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

Terminator Detection by Support Vector Machine Utilizing a Stochastic Context-Free Grammar

A 2-stage detector was designed to find rho-independent transcription terminators in the Escherichia coli genome. The detector includes a stochastic context free grammar (SCFG) component and a support vector machine (SVM) component. To find terminators, the SCFG searches the intergenic regions of nucleotide sequence for local matches to a terminator grammar that was designed and trained utilizing examples of known terminators. The grammar selects sequences that are the best candidates for terminators and assigns them a prefix, stem-loop, suffix structure using the Cocke-Younger-Kasaami (CYK) algorithm, modified to incorporate energy effects of base pairing. The parameters from this inferred structure are passed to the SVM classifier, which distinguishes terminators from non-terminators that score high according to the terminator grammar. The SVM was trained with negative examples drawn from intergenic sequences that include both featureless and RNA gene regions (which were assigned prefix, stem-loop, suffix structure by the SCFG), so that it successfully distinguishes terminators from either of these. The classifier was found to be 96.4% successful during testin

A Machine Learning Model for Discovery of Protein Isoforms as Biomarkers

Prostate cancer is the most common cancer in men. One in eight Canadian men will be diagnosed with prostate cancer in their lifetime. The accurate detection of the disease’s subtypes is critical for providing adequate therapy; hence, it is critical for increasing both survival rates and quality of life. Next generation sequencing can be beneficial when studying cancer. This technology generates a large amount of data that can be used to extract information about biomarkers. This thesis proposes a model that discovers protein isoforms for different stages of prostate cancer progression. A tool has been developed that utilizes RNA-Seq data to infer open reading frames (ORFs) corresponding to transcripts. These ORFs are used as features for classificatio. A quantification measurement, Adaptive Fragments Per Kilobase of transcript per Million mapped reads (AFPKM), is proposed to compute the expression level for ORFs. The new measurement considers the actual length of the ORF and the length of the transcript. Using these ORFs and the new expression measure, several classifiers were built using different machine learning techniques. That enabled the identification of some protein isoforms related to prostate cancer progression. The biomarkers have had a great impact on the discrimination of prostate cancer stages and are worth further investigation

Automatic detection of exonic splicing enhancers (ESEs) using SVMs

<p>Abstract</p> <p>Background</p> <p>Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins.</p> <p>Results</p> <p>The choice of training examples needed for machine learning approaches is difficult since there are only few exact locations of human ESEs described in the literature which could be considered as positive examples. Additionally, it is unclear which sequences are suitable as negative examples. Therefore, we developed a motif-oriented data-extraction method that extracts exon sequences around experimentally or theoretically determined ESE patterns. Positive examples are restricted by heuristics based on known properties of ESEs, e.g. location in the vicinity of a splice site, whereas negative examples are taken in the same way from the middle of long exons. We show that a suitably chosen SVM using optimized sequence kernels (e.g., combined oligo kernel) can extract meaningful properties from these training examples. Once the classifier is trained, every potential ESE sequence can be passed to the SVM for verification. Using SVMs with the combined oligo kernel yields a high accuracy of about 90 percent and well interpretable parameters.</p> <p>Conclusion</p> <p>The motif-oriented data-extraction method seems to produce consistent training and test data leading to good classification rates and thus allows verification of potential ESE motifs. The best results were obtained using an SVM with the combined oligo kernel, while oligo kernels with oligomers of a certain length could be used to extract relevant features.</p

Identifying Interaction Sites in Recalcitrant Proteins: Predicted Protein and RNA Binding Sites in Rev Proteins of HIV-1 and EIAV Agree with Experimental Data

Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the protein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-1 and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these <it>ab initio </it>approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.</p> <p>Results</p> <p>This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G²DE) based classifier. The G²DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set.</p> <p>Conclusion</p> <p>Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G²DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G²DE based predictor.</p

Comparing Kernels For Predicting Protein Binding Sites From Amino Acid Sequence

The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. Support vector machines (SVM) and related kernel methods offer an attractive approach to predicting protein binding sites. An appropriate choice of the kernel function is critical to the performance of SVM. Kernel functions offer a way to incorporate domain-specific knowledge into the classifier. We compare the performance of 3 types of kernels functions: identity kernel, sequence-alignment kernel, and amino acid substitution matrix kernel for predicting protein-protein, protein-DNA and protein-RNA binding sites. The results show that the identity kernel is quite effective in on all three tasks, with the substitution kernel based on amino acid substitution matrices that take into account structural or evolutionary conservation or physicochemical properties of amino acids yields modest improvement in the performance of the resulting SVM classifiers for predicting protein-protein, protein-DNA and protein-RNA binding sites

Algorithms in comparative genomics

The field of comparative genomics is abundant with problems of interest to computer scientists. In this thesis, the author presents solutions to three contemporary problems: obtaining better alignments for phylogeny reconstruction, identifying related RNA sequences in genomes, and ranking Single Nucleotide Polymorphisms (SNPs) in genome-wide association studies (GWAS). Sequence alignment is a basic and widely used task in bioinformatics. Its applications include identifying protein structure, RNAs and transcription factor binding sites in genomes, and phylogeny reconstruction. Phylogenetic descriptions depend not only on the employed reconstruction technique, but also on the underlying sequence alignment. The author has studied and established a simple prescription for obtaining a better phylogeny by improving the underlying alignments used in phylogeny reconstruction. This was achieved by improving upon Gotoh\u27s iterative heuristic by iterating with maximum parsimony guide-trees. This approach has shown an improvement in accuracy over standard alignment programs. A novel alignment algorithm named Probalign-RNAgenome that can identify non-coding RNAs in genomic sequences was also developed. Non-coding RNAs play a critical role in the cell such as gene regulation. It is thought that many such RNAs lie undiscovered in the genome. To date, alignment based approaches have shown to be more accurate than thermodynamic methods for identifying such non-coding RNAs. Probalign-RNAgenome employs a probabilistic consistency based approach for aligning a query RNA sequence to its homolog in a genomic sequence. Results show that this approach is more accurate on real data than the widely used BLAST and Smith- Waterman algorithms. Within the realm of comparative genomics are also a large number of recently conducted GWAS. GWAS aim to identify regions in the genome that are associated with a given disease. The support vector machine (SVM) provides a discriminative alternative to the widely used chi-square statistic in GWAS. A novel hybrid strategy that combines the chi-square statistic with the SVM was developed and implemented. Its performance was studied on simulated data and the Wellcome Trust Case Control Consortium (WTCCC) studies. Results presented in this thesis show that the hybrid strategy ranks causal SNPs in simulated data significantly higher than the chi-square test and SVM alone. The results also show that the hybrid strategy ranks previously replicated SNPs and associated regions (where applicable) of type 1 diabetes, rheumatoid arthritis, and Crohn\u27s disease higher than the chi-square, SVM, and SVM Recursive Feature Elimination (SVM-RFE)