Big data analytics in computational biology and bioinformatics

Big data analytics in computational biology and bioinformatics refers to an array of operations including biological pattern discovery, classification, prediction, inference, clustering as well as data mining in the cloud, among others. This dissertation addresses big data analytics by investigating two important operations, namely pattern discovery and network inference. The dissertation starts by focusing on biological pattern discovery at a genomic scale. Research reveals that the secondary structure in non-coding RNA (ncRNA) is more conserved during evolution than its primary nucleotide sequence. Using a covariance model approach, the stems and loops of an ncRNA secondary structure are represented as a statistical image against which an entire genome can be efficiently scanned for matching patterns. The covariance model approach is then further extended, in combination with a structural clustering algorithm and a random forests classifier, to perform genome-wide search for similarities in ncRNA tertiary structures. The dissertation then presents methods for gene network inference. Vast bodies of genomic data containing gene and protein expression patterns are now available for analysis. One challenge is to apply efficient methodologies to uncover more knowledge about the cellular functions. Very little is known concerning how genes regulate cellular activities. A gene regulatory network (GRN) can be represented by a directed graph in which each node is a gene and each edge or link is a regulatory effect that one gene has on another gene. By evaluating gene expression patterns, researchers perform in silico data analyses in systems biology, in particular GRN inference, where the “reverse engineering” is involved in predicting how a system works by looking at the system output alone. Many algorithmic and statistical approaches have been developed to computationally reverse engineer biological systems. However, there are no known bioin-formatics tools capable of performing perfect GRN inference. Here, extensive experiments are conducted to evaluate and compare recent bioinformatics tools for inferring GRNs from time-series gene expression data. Standard performance metrics for these tools based on both simulated and real data sets are generally low, suggesting that further efforts are needed to develop more reliable GRN inference tools. It is also observed that using multiple tools together can help identify true regulatory interactions between genes, a finding consistent with those reported in the literature. Finally, the dissertation discusses and presents a framework for parallelizing GRN inference methods using Apache Hadoop in a cloud environment

Emerging applications of read profiles towards the functional annotation of the genome

Functional annotation of the genome in various species is important to understand their phenotypic complexity. The road towards functional annotation involves several challenges ranging from experiments on individual molecules to large-scale analysis of high-throughput sequencing (HTS) data. HTS data is typically a result of the protocol designed to address specific research questions. The sequencing results in reads, which when mapped to a reference genome often leads to the formation of distinct patterns (read profiles). Interpretation of these read profiles are essential for the analysis in relation to the research question addressed. Several strategies have been employed at varying levels of abstraction ranging from a somewhat ad hoc to a more systematic analysis of read profiles. These include methods which can compare read profiles, e.g. from direct (non-sequence based) alignments to classification of patterns into functional groups. In this review, we highlight the emerging applications of read profiles for the annotation of non-coding RNA and cis-regulatory regions such as enhancers and promoters. We also discuss the biological rationale behind their formation

Workflows for the Large-Scale Assessment of miRNA Evolution: Birth and Death of miRNA Genes in Tunicates

As described over 20 years ago with the discovery of RNA interference (RNAi), double-stranded RNAs occupied key roles in regulation and as defense-line in animal cells. This thesis focuses on metazoan microRNAs (miRNAs). These small non-coding RNAs are distinguished from their small-interfering RNA (siRNA) relatives by their tightly controlled, efficient and flexible biogenesis, together with a broader flexibility to target multiple mRNAs by a seed imperfect base-pairing. As potent regulators, miRNAs are involved in mRNA stability and post-transcriptional regulation tasks, being a conserved mechanism used repetitively by the evolution, not only in metazoans, but plants and unicellular organisms. Through a comprehensive revision of the current animal miRNA model, the canonical pathway dominates the extensive literature about miRNAs, and served as a scaffold to understand the scenes behind the regulatory landscape performed by the cell. The characterization of a diverse set of non-canonical pathways has expanded this view, suggesting a diverse, rich and flexible regulatory landscape to generate mature miRNAs. The production of miRNAs, derived from isolated or clustered transcripts, is an efficient and highly conserved mechanism traced back to animals with high fidelity at family level. In evolutionary terms, expansions of miRNA families have been associated with an increasing morphological and developmental complexity. In particular, the Chordata clade (the ancient cephalochordates, highly derived and secondary simplified tunicates, and the well-known vertebrates) represents an interesting scenario to study miRNA evolution. Despite clearly conserved miRNAs along these clades, tunicates display massive restructuring events, including emergence of highly derived miRNAs. As shown in this thesis, model organisms or vertebrate-specific bias exist in current animal miRNA annotations, misrepresenting more diverse groups, such as marine invertebrates. Current miRNA databases, such as miRBase and Rfam, classified miRNAs under different definitions and possessed annotations that are not simple to be linked. As an alternative, this thesis proposes a method to curate and merge those annotations, making use of miRBase precursor/mature annotations and genomes together with Rfam predicted sequences. This approach generated structural models for shared miRNA families, based on the alignment of their correct-positioned mature sequences as anchors. In this process, the developed structural curation steps flagged 33 miRNA families from the Rfam as questionable. Curated Rfam and miRBase anchored-structural alignments provided a rich resource for constructing predictive miRNA profiles, using correspondent hidden Markov (HMMs) and covariance models (CMs). As a direct application, the use of those models is time-consuming, and the user has to deal with multiple iterations to achieve a genome-wide non-overlapping annotation. To resolve this, the proposed miRNAture pipeline provides an automatic and flexible solution to annotate miRNAs. It combines multiple homology approaches to generate the best candidates validated at sequence and structural levels. This increases the achievable sensitivity to annotate canonical miRNAs, and the evaluation against human annotation shows that clear false positive calls are rare and additional counterparts lie in retained-introns, transcribed lncRNAs or repeat families. Further development of miRNAture suggests an inclusion of multiple rules to distinguish non-canonical miRNA families. This thesis describes multiple homology approaches to annotate the genomic information from a non-model chordate: the colonial tunicate Didemnum vexillum. Detected high levels of genetic variance and unexpected levels of DNA degradation were evidenced through a comprehensive analysis of genome-assembly methods and gene annotation. Despite those challenges, it was possible to find candidate homeobox and skeletogenesis- related genes. On its own, the ncRNA annotation included expected conserved families, and an extensive search of the Rhabdomyosarcoma 2-associated transcript (RMST) lncRNA family traced-back at the divergence of deuterostomes. In addition, a complete study of the annotation thresholds suggested variations to detect miRNAs, later implemented on the miRNAture tool. This chapter is a showcase of the usual workflow that should follow comprehensive sequencing, assembly and annotation project, in the light of the increasing research approaching DNA sequencing. In the last 10 years, the remarkable increment in tunicate sequencing projects boosted the access to an expanded miRNA annotation landscape. In this way, a comprehensive homology approach annotated the miRNA complement of 28 deuterostome genomes (including current 16 reported tunicates) using miRNAture. To get proper structural models as input, corrected miRBase structural alignments served as a scaffold for building correspondent CMs, based on a developed genetic algorithm. By this means, this automatic approach selected the set of sequences that composed the alignments, generating 2492 miRNA CMs. Despite the multiple sources and associated heterogeneity of the studied genomes, a clustering approach successfully gathered five groups of similar assemblies and highlighted low quality assemblies. The overall family and loci reduction on tunicates is notorious, showing on average 374 microRNA (miRNA) loci, in comparison to other clades: Cephalochordata (2119), Vertebrata (3638), Hemichordata (1092) and Echinodermata (2737). Detection of 533 miRNA families on the divergence of tunicates shows an expanded landscape regarding currently miRNA annotated families. Shared sets of ancestral, chordates, Olfactores, and specific clade-specific miRNAs were uncovered using a phyloge- netic conservation criteria. Compared to current annotations, the family repertories were expanded in all cases. Finally, relying on the adjacent elements from annotated miRNAs, this thesis proposes an additional syntenic support to cluster miRNA loci. In this way, the structural alignment of miR-1497, originally annotated in three model tunicates, was expanded with a clear syntenic support on tunicates

The evolution, modifications and interactions of proteins and RNAs

Proteins and RNAs are two of the most versatile macromolecules that carry out almost all functions within living organisms. In this thesis I have explored evolutionary and regulatory aspects of proteins and RNAs by studying their structures, modifications and interactions. In the first chapter of my thesis I investigate domain atrophy, a term I coined to describe large-scale deletions of core structural elements within protein domains. By looking into truncated domain boundaries across several domain families using Pfam, I was able to identify rare cases of domains that showed atrophy. Given that even point mutations can be deleterious, it is surprising that proteins can tolerate such large-scale deletions. Some of the structures of atrophied domains show novel protein-protein interaction interfaces that appear to compensate and stabilise their folds. Protein-protein interactions are largely influenced by the surface and charge complementarity, while RNA-RNA interactions are governed by base-pair complementarity; both interaction types are inherently different and these differences might be observed in their interaction networks. Based on this hypothesis I have explored the protein-protein, RNA-protein and the RNA-RNA interaction networks of yeast in the second chapter. By analysing the three networks I found no major differences in their network properties, which indicates an underlying uniformity in their interactomes despite their individual differences. In the third chapter I focus on RNA-protein interactions by investigating post-translational modifications (PTMs) in RNA-binding proteins (RBPs). By comparing occurrences of PTMs, I observe that RBPs significantly undergo more PTMs than non-RBPs. I also found that within RBPs, PTMs are more frequently targeted at regions that directly interact with RNA compared to regions that do not. Moreover disorderedness and amino acid composition were not observed to significantly influence the differential PTMs observed between RBPs and nonRBPs. The results point to a direct regulatory role of PTMs in RNA-protein interactions of RBPs. In the last chapter, I explore regulatory RNA-RNA interactions. Using differential expression data of mRNAs and lncRNAs from mouse models of hereditary hemochromatosis, I investigated competing regulatory interactions between mRNA, lncRNA and miRNA. A mutual interaction network was created from the predicted miRNA interaction sites on mRNAs and lncRNAs to identify regulatory RNAs in the disease. I also observed interesting relations between the sense-antisense mRNA-lncRNA pairs that indicate mutual regulation of expression levels through a yet unknown mechanism

Sequencing and Analysis of Full-Length cDNAs, 5′-ESTs and 3′-ESTs from a Cartilaginous Fish, the Elephant Shark (Callorhinchus milii)

10.1371/journal.pone.0047174PLoS ONE710

Genomic data mining for the computational prediction of small non-coding RNA genes

The objective of this research is to develop a novel computational prediction algorithm for non-coding RNA (ncRNA) genes using features computable for any genomic sequence without the need for comparative analysis. Existing comparative-based methods require the knowledge of closely related organisms in order to search for sequence and structural similarities. This approach imposes constraints on the type of ncRNAs, the organism, and the regions where the ncRNAs can be found. We have developed a novel approach for ncRNA gene prediction without the limitations of current comparative-based methods. Our work has established a ncRNA database required for subsequent feature and genomic analysis. Furthermore, we have identified significant features from folding-, structural-, and ensemble-based statistics for use in ncRNA prediction. We have also examined higher-order gene structures, namely operons, to discover potential insights into how ncRNAs are transcribed. Being able to automatically identify ncRNAs on a genome-wide scale is immensely powerful for incorporating it into a pipeline for large-scale genome annotation. This work will contribute to a more comprehensive annotation of ncRNA genes in microbial genomes to meet the demands of functional and regulatory genomic studies.Ph.D.Committee Chair: Dr. G. Tong Zhou; Committee Member: Dr. Arthur Koblasz; Committee Member: Dr. Eberhard Voit; Committee Member: Dr. Xiaoli Ma; Committee Member: Dr. Ying X

New Computational Approaches For Multiple Rna Alignment And Rna Search

In this thesis we explore the the theory and history behind RNA alignment. Normal sequence alignments as studied by computer scientists can be completed in O(n2) time in the naive case. The process involves taking two input sequences and finding the list of edits that can transform one sequence into the other. This process is applied to biology in many forms, such as the creation of multiple alignments and the search of genomic sequences. When you take into account the RNA sequence structure the problem becomes even harder. Multiple RNA structure alignment is particularly challenging because covarying mutations make sequence information alone insufficient. Existing tools for multiple RNA alignments first generate pair-wise RNA structure alignments and then build the multiple alignment using only the sequence information. Here we present PMFastR, an algorithm which iteratively uses a sequence-structure alignment procedure to build a multiple RNA structure alignment. PMFastR also has low memory consumption allowing for the alignment of large sequences such as 16S and 23S rRNA. Specifically, we reduce the memory consumption to ∼O(band2 ∗ m) where band is the banding size. Other solutions are ∼ O(n2 ∗ m) where n and m are the lengths of the target and query respectively. The algorithm also provides a method to utilize a multi-core environment. We present results on benchmark data sets from BRAliBase, which shows PMFastR outperforms other state-of-the-art programs. Furthermore, we regenerate 607 Rfam seed alignments and show that our automated process creates similar multiple alignments to the manually-curated Rfam seed alignments. While these methods can also be applied directly to genome sequence search, the abundance of new multiple species genome alignments presents a new area for exploration. Many multiple alignments of whole genomes are available and these alignments keep growing in size. These alignments can provide more information to the searcher than just a single sequence. Using the methodology from sequence-structure alignment we developed AlnAlign, which searches an entire genome alignment using RNA sequence structure. While programs have been readily available to align alignments, this is the first to our knowledge that is specifically designed for RNA sequences. This algorithm is presented only in theory and is yet to be tested