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

Efficiently Dispersing Carbon Nanotubes in Polyphenylene Sulfide

Thermal plastics are replacing conventional metals in the aerospace, sporting, electronics, and other industries. Thermal plastics are able to withstand relatively high temperatures, have good fatigue properties, and are lighter than metals. Unfortunately, they are not very electrically conductive. However, adding carbon nanotubes to thermal plastics such as polyphenylene sulfide (PPS) can drastically increase the plastic\u27s conductivity at a low weight percent of nanotubes called the percolation threshold. The percolation threshold is the point where adding a little more carbon nanotubes brings together the network of nanotubes and greatly increases the conductivity. We need to learn how to increase the dispersion of nanotubes in PPS to reduce the amount of expensive nanotubes necesarry to reach the percolation threshold. Adding nanotubes to thermal plastics is a difficult procedure. A few different melting and mixing methods have been utilized in previous studies. Initially, we tested how to best disperse the nanotubes using an extruder after physically mixing the two components. We have determined that grinding the PPS pellets to 400 microns and smaller and then coating the PPS powder with the carbon nanotubes in a pulverizer reduces the size and number of carbon nanotube agglomerates in the PPS versus using pellets and mixing by hand. In addition, using moderate screw speeds such as 70 rpm in the extruder helped reduce agglomerates. These results will help us reach the percolation threshold of carbon nanotubes in polyphenylene sulfide while using a smaller amount of the costly nanotubes

Non-syncytium-inducing HIV type 1 isolated from infected individuals replicates in MT-2 cells

Human immunodeficiency virus type 1 (HIV-1) isolates from six infected individuals less then 4 years of age were phenotyped for their syncytium-inducing (SI) ability in MT-2 cells. Three viral isolates that induced syncytia were detected. One SI isolate was from an individual who was in disease stage P2A,B,C and two SI isolates were recovered sequentially from another individual who switched from disease stage P1B to P2F. Non-syncytium-inducing (NSI) isolates were detected in two individuals who were in stage P1B of disease, and in a third individual who was in stage P2A of disease. Three sequential isolates obtained over a 2-year period from a fourth individual who progressed from disease stage P1B to P2A,B,C and subsequently died of AIDS-related disease were also found to have the NSI phenotype. To test whether NSI isolates can replicate in the absence of syncytium formation, we analyzed NSI-infected MT-2 cells for production of viral p24 antigen and expression of viral RNA by in situ hybridization. By day 12 postinfection, 6 of 7 NSI viral isolates produced 7- to 36-fold increases in p24 antigen compared to day 6, and expressed viral RNA in 13-20% of cells. A single NSI isolate that did not replicate in MT-2 cells was obtained from an individual who was asymptomatic (stage P1B). The individual rapidly progressed to symptomatic stage P2F and two sequential SI viruses were isolated. These SI isolates replicated in MT-2 cells and induced cytopathic effects.(ABSTRACT TRUNCATED AT 250 WORDS

Detection of HIV-1-infected cells from patients using nonisotopic in situ hybridization

We have demonstrated that a sensitive, nonisotopic in situ hybridization (ISH) assay can be used to detect HIV-infected cells from seropositive, asymptomatic individuals. Our assay is based on the detection of a biotinated HIV DNA probe hybridized to human immunodeficiency virus (HIV)-infected peripheral blood lymphocytes (PBL) using streptavidin and alkaline phosphatase to identify positive cells. This assay is rapid in that it can be performed within a day and is sensitive enough to unambiguously identify a rare, single, positive cell. Patient samples derived from HIV-seropositive hemophiliacs and HIV-seropositive infants were analyzed before and after coculture with normal PBL. The same samples were investigated using a Dupont P24 antigen-capture kit. It was found that ISH always detected the same positive samples as antigen capture, often in shorter times of coculture. In situ hybridization detected over half of our HIV-infected hemophilia patient population as virus positive, whereas the antigen capture assay detected less than one fourth as virus positive. In situ hybridization detected positive cells directly, without coculture, in 12 out of 35 (34%) hemophiliacs and in three out of eight (37%) infants. The speed, sensitivity, and confidence of ISH and nonisotopic detection indicates that it will be useful as a tool for clinical research and diagnosis

Ethnic variations in sexual behaviour in Great Britain and risk of sexually transmitted infections: a probability survey.

BACKGROUND: Ethnic variations in the rate of diagnosed sexually transmitted infections (STIs) have been reported in many developed countries. We used data from the second British National Survey of Sexual Attitudes and Lifestyles (Natsal 2000) to investigate the frequency of high-risk sexual behaviours and adverse sexual health outcomes in five ethnic groups in Great Britain. METHODS: We did a stratified probability sample survey of 11161 men and women aged 16-44 years, resident in Great Britain, using computer-assisted interviews. Additional sampling enabled us to do more detailed analyses for 949 black Caribbean, black African, Indian, and Pakistani respondents. We used logistic regression to assess reporting of STI diagnoses in the past 5 years, after controlling for demographic and behavioural variables. FINDINGS: We noted striking variations in number of sexual partnerships by ethnic group and between men and women. Reported numbers of sexual partnerships in a lifetime were highest in black Caribbean (median 9 [IQR 4-20]) and black African (9 [3-20]) men, and in white (5 [2-9]) and black Caribbean (4 [2-7]) women. Indian and Pakistani men and women reported fewer sexual partnerships, later first intercourse, and substantially lower prevalence of diagnosed STIs than did other groups. We recorded a significant association between ethnic origin and reported STIs in the past 5 years with increased risk in sexually active black Caribbean (OR 2.74 [95% CI 1.22-6.15]) and black African (2.95 [1.45-5.99]) men compared with white men, and black Caribbean (2.41 [1.35-4.28]) women compared with white women. Odds ratios changed little after controlling for age, number of sexual partnerships, homosexual and overseas partnerships, and condom use at last sexual intercourse. INTERPRETATION: Individual sexual behaviour is a key determinant of STI transmission risk, but alone does not explain the varying risk across ethnic groups. Our findings suggest a need for targeted and culturally competent prevention interventions

Capture, Concentration and Detection of Salmonella in Foods Using Magnetic Ionic Liquids and Recombinase Polymerase Amplification

We have previously investigated the extraction and concentration of bacteria from model systems using magnetic ionic liquid (MIL) solvents, while retaining their viability. Here, we combine MIL-based sample preparation with isothermal amplification and detection of Salmonella-specific DNA using Recombinase Polymerase Amplification (RPA). After initial developmental work with Serratia marcescens in water, Salmonella Typhimurium ATCC 14028 was inoculated in water, 2% milk, almond milk or liquid egg samples and extracted using one of two MILs, including: trihexyl(tetradecyl)phosphonium cobalt(II) hexafluoroacetylacetonate ([P66614+][Co(hfacac)3–]) and trihexyl(tetradecyl)phosphonium nickel(II) hexafluoroacetylacetonate ([P66614+][Ni(hfacac)3–]). Viable cells were recovered from the MIL extraction phase after the addition of modified LB broth, followed by a 20 min isothermal RPA assay. Amplification was carried out using supersaturated sodium acetate heat packs and results compared to those using a conventional laboratory thermocycler set to a single temperature. Results were visualized using either gel electrophoresis or nucleic acid lateral flow immunoassay (NALFIA). The combined MIL-RPA approach enabled detection of Salmonella at levels as low as 103 CFU mL-1. MIL-based sample preparation required less than 5 min to capture and concentrate sufficient cells for detection using RPA, which (including NALFIA or gel-based analysis) required approximately 30 - 45 min. Our results suggest the utility of MILs for the rapid extraction and concentration of pathogenic microorganisms in food samples, providing a means for physical enrichment that is compatible with downstream analysis using RPA

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections, are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.Comment: 19 pages, 5 figure

Biomass energy potential in Manabí province

The present work aims to determine the energy potential of biomass in the province of Manabí, having as main actors the residues of cocoa, dry corn, bananas, and African palm, these being the products with the greatest abundance within the province since during Its production is constant throughout the year and this allows it to be used as a base for energy production. The increase in greenhouse gases in the production of consumable electrical energy has led to a significant advance in the development of biologically friendly alternatives. Among these alternatives, one of the options for immediate implementation is obtaining energy through the combustion of conventionally wasted waste, also known as biomass