The emergence of tuberculosis is at the peak; therefore to station it at its lower level we hereby try bioinformatics approach against Mycobacterium tuberculosis [M. tuberculosis] pathogenesis. Rv3906c is a conserved hypothetical gene of M. tuberculosis and contains many GTP binding protein motif DXXG which demonstrate that this gene might be processed in a GTP binding or in GTP hydrolyzing manner. This gene shows interaction with its adjacent genes as well as pcnA which is a polymerase and localized in the extracellular region and found to be a soluble protein. Rv3906c has binding pockets for calcium atom at various positions which prove that calcium might have some role during the process of this gene. GTP binding protein motif DXXG is present in various positions and calcium binds at this site with a C-score of 0.25. Mutational analysis on this motif shows the large decrease of stability after mutation of aspartate residue with glycine. Stress conditions like pH and temperature also change stability of the protein. A decrease in stability at this position might play a role in inhibition of survival of the pathogen. These computational studies of this gene might be a successful step towards drug development against tuberculosis.

Md. Amjad Beg

Shivangi

Sonu Chand Thakur

Laxman S. Meena

Structural Prediction and Mutational Analysis of Rv3906c Gene of Mycobacterium tuberculosis H37Rv to Determine Its Essentiality in Survival

We performed locus similarity calculation by measuring fuzzy intersection between individual locus and reference locus and then performed CODIS STR-DNA similarity calculation. The fuzzy intersection calculation enables a more robust CODIS STR-DNA similarity calculation due to imprecision caused by noise produced by PCR machine. We also proposed shifted convoluted Gaussian fuzzy number (SCGFN) and Gaussian fuzzy number (GFN) to represent each locus value as improvement of triangular fuzzy number (TFN) as used in previous research. Compared to triangular fuzzy number (TFN), GFN is more realistic to represent uncertainty of locus information because the distribution is assumed to be Gaussian. Then, the original Gaussian fuzzy number (GFN) is convoluted with distribution of certain ethnic locus information to produce the new SCGFN which more represents ethnic information compared to original GFN. Experiments were done for the following cases: people with family relationships, people of the same tribe, and certain tribal populations. The statistical test with analysis of variance (ANOVA) shows the difference in similarity between SCGFN, GFN, and TFN with a significant level of 95&#x25;. The Tukey method in ANOVA shows that SCGFN yields a higher similarity which means being better than the GFN and TFN methods. The proposed method enables CODIS STR-DNA similarity calculation which is more robust to noise and performed better CODIS similarity calculation involving familial and tribal relationships.

Maria Susan Anggreainy

M. Rahmat Widyanto

Belawati H. Widjaja

Nurtami Soedarsono

Gaussian Fuzzy Number for STR-DNA Similarity Calculation Involving Familial and Tribal Relationships

Concept-centered semantic maps were created based on a text-mining analysis of PubMed using the BiblioEngine&#x5f;v2018 software. The objects (&#x201c;concepts&#x201d;) of a semantic map can be MeSH-terms or other terms (names of proteins, diseases, chemical compounds, etc.) structured in the form of controlled vocabularies. The edges between the two objects were automatically calculated based on the index of semantic similarity, which is proportional to the number of publications related to both objects simultaneously. On the one hand, an individual semantic map created based on the already published papers allows us to trace scientific inquiry. On the other hand, a prospective analysis based on the study of PubMed search history enables us to determine the possible directions for future research.

Ekaterina Ilgisonis

Andrey Lisitsa

Valerya Kudryavtseva

Elena Ponomarenko

Creation of Individual Scientific Concept-Centered Semantic Maps Based on Automated Text-Mining Analysis of PubMed

The growing amount of data in healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition. Based on the proposed architecture, a prototype implementation has been built for healthcare systems in order to generate real-time alerts. The suggested prototype is based on spark and MongoDB tools.

Naoual El aboudi

Laila Benhlima

Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation

Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation. The proposed algorithm achieves this by incorporating the following novel features: (i) new initialization method which generates only valid individuals with (rather than random) better fitness values; (ii) use of probability-based selection operators that limit the local convergence; (iii) use of secondary structure based mutation operator that makes the structure more closely to the laboratory determined structure; and (iv) incorporating all the above-mentioned features developed a complete two-tier framework. The developed framework builds the protein conformation on the square and triangular lattice. The test has been performed using benchmark sequences, and a comparative evaluation is done with various state-of-the-art algorithms. Moreover, in addition to hypothetical test sequences, we have tested protein sequences deposited in protein database repository. It has been observed that the proposed framework has shown superior performance regarding accuracy (fitness value) and speed (number of generations needed to attain the final conformation). The concepts used to enhance the performance are generic and can be used with any other population-based search algorithm such as genetic algorithm, ant colony optimization, and immune algorithm.

Sandhya Parasnath Dubey

S. Balaji

N. Gopalakrishna Kini

M. Sathish Kumar

A Novel Framework for Ab Initio Coarse Protein Structure Prediction

Ticks cause approximately &#x24;17&#x2013;19 billion economic losses to the livestock industry globally. Development of recombinant antitick vaccine is greatly hindered by insufficient knowledge and understanding of proteins expressed by ticks. Ticks secrete immunosuppressant proteins that modulate the host&#x2019;s immune system during blood feeding; these molecules could be a target for antivector vaccine development. Recombinant p36, a 36&#x2009;kDa immunosuppressor from the saliva of female Dermacentor andersoni, suppresses T-lymphocytes proliferation in vitro. To identify potential unique structural and dynamic properties responsible for the immunosuppressive function of p36 proteins, this study utilized bioinformatic tool to characterize and model structure of D. andersoni p36 protein. Evaluation of p36 protein family as suitable vaccine antigens predicted a p36 homolog in Rhipicephalus appendiculatus, the tick vector of East Coast fever, with an antigenicity score of 0.7701 that compares well with that of Bm86 (0.7681), the protein antigen that constitute commercial tick vaccine Tickgard&#x2122;. Ab initio modeling of the D. andersoni p36 protein yielded a 3D structure that predicted conserved antigenic region, which has potential of binding immunomodulating ligands including glycerol and lactose, found located within exposed loop, suggesting a likely role in immunosuppressive function of tick p36 proteins. Laboratory confirmation of these preliminary results is necessary in future studies.

Martin Omulindi Oyugi

Johnson Kangethe Kinyua

Esther Nkirote Magiri

Milcah Wagio Kigoni

Evenilton Pessoa Costa

Naftaly Wang&#x2019;ombe Githaka

In Silico Characterization and Structural Modeling of Dermacentor andersoni p36 Immunosuppressive Protein

Nowadays, microarray technology has become one of the popular ways to study gene expression and diagnosis of disease. National Center for Biology Information (NCBI) hosts public databases containing large volumes of biological data required to be preprocessed, since they carry high levels of noise and bias. Robust Multiarray Average (RMA) is one of the standard and popular methods that is utilized to preprocess the data and remove the noises. Most of the preprocessing algorithms are time-consuming and not able to handle a large number of datasets with thousands of experiments. Parallel processing can be used to address the above-mentioned issues. Hadoop is a well-known and ideal distributed file system framework that provides a parallel environment to run the experiment. In this research, for the first time, the capability of Hadoop and statistical power of R have been leveraged to parallelize the available preprocessing algorithm called RMA to efficiently process microarray data. The experiment has been run on cluster containing 5 nodes, while each node has 16 cores and 16&#x2009;GB memory. It compares efficiency and the performance of parallelized RMA using Hadoop with parallelized RMA using affyPara package as well as sequential RMA. The result shows the speed-up rate of the proposed approach outperforms the sequential approach and affyPara approach.

Amirhossein Sahlabadi

Ravie Chandren Muniyandi

Mahdi Sahlabadi

Hossein Golshanbafghy

Framework for Parallel Preprocessing of Microarray Data Using Hadoop

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Advances in Bioinformatics - Table of contents 2018

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Advances in Bioinformatics ceased publication in 2019 and is no longer accepting submissions.

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Advances in Bioinformatics publishes original research articles and review articles focusing on computational and statistical methods to address biological problems.

Advances in Bioinformatics