Pair HMM based gap statistics for re-evaluation of indels in alignments with affine gap penalties: Extended Version

Although computationally aligning sequence is a crucial step in the vast majority of comparative genomics studies our understanding of alignment biases still needs to be improved. To infer true structural or homologous regions computational alignments need further evaluation. It has been shown that the accuracy of aligned positions can drop substantially in particular around gaps. Here we focus on re-evaluation of score-based alignments with affine gap penalty costs. We exploit their relationships with pair hidden Markov models and develop efficient algorithms by which to identify gaps which are significant in terms of length and multiplicity. We evaluate our statistics with respect to the well-established structural alignments from SABmark and find that indel reliability substantially increases with their significance in particular in worst-case twilight zone alignments. This points out that our statistics can reliably complement other methods which mostly focus on the reliability of match positions.Comment: 17 pages, 7 figure

An Investigation and Application of Biology and Bioinformatics for Activity Recognition

Activity recognition in a smart home context is inherently difficult due to the variable nature of human activities and tracking artifacts introduced by video-based tracking systems. This thesis addresses the activity recognition problem via introducing a biologically-inspired chemotactic approach and bioinformatics-inspired sequence alignment techniques to recognise spatial activities. The approaches are demonstrated in real world conditions to improve robustness and recognise activities in the presence of innate activity variability and tracking noise

Modeling the Evolution of Regulatory Elements by Simultaneous Detection and Alignment with Phylogenetic Pair HMMs

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation

Prediction of Indel Flanking Regions and Its Application in the Alignment of Multiple Protein Sequences

Proteins are the most important molecules in living organism, and they are involved in every function of the cells, such as signal transmission, metabolic regulation, transportation of molecules, and defense mechanism. As new protein sequences are discovered on an everyday basis and protein databases continue to grow exponentially with time, analysis of protein families, understanding their evolutionary trends and detection of remote homologues have become extremely important. The traditional laboratory techniques of studying these proteins are very slow and time consuming. Therefore, biologists have turned to automated methods that are fast and capable of analyzing large amounts of data and determining relationships between proteins that would be difficult, if not impossible, for humans to identify through the traditional techniques. Insertion/deletion (indel) and substitution of an amino acid are two common events that lead to the evolution of and variations in protein sequences. Further, many of the human diseases and functional divergence between homologous proteins are related more to the indel mutations than to the substitution mutations, even though the former occurs less often than the latter. A reliable detection of indels and their flanking regions is a major challenge in research related to protein evolution, structures and functions. The first and most important step in studying a newly discovered protein sequence is to search protein databases for proteins that are similar or closely-related to the new protein, and then to align the new protein sequence to these proteins. Thus, the alignment of multiple protein sequences is one of the most commonly performed tasks in bioinformatics analyses, and has been used in many applications, including sequence annotation, phylogenetic tree estimation, evolutionary analysis, secondary structure prediction and protein database search. In spite of considerable research and efforts that have been recently deployed for improving the performance of multiple sequence alignment (MSA) algorithms, finding a highly accurate alignment between multiple protein sequences still remains a challenging problem. The objectives of this thesis are to develop a novel scheme to predict indel flanking regions (IndelFRs) in a protein sequence and to develop an efficient algorithm for the alignment of multiple protein sequences incorporating the information on the predicted IndelFRs. In the first part of the thesis, a variable-order Markov model-based scheme to predict indel flanking regions in a protein sequence for a given protein fold is proposed. In this scheme, two predictors, referred to as the PPM IndelFR and PST IndelFR predictors, are designed based on prediction by partial match and probabilistic suffix tree, respectively. The performance of the proposed IndelFR predictors is evaluated in terms of the commonly used metrics, namely, accuracy of prediction and F1-measure. It is shown through extensive performance evaluation that the proposed predictors are able to predict IndelFRs in the protein sequences with high values of accuracy and F1-measure. It is also shown that if one is interested only in predicting IndelFRs in protein sequences, it would be preferable to use the proposed predictors instead of HMMER 3.0 in view of the substantially superior performance of the former. In the second part of the thesis, a novel and efficient algorithm incorporating the information on the predicted IndelFRs for the alignment of multiple protein sequences is proposed. A new variable gap penalty function is introduced, which makes the gap placement in protein sequences more accurate for the protein alignment. The performance of the proposed alignment algorithm, named as MSAIndelFR algorithm, is evaluated in terms of the so called metrics, sum-of-pairs (SP) and total columns (TC). It is shown through extensive performance evaluation using four popular benchmarks, BAliBASE 3.0, OXBENCH, PREFAB 4.0, and SABmark 1.65, that the performance of MSAIndelFR is superior to that of the six most-widely used alignment algorithms, namely, Clustal W2, Clustal Omega, MSAProbs, Kalign2, MAFFT and MUSCLE. Through the study undertaken in this thesis it is shown that a reliable detection of indels and their flanking regions can be achieved by using the proposed IndelFR predictors, and a substantial improvement in the protein alignment accuracy can be achieved by using the proposed variable gap penalty function. Thus, it is anticipated that this investigation will not only facilitate future studies on the modeling of indel mutations and protein sequence alignment, but will also open up new avenues for research concerning protein evolution, structures, and functions as well as for research concerning protein sequence alignment

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)

Structural RNA Homology Search and Alignment Using Covariance Models

Functional RNA elements do not encode proteins, but rather function directly as RNAs. Many different types of RNAs play important roles in a wide range of cellular processes, including protein synthesis, gene regulation, protein transport, splicing, and more. Because important sequence and structural features tend to be evolutionarily conserved, one way to learn about functional RNAs is through comparative sequence analysis - by collecting and aligning examples of homologous RNAs and comparing them. Covariance models: CMs) are powerful computational tools for homology search and alignment that score both the conserved sequence and secondary structure of an RNA family. However, due to the high computational complexity of their search and alignment algorithms, searches against large databases and alignment of large RNAs like small subunit ribosomal RNA: SSU rRNA) are prohibitively slow. Large-scale alignment of SSU rRNA is of particular utility for environmental survey studies of microbial diversity which often use the rRNA as a phylogenetic marker of microorganisms. In this work, we improve CM methods by making them faster and more sensitive to remote homology. To accelerate searches, we introduce a query-dependent banding: QDB) technique that makes scoring sequences more efficient by restricting the possible lengths of structural elements based on their probability given the model. We combine QDB with a complementary filtering method that quickly prunes away database subsequences deemed unlikely to receive high CM scores based on sequence conservation alone. To increase search sensitivity, we apply two model parameterization strategies from protein homology search tools to CMs. As judged by our benchmark, these combined approaches yield about a 250-fold speedup and significant increase in search sensitivity compared with previous implementations. To accelerate alignment, we apply a method that uses a fast sequence-based alignment of a target sequence to determine constraints for the more expensive CM sequence- and structure-based alignment. This technique reduces the time required to align one SSU rRNA sequence from about 15 minutes to 1 second with a negligible effect on alignment accuracy. Collectively, these improvements make CMs more powerful and practical tools for RNA homology search and alignment