Spectral Preprocessing for Clustering Time-Series Gene Expressions

<p/> <p>Based on gene expression profiles, genes can be partitioned into clusters, which might be associated with biological processes or functions, for example, cell cycle, circadian rhythm, and so forth. This paper proposes a novel clustering preprocessing strategy which combines clustering with spectral estimation techniques so that the time information present in time series gene expressions is fully exploited. By comparing the clustering results with a set of biologically annotated yeast cell-cycle genes, the proposed clustering strategy is corroborated to yield significantly different clusters from those created by the traditional expression-based schemes. The proposed technique is especially helpful in grouping genes participating in time-regulated processes.</p

Optimal reference sequence selection for genome assembly using minimum description length principle

Reference assisted assembly requires the use of a reference sequence, as a model, to assist in the assembly of the novel genome. The standard method for identifying the best reference sequence for the assembly of a novel genome aims at counting the number of reads that align to the reference sequence, and then choosing the reference sequence which has the highest number of reads aligning to it. This article explores the use of minimum description length (MDL) principle and its two variants, the two-part MDL and Sophisticated MDL, in identifying the optimal reference sequence for genome assembly. The article compares the MDL based proposed scheme with the standard method coming to the conclusion that “counting the number of reads of the novel genome present in the reference sequence” is not a sufficient condition. Therefore, the proposed MDL scheme includes within itself the standard method of “counting the number of reads that align to the reference sequence” and also moves forward towards looking at the model, the reference sequence, as well, in identifying the optimal reference sequence. The proposed MDL based scheme not only becomes the sufficient criterion for identifying the optimal reference sequence for genome assembly but also improves the reference sequence so that it becomes more suitable for the assembly of the novel genome

Information Theory, Graph Theory and Bayesian Statistics based improved and robust methods in Genome Assembly

Bioinformatics skills required for genome sequencing often represent a significant hurdle for many researchers working in computational biology. This dissertation highlights the significance of genome assembly as a research area, focuses on its need to remain accurate, provides details about the characteristics of the raw data, examines some key metrics, emphasizes some tools and outlines the whole pipeline for next-generation sequencing. Currently, a major effort is being put towards the assembly of the genomes of all living organisms. Given the importance of comparative genome assembly, herein dissertation, the principle of Minimum Description Length (MDL) and its two variants, the Two-Part MDL and Sophisticated MDL, are explored in identifying the optimal reference sequence for genome assembly. Thereafter, a Modular Approach to Reference Assisted Genome Assembly Pipeline, referred to as MARAGAP, is developed. MARAGAP uses the principle of Minimum Description Length (MDL) to determine the optimal reference sequence for the assembly. The optimal reference sequence is used as a template to infer inversions, insertions, deletions and Single Nucleotide Polymorphisms (SNPs) in the target genome. MARAGAP uses an algorithmic approach to detect and correct inversions and deletions, a De-Bruijn graph based approach to infer insertions, an affine-match affine-gap local alignment tool to estimate the locations of insertions and a Bayesian estimation framework for detecting SNPs (called BECA). BECA effectively capitalizes on the `alignment-layout-consensus' paradigm and Quality (Q-) values for detecting and correcting SNPs by evaluating a number of probabilistic measures. However, the entire process is conducted once. BECA's framework is further extended by using Gibbs Sampling for further iterations of BECA. After each assembly the reference sequence is updated and the probabilistic score for each base call renewed. The revised reference sequence and probabilities are then further used to identify the alignments and consensus sequence, thereby, yielding an algorithm referred to as Gibbs-BECA. Gibbs-BECA further improves the performance both in terms of rectifying more SNPs and offering a robust performance even in the presence of a poor reference sequence. Lastly, another major effort in this dissertation was the development of two cohesive software platforms that combine many different genome assembly pipelines in two distinct environments, referred to as Baari and Genobuntu, respectively. Baari and Genobuntu support pre-assembly tools, genome assemblers as well as post-assembly tools. Additionally, a library of tools developed by the authors for Next Generation Sequencing (NGS) data and commonly used biological software have also been provided in these software platforms. Baari and Genobuntu are free, easily distributable and facilitate building laboratories and software workstations both for personal use as well as for a college/university laboratory. Baari is a customized Ubuntu OS packed with the tools mentioned beforehand whereas Genobuntu is a software package containing the same tools for users who already have Ubuntu OS pre-installed on their systems

Genomic applications of statistical signal processing

Biological phenomena in the cells can be explained in terms of the interactions among biological macro-molecules, e.g., DNAs, RNAs and proteins. These interactions can be modeled by genetic regulatory networks (GRNs). This dissertation proposes to reverse engineering the GRNs based on heterogeneous biological data sets, including time-series and time-independent gene expressions, Chromatin ImmunoPrecipatation (ChIP) data, gene sequence and motifs and other possible sources of knowledge. The objective of this research is to propose novel computational methods to catch pace with the fast evolving biological databases. Signal processing techniques are exploited to develop computationally efficient, accurate and robust algorithms, which deal individually or collectively with various data sets. Methods of power spectral density estimation are discussed to identify genes participating in various biological processes. Information theoretic methods are applied for non-parametric inference. Bayesian methods are adopted to incorporate several sources with prior knowledge. This work aims to construct an inference system which takes into account different sources of information such that the absence of some components will not interfere with the rest of the system. It has been verified that the proposed algorithms achieve better inference accuracy and higher computational efficiency compared with other state-of-the-art schemes, e.g. REVEAL, ARACNE, Bayesian Networks and Relevance Networks, at presence of artificial time series and steady state microarray measurements. The proposed algorithms are especially appealing when the the sample size is small. Besides, they are able to integrate multiple heterogeneous data sources, e.g. ChIP and sequence data, so that a unified GRN can be inferred. The analysis of biological literature and in silico experiments on real data sets for fruit fly, yeast and human have corroborated part of the inferred GRN. The research has also produced a set of potential control targets for designing gene therapy strategies