those that are required for data processing, management, and quality control, and those used for interpretation of the data. This thesis focuses on the application of genome sequencing, assembly and annotation to two members of the Solanaceae family, tomato and potato. Potato is the economically most important species within the Solanaceae, and its tubers contribute to dietary intake of starch, protein, antioxidants, and vitamins. Tomato fruits are the second most consumed vegetable after potato, and are a globally important dietary source of lycopene, beta-carotene, vitamin C, and fiber. The chapters in this thesis document the generation, exploitation and interpretation of genomic sequence resources for these two species and shed light on the contents, structure and evolution of their genomes. Chapter 1introduces the concepts of genome sequencing, assembly and annotation, and explains the novel genome sequencing technologies that have been developed in the past decade. These so-called Next Generation Sequencing platforms display considerable variation in chemistry and workflow, and as a consequence the throughput and data quality differs by orders of magnitude between the platforms. The currently available sequencing platforms produce a vast variety of read lengths and facilitate the generation of paired sequences with an approximately fixed distance between them. The choice of sequencing chemistry and platform combined with the type of sequencing template demands specifically adapted bioinformatics for data processing and interpretation. Irrespective of the sequencing and assembly strategy that is chosen, the resulting genome sequence, often represented by a collection of long linear strings of nucleotides, is of limited interest by itself. Interpretation of the genome can only be achieved through sequence annotation – that is, identification and classification of all functional elements in a genome sequence. Once these elements have been annotated, sequence alignments between multiple genomes of related accessions or species can be utilized to reveal the genetic variation on both the nucleotide and the structural level that underlies the difference between these species or accessions. Chapter 2describes BlastIf, a novel software tool that exploits sequence similarity searches with BLAST to provide a straightforward annotation of long nucleotide sequences. Generally, two problems are associated with the alignment of a long nucleotide sequence to a database of short gene or protein sequences: (i) the large number of similar hits that can be generated due to database redundancy; and (ii) the relationships implied between aligned segments within a hit that in fact correspond to distinct elements on the sequence such as genes. BlastIf generates a comprehensible BLAST output for long nucleotide sequences by reducing the number of similar hits while revealing most of the variation present between hits. It is a valuable tool for molecular biologists who wish to get a quick overview of the genetic elements present in a newly sequenced segment of DNA, prior to more elaborate efforts of gene structure prediction and annotation. In Chapter 3 a first genome-wide comparison between the emerging genomic sequence resources of tomato and potato is presented. Large collections of BAC end sequences from both species were annotated through repeat searches, transcript alignments and protein domain identification. In-depth comparisons of the annotated sequences revealed remarkable differences in both gene and repeat content between these closely related genomes. The tomato genome was found to be more repetitive than the potato genome, and substantial differences in the distribution of Gypsy and Copia retrotransposable elements as well as microsatellites were observed between the two genomes. A higher gene content was identified in the potato sequences, and in particular several large gene families including cytochrome P450 mono-oxygenases and serine-threonine protein kinases were significantly overrepresented in potato compared to tomato. Moreover, the cytochrome P450 gene family was found to be expanded in both tomato and potato when compared to Arabidopsis thaliana, suggesting an expanded network of secondary metabolic pathways in the Solanaceae. Together these findings present a first glimpse into the evolution of Solanaceous genomes, both within the family and relative to other plant species. Chapter 4explores the physical and genetic organization of tomato chromosome 6 through integration of BAC sequence analysis, High Information Content Fingerprinting, genetic analysis, and BAC-FISH mapping data. A collection of BACs spanning substantial parts of the short and long arm euchromatin and several dispersed regions of the pericentrometric heterochromatin were sequenced and assembled into several tiling paths spanning approximately 11 Mb. Overall, the cytogenetic order of BACs was in agreement with the order of BACs anchored to the Tomato EXPEN 2000 genetic map, although a few striking discrepancies were observed. The integration of BAC-FISH, sequence and genetic mapping data furthermore provided a clear picture of the borders between eu- and heterochromatin on chromosome 6. Annotation of the BAC sequences revealed that, although the majority of protein-coding genes were located in the euchromatin, the highly repetitive pericentromeric heterochromatin displayed an unexpectedly high gene content. Moreover, the short arm euchromatin was relatively rich in repeats, but the ratio of Gypsy and Copia retrotransposons across the different domains of the chromosome clearly distinguished euchromatin from heterochromatin. The ongoing whole-genome sequencing effort will reveal if these properties are unique for tomato chromosome 6, or a more general property of the tomato genome. Chapter 5presents the potato genome, the first genome sequence of an Asterid. To overcome the problems associated with genome assembly due tothe high level of heterozygosity that is observed in commercial tetraploid potato varieties, a homozygous doubled-monoploid potato clone was exploited to sequence and assemble 86% of the 844 Mb genome. This potato reference genome sequence was complemented with re-sequencing of aheterozygous diploid clone, revealing the form and extent of sequence polymorphism both between different genotypes and within a single heterozygous genotype. Gene presence/absence variants and other potentially deleterious mutations were found to occur frequently in potato and are a likely cause of inbreeding depression. Annotation of the genome was supported by deep transcriptome sequencing of both the doubled-monoploid and the heterozygous potato, resulting in the prediction of more than 39,000 protein coding genes. Transcriptome analysis provided evidence for the contribution of gene family expansion, tissue specific expression, and recruitment of genes to new pathways to the evolution of tuber development. The sequence of the potato genome has provided new insights into Eudicot genome evolution and has provided a solid basis for the elucidation of the evolution of tuberisation. Many traits of interest to plant breeders are quantitative in nature and the potato sequence will simplify both their characterization and deployment to generate novel cultivars. The outstanding challenges in plant genome sequencing are addressed in Chapter 6. The high concentration of repetitive elements and the heterozygosity and polyploidy of many interesting crop plant species currently pose a barrier for the efficient reconstruction of their genome sequences. Nonetheless, the completion of a large number of new genome sequences in recent years and the ongoing advances in sequencing technology provide many excitingopportunities for plant breeding and genome research. Current sequencing platforms are being continuously updated and improved, and novel technologies are being developed and implemented in third-generation sequencing platforms that sequence individual molecules without need for amplification. While these technologies create exciting opportunities for new sequencing applications, they also require robust software tools to process the data produced through them efficiently. The ever increasing amount of available genome sequences creates the need for an intuitive platform for the automated and reproducible interrogation of these data in order to formulate new biologically relevant questions on datasets spanning hundreds or thousands of genome sequences. </p