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

Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

Prediction of Alternative Splice Sites in Human Genes

This thesis addresses the problem of predicting alternative splice sites in human genes. The most common way to identify alternative splice sites are the use of expressed sequence tags and microarray data. Since genes only produce alternative proteins under certain conditions, these methods are limited to detecting only alternative splice sites in genes whose alternative protein forms are expressed under the tested conditions. I have introduced three multiclass support vector machines that predict upstream and downstream alternative 3’ splice sites, upstream and downstream alternative 5’ splice sites, and the 3’ splice site of skipped and cryptic exons. On a test set extracted from the Alternative Splice Annotation Project database, I was able to correctly classify about 68% of the splice sites in the alternative 3’ set, about 62% of the splice sites in the alternative 5’ set, and about 66% in the exon skipping set

Methods to study splicing from high-throughput RNA Sequencing data

The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data. We group the methods according to the different questions they address: 1) Assignment of the sequencing reads to their likely gene of origin. This is addressed by methods that map reads to the genome and/or to the available gene annotations. 2) Recovering the sequence of splicing events and isoforms. This is addressed by transcript reconstruction and de novo assembly methods. 3) Quantification of events and isoforms. Either after reconstructing transcripts or using an annotation, many methods estimate the expression level or the relative usage of isoforms and/or events. 4) Providing an isoform or event view of differential splicing or expression. These include methods that compare relative event/isoform abundance or isoform expression across two or more conditions. 5) Visualizing splicing regulation. Various tools facilitate the visualization of the RNA-Seq data in the context of alternative splicing. In this review, we do not describe the specific mathematical models behind each method. Our aim is rather to provide an overview that could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.Comment: 31 pages, 1 figure, 9 tables. Small corrections adde

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

Global Discriminative Learning for Higher-Accuracy Computational Gene Prediction

Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM) in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns

Alu Exonization Events Reveal Features Required for Precise Recognition of Exons by the Splicing Machinery

Despite decades of research, the question of how the mRNA splicing machinery precisely identifies short exonic islands within the vast intronic oceans remains to a large extent obscure. In this study, we analyzed Alu exonization events, aiming to understand the requirements for correct selection of exons. Comparison of exonizing Alus to their non-exonizing counterparts is informative because Alus in these two groups have retained high sequence similarity but are perceived differently by the splicing machinery. We identified and characterized numerous features used by the splicing machinery to discriminate between Alu exons and their non-exonizing counterparts. Of these, the most novel is secondary structure: Alu exons in general and their 5′ splice sites (5′ss) in particular are characterized by decreased stability of local secondary structures with respect to their non-exonizing counterparts. We detected numerous further differences between Alu exons and their non-exonizing counterparts, among others in terms of exon–intron architecture and strength of splicing signals, enhancers, and silencers. Support vector machine analysis revealed that these features allow a high level of discrimination (AUC = 0.91) between exonizing and non-exonizing Alus. Moreover, the computationally derived probabilities of exonization significantly correlated with the biological inclusion level of the Alu exons, and the model could also be extended to general datasets of constitutive and alternative exons. This indicates that the features detected and explored in this study provide the basis not only for precise exon selection but also for the fine-tuned regulation thereof, manifested in cases of alternative splicing

Improved identification of conserved cassette exons using Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Alternative splicing is a major contributor to the diversity of eukaryotic transcriptomes and proteomes. Currently, large scale detection of alternative splicing using expressed sequence tags (ESTs) or microarrays does not capture all alternative splicing events. Moreover, for many species genomic data is being produced at a far greater rate than corresponding transcript data, hence <it>in silico </it>methods of predicting alternative splicing have to be improved.</p> <p>Results</p> <p>Here, we show that the use of Bayesian networks (BNs) allows accurate prediction of evolutionary conserved exon skipping events. At a stringent false positive rate of 0.5%, our BN achieves an improved true positive rate of 61%, compared to a previously reported 50% on the same dataset using support vector machines (SVMs). Incorporating several novel discriminative features such as intronic splicing regulatory elements leads to the improvement. Features related to mRNA secondary structure increase the prediction performance, corroborating previous findings that secondary structures are important for exon recognition. Random labelling tests rule out overfitting. Cross-validation on another dataset confirms the increased performance. When using the same dataset and the same set of features, the BN matches the performance of an SVM in earlier literature. Remarkably, we could show that about half of the exons which are labelled constitutive but receive a high probability of being alternative by the BN, are in fact alternative exons according to the latest EST data. Finally, we predict exon skipping without using conservation-based features, and achieve a true positive rate of 29% at a false positive rate of 0.5%.</p> <p>Conclusion</p> <p>BNs can be used to achieve accurate identification of alternative exons and provide clues about possible dependencies between relevant features. The near-identical performance of the BN and SVM when using the same features shows that good classification depends more on features than on the choice of classifier. Conservation based features continue to be the most informative, and hence distinguishing alternative exons from constitutive ones without using conservation based features remains a challenging problem.</p