Systems Biology and the Development of Vaccines and Drugs for Malaria Treatments

The sequencing race has ended and the functional race has already begun. Microarray technology enables simultaneous gene expression analysis of thousands of genes, enabling a snapshot of an organisms’transcriptome at an unprecedented resolution. The close correlation between gene transcription and function, allow the inference of biological processes from the assessed transcriptome profile. Among the sophisticated analytical problems in microarray technology at the front and back ends respectively, are the selection of optimal DNA oligos and computational analysis of the genes expression. In this review paper, we analyse important methods in use today in customized oligos design. In the course of executing this, we discovered that the oligos designer algorithm hanged on gene PFA0135w of chromosome 1, while designing oligos for the gene sequences of Plasmodium falciparum. We do not know the reason for this yet, as the algorithm runs on other sequences like the yeast (Saccharomyces cervisiae) and Neurospora crassa. We conclude the paper highlighting the procedures encompassing the back end phase and discuss their application to the development of vaccines and drugs for malaria treatment. Note that, malaria is the cause of significant global morbidity and mortality with 300-500 million cases annually. Our aims are not ends, but a means to achieve the following: Iterate the need for experimental biologists to (i) know how to design their customized oligos and (ii) have some idea about gene expression analysis and the need for cooperation between experimental biologists and their counterpart, the computational biologists. These will help experimental biologists to coordinate very well the front and the back ends of the system biology analysis of the whole genome effectively

A comparative analysis of existing oligonucleotides selection algorithms for microarray technology

In system biology, DNA microarray technology is an indispensable tool for the biological analysis involved at the level of the whole genome. Among the sophisticated analytical problems in microarraytechnology at the front and back ends, respectively, are the selection of optimal DNA oligonucleotides (henceforth oligos) and computational analysis of the genes expression data. A computational comparative analysis of the methods used to select oligos is important since the design and quality of the microarray probes are of critical importance for the hybridization experiments as well as subsequent analysis of the data. In an attempt to enhance efficient and effective design at the front end, a computational comparative analysis was performed on oligos selection tools using the barley ESTs, as well as the Saccharomyces cerevisiae, Encephalitozoon cuniculi and human genomes. The analysis also shows that a large number of the existing tools are difficult to install and configure. For cross hybridization test, most rely on BLAST and therefore design ill specific oligonucleotides. Furthermore,most are non-intuitive to use and lack important oligo design and software features

Computational Biology and Bioinformatics in Nigeria

Over the past few decades, major advances in the field of molecular biology, coupled with advances in genomic technologies, have led to an explosive growth in the biological data generated by the scientific community. The critical need to process and analyze such a deluge of data and turn it into useful knowledge has caused bioinformatics to gain prominence and importance. Bioinformatics is an interdisciplinary research area that applies techniques, methodologies, and tools in computer and information science to solve biological problems. In Nigeria, bioinformatics has recently played a vital role in the advancement of biological sciences. As a developing country, the importance of bioinformatics is rapidly gaining acceptance, and bioinformatics groups comprised of biologists, computer scientists, and computer engineers are being constituted at Nigerian universities and research institutes. In this article, we present an overview of bioinformatics education and research in Nigeria. We also discuss professional societies and academic and research institutions that play central roles in advancing the discipline in Nigeria. Finally, we propose strategies that can bolster bioinformatics education and support from policy makers in Nigeria, with potential positive implications for other developing countries. © 2014 Fatumo et al.SAF was supported by H3ABioNet NABDA Node, Abuja, Nigeria with NIH Common Fund Award/NHGRI Grant Number U41HG006941 and Genetic Epidemiology Group at Wellcome Trust Sanger Institute.Published versio

Estimates of global, regional, and national incidence, prevalence, and mortality of HIV, 1980-2015 : the Global Burden of Disease Study 2015

Background Timely assessment of the burden of HIV/AIDS is essential for policy setting and programme evaluation. In this report from the Global Burden of Disease Study 2015 (GBD 2015), we provide national estimates of levels and trends of HIV/AIDS incidence, prevalence, coverage of antiretroviral therapy (ART), and mortality for 195 countries and territories from 1980 to 2015. Methods For countries without high-quality vital registration data, we estimated prevalence and incidence with data from antenatal care clinics and population-based seroprevalence surveys, and with assumptions by age and sex on initial CD4 distribution at infection, CD4 progression rates (probability of progression from higher to lower CD4 cell-count category), on and off antiretroviral therapy (ART) mortality, and mortality from all other causes. Our estimation strategy links the GBD 2015 assessment of all-cause mortality and estimation of incidence and prevalence so that for each draw from the uncertainty distribution all assumptions used in each step are internally consistent. We estimated incidence, prevalence, and death with GBD versions of the Estimation and Projection Package (EPP) and Spectrum software originally developed by the Joint United Nations Programme on HIV/AIDS (UNAIDS). We used an open-source version of EPP and recoded Spectrum for speed, and used updated assumptions from systematic reviews of the literature and GBD demographic data. For countries with high-quality vital registration data, we developed the cohort incidence bias adjustment model to estimate HIV incidence and prevalence largely from the number of deaths caused by HIV recorded in cause-of-death statistics. We corrected these statistics for garbage coding and HIV misclassification. Findings Global HIV incidence reached its peak in 1997, at 3.3 million new infections (95% uncertainty interval [UI] 3.1-3.4 million). Annual incidence has stayed relatively constant at about 2.6 million per year (range 2.5-2.8 million) since 2005, after a period of fast decline between 1997 and 2005. The number of people living with HIV/AIDS has been steadily increasing and reached 38.8 million (95% UI 37.6-40.4 million) in 2015. At the same time, HIV/AIDS mortality has been declining at a steady pace, from a peak of 1.8 million deaths (95% UI 1.7-1.9 million) in 2005, to 1.2 million deaths (1.1-1.3 million) in 2015. We recorded substantial heterogeneity in the levels and trends of HIV/AIDS across countries. Although many countries have experienced decreases in HIV/AIDS mortality and in annual new infections, other countries have had slowdowns or increases in rates of change in annual new infections. Interpretation Scale-up of ART and prevention of mother-to-child transmission has been one of the great successes of global health in the past two decades. However, in the past decade, progress in reducing new infections has been slow, development assistance for health devoted to HIV has stagnated, and resources for health in low-income countries have grown slowly. Achievement of the new ambitious goals for HIV enshrined in Sustainable Development Goal 3 and the 90-90-90 UNAIDS targets will be challenging, and will need continued efforts from governments and international agencies in the next 15 years to end AIDS by 2030. Copyright (C) The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY licensePeer reviewe

PQ trees, consecutive ones problem and applications

A PQ tree is an advanced tree–based data structure, which represents a family of permutations on a set of elements. In this research article, we considered the significance of PQ trees and the Consecutive ones Problem to Computer Science and bioinformatics and their various applications. We also went further to demonstrate the operations of the characteristics of the Consecutive ones property by simulation, using high level programming languages. Attempt was also made at developing a PQ tree–Consecutive Ones analyzer, which could be instrumental not only as an educative tool to inquisitive students, but also serve as an important tool in developing clustering software in the field of bioinformatics and other application domains, with respect to solving real life problems.Keywords: PQ trees, Consecutive ones problem, bioinformatics, computer scienc

High-performance exact algorithms for motif search

OBJECTIVE: The human genome project has resulted in the generation of voluminous biological data. Novel computational techniques are called for to extract useful information from this data. One such technique is that of finding patterns that are repeated over many sequences (and possibly over many species). In this paper we study the problem of identifying meaningful patterns (i.e., motifs) from biological data, the motif search problem. METHODS: The general version of the motif search problem is NP-hard. Numerous algorithms have been proposed in the literature to solve this problem. Many of these algorithms fall under the category of heuristics. We concentrate on exact algorithms in this paper. In particular, we concentrate on two different versions of the motif search problem and offer exact algorithms for them. RESULTS: In this paper we present algorithms for two versions of the motif search problem. All of our algorithms are elegant and use only such simple data structures as arrays. For the first version of the problem described as Problem 1 in the paper, we present a simple sorting based algorithm, SMS (Simple Motif Search). This algorithm has been coded and experimental results have been obtained. For the second version of the problem (described in the paper as Problem 2), we present two different algorithms--a deterministic algorithm (called DMS) and a randomized algorithm (Monte Carlo algorithm). We also show how these algorithms can be parallelized. CONCLUSIONS: All the algorithms proposed in this paper are improvements over existing algorithms for these versions of motif search in biological sequence data. The algorithms presented have the potential of performing well in practice

Deep Neural Trading: Comparative Study With Feed Forward, Recurrent and Autoencoder Networks

Algorithmic trading approaches based on news or social network posts claim to outperform classical methods that use only price time series and other economics values. However combining financial time series with news or posts, requires daily huge amount of relevant text which are impracticable to gather in real time, even because the online sources of news and social networks no longer allow unconditional massive download of data. These difficulties have renewed the interest in simpler methods based on financial time series. This work presents a wide experimental comparisons of the performance of 7 trading protocols applied to 27 component stocks of the Dow Jones Industrial Average (DJIA). The buy/sell trading actions are driven by the stock value predictions performed with 3 types of neural network architectures: feed forward, recurrent and autoencoder. Each architecture types in turn has been experimented with different sizes and hyperparameters over all the multivariate time series. The combinations of trading protocols with variants of the 3 neural network types have been in turn applied to time series, varying the input variables from 4 to 17 and the training period from 8 to 16 years while the test period from 1 to 2 years