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Displaying publications 21 - 31 of 31 in total

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Iterative local Gaussian clustering for expressed genes identification linked to malignancy of human colorectal carcinoma

Wasito I, Hashim SZ, Sukmaningrum S

Bioinformation, 2007 Dec 30;2(5):175-81.
PMID: 18305825

Gene expression profiling plays an important role in the identification of biological and clinical properties of human solid tumors such as colorectal carcinoma. Profiling is required to reveal underlying molecular features for diagnostic and therapeutic purposes. A non-parametric density-estimation-based approach called iterative local Gaussian clustering (ILGC), was used to identify clusters of expressed genes. We used experimental data from a previous study by Muro and others consisting of 1,536 genes in 100 colorectal cancer and 11 normal tissues. In this dataset, the ILGC finds three clusters, two large and one small gene clusters, similar to their results which used Gaussian mixture clustering. The correlation of each cluster of genes and clinical properties of malignancy of human colorectal cancer was analysed for the existence of tumor or normal, the existence of distant metastasis and the existence of lymph node metastasis.
Fulltext Structure and functional annotation of hypothetical proteins having putative Rubisco activase function from Vitis vinifera

Kumar S

Bioinformation, 2015;11(1):11-6.
PMID: 25780274 DOI: 10.6026/97320630011011

Rubisco is a very large, complex and one of the most abundant proteins in the world and comprises up to 50% of all soluble protein in plants. The activity of Rubisco, the enzyme that catalyzes CO2 assimilation in photosynthesis, is regulated by Rubisco activase (Rca). In the present study, we searched for hypothetical protein of Vitis vinifera which has putative Rubisco activase function. The Arabidopsis and tobacco Rubisco activase protein sequences were used as seed sequences to search against Vitis vinifera in UniprotKB database. The selected hypothetical proteins of Vitis vinifera were subjected to sequence, structural and functional annotation. Subcellular localization predictions suggested it to be cytoplasmic protein. Homology modelling was used to define the three-dimensional (3D) structure of selected hypothetical proteins of Vitis vinifera. Template search revealed that all the hypothetical proteins share more than 80% sequence identity with structure of green-type Rubisco activase from tobacco, indicating proteins are evolutionary conserved. The homology modelling was generated using SWISS-MODEL. Several quality assessment and validation parameters computed indicated that homology models are reliable. Further, functional annotation through PFAM, CATH, SUPERFAMILY, CDART suggested that selected hypothetical proteins of Vitis vinifera contain ATPase family associated with various cellular activities (AAA) and belong to the AAA+ super family of ring-shaped P-loop containing nucleoside triphosphate hydrolases. This study will lead to research in the optimization of the functionality of Rubisco which has large implication in the improvement of plant productivity and resource use efficiency.
An improved hybrid of SVM and SCAD for pathway analysis

Misman MF, Mohamad MS, Deris S, Abdullah A, Hashim SZ

Bioinformation, 2011;7(4):169-75.
PMID: 22102773

Pathway analysis has lead to a new era in genomic research by providing further biological process information compared to traditional single gene analysis. Beside the advantage, pathway analysis provides some challenges to the researchers, one of which is the quality of pathway data itself. The pathway data usually defined from biological context free, when it comes to a specific biological context (e.g. lung cancer disease), typically only several genes within pathways are responsible for the corresponding cellular process. It also can be that some pathways may be included with uninformative genes or perhaps informative genes were excluded. Moreover, many algorithms in pathway analysis neglect these limitations by treating all the genes within pathways as significant. In previous study, a hybrid of support vector machines and smoothly clipped absolute deviation with groups-specific tuning parameters (gSVM-SCAD) was proposed in order to identify and select the informative genes before the pathway evaluation process. However, gSVM-SCAD had showed a limitation in terms of the performance of classification accuracy. In order to deal with this limitation, we made an enhancement to the tuning parameter method for gSVM-SCAD by applying the B-Type generalized approximate cross validation (BGACV). Experimental analyses using one simulated data and two gene expression data have shown that the proposed method obtains significant results in identifying biologically significant genes and pathways, and in classification accuracy.
EuDBase: An online resource for automated EST analysis pipeline (ESTFrontier) and database for red seaweed Eucheuma denticulatum

Hussein ZA, Loke KK, Abidin RA, Othman R

Bioinformation, 2011;7(4):157-62.
PMID: 22102771

Functional genomics has proven to be an efficient tool in identifying genes involved in various biological functions. However the availability of commercially important seaweed Eucheuma denticulatum functional resources is still limited. EuDBase is the first seaweed online repository that provides integrated access to ESTs of Eucheuma denticulatum generated from samples collected from Kudat and Semporna in Sabah, Malaysia. The database stored 10,031 ESTs that are clustered and assembled into 2,275 unique transcripts (UT) and 955 singletons. Raw data were automatically processed using ESTFrontier, an in-house automated EST analysis pipeline. Data was collected in MySQL database. Web interface is implemented using PHP and it allows browsing and querying EuDBase through search engine. Data is searchable via BLAST hit, domain search, Gene Ontology or KEGG Pathway. A user-friendly interface allows the identification of sequences either using a simple text query or similarity search. The development of EuDBase is initiated to store, manage and analyze the E. denticulatum ESTs and to provide accumulative digital resources for the use of global scientific community. EuDBase is freely available from http://www.inbiosis.ukm.my/eudbase/.
Random forest for gene selection and microarray data classification

Moorthy K, Mohamad MS

Bioinformation, 2011;7(3):142-6.
PMID: 22125385

A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods.
Fulltext Comparative sequence-structure analysis of Aves insulin

Islam MM, Aktaruzzaman M, Mohamed Z

Bioinformation, 2015;11(2):67-72.
PMID: 25848166 DOI: 10.6026/97320630011067

Normal blood glucose level depends on the availability of insulin and its ability to bind insulin receptor (IR) that regulates the downstream signaling pathway. Insulin sequence and blood glucose level usually vary among animals due to species specificity. The study of genetic variation of insulin, blood glucose level and diabetics symptoms development in Aves is interesting because of its optimal high blood glucose level than mammals. Therefore, it is of interest to study its evolutionary relationship with other mammals using sequence data. Hence, we compiled 32 Aves insulin from GenBank to compare its sequence-structure features with phylogeny for evolutionary inference. The analysis shows long conserved motifs (about 14 residues) for functional inference. These sequences show high leucine content (20%) with high instability index (>40). Amino acid position 11, 14, 16 and 20 are variable that may have contribution to binding to IR. We identified functionally critical variable residues in the dataset for possible genetic implication. Structural models of these sequences were developed for surface analysis towards functional representation. These data find application in the understanding of insulin function across species.
Fulltext Artificial signal peptide prediction by a hidden markov model to improve protein secretion via Lactococcus lactis bacteria

Razmara J, Deris SB, Illias RB, Parvizpour S

Bioinformation, 2013;9(7):345-8.
PMID: 23750078 DOI: 10.6026/97320630009345

A hidden Markov model (HMM) has been utilized to predict and generate artificial secretory signal peptide sequences. The strength of signal peptides of proteins from different subcellular locations via Lactococcus lactis bacteria correlated with their HMM bit scores in the model. The results show that the HMM bit score +12 are determined as the threshold for discriminating secreteory signal sequences from the others. The model is used to generate artificial signal peptides with different bit scores for secretory proteins. The signal peptide with the maximum bit score strongly directs proteins secretion.
A decision tree model for the prediction of homodimer folding mechanism

Suresh A, Karthikraja V, Lulu S, Kangueane U, Kangueane P

Bioinformation, 2009 Nov 17;4(5):197-205.
PMID: 20461159

The formation of protein homodimer complexes for molecular catalysis and regulation is fascinating. The homodimer formation through 2S (2 state), 3SMI (3 state with monomer intermediate) and 3SDI (3 state with dimer intermediate) folding mechanism is known for 47 homodimer structures. Our dataset of forty-seven homodimers consists of twenty-eight 2S, twelve 3SMI and seven 3SDI. The dataset is characterized using monomer length, interface area and interface/total (I/T) residue ratio. It is found that 2S are often small in size with large I/T ratio and 3SDI are frequently large in size with small I/T ratio. Nonetheless, 3SMI have a mixture of these features. Hence, we used these parameters to develop a decision tree model. The decision tree model produced positive predictive values (PPV) of 72% for 2S, 58% for 3SMI and 57% for 3SDI in cross validation. Thus, the method finds application in assigning homodimers with folding mechanism.
Fulltext Three-dimensional (3D) structure prediction of the American and African oil-palms β-ketoacyl-[ACP] synthase-II protein by comparative modelling

Wang E, Chinni S, Bhore SJ

Bioinformation, 2014;10(3):130-7.
PMID: 24748752 DOI: 10.6026/97320630010130

BACKGROUND: The fatty-acid profile of the vegetable oils determines its properties and nutritional value. Palm-oil obtained from the African oil-palm [Elaeis guineensis Jacq. (Tenera)] contains 44% palmitic acid (C16:0), but, palm-oil obtained from the American oilpalm [Elaeis oleifera] contains only 25% C16:0. In part, the b-ketoacyl-[ACP] synthase II (KASII) [EC: 2.3.1.179] protein is responsible for the high level of C16:0 in palm-oil derived from the African oil-palm. To understand more about E. guineensis KASII (EgKASII) and E. oleifera KASII (EoKASII) proteins, it is essential to know its structures. Hence, this study was undertaken.
OBJECTIVE: The objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools.
MATERIALS AND METHODS: The amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated.
RESULTS: The homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate.
CONCLUSION: The 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates.
Fulltext Computer aided analysis of disease linked protein networks

Sabetian S, Shamsir MS

Bioinformation, 2019;15(7):513-522.
PMID: 31485137 DOI: 10.6026/97320630015513

Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of complex biological systems including network pharmacology and disease network has also been discussed in this review.
Ethics on academic procurement of cadavers

Bala Ganesh KA, Panda P, Gurawa T, Gopalakrishna PK, Jagadeesan S, Vishnumukkala T

Bioinformation, 2024;20(8):872-876.
PMID: 39411772 DOI: 10.6026/973206300200872

Anatomy is a vital discipline in the realm of Medicine, and its primary means of study is through the use of cadavers. Experts in the medical, legal, and ethical domains have discussed the use of cadavers or their anatomical parts for educational purposes, which are considered a severe drawback. The advantages of using cadavers include their contribution to medical education, research, the investigation of innovative surgical procedures or techniques, the detection of anatomical differences at an individual or population level, the enhancement of surgical skills, and the support of other anatomical investigations. This review highlights the issues like consent, respect for the deceased and cultural beliefs on cadaver procurement practices and also it emphasizes the need for better body donation initiatives and public awareness campaigns to ensure sustainable cadaver procurement practices, ensuring consent, respect for the deceased and cultural beliefs.