Companion: a web server for annotation and analysis of parasite genomes

Currently available sequencing technologies enable quick and economical sequencing of many new eukaryotic parasite (apicomplexan or kinetoplastid) species or strains. Compared to SNP calling approaches, de novo assembly of these genomes enables researchers to additionally determine insertion, deletion and recombination events as well as to detect complex sequence diversity, such as that seen in variable multigene families. However, there currently are no automated eukaryotic annotation pipelines offering the required range of results to facilitate such analyses. A suitable pipeline needs to perform evidence-supported gene finding as well as functional annotation and pseudogene detection up to the generation of output ready to be submitted to a public database. Moreover, no current tool includes quick yet informative comparative analyses and a first pass visualization of both annotation and analysis results. To overcome those needs we have developed the Companion web server (http://companion.sanger.ac.uk) providing parasite genome annotation as a service using a reference-based approach. We demonstrate the use and performance of Companion by annotating two Leishmania and Plasmodium genomes as typical parasite cases and evaluate the results compared to manually annotated references

Doctor of Philosophy

dissertationThe MAKER genome annotation and curation software tool was developed in response to increased demand for genome annotation services, secondary to decreased genome sequencing costs. MAKER currently has over 1000 registered users throughout the world. This wide adoption of MAKER has uncovered the need for additional functionalities. Here I addressed moving MAKER into the domain of plant annotation, expanding MAKER to include new methods of gene and noncoding RNA annotation, and improving usability of MAKER through documentation and community outreach. To move MAKER into the plant annotation domain, I benchmarked MAKER on the well-annotated Arabidopsis thaliana genome. MAKER performs well on the Arabidopsis genome in de novo genome annotation and was able to improve the current TAIR10 gene models by incorporating mRNA-seq data not available during the original annotation efforts. In addition to this benchmarking, I annotated the genome of the sacred lotus Nelumbo Nucifera. I enabled noncoding RNA annotation in MAKER by adding the ability for MAKER to run and process the outputs of tRNAscan-SE and snoscan. These functionalities were tested on the Arabidopsis genome and used MAKER to annotate tRNAs and snoRNAs in Zea mays. The resulting version of MAKER was named MAKER-P. I added the functionality of a combiner by adding EVidence Modeler to the MAKER code base. iv As the number of MAKER users has grown, so have the help requests sent to the MAKER developers list. Motivated by the belief that improving the MAKER documentation would obviate the need for many of these requests, I created a media wiki that was linked to the MAKER download page, and the MAKER developers list was made searchable. Additionally I have written a unit on genome annotation using MAKER for Current Protocols in Bioinformatics. In response to these efforts I have seen a corresponding decrease in help requests, even though the number of registered MAKER users continues to increase. Taken together these products and activities have moved MAKER into the domain of plant annotation, expanded MAKER to include new methods of gene and noncoding RNA annotation, and improved the usability of MAKER through documentation and community outreach

Work ow-based systematic design of high throughput genome annotation

The genus Eimeria belongs to the phylum Apicomplexa, which includes many obligate intra-cellular protozoan parasites of man and livestock. E. tenella is one of seven species that infect the domestic chicken and cause the intestinal disease coccidiosis which is economy important for poultry industry. E. tenella is highly pathogenic and is often used as a model species for the Eimeria biology studies. In this PhD thesis, a comprehensive annotation system named as \WAGA" (Workflow-based Automatically Genome Annotation) was built and applied to the E. tenella genome. InforSense KDE, and its BioSense plug-in (products of the InforSense Company), were the core softwares used to build the workflows. Workflows were made by integrating individual bioinformatics tools into a single platform. Each workflow was designed to provide a standalone service for a particular task. Three major workflows were developed based on the genomic resources currently available for E. tenella. These were of ESTs-based gene construction, HMM-based gene prediction and protein-based annotation. Finally, a combining workflow was built to sit above the individual ones to generate a set of automatic annotations using all of the available information. The overall system and its three major components were deployed as web servers that are fully tuneable and reusable for end users. WAGA does not require users to have programming skills or knowledge of the underlying algorithms or mechanisms of its low level components. E. tenella was the target genome here and all the results obtained were displayed by GBrowse. A sample of the results is selected for experimental validation. For evaluation purpose, WAGA was also applied to another Apicomplexa parasite, Plasmodium falciparum, the causative agent of human malaria, which has been extensively annotated. The results obtained were compared with gene predictions of PHAT, a gene finder designed for and used in the P. falciparum genome project

Reranking candidate gene models with cross-species comparison for improved gene prediction

Background: Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results: We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion: Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models

Unsupervised Algorithms for Automated Gene Prediction in Novel Eukaryotic Genomes

Gene prediction, the identification of the location and structure of protein-coding genes in genomic sequences, is one of the first and most important steps in the analysis of assembled genomes. The exponential growth of sequenced eukaryotic genomes necessitates fully automated computational gene prediction methods. Due to the complexity and diversity of eukaryotic genomes, the task of accurate automatic eukaryotic gene prediction remains an open challenge. This work presents three novel gene prediction algorithms that address specific aspects of this challenge and thus improve over existing gene prediction methods. The first part of this thesis describes GeneMark-EP+, an unsupervised gene prediction algorithm that uses homologous cross-species proteins to guide its model training and gene prediction steps. In contrast to existing homology-based gene finders, which can only extract information from proteins of closely related species, GeneMark-EP+ is designed to utilize proteins of any evolutionary distance, including remote homologs. Consequently, GeneMark-EP+ can fully exploit the information contained in large and ever-growing protein databases that are, unlike transcriptomic data, always readily available prior to a genome annotation project start. GeneMark-EP+ is shown to significantly improve over previous GeneMark versions, including ones integrating transcriptomic data. In the second part, BRAKER2 is presented---a fully automated protein homology-based gene prediction pipeline that integrates GeneMark-EP+ with AUGUSTUS, an accurate gene finder that requires supervised training. By combining complementary strengths of these two gene prediction tools, BRAKER2 achieves state-of-the-art gene prediction accuracy in a fully unsupervised manner. The high gene prediction accuracy of BRAKER2 is demonstrated in tests on a wide range of plant and animal genomes. Further, it is shown that BRAKER2 compares favorably with MAKER2, one of the most popular gene prediction pipelines. Finally, this thesis describes GeneMark-ETP+, a self-training gene prediction algorithm that simultaneously utilizes diverse information streams---genomic, transcriptomic, and protein homology---throughout all stages of its model training and gene prediction. This evidence integration is achieved by, among other things, creating a novel method for simultaneous gene prediction in transcripts and genomic DNA. Notably, GeneMark-ETP+ builds upon the previous work of this thesis: its training is fully unsupervised and proteins of any evolutionary distance are utilized. The integrative approach of GeneMark-ETP+ is demonstrated to reach better prediction accuracy compared with competing tools combining ab initio-, protein homology-, and transcriptome-based predictions.Ph.D

Doctor of Philosophy

dissertationWhole genome sequencing projects have expanded our understanding of evolution, organism development, and human disease. Now advances in secondgeneration technologies are making whole genome sequencing routine even for small laboratories. However, advances in annotation technology have not kept pace with genome sequencing, and annotation has become the major bottleneck for many genome projects (especially those with limited bioinformatics expertise). At the same time, challenges associated with genomics research extend beyond merely annotating genomes, as annotations must be subjected to diverse downstream analyses, the complexities of which can confound smaller research groups. Additionally, with improvements in genome assembly and the wide availability of next generation transcriptome data (mRNA-seq), researchers have the opportunity to re-annotate previously published genomes, which creates new difficulties for data integration and management that are not well addressed by existing tools. In response to the challenges facing second-generation genome projects, I have developed the annotation pipeline MAKER2 together with accessory software for downstream analysis and data management. The MAKER2 annotation pipeline finds repeats within a genome, aligns ESTs and cDNAs, identifies sites of protein homology, and produces database-ready gene annotations in association with supporting evidence. However MAKER2 can go beyond structural annotation to identify and integrate functional annotations. MAKER2 also provides researchers iv with the capability to re-annotate legacy genome datasets and to incorporate mRNAseq. Additionally, MAKER2 supports distributed parallelization on computer clusters, thus providing a scalable solution for datasets of any size. Annotations produced by MAKER2 can be directly loaded into many popular downstream annotation analysis and management tools from the Generic Model Organism Database Project. By using MAKER2 with these tools, research groups can quickly build genome annotations, perform analyses, and distribute their data to the wider scientific community. Here I describe the internal architecture of MAKER2, and document its computational capabilities. I also describe my work to annotate and analyze eight emerging model organism genomes in collaboration with their associated genome projects. Thus, in the course of my thesis work, I have addressed a specific need within the scientific community for easy-to-use annotation and analysis tools while also expanding our understanding of evolution and biology

Unsupervised and semi-supervised training methods for eukaryotic gene prediction

This thesis describes new gene finding methods for eukaryotic gene prediction. The current methods for deriving model parameters for gene prediction algorithms are based on curated or experimentally validated set of genes or gene elements. These training sets often require time and additional expert efforts especially for the species that are in the initial stages of genome sequencing. Unsupervised training allows determination of model parameters from anonymous genomic sequence with. The importance and the practical applicability of the unsupervised training is critical for ever growing rate of eukaryotic genome sequencing. Three distinct training procedures are developed for diverse group of eukaryotic species. GeneMark-ES is developed for species with strong donor and acceptor site signals such as Arabidopsis thaliana, Caenorhabditis elegans and Drosophila melanogaster. The second version of the algorithm, GeneMark-ES-2, introduces enhanced intron model to better describe the gene structure of fungal species with posses with relatively weak donor and acceptor splice sites and well conserved branch point signal. GeneMark-LE, semi-supervised training approach is designed for eukaryotic species with small number of introns. The results indicate that the developed unsupervised training methods perform well as compared to other training methods and as estimated from the set of genes supported by EST-to-genome alignments. Analysis of novel genomes reveals interesting biological findings and show that several candidates of under-annotated and over-annotated fungal species are present in the current set of annotated of fungal genomes.Ph.D.Committee Chair: Mark Borodovky; Committee Member: Jung H. Choi; Committee Member: King Jordan; Committee Member: Leonid Bunimovich; Committee Member: Yury Chernof

A Brief Review of Computational Gene Prediction Methods

With the development of genome sequencing for many organisms, more and more raw sequences need to be annotated. Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Two classes of methods are generally adopted: similarity based searches and ab initio prediction. Here, we review the development of gene prediction methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions

Improving algorithms of gene prediction in prokaryotic genomes, metagenomes, and eukaryotic transcriptomes

Next-generation sequencing has generated enormous amount of DNA and RNA sequences that potentially carry volumes of genetic information, e.g. protein-coding genes. The thesis is divided into three main parts describing i) GeneMarkS-2, ii) GeneMarkS-T, and iii) MetaGeneTack. In prokaryotic genomes, ab initio gene finders can predict genes with high accuracy. However, the error rate is not negligible and largely species-specific. Most errors in gene prediction are made in genes located in genomic regions with atypical GC composition, e.g. genes in pathogenicity islands. We describe a new algorithm GeneMarkS-2 that uses local GC-specific heuristic models for scoring individual ORFs in the first step of analysis. Predicted atypical genes are retained and serve as ‘external’ evidence in subsequent runs of self-training. GeneMarkS-2 also controls the quality of training process by effectively selecting optimal orders of the Markov chain models as well as duration parameters in the hidden semi-Markov model. GeneMarkS-2 has shown significantly improved accuracy compared with other state-of-the-art gene prediction tools. Massive parallel sequencing of RNA transcripts by the next generation technology (RNA-Seq) provides large amount of RNA reads that can be assembled to full transcriptome. We have developed a new tool, GeneMarkS-T, for ab initio identification of protein-coding regions in RNA transcripts. Unsupervised estimation of parameters of the algorithm makes unnecessary several steps in the conventional gene prediction protocols, most importantly the manually curated preparation of training sets. We have demonstrated that the GeneMarkS-T self-training is robust with respect to the presence of errors in assembled transcripts and the accuracy of GeneMarkS-T in identifying protein-coding regions and, particularly, in predicting gene starts compares favorably to other existing methods. Frameshift prediction (FS) is important for analysis and biological interpretation of metagenomic sequences. Reads in metagenomic samples are prone to sequencing errors. Insertion and deletion errors that change the coding frame impair the accurate identification of protein coding genes. Accurate frameshift prediction requires sufficient amount of data to estimate parameters of species-specific statistical models of protein-coding and non-coding regions. However, this data is not available; all we have is metagenomic sequences of unknown origin. The challenge of ab initio FS detection is, therefore, twofold: (i) to find a way to infer necessary model parameters and (ii) to identify positions of frameshifts (if any). We describe a new tool, MetaGeneTack, which uses a heuristic method to estimate parameters of sequence models used in the FS detection algorithm. It was shown on several test sets that the performance of MetaGeneTack FS detection is comparable or better than the one of earlier developed program FragGeneScan.Ph.D