Advances in Bioinformatics The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks Sun, 23 Aug 2015 09:30:44 +0000 Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer’s disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer’s disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively. Fayroz F. Sherif, Nourhan Zayed, and Mahmoud Fakhr Copyright © 2015 Fayroz F. Sherif et al. All rights reserved. Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine Tue, 11 Aug 2015 11:22:47 +0000 Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP) and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs), and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions. Masayuki Yarimizu, Cao Wei, Yusuke Komiyama, Kokoro Ueki, Shugo Nakamura, Kazuya Sumikoshi, Tohru Terada, and Kentaro Shimizu Copyright © 2015 Masayuki Yarimizu et al. All rights reserved. A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data Thu, 11 Jun 2015 13:48:13 +0000 We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources. Zena M. Hira and Duncan F. Gillies Copyright © 2015 Zena M. Hira and Duncan F. Gillies. All rights reserved. Semantic Annotation for Biological Information Retrieval System Mon, 09 Feb 2015 06:37:38 +0000 Online literatures are increasing in a tremendous rate. Biological domain is one of the fast growing domains. Biological researchers face a problem finding what they are searching for effectively and efficiently. The aim of this research is to find documents that contain any combination of biological process and/or molecular function and/or cellular component. This research proposes a framework that helps researchers to retrieve meaningful documents related to their asserted terms based on gene ontology (GO). The system utilizes GO by semantically decomposing it into three subontologies (cellular component, biological process, and molecular function). Researcher has the flexibility to choose searching terms from any combination of the three subontologies. Document annotation is taking a place in this research to create an index of biological terms in documents to speed the searching process. Query expansion is used to infer semantically related terms to asserted terms. It increases the search meaningful results using the term synonyms and term relationships. The system uses a ranking method to order the retrieved documents based on the ranking weights. The proposed system achieves researchers’ needs to find documents that fit the asserted terms semantically. Mohamed Marouf Z. Oshaiba, Enas M. F. El Houby, and Akram Salah Copyright © 2015 Mohamed Marouf Z. Oshaiba et al. All rights reserved. A Highly Conserved GEQYQQLR Epitope Has Been Identified in the Nucleoprotein of Ebola Virus by Using an In Silico Approach Sun, 01 Feb 2015 09:50:26 +0000 Ebola virus (EBOV) is a deadly virus that has caused several fatal outbreaks. Recently it caused another outbreak and resulted in thousands afflicted cases. Effective and approved vaccine or therapeutic treatment against this virus is still absent. In this study, we aimed to predict B-cell epitopes from several EBOV encoded proteins which may aid in developing new antibody-based therapeutics or viral antigen detection method against this virus. Multiple sequence alignment (MSA) was performed for the identification of conserved region among glycoprotein (GP), nucleoprotein (NP), and viral structural proteins (VP40, VP35, and VP24) of EBOV. Next, different consensus immunogenic and conserved sites were predicted from the conserved region(s) using various computational tools which are available in Immune Epitope Database (IEDB). Among GP, NP, VP40, VP35, and VP30 protein, only NP gave a 100% conserved GEQYQQLR B-cell epitope that fulfills the ideal features of an effective B-cell epitope and could lead a way in the milieu of Ebola treatment. However, successful in vivo and in vitro studies are prerequisite to determine the actual potency of our predicted epitope and establishing it as a preventing medication against all the fatal strains of EBOV. Mohammad Tuhin Ali and Md Ohedul Islam Copyright © 2015 Mohammad Tuhin Ali and Md Ohedul Islam. All rights reserved. Development of a Machine Learning Method to Predict Membrane Protein-Ligand Binding Residues Using Basic Sequence Information Sat, 31 Jan 2015 08:20:58 +0000 Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment. M. Xavier Suresh, M. Michael Gromiha, and Makiko Suwa Copyright © 2015 M. Xavier Suresh et al. All rights reserved. PhosphoHunter: An Efficient Software Tool for Phosphopeptide Identification Mon, 12 Jan 2015 12:58:05 +0000 Phosphorylation is a protein posttranslational modification. It is responsible of the activation/inactivation of disease-related pathways, thanks to its role of “molecular switch.” The study of phosphorylated proteins becomes a key point for the proteomic analyses focused on the identification of diagnostic/therapeutic targets. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the most widely used analytical approach. Although unmodified peptides are automatically identified by consolidated algorithms, phosphopeptides still require automated tools to avoid time-consuming manual interpretation. To improve phosphopeptide identification efficiency, a novel procedure was developed and implemented in a Perl/C tool called PhosphoHunter, here proposed and evaluated. It includes a preliminary heuristic step for filtering out the MS/MS spectra produced by nonphosphorylated peptides before sequence identification. A method to assess the statistical significance of identified phosphopeptides was also formulated. PhosphoHunter performance was tested on a dataset of 1500 MS/MS spectra and it was compared with two other tools: Mascot and Inspect. Comparisons demonstrated that a strong point of PhosphoHunter is sensitivity, suggesting that it is able to identify real phosphopeptides with superior performance. Performance indexes depend on a single parameter (intensity threshold) that users can tune according to the study aim. All the three tools localized >90% of phosphosites. Alessandra Tiengo, Lorenzo Pasotti, Nicola Barbarini, and Paolo Magni Copyright © 2015 Alessandra Tiengo et al. All rights reserved. CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences Tue, 06 Jan 2015 07:20:06 +0000 Complex informational spectrum analysis for protein sequences (CISAPS) and its web-based server are developed and presented. As recent studies show, only the use of the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient. Therefore, CISAPS is developed to consider and provide results in three forms including absolute, real, and imaginary spectrum. Biologically related features to the analysis of influenza A subtypes as presented as a case study in this study can also appear individually either in the real or imaginary spectrum. As the results presented, protein classes can present similarities or differences according to the features extracted from CISAPS web server. These associations are probable to be related with the protein feature that the specific amino acid index represents. In addition, various technical issues such as zero-padding and windowing that may affect the analysis are also addressed. CISAPS uses an expanded list of 611 unique amino acid indices where each one represents a different property to perform the analysis. This web-based server enables researchers with little knowledge of signal processing methods to apply and include complex informational spectrum analysis to their work. Charalambos Chrysostomou, Huseyin Seker, and Nizamettin Aydin Copyright © 2015 Charalambos Chrysostomou et al. All rights reserved. A Computational Approach for Predicting Role of Human MicroRNAs in MERS-CoV Genome Tue, 23 Dec 2014 00:10:06 +0000 The new epidemic Middle East Respiratory Syndrome (MERS) is caused by a type of human coronavirus called MERS-CoV which has global fatality rate of about 30%. We are investigating potential antiviral therapeutics against MERS-CoV by using host microRNAs (miRNAs) which may downregulate viral gene expression to quell viral replication. We computationally predicted potential 13 cellular miRNAs from 11 potential hairpin sequences of MERS-CoV genome. Our study provided an interesting hypothesis that those miRNAs, that is, hsa-miR-628-5p, hsa-miR-6804-3p, hsa-miR-4289, hsa-miR-208a-3p, hsa-miR-510-3p, hsa-miR-18a-3p, hsa-miR-329-3p, hsa-miR-548ax, hsa-miR-3934-5p, hsa-miR-4474-5p, hsa-miR-7974, hsa-miR-6865-5p, and hsa-miR-342-3p, would be antiviral therapeutics against MERS-CoV infection. Md Mahmudul Hasan, Rozina Akter, Md. Shahin Ullah, Md. Jaynul Abedin, G. M. Ahsan Ullah, and Md. Zakir Hossain Copyright © 2014 Md Mahmudul Hasan et al. All rights reserved. Alternate Phosphorylation/O-GlcNAc Modification on Human Insulin IRSs: A Road towards Impaired Insulin Signaling in Alzheimer and Diabetes Wed, 17 Dec 2014 13:26:37 +0000 Impaired insulin signaling has been thought of as important step in both Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM). Posttranslational modifications (PTMs) regulate functions and interaction of insulin with insulin receptors substrates (IRSs) and activate insulin signaling downstream pathways via autophosphorylation on several tyrosine (TYR) residues on IRSs. Two important insulin receptor substrates 1 and 2 are widely expressed in human, and alternative phosphorylation on their serine (Ser) and threonine (Thr) residues has been known to block the Tyr phosphorylation of IRSs, thus inhibiting insulin signaling and promoting insulin resistance. Like phosphorylation, O-glycosylation modification is important PTM and inhibits phosphorylation on same or neighboring Ser/Thr residues, often called Yin Yang sites. Both IRS-1 and IRS-2 have been shown to be O-glycosylated; however exact sites are not determined yet. In this study, by using neuronal network based prediction methods, we found more than 50 Ser/Thr residues that have potential to be O-glycosylated and may act as possible sites as well. Moreover, alternative phosphorylation and O-glycosylation on IRS-1 Ser-312, 984, 1037, and 1101 may act as possible therapeutic targets to minimize the risk of AD and T2DM. Zainab Jahangir, Waqar Ahmad, and Khadija Shabbiri Copyright © 2014 Zainab Jahangir et al. All rights reserved. An In Silico Approach towards the Prediction of Druglikeness Properties of Inhibitors of Plasminogen Activator Inhibitor1 Mon, 15 Dec 2014 11:28:23 +0000 Diabetic retinopathy is the leading cause of blindness worldwide. It is caused by the abnormal growth of the retinal blood vessels. Plasminogen activator inhibitor1 (PAI1) is the key growth factor and the inhibition of PAI1 can reduce the angiogenesis. In this study, currently available inhibitors are taken and tested for the toxicity, binding affinity, and bioactivities of the compounds by in silico approach. Five toxic free inhibitors were identified, among which N-acetyl-D-glucosamine shows the significant binding affinity and two of the molecules are having the better bioactivity properties. The molecular optimization of 2-(acetylamino)-2-deoxy-A-D-glucopyranose and alpha-L-fucose can be used for the treatment of diabetic retinopathy. Umadevi Subramanian, Ashok Sivapunniyam, Ayyasamy Pudukadu Munusamy, and Rajakumar Sundaram Copyright © 2014 Umadevi Subramanian et al. All rights reserved. A Framework for Prediction of Response to HCV Therapy Using Different Data Mining Techniques Thu, 11 Dec 2014 11:37:33 +0000 Hepatitis C which is a widely spread disease all over the world is a fatal liver disease caused by Hepatitis C Virus (HCV). The only approved therapy is interferon plus ribavirin. The number of responders to this treatment is low, while its cost is high and side effects are undesirable. Treatment response prediction will help in reducing the patients who suffer from the side effects and high costs without achieving recovery. The aim of this research is to develop a framework which can select the best model to predict HCV patients’ response to the treatment of HCV from clinical information. The framework contains three phases which are preprocessing phase to prepare the data for applying Data Mining (DM) techniques, DM phase to apply different DM techniques, and evaluation phase to evaluate and compare the performance of the built models and select the best model as the recommended one. Different DM techniques had been applied which are associative classification, artificial neural network, and decision tree to evaluate the framework. The experimental results showed the effectiveness of the framework in selecting the best model which is the model built by associative classification using histology activity index, fibrosis stage, and alanine amino transferase. Enas M. F. El Houby Copyright © 2014 Enas M. F. El Houby. All rights reserved. Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors Wed, 26 Nov 2014 12:59:09 +0000 Histone deacetylases 2 (HDAC2), Class I histone deacetylase (HDAC) family, emerged as an important therapeutic target for the treatment of various cancers. A total of 48 inhibitors of two different chemotypes were used to generate pharmacophore model using 3D QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best HypoGen model consists of four pharmacophore features namely, one hydrogen bond acceptor (HBA), and one hydrogen donor (HBD), one hydrophobic (HYP) and one aromatic centres, (RA). This model was validated against 20 test set compounds and this model was utilized as a 3D query for virtual screening to validate against NCI and Maybridge database and the hits further screened by Lipinski’s rule of 5, and a total of 382 hit compounds from NCI and 243 hit compounds from Maybridge were found and were subjected to molecular docking in the active site of HDAC2 (PDB: 3MAX). Finally eight hit compounds, NSC108392, NSC127064, NSC110782, and NSC748337 from NCI database and MFCD01935795, MFCD00830779, MFCD00661790, and MFCD00124221 from Maybridge database, were considered as novel potential HDAC2 inhibitors. Naresh Kandakatla and Geetha Ramakrishnan Copyright © 2014 Naresh Kandakatla and Geetha Ramakrishnan. All rights reserved. Binding Energy Calculation of Patchouli Alcohol Isomer Cyclooxygenase Complexes Suggested as COX-1/COX-2 Selective Inhibitor Mon, 17 Nov 2014 09:58:17 +0000 To understand the structural features that dictate the selectivity of the two isoforms of the prostaglandin H2 synthase (PGHS/COX), the three-dimensional (3D) structure of COX-1/COX-2 was assessed by means of binding energy calculation of virtual molecular dynamic with using ligand alpha-Patchouli alcohol isomers. Molecular interaction studies with COX-1 and COX-2 were done using the molecular docking tools by Hex 8.0. Interactions were further visualized by using Discovery Studio Client 3.5 software tool. The binding energy of molecular interaction was calculated by AMBER12 and Virtual Molecular Dynamic 1.9.1 software. The analysis of the alpha-Patchouli alcohol isomer compounds showed that all alpha-Patchouli alcohol isomers were suggested as inhibitor of COX-1 and COX-2. Collectively, the scoring binding energy calculation (with PBSA Model Solvent) of alpha-Patchouli alcohol isomer compounds (CID442384, CID6432585, CID3080622, CID10955174, and CID56928117) was suggested as candidate for a selective COX-1 inhibitor and CID521903 as nonselective COX-1/COX-2. Sentot Joko Raharjo, Chanif Mahdi, Nurdiana Nurdiana, Takheshi Kikuchi, and Fatchiyah Fatchiyah Copyright © 2014 Sentot Joko Raharjo et al. All rights reserved. Comparative Genomics of Ten Solanaceous Plastomes Mon, 17 Nov 2014 00:00:00 +0000 Availability of complete plastid genomes of ten solanaceous species, Atropa belladonna, Capsicum annuum, Datura stramonium, Nicotiana sylvestris, Nicotiana tabacum, Nicotiana tomentosiformis, Nicotiana undulata, Solanum bulbocastanum, Solanum lycopersicum, and Solanum tuberosum provided us with an opportunity to conduct their in silico comparative analysis in depth. The size of complete chloroplast genomes and LSC and SSC regions of three species of Solanum is comparatively smaller than that of any other species studied till date (exception: SSC region of A. belladonna). AT content of coding regions was found to be less than noncoding regions. A duplicate copy of trnH gene in C. annuum and two alternative tRNA genes for proline in D. stramonium were observed for the first time in this analysis. Further, homology search revealed the presence of rps19 pseudogene and infA genes in A. belladonna and D. stramonium, a region identical to rps19 pseudogene in C. annum and orthologues of sprA gene in another six species. Among the eighteen intron-containing genes, 3 genes have two introns and 15 genes have one intron. The longest insertion was found in accD gene in C. annuum. Phylogenetic analysis using concatenated protein coding sequences gave two clades, one for Nicotiana species and another for Solanum, Capsicum, Atropa, and Datura. Harpreet Kaur, Bhupinder Pal Singh, Harpreet Singh, and Avinash Kaur Nagpal Copyright © 2014 Harpreet Kaur et al. All rights reserved. Computational Studies of Beta Amyloid (Aβ42) with p75NTR Receptor: A Novel Therapeutic Target in Alzheimer’s Disease Tue, 11 Nov 2014 13:14:00 +0000 Alzheimer’s disease is a neurodegenerative disorder characterized by the accumulation of beta amyloid plaques (Aβ) which can induce neurite degeneration and progressive dementia. It has been identified that neuronal apoptosis is induced by binding of Aβ42 to pan neurotrophin receptor (p75NTR) and gave the possibility that beta amyloid oligomer is a ligand for p75NTR. However, the atomic contact point responsible for molecular interactions and conformational changes of the protein upon binding was not studied in detail. In view of this, we conducted a molecular docking and simulation study to investigate the binding behaviour of Aβ42 monomer with p75NTR ectodomain. Furthermore, we proposed a p75NTR-ectodomain-Aβ42 complex model. Our data revealed that, Aβ42 specifically recognizes CRD1 and CRD2 domains of the receptor and formed a “cap” like structure at the N-terminal of receptor which is stabilized by a network of hydrogen bonds. These findings are supported by molecular dynamics simulation that Aβ42 showed distinct structural alterations at N- and C-terminal regions due to the influence of the receptor binding site. Overall, the present study gives more structural insight on the molecular interactions of beta amyloid protein involved in the activation of p75NTR receptor. Shine Devarajan and Jeya Sundara Sharmila Copyright © 2014 Shine Devarajan and Jeya Sundara Sharmila. All rights reserved. Computational Analysis Reveals the Association of Threonine 118 Methionine Mutation in PMP22 Resulting in CMT-1A Mon, 20 Oct 2014 06:47:05 +0000 The T118M mutation in PMP22 gene is associated with Charcot Marie Tooth, type 1A (CMT1A). CMT1A is a form of Charcot-Marie-Tooth disease, the most common inherited disorder of the peripheral nervous system. Mutations in CMT related disorder are seen to increase the stability of the protein resulting in the diseased state. We performed SNP analysis for all the nsSNPs of PMP22 protein and carried out molecular dynamics simulation for T118M mutation to compare the stability difference between the wild type protein structure and the mutant protein structure. The mutation T118M resulted in the overall increase in the stability of the mutant protein. The superimposed structure shows marked structural variation between the wild type and the mutant protein structures. Chundi Vinay Kumar, Rayapadi G. Swetha, Anand Anbarasu, and Sudha Ramaiah Copyright © 2014 Chundi Vinay Kumar et al. All rights reserved. A Hybrid Method for Endocardial Contour Extraction of Right Ventricle in 4-Slices from 3D Echocardiography Dataset Sun, 12 Oct 2014 13:38:16 +0000 This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours. Faten A. Dawood, Rahmita W. Rahmat, Suhaini B. Kadiman, Lili N. Abdullah, and Mohd D. Zamrin Copyright © 2014 Faten A. Dawood et al. All rights reserved. In Silico Screening of Mutated K-Ras Inhibitors from Malaysian Typhonium flagelliforme for Non-Small Cell Lung Cancer Sun, 21 Sep 2014 00:00:00 +0000 K-ras is an oncogenic GTPase responsible for at least 15–25% of all non-small cell lung cancer cases worldwide. Lung cancer of both types is increasing with an alarming rate due to smoking habits in Malaysia among men and women. Natural products always offer alternate treatment therapies that are safe and effective. Typhonium flagelliforme or Keladi Tikus is a local plant known to possess anticancer properties. The whole extract is considered more potent than individual constituents. Since K-ras is the key protein in lung cancer, our aim was to identify the constituents of the plant that could target the mutated K-ras. Using docking strategies, reported potentially active compounds of Typhonium flagelliforme were docked into the allosteric surface pockets and switch regions of the K-ras protein to identify possible inhibitors. The selected ligands were found to have a high binding affinity for the switch II and the interphase region of the ras-SOS binding surface. Ayesha Fatima and H. F. Yee Copyright © 2014 Ayesha Fatima and H. F. Yee. All rights reserved. Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images Tue, 16 Sep 2014 00:00:00 +0000 The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data. Karthik Kalyan, Binal Jakhia, Ramachandra Dattatraya Lele, Mukund Joshi, and Abhay Chowdhary Copyright © 2014 Karthik Kalyan et al. All rights reserved. Breast Cancer Nodes Detection Using Ultrasonic Microscale Subarrayed MIMO RADAR Mon, 15 Sep 2014 08:37:25 +0000 This paper proposes the use of ultrasonic microscale subarrayed MIMO RADARs to estimate the position of breast cancer nodes. The transmit and receive antenna arrays are divided into subarrays. In order to increase the signal diversity each subarray is assigned a different waveform from an orthogonal set. High-frequency ultrasonic transducers are used since a breast is considered to be a superficial structure. Closed form expressions for the optimal Neyman-Pearson detector are derived. The combination of the waveform diversity present in the subarrayed deployment and traditional phased-array RADAR techniques provides promising results. Attaphongse Taparugssanagorn, Siwaruk Siwamogsatham, and Carlos Pomalaza-Ráez Copyright © 2014 Attaphongse Taparugssanagorn et al. All rights reserved. Utilization of Boron Compounds for the Modification of Suberoyl Anilide Hydroxamic Acid as Inhibitor of Histone Deacetylase Class II Homo sapiens Sun, 24 Aug 2014 10:43:10 +0000 Histone deacetylase (HDAC) has a critical function in regulating gene expression. The inhibition of HDAC has developed as an interesting anticancer research area that targets biological processes such as cell cycle, apoptosis, and cell differentiation. In this study, an HDAC inhibitor that is available commercially, suberoyl anilide hydroxamic acid (SAHA), has been modified to improve its efficacy and reduce the side effects of the compound. Hydrophobic cap and zinc-binding group of these compounds were substituted with boron-based compounds, whereas the linker region was substituted with p-aminobenzoic acid. The molecular docking analysis resulted in 8 ligands with Δ value more negative than the standards, SAHA and trichostatin A (TSA). That ligands were analyzed based on the nature of QSAR, pharmacological properties, and ADME-Tox. It is conducted to obtain a potent inhibitor of HDAC class II Homo sapiens. The screening process result gave one best ligand, Nova2 (513246-99-6), which was then further studied by molecular dynamics simulations. Ridla Bakri, Arli Aditya Parikesit, Cipta Prio Satriyanto, Djati Kerami, and Usman Sumo Friend Tambunan Copyright © 2014 Ridla Bakri et al. All rights reserved. AUTO-MUTE 2.0: A Portable Framework with Enhanced Capabilities for Predicting Protein Functional Consequences upon Mutation Sun, 17 Aug 2014 08:33:10 +0000 The AUTO-MUTE 2.0 stand-alone software package includes a collection of programs for predicting functional changes to proteins upon single residue substitutions, developed by combining structure-based features with trained statistical learning models. Three of the predictors evaluate changes to protein stability upon mutation, each complementing a distinct experimental approach. Two additional classifiers are available, one for predicting activity changes due to residue replacements and the other for determining the disease potential of mutations associated with nonsynonymous single nucleotide polymorphisms (nsSNPs) in human proteins. These five command-line driven tools, as well as all the supporting programs, complement those that run our AUTO-MUTE web-based server. Nevertheless, all the codes have been rewritten and substantially altered for the new portable software, and they incorporate several new features based on user feedback. Included among these upgrades is the ability to perform three highly requested tasks: to run “big data” batch jobs; to generate predictions using modified protein data bank (PDB) structures, and unpublished personal models prepared using standard PDB file formatting; and to utilize NMR structure files that contain multiple models. Majid Masso and Iosif I. Vaisman Copyright © 2014 Majid Masso and Iosif I. Vaisman. All rights reserved. Multiplex Degenerate Primer Design for Targeted Whole Genome Amplification of Many Viral Genomes Sun, 03 Aug 2014 10:37:23 +0000 Background. Targeted enrichment improves coverage of highly mutable viruses at low concentration in complex samples. Degenerate primers that anneal to conserved regions can facilitate amplification of divergent, low concentration variants, even when the strain present is unknown. Results. A tool for designing multiplex sets of degenerate sequencing primers to tile overlapping amplicons across multiple whole genomes is described. The new script, run_tiled_primers, is part of the PriMux software. Primers were designed for each segment of South American hemorrhagic fever viruses, tick-borne encephalitis, Henipaviruses, Arenaviruses, Filoviruses, Crimean-Congo hemorrhagic fever virus, Rift Valley fever virus, and Japanese encephalitis virus. Each group is highly diverse with as little as 5% genome consensus. Primer sets were computationally checked for nontarget cross reactions against the NCBI nucleotide sequence database. Primers for murine hepatitis virus were demonstrated in the lab to specifically amplify selected genes from a laboratory cultured strain that had undergone extensive passage in vitro and in vivo. Conclusions. This software should help researchers design multiplex sets of primers for targeted whole genome enrichment prior to sequencing to obtain better coverage of low titer, divergent viruses. Applications include viral discovery from a complex background and improved sensitivity and coverage of rapidly evolving strains or variants in a gene family. Shea N. Gardner, Crystal J. Jaing, Maher M. Elsheikh, José Peña, David A. Hysom, and Monica K. Borucki Copyright © 2014 Shea N. Gardner et al. All rights reserved. Prediction of Epitope-Based Peptides for the Utility of Vaccine Development from Fusion and Glycoprotein of Nipah Virus Using In Silico Approach Thu, 24 Jul 2014 07:20:33 +0000 This study aims to design epitope-based peptides for the utility of vaccine development by targeting glycoprotein G and envelope protein F of Nipah virus (NiV) that, respectively, facilitate attachment and fusion of NiV with host cells. Using various databases and tools, immune parameters of conserved sequence(s) from G and F proteins of different isolates of NiV were tested to predict probable epitope(s). Binding analyses of the peptides with MHC class-I and class-II molecules, epitope conservancy, population coverage, and linear B cell epitope prediction were analyzed. Predicted peptides interacted with seven or more MHC alleles and illustrated population coverage of more than 99% and 95%, for G and F proteins, respectively. The predicted class-I nonamers, SLIDTSSTI and EWISIVPNF, superimposed on the putative decameric B cell epitopes, were also identified as core sequences of the most probable class-II 15-mer peptides GPKVSLIDTSSTITI and EWISIVPNFILVRNT. These peptides were further validated for their binding to specific HLA alleles using in silico docking technique. Our in silico analysis suggested that the predicted epitopes, either GPKVSLIDTSSTITI or EWISIVPNFILVRNT, could be a better choice as universal vaccine component against NiV irrespective of different isolates which may elicit both humoral and cell-mediated immunity. M. Sadman Sakib, Md. Rezaul Islam, A. K. M. Mahbub Hasan, and A. H. M. Nurun Nabi Copyright © 2014 M. Sadman Sakib et al. All rights reserved. IN-MACA-MCC: Integrated Multiple Attractor Cellular Automata with Modified Clonal Classifier for Human Protein Coding and Promoter Prediction Tue, 15 Jul 2014 09:40:11 +0000 Protein coding and promoter region predictions are very important challenges of bioinformatics (Attwood and Teresa, 2000). The identification of these regions plays a crucial role in understanding the genes. Many novel computational and mathematical methods are introduced as well as existing methods that are getting refined for predicting both of the regions separately; still there is a scope for improvement. We propose a classifier that is built with MACA (multiple attractor cellular automata) and MCC (modified clonal classifier) to predict both regions with a single classifier. The proposed classifier is trained and tested with Fickett and Tung (1992) datasets for protein coding region prediction for DNA sequences of lengths 54, 108, and 162. This classifier is trained and tested with MMCRI datasets for protein coding region prediction for DNA sequences of lengths 252 and 354. The proposed classifier is trained and tested with promoter sequences from DBTSS (Yamashita et al., 2006) dataset and nonpromoters from EID (Saxonov et al., 2000) and UTRdb (Pesole et al., 2002) datasets. The proposed model can predict both regions with an average accuracy of 90.5% for promoter and 89.6% for protein coding region predictions. The specificity and sensitivity values of promoter and protein coding region predictions are 0.89 and 0.92, respectively. Kiran Sree Pokkuluri, Ramesh Babu Inampudi, and S. S. S. N. Usha Devi Nedunuri Copyright © 2014 Kiran Sree Pokkuluri et al. All rights reserved. Pharmacophore Modeling and Molecular Docking Studies on Pinus roxburghii as a Target for Diabetes Mellitus Thu, 10 Jul 2014 10:07:59 +0000 The present study attempts to establish a relationship between ethnopharmacological claims and bioactive constituents present in Pinus roxburghii against all possible targets for diabetes through molecular docking and to develop a pharmacophore model for the active target. The process of molecular docking involves study of different bonding modes of one ligand with active cavities of target receptors protein tyrosine phosphatase 1-beta (PTP-1β), dipeptidyl peptidase-IV (DPP-IV), aldose reductase (AR), and insulin receptor (IR) with help of docking software Molegro virtual docker (MVD). From the results of docking score values on different receptors for antidiabetic activity, it is observed that constituents, namely, secoisoresinol, pinoresinol, and cedeodarin, showed the best docking results on almost all the receptors, while the most significant results were observed on AR. Then, LigandScout was applied to develop a pharmacophore model for active target. LigandScout revealed that 2 hydrogen bond donors pointing towards Tyr 48 and His 110 are a major requirement of the pharmacophore generated. In our molecular docking studies, the active constituent, secoisoresinol, has also shown hydrogen bonding with His 110 residue which is a part of the pharmacophore. The docking results have given better insights into the development of better aldose reductase inhibitor so as to treat diabetes related secondary complications. Pawan Kaushik, Sukhbir Lal Khokra, A. C. Rana, and Dhirender Kaushik Copyright © 2014 Pawan Kaushik et al. All rights reserved. How Good Are Simplified Models for Protein Structure Prediction? Tue, 29 Apr 2014 07:15:44 +0000 Protein structure prediction (PSP) has been one of the most challenging problems in computational biology for several decades. The challenge is largely due to the complexity of the all-atomic details and the unknown nature of the energy function. Researchers have therefore used simplified energy models that consider interaction potentials only between the amino acid monomers in contact on discrete lattices. The restricted nature of the lattices and the energy models poses a twofold concern regarding the assessment of the models. Can a native or a very close structure be obtained when structures are mapped to lattices? Can the contact based energy models on discrete lattices guide the search towards the native structures? In this paper, we use the protein chain lattice fitting (PCLF) problem to address the first concern; we developed a constraint-based local search algorithm for the PCLF problem for cubic and face-centered cubic lattices and found very close lattice fits for the native structures. For the second concern, we use a number of techniques to sample the conformation space and find correlations between energy functions and root mean square deviation (RMSD) distance of the lattice-based structures with the native structures. Our analysis reveals weakness of several contact based energy models used that are popular in PSP. Swakkhar Shatabda, M. A. Hakim Newton, Mahmood A. Rashid, Duc Nghia Pham, and Abdul Sattar Copyright © 2014 Swakkhar Shatabda et al. All rights reserved. Elementary Flux Mode Analysis of Acetyl-CoA Pathway in Carboxydothermus hydrogenoformans Z-2901 Wed, 16 Apr 2014 08:04:40 +0000 Carboxydothermus hydrogenoformans is a carboxydotrophic hydrogenogenic bacterium species that produces hydrogen molecule by utilizing carbon monoxide (CO) or pyruvate as a carbon source. To investigate the underlying biochemical mechanism of hydrogen production, an elementary mode analysis of acetyl-CoA pathway was performed to determine the intermediate fluxes by combining linear programming (LP) method available in CellNetAnalyzer software. We hypothesized that addition of enzymes necessary for carbon monoxide fixation and pyruvate dissimilation would enhance the theoretical yield of hydrogen. An in silico gene knockout of pyk, pykC, and mdh genes of modeled acetyl-CoA pathway allows the maximum theoretical hydrogen yield of 47.62 mmol/gCDW/h for 1 mole of carbon monoxide (CO) uptake. The obtained hydrogen yield is comparatively two times greater than the previous experimental data. Therefore, it could be concluded that this elementary flux mode analysis is a crucial way to achieve efficient hydrogen production through acetyl-CoA pathway and act as a model for strain improvement. Rajadurai Chinnasamy Perumal, Ashok Selvaraj, and Gopal Ramesh Kumar Copyright © 2014 Rajadurai Chinnasamy Perumal et al. All rights reserved. Objective and Comprehensive Evaluation of Bisulfite Short Read Mapping Tools Tue, 15 Apr 2014 16:28:46 +0000 Background. Large-scale bisulfite treatment and short reads sequencing technology allow comprehensive estimation of methylation states of Cs in the genomes of different tissues, cell types, and developmental stages. Accurate characterization of DNA methylation is essential for understanding genotype phenotype association, gene and environment interaction, diseases, and cancer. Aligning bisulfite short reads to a reference genome has been a challenging task. We compared five bisulfite short read mapping tools, BSMAP, Bismark, BS-Seeker, BiSS, and BRAT-BW, representing two classes of mapping algorithms (hash table and suffix/prefix tries). We examined their mapping efficiency (i.e., the percentage of reads that can be mapped to the genomes), usability, running time, and effects of changing default parameter settings using both real and simulated reads. We also investigated how preprocessing data might affect mapping efficiency. Conclusion. Among the five programs compared, in terms of mapping efficiency, Bismark performs the best on the real data, followed by BiSS, BSMAP, and finally BRAT-BW and BS-Seeker with very similar performance. If CPU time is not a constraint, Bismark is a good choice of program for mapping bisulfite treated short reads. Data quality impacts a great deal mapping efficiency. Although increasing the number of mismatches allowed can increase mapping efficiency, it not only significantly slows down the program, but also runs the risk of having increased false positives. Therefore, users should carefully set the related parameters depending on the quality of their sequencing data. Hong Tran, Jacob Porter, Ming-an Sun, Hehuang Xie, and Liqing Zhang Copyright © 2014 Hong Tran et al. All rights reserved.