Survival analysis of DNA mutation motifs with penalized proportional hazards

Antibodies, an essential part of our immune system, develop through an intricate process to bind a wide array of pathogens. This process involves randomly mutating DNA sequences encoding these antibodies to find variants with improved binding, though mutations are not distributed uniformly across sequence sites. Immunologists observe this nonuniformity to be consistent with "mutation motifs", which are short DNA subsequences that affect how likely a given site is to experience a mutation. Quantifying the effect of motifs on mutation rates is challenging: a large number of possible motifs makes this statistical problem high dimensional, while the unobserved history of the mutation process leads to a nontrivial missing data problem. We introduce an $\ell_1$-penalized proportional hazards model to infer mutation motifs and their effects. In order to estimate model parameters, our method uses a Monte Carlo EM algorithm to marginalize over the unknown ordering of mutations. We show that our method performs better on simulated data compared to current methods and leads to more parsimonious models. The application of proportional hazards to mutation processes is, to our knowledge, novel and formalizes the current methods in a statistical framework that can be easily extended to analyze the effect of other biological features on mutation rates

A Survey on Identification of Motifs and Ontology in Medical Database

Motifs and ontology are used in medical database for identifyingand diagnose of the disease. A motif is a pattern network used for analysis of the disease. It also identifies the pattern of the signal. Based on the motifs the disease can be predicted, classified and diagnosed. Ontology is knowledge based representation, and it is used as a user interface to diagnose the disease. Ontology is also used by medical expert to diagnose and analyse the disease easily. Gene ontology is used to express the gene of the disease

Analysis Of DNA Motifs In The Human Genome

DNA motifs include repeat elements, promoter elements and gene regulator elements, and play a critical role in the human genome. This thesis describes a genome-wide computational study on two groups of motifs: tandem repeats and core promoter elements. Tandem repeats in DNA sequences are extremely relevant in biological phenomena and diagnostic tools. Computational programs that discover tandem repeats generate a huge volume of data, which can be difficult to decipher without further organization. A new method is presented here to organize and rank detected tandem repeats through clustering and classification. Our work presents multiple ways of expressing tandem repeats using the n-gram model with different clustering distance measures. Analysis of the clusters for the tandem repeats in the human genome shows that the method yields a well-defined grouping in which similarity among repeats is apparent. Our new, alignment-free method facilitates the analysis of the myriad of tandem repeats replete in the human genome. We believe that this work will lead to new discoveries on the roles, origins, and significance of tandem repeats. As with tandem repeats, promoter sequences of genes contain binding sites for proteins that play critical roles in mediating expression levels. Promoter region binding proteins and their co-factors influence timing and context of transcription. Despite the critical regulatory role of these non-coding sequences, computational methods to identify and predict DNA binding sites are extremely limited. The work reported here analyzes the relative occurrence of core promoter elements (CPEs) in and around transcription start sites. We found that out of all the data sets 49\%-63\% upstream regions have either TATA box or DPE elements. Our results suggest the possibility of predicting transcription start sites through combining CPEs signals with other promoter signals such as CpG islands and clusters of specific transcription binding sites

Systematically Mapping the Epigenetic Context Dependence of Transcription Factor Binding

At the core of gene regulatory networks are transcription factors (TFs) that recognize specific DNA sequences and target distinct gene sets. Characterizing the DNA binding specificity of all TFs is a prerequisite for understanding global gene regulatory logic, which in recent years has resulted in the development of high-throughput methods that probe TF specificity in vitro and are now routinely used to inform or interpret in vivo studies. Despite the broad success of such methods, several challenges remain, two of which are addressed in this thesis. Genomic DNA can harbor different epigenetic marks that have the potential to alter TF binding, the most prominent being CpG methylation. Given the vast number of modified CpGs in the human genome and an increasing body of literature suggesting a link between epigenetic changes and genome instability, or the onset of disease such as cancer, methods that can characterize the sensitivity of TFs to DNA methylation are needed to mechanistically interpret its impact on gene expression. We developed a high-throughput in vitro method (EpiSELEX-seq) that probes TF binding to unmodified and modified DNA sequences in competition, resulting in high-resolution maps of TF binding preferences. We found that methylation sensitivity can vary between TFs of the the same structural family and is dependent on the position of the 5mCpG within the TF binding site. The importance of our in vitro profiling of methylation sensitivity is demonstrated by the preference of human p53 tetramers for 5mCpGs within its binding site core. This previously unknown, stabilizing effect is also detectable in p53 ChIP-seq data when comparing methylated and unmethylated sites genome-wide. A second impediment to predicting TF binding is our limited understanding of i) how cooperative participation of a TF in different complexes can alter their binding preference, and ii) how the detailed shape of DNA aids in creating a substrate for adaptive multi-TF binding. To address these questions in detail, we studied the in vitro binding preferences of three D. melanogaster homeodomain TFs: Homothorax (Hth), Extradenticle(Exd) and one of the eight Hox proteins. In vivo, Hth occurs in two splice forms: with (HthFL) and without (HthHM) the DNA binding domain (DBD). HthHM-Exd itself is a Hox cofactor that has been shown to induce latent sequence specificity upon complex formation with Hox proteins. There are three possible complexes that can be formed, all potentially having specific target genes: HthHM-Exd-Hox, HthFL-Exd-Hox, and HthFL-Exd. We characterized the in vitro binding preferences of each of these by developing new computational approaches to analyze high-throughput SELEX-seq data. We found distinct orientation and spacing preference for HthFL-Exd-Hox, alternative recognition modes that depend on the affinity class a sequence falls into, and a strong preference for a narrow DNA minor grove near Exd's N-terminal DBD. Strikingly, this shape readout is crucial to stabilize the HthHM-Exd-Hox complex in the absence of a Hth DBD and can thus be used to distinguish HthHM from HthFL isoform binding. Mutating the amino acids responsible for the shape readout by Exd and reinserting the engineered protein into the fly genome allowed us to classify in vivo binding sites based on ChIP-seq signal comparison between “shape-mutant” and wild-type Exd. In summary, the research presented here has investigated TF binding preferences beyond sequence context by combining novel high-throughput experimental and computational methods. This interdisciplinary approach has enabled us to study binding preferences of TF complexes with respect to the epigenetic landscape of their cognate binding sites. Our novel mechanistic insights into DNA shape readout have provided a new avenue of exploiting guided protein engineering to probe how specific TFs interact with their co-factors in a cellular context, and how flanking genomic sequence helps determine which multi-TF complexes will form and which binding mode a complex adopts

Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations

Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences\u27 structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity. In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm

Application of motif scoring algorithms for enhancer prediction in distantly related species

Although many studies proposed methods for the identification of enhancers, reliable prediction on a genome-wide scale is still an unsolved problem. One of the reasons for this is the highly flexible regulatory logic underlying a detectable enhancer activity. In each cell type or tissue and at any given time, a mostly unknown set of transcription factors activates specific regulatory elements by coordinated binding to the corresponding genomic region. Position, spacing, and orientation of the individual bound factors can thereby vary between different enhancers yet result in a highly similar spatio-temporal activity. Due to this inner flexibility, so-called “alignment-free” methods have been proposed for enhancer prediction, as they are able to cope with rearrangements by comparison of word profiles rather than linear sequence. However, the problems caused by allowing for permutation in sequence comparison have not been investigated so far. In this study I implemented several published alignment-free metrics and analysed, which parameters affect their ability to successfully predict regulatory regions. As results show, single point mutations and the increasing amount of spurious matches with decreasing word size pose the biggest challenge to alignment-free techniques, especially when applied on a genome-wide scale. Alignment algorithms usually solve these problems quite efficiently but cannot handle permutation. I therefore implemented a new technique for enhancer prediction that combines the advantages of both algorithm types and used it for the identification of regulatory regions in the teleost fish Oryzias latipes (Medaka) based on a set of known and validated human enhancers. Predicted medaka regions and human enhancers were subsequently used in an in vivo enhancer assay and analysed for their activity. In total, 12 predicted regions corresponding to 9 human enhancers showed clear enhancing activity in the fish. This shows that the principle implemented here is able to predict active enhancers at a high rate on a genome-wide scale even in species as diverged as human and fish. Furthermore, evidence for motif-level conservation between some of the human and medaka enhancers could be found that was invisible for most of the alignment-algorithms used for comparison

Computational Discovery of Structured Non-coding RNA Motifs in Bacteria

This dissertation describes a range of computational efforts to discover novel structured non-coding RNA (ncRNA) motifs in bacteria and generate hypotheses regarding their potential functions. This includes an introductory description of key advances in comparative genomics and RNA structure prediction as well as some of the most commonly found ncRNA candidates. Beyond that, I describe efforts for the comprehensive discovery of ncRNA candidates in 25 bacterial genomes and a catalog of the various functions hypothesized for these new motifs. Finally, I describe the Discovery of Intergenic Motifs PipeLine (DIMPL) which is a new computational toolset that harnesses the power of support vector machine (SVM) classifiers to identify bacterial intergenic regions most likely to contain novel structured ncRNA and automates the bulk of the subsequent analysis steps required to predict function. In totality, the body of work will enable the large scale discovery of novel structured ncRNA motifs at a far greater pace than possible before

Front Matter - Soft Computing for Data Mining Applications

Efficient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the capability of computers to search huge amounts of data in a fast and effective manner. However, the data to be analyzed is imprecise and afflicted with uncertainty. In the case of heterogeneous data sources such as text, audio and video, the data might moreover be ambiguous and partly conflicting. Besides, patterns and relationships of interest are usually vague and approximate. Thus, in order to make the information mining process more robust or say, human-like methods for searching and learning it requires tolerance towards imprecision, uncertainty and exceptions. Thus, they have approximate reasoning capabilities and are capable of handling partial truth. Properties of the aforementioned kind are typical soft computing. Soft computing techniques like Genetic

Bayesian machine learning methods for predicting protein-peptide interactions and detecting mosaic structures in DNA sequences alignments

Short well-defined domains known as peptide recognition modules (PRMs) regulate many important protein-protein interactions involved in the formation of macromolecular complexes and biochemical pathways. High-throughput experiments like yeast two-hybrid and phage display are expensive and intrinsically noisy, therefore it would be desirable to target informative interactions and pursue in silico approaches. We propose a probabilistic discriminative approach for predicting PRM-mediated protein-protein interactions from sequence data. The model suffered from over-fitting, so Laplacian regularisation was found to be important in achieving a reasonable generalisation performance. A hybrid approach yielded the best performance, where the binding site motifs were initialised with the predictions of a generative model. We also propose another discriminative model which can be applied to all sequences present in the organism at a significantly lower computational cost. This is due to its additional assumption that the underlying binding sites tend to be similar.It is difficult to distinguish between the binding site motifs of the PRM due to the small number of instances of each binding site motif. However, closely related species are expected to share similar binding sites, which would be expected to be highly conserved. We investigated rate variation along DNA sequence alignments, modelling confounding effects such as recombination. Traditional approaches to phylogenetic inference assume that a single phylogenetic tree can represent the relationships and divergences between the taxa. However, taxa sequences exhibit varying levels of conservation, e.g. due to regulatory elements and active binding sites, and certain bacteria and viruses undergo interspecific recombination. We propose a phylogenetic factorial hidden Markov model to infer recombination and rate variation. We examined the performance of our model and inference scheme on various synthetic alignments, and compared it to state of the art breakpoint models. We investigated three DNA sequence alignments: one of maize actin genes, one bacterial (Neisseria), and the other of HIV-1. Inference is carried out in the Bayesian framework, using Reversible Jump Markov Chain Monte Carlo