Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

For modern biology, precise genome annotations are of prime importance, as they allow the accurate definition of genic regions. We employ state-of-the-art machine learning methods to assay and improve the accuracy of the genome annotation of the nematode Caenorhabditis elegans. The proposed machine learning system is trained to recognize exons and introns on the unspliced mRNA, utilizing recent advances in support vector machines and label sequence learning. In 87% (coding and untranslated regions) and 95% (coding regions only) of all genes tested in several out-of-sample evaluations, our method correctly identified all exons and introns. Notably, only 37% and 50%, respectively, of the presently unconfirmed genes in the C. elegans genome annotation agree with our predictions, thus we hypothesize that a sizable fraction of those genes are not correctly annotated. A retrospective evaluation of the Wormbase WS120 annotation [1] of C. elegans reveals that splice form predictions on unconfirmed genes in WS120 are inaccurate in about 18% of the considered cases, while our predictions deviate from the truth only in 10%–13%. We experimentally analyzed 20 controversial genes on which our system and the annotation disagree, confirming the superiority of our predictions. While our method correctly predicted 75% of those cases, the standard annotation was never completely correct. The accuracy of our system is further corroborated by a comparison with two other recently proposed systems that can be used for splice form prediction: SNAP and ExonHunter. We conclude that the genome annotation of C. elegans and other organisms can be greatly enhanced using modern machine learning technology

A phylogenetic generalized hidden Markov model for predicting alternatively spliced exons

BACKGROUND: An important challenge in eukaryotic gene prediction is accurate identification of alternatively spliced exons. Functional transcripts can go undetected in gene expression studies when alternative splicing only occurs under specific biological conditions. Non-expression based computational methods support identification of rarely expressed transcripts. RESULTS: A non-expression based statistical method is presented to annotate alternatively spliced exons using a single genome sequence and evidence from cross-species sequence conservation. The computational method is implemented in the program ExAlt and an analysis of prediction accuracy is given for Drosophila melanogaster. CONCLUSION: ExAlt identifies the structure of most alternatively spliced exons in the test set and cross-species sequence conservation is shown to improve the precision of predictions. The software package is available to run on Drosophila genomes to search for new cases of alternative splicing

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

Genome-Wide Association between Branch Point Properties and Alternative Splicing

The branch point (BP) is one of the three obligatory signals required for pre-mRNA splicing. In mammals, the degeneracy of the motif combined with the lack of a large set of experimentally verified BPs complicates the task of modeling it in silico, and therefore of predicting the location of natural BPs. Consequently, BPs have been disregarded in a considerable fraction of the genome-wide studies on the regulation of splicing in mammals. We present a new computational approach for mammalian BP prediction. Using sequence conservation and positional bias we obtained a set of motifs with good agreement with U2 snRNA binding stability. Using a Support Vector Machine algorithm, we created a model complemented with polypyrimidine tract features, which considerably improves the prediction accuracy over previously published methods. Applying our algorithm to human introns, we show that BP position is highly dependent on the presence of AG dinucleotides in the 3′ end of introns, with distance to the 3′ splice site and BP strength strongly correlating with alternative splicing. Furthermore, experimental BP mapping for five exons preceded by long AG-dinucleotide exclusion zones revealed that, for a given intron, more than one BP can be chosen throughout the course of splicing. Finally, the comparison between exons of different evolutionary ages and pseudo exons suggests a key role of the BP in the pathway of exon creation in human. Our computational and experimental analyses suggest that BP recognition is more flexible than previously assumed, and it appears highly dependent on the presence of downstream polypyrimidine tracts. The reported association between BP features and the splicing outcome suggests that this, so far disregarded but yet crucial, element buries information that can complement current acceptor site models

Combining in silico prediction and ribosome profiling in a genome-wide search for novel putatively coding sORFs

Background: It was long assumed that proteins are at least 100 amino acids (AAs) long. Moreover, the detection of short translation products (e. g. coded from small Open Reading Frames, sORFs) is very difficult as the short length makes it hard to distinguish true coding ORFs from ORFs occurring by chance. Nevertheless, over the past few years many such non-canonical genes (with ORFs < 100 AAs) have been discovered in different organisms like Arabidopsis thaliana, Saccharomyces cerevisiae, and Drosophila melanogaster. Thanks to advances in sequencing, bioinformatics and computing power, it is now possible to scan the genome in unprecedented scrutiny, for example in a search of this type of small ORFs. Results: Using bioinformatics methods, we performed a systematic search for putatively functional sORFs in the Mus musculus genome. A genome-wide scan detected all sORFs which were subsequently analyzed for their coding potential, based on evolutionary conservation at the AA level, and ranked using a Support Vector Machine (SVM) learning model. The ranked sORFs are finally overlapped with ribosome profiling data, hinting to sORF translation. All candidates are visually inspected using an in-house developed genome browser. In this way dozens of highly conserved sORFs, targeted by ribosomes were identified in the mouse genome, putatively encoding micropeptides. Conclusion: Our combined genome-wide approach leads to the prediction of a comprehensive but manageable set of putatively coding sORFs, a very important first step towards the identification of a new class of bioactive peptides, called micropeptides

A procedure for identifying homologous alternative splicing events

<p>Abstract</p> <p>Background</p> <p>The study of the functional role of alternative splice isoforms of a gene is a very active area of research in biology. The difficulty of the experimental approach (in particular, in its high-throughput version) leaves ample room for the development of bioinformatics tools that can provide a useful first picture of the problem. Among the possible approaches, one of the simplest is to follow classical protein function annotation protocols and annotate target alternative splice events with the information available from conserved events in other species. However, the application of this protocol requires a procedure capable of recognising such events. Here we present a simple but accurate method developed for this purpose.</p> <p>Results</p> <p>We have developed a method for identifying homologous, or equivalent, alternative splicing events, based on the combined use of neural networks and sequence searches. The procedure comprises four steps: (i) BLAST search for homologues of the two isoforms defining the target alternative splicing event; (ii) construction of all possible candidate events; (iii) scoring of the latter with a series of neural networks; and (iv) filtering of the results. When tested in a set of 473 manually annotated pairs of homologous events, our method showed a good performance, with an accuracy of 0.99, a precision of 0.98 and a sensitivity of 0.93. When no candidates were available, the specificity of our method varied between 0.81 and 0.91.</p> <p>Conclusion</p> <p>The method described in this article allows the identification of homologous alternative splicing events, with a good success rate, indicating that such method could be used for the development of functional annotation of alternative splice isoforms.</p

Leveraging EST Evidence to Automatically Predict Alternatively Spliced Genes, Master\u27s Thesis, December 2006

Current methods for high-throughput automatic annotation of newly sequenced genomes are largely limited to tools which predict only one transcript per gene locus. Evidence suggests that 20-50% of genes in higher eukariotic organisms are alternatively spliced. This leaves the remainder of the transcripts to be annotated by hand, an expensive time-consuming process. Genomes are being sequenced at a much higher rate than they can be annotated. We present three methods for using the alignments of inexpensive Expressed Sequence Tags in combination with HMM-based gene prediction with N-SCAN EST to recreate the vast majority of hand annotations in the D.melanogaster genome. In our first method, we “piece together” N-SCAN EST predictions with clustered EST alignments to increase the number of transcripts per locus predicted. This is shown to be a sensitve and accurate method, predicting the vast majority of known transcripts in the D.melanogaster genome. We present an approach of using these clusters of EST alignments to construct a Multi-Pass gene prediction phase, again, piecing it together with clusters of EST alignments. While time consuming, Multi-Pass gene prediction is very accurate and more sensitive than single-pass. Finally, we present a new Hidden Markov Model instance, which augments the current N-SCAN EST HMM, that predicts multiple splice forms in a single pass of prediction. This method is less time consuming, and performs nearly as well as the multi-pass approach

Machine learning models towards elucidating the plant intron retention code

2017 Fall.Includes bibliographical references.Alternative Splicing is a process that allows a single gene to encode multiple proteins. Intron Retention (IR) is a type of alternative splicing which is mainly prevalent in plants, but has been shown to regulate gene expression in various organisms and is often involved in rare human diseases. Despite its important role, not much research has been done to understand IR. The motivation behind this research work is to better understand IR and how it is regulated by various biological factors. We designed a combination of 137 features, forming an "intron retention code", to reveal the factors that contribute to IR. Using random forest and support vector machine classifiers, we show the usefulness of these features for the task of predicting whether an intron is subject to IR or not. An analysis of the top-ranking features for this task reveals a high level of similarity of the most predictive features across the three plant species, demonstrating the conservation of the factors that determine IR. We also found a high level of similarity to the top features contributing to IR in mammals. The task of predicting the response to drought stress proved more difficult, with lower levels of accuracy and lower levels of similarity across species, suggesting that additional features need to be considered for predicting condition-specific IR

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