Biomedical subjects

Kuo-Bin Li

Publications and source records attributed to Kuo-Bin Li.

17 recordsLinked to original sources

PubMed2006

iHAP--integrated haplotype analysis pipeline for characterizing the haplotype structure of genes.

BACKGROUND: The advent of genotype data from large-scale efforts that catalog the genetic variants of different populations have given rise to new avenues for multifactorial disease association studies. Recent work shows that genotype data from the International HapMap Project have a high degree of transferability to the wider population. This implies that the design of genotyping studies on local populations may be facilitated through inferences drawn from information contained in HapMap populations. RESULTS: To facilitate analysis of HapMap data for characterizing the haplotype structure of genes or any chromosomal regions, we have developed an integrated web-based resource, iHAP. In addition to incorporating genotype and haplotype data from the International HapMap Project and gene information from the UCSC Genome Browser Database, iHAP also provides capabilities for inferring haplotype blocks and selecting tag SNPs that are representative of haplotype patterns. These include block partitioning algorithms, block definitions, tag SNP definitions, as well as SNPs to be "force included" as tags. Based on the parameters defined at the input stage, iHAP performs on-the-fly analysis and displays the result graphically as a webpage. To facilitate analysis, intermediate and final result files can be downloaded. CONCLUSION: The iHAP resource, available at http://ihap.bii.a-star.edu.sg, provides a convenient yet flexible approach for the user community to analyze HapMap data and identify candidate targets for genotyping studies.

Algorithms↗

PubMed2006

How well do HapMap SNPs capture the untyped SNPs?

BACKGROUND: The recent advancement in human genome sequencing and genotyping has revealed millions of single nucleotide polymorphisms (SNP) which determine the variation among human beings. One of the particular important projects is The International HapMap Project which provides the catalogue of human genetic variation for disease association studies. In this paper, we analyzed the genotype data in HapMap project by using National Institute of Environmental Health Sciences Environmental Genome Project (NIEHS EGP) SNPs. We first determine whether the HapMap data are transferable to the NIEHS data. Then, we study how well the HapMap SNPs capture the untyped SNPs in the region. Finally, we provide general guidelines for determining whether the SNPs chosen from HapMap may be able to capture most of the untyped SNPs. RESULTS: Our analysis shows that HapMap data are not robust enough to capture the untyped variants for most of the human genes. The performance of SNPs for European and Asian samples are marginal in capturing the untyped variants, i.e. approximately 55%. Expectedly, the SNPs from HapMap YRI panel can only capture approximately 30% of the variants. Although the overall performance is low, however, the SNPs for some genes perform very well and are able to capture most of the variants along the gene. This is observed in the European and Asian panel, but not in African panel. Through observation, we concluded that in order to have a well covered SNPs reference panel, the SNPs density and the association among reference SNPs are important to estimate the robustness of the chosen SNPs. CONCLUSION: We have analyzed the coverage of HapMap SNPs using NIEHS EGP data. The results show that HapMap SNPs are transferable to the NIEHS SNPs. However, HapMap SNPs cannot capture some of the untyped SNPs and therefore resequencing may be needed to uncover more SNPs in the missing region.

Asian People↗

PubMed2005

Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines.

BACKGROUND: Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in Gram-negative bacteria obtained good results. However, these methods had relatively low accuracies for the localization of extracellular proteins. This paper studies ways to improve the accuracy for predicting extracellular localization in Gram-negative bacteria. RESULTS: We have developed a system for predicting the subcellular localization of proteins for Gram-negative bacteria based on amino acid subalphabets and a combination of multiple support vector machines. The recall of the extracellular site and overall recall of our predictor reach 86.0% and 89.8%, respectively, in 5-fold cross-validation. To the best of our knowledge, these are the most accurate results for predicting subcellular localization in Gram-negative bacteria. CONCLUSION: Clustering 20 amino acids into a few groups by the proposed greedy algorithm provides a new way to extract features from protein sequences to cover more adjacent amino acids and hence reduce the dimensionality of the input vector of protein features. It was observed that a good amino acid grouping leads to an increase in prediction performance. Furthermore, a proper choice of a subset of complementary support vector machines constructed by different features of proteins maximizes the prediction accuracy.

Algorithms↗

PubMed2005

pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties.

BACKGROUND: Protein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. However, such approaches lead to a loss of contextual information. Moreover, where information about the physicochemical properties of amino acids has been used, the methods employed to exploit that information are less than optimal and could use the information more effectively. RESULTS: In this paper, we propose a new algorithm called pSLIP which uses Support Vector Machines (SVMs) in conjunction with multiple physicochemical properties of amino acids to predict protein subcellular localization in eukaryotes across six different locations, namely, chloroplast, cytoplasmic, extracellular, mitochondrial, nuclear and plasma membrane. The algorithm was applied to the dataset provided by Park and Kanehisa and we obtained prediction accuracies for the different classes ranging from 87.7%-97.0% with an overall accuracy of 93.1%. CONCLUSION: This study presents a physicochemical property based protein localization prediction algorithm. Unlike other algorithms, contextual information is preserved by dividing the protein sequences into clusters. The prediction accuracy shows an improvement over other algorithms based on various types of amino acid composition (single, pair and gapped pair). We have also implemented a web server to predict protein localization across the six classes (available at http://pslip.bii.a-star.edu.sg/).

Algorithms↗

PubMed2005

WebAllergen: a web server for predicting allergenic proteins.

UNLABELLED: WebAllergen is a web server that predicts the potential allergenicity of proteins. The query protein will be compared against a set of prebuilt allergenic motifs that have been obtained from 664 known allergen proteins. The query will also be compared with known allergens that do not have detectable allergenic motifs. Moreover, users are allowed to upload their own allergens as alternative training sequences on which a new set of allergenic motifs will be built. The query sequences can also be compared with these motifs. AVAILABILITY: http://weballergen.bii.a-star.edu.sg/

Algorithms↗

PubMed2005

Immunohistochemical detection of Ki67 in breast cancer correlates with transcriptional regulation of genes related to apoptosis and cell death.

Ki67 is a nuclear protein that is tightly linked to the cell cycle. It is a marker of cell proliferation and has been used to stratify good and poor prognostic categories in invasive breast cancer. Its correlation with gene expression patterns has not been fully elucidated. In this study, Ki67 immunohistochemistry using the MIB-1 antibody was performed on sections cut from 21 formalin-fixed, paraffin-embedded invasive breast cancers. Scoring was determined as nil (no immunostaining), low (10% or less immunopositivity) or high (>10% immunoreactive cells) respectively. The relationship of Ki67 immunohistochemical detection with clinicopathologic parameters was evaluated. Using Affymetrix U133A GeneChips, expression profiles for these tumors were generated and correlated with Ki67 immunohistochemical findings. Analysis of variance was used to define genes that were differentially regulated between the groups. Real-time polymerase chain reaction (PCR) was used to confirm the presence of a downregulated gene. Our results showed high, low and nil Ki67 immunostaining in nine (43%), six (28.5%) and six (28.5%) invasive breast cancers respectively, with increased Ki67 protein expression correlating with high histologic grade (P=0.02), mitotic score (P=0.001) and estrogen receptor immunonegativity (P=0.002). Expression profiling trends of the Ki67 gene mirrored the observed proportions of immunostained cells when the Ki67 immunoscore was >10%. Genes related to apoptosis and cell death (bcl2, MAP2K4, TNF10) were noted to be downregulated in tumors that disclosed >40% Ki67 immunostaining (P<0.001). Downregulation of the bcl2 gene was confirmed at the RNA level by real-time RT-PCR. Differential regulation of these genes, especially bcl2, may contribute to the biological nature of clinically more aggressive and highly proliferative breast cancers.

Adult↗

PubMed2005

A tabu search algorithm for post-processing multiple sequence alignment.

Tabu search is a meta-heuristic approach that is proven to be useful in solving combinatorial optimization problems. We implement the adaptive memory features of tabu search to refine a multiple sequence alignment. Adaptive memory helps the search process to avoid local optima and explores the solution space economically and effectively without getting trapped into cycles. The algorithm is further enhanced by introducing extended tabu search features such as intensification and diversification. The neighborhoods of a solution are generated stochastically and a consistency-based objective function is employed to measure its quality. The algorithm is tested with the datasets from BAliBASE benchmarking database. We have observed through experiments that tabu search is able to improve the quality of multiple alignments generated by other software such as ClustalW and T-Coffee. The source code of our algorithm is available at http://www.bii.a-star.edu.sg/~tariq/tabu/.

Algorithms↗

PubMed2005

Multiple sequence alignment using an exhaustive and greedy algorithm.

We describe an exhaustive and greedy algorithm for improving the accuracy of multiple sequence alignment. A simple progressive alignment approach is employed to provide initial alignments. The initial alignment is then iteratively optimized against an objective function. For any working alignment, the optimization involves three operations: insertions, deletions and shuffles of gaps. The optimization is exhaustive since the algorithm applies the above operations to all eligible positions of an alignment. It is also greedy since only the operation that gives the best improving objective score will be accepted. The algorithms have been implemented in the EGMA (Exhaustive and Greedy Multiple Alignment) package using Java programming language, and have been evaluated using the BAliBASE benchmark alignment database. Although EGMA is not guaranteed to produce globally optimized alignment, the tests indicate that EGMA is able to build alignments with high quality consistently, compared with other commonly used iterative and non-iterative alignment programs. It is also useful for refining multiple alignments obtained by other methods.

Algorithms↗

PubMed2005

Y-box binding protein, YB-1, as a marker of tumor aggressiveness and response to adjuvant chemotherapy in breast cancer.

The Y-box binding protein 1 (YB-1) regulates gene expression through transcription and translation. YB-1 has been shown to be associated with up-regulation of P-glycoprotein (Pgp), an ATP-binding transporter involved in multi-drug resistance. In this study, we determined the prognostic significance of YB-1 and its relationship with Pgp in patients with breast cancer. YB-1 and Pgp expression were evaluated by immunohistochemistry in resected specimens of infiltrative ductal breast cancers from 99 patients and 57 patients respectively and correlated with clinicopathological parameters and adjuvant chemotherapy regimes. The antibody for the YB-1 protein was prepared by injecting a rabbit with a purified recombinant chicken YB1 protein. The relationship between YB-1 and Pgp was also evaluated by a computational approach using the Resonant Recognition Model (RRM). We found that breast tumors which were both estrogen receptor-negative and lymph node positive were associated with high YB-1 expression (P=0.017). In patients who did not receive adjuvant chemotherapy, recurrence risk was reduced in breast cancers having lower YB-1 expression (P=0.034), suggesting that high levels of YB-1 expression in breast cancer is associated with tumor aggressiveness. We were able to demonstrate a direct interaction between YB-1 and Pgp using the computer-based RRM. Interestingly, we found that patients who were on a chemotherapy regime which contained an anthracycline (a Pgp substrate) and subsequently developed recurrence, had a higher YB-1 score compared to patients on the Cyclophosphamide/Methotrexate/5-Fluorouracil regime (P=0.024). YB-1 expression in breast cancer may be a potential marker of chemoresistance and could possibly aid in selection of the appropriate adjuvant chemotherapy regime for breast cancers.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2005

Natural antisense as potential regulator of alternative initiation, splicing and termination.

In humans an estimated 35-60% of genes are alternatively spliced. A large number of genes also show alternative initiation or termination. Regulation of these processes is still poorly understood. For alternative splicing it is believed that the relative concentration of certain proteins and the presence of certain regulatory elements are the key factors determining alterations in splicing pattern. However, there is evidence that antisense RNA might be part of the regulatory processes. Antisense RNA molecules could bind to the target pre-mRNA in a sequence-specific fashion, sterically blocking targeted splice sites and redirecting the spliceosome to available and unhindered splice sites. Here we describe an in silico investigation to identify human sense/antisense pairs with alternative initiation or termination in the sense gene and where only one of the isoforms overlaps the antisense transcript. Alternatively spliced genes with antisense transcripts covering the alternatively used splice site are also identified. Our analyses are based on the ASAP splicing annotation database from UCLA, the antisense transcripts data from Yelin et al., 2003, and the H-invitational full-length cDNA database from JBIRC, Japan. These data gives new insight into the complexity of genomic organization and provide candidate loci for experimentalists to study antisense mediated regulation of alternative initiation, splicing and termination. Our result contains 468 clusters with this characteristic genomic organization and can be found at http://aistar.bii.a-star.edu.sg/.

Alternative Splicing↗

PubMed2005

CMDWave: conserved motifs detection using wavelets.

CMDWave (Conserved Motif Detection using WAVElets) is a web server that predicts conserved motifs in protein sequences. A set of query protein sequences are first aligned using ClustalW to obtain equal sized sequences. CMDWave then converts the sequences into a numerical representation using electron-ion interaction potential (EIIP). This is followed by a wavelet decomposition and reconstruction. A new similarity metric along with thresholding is then used to identify conserved motifs across all the query sequences. Users need not specify the number of motifs to be identified. For larger groups of sequences, results can be emailed to the users.

Amino Acid Motifs↗

PubMed2004

Predicting allergenic proteins using wavelet transform.

MOTIVATION: With many transgenic proteins introduced today, the ability to predict their potential allergenicity has become an important issue. Previous studies were based on either sequence similarity or the protein motifs identified from known allergen databases. The similarity-based approaches, although being able to produce high recalls, usually have low prediction precisions. Previous motif-based approaches have been shown to be able to improve the precisions on cross-validation experiments. In this study, a system that combines the advantages of similarity-based and motif-based prediction is described. RESULTS: The new prediction system uses a clustering algorithm that groups the known allergenic proteins into clusters. Proteins within each cluster are assumed to carry one or more common motifs. After a multiple sequence alignment, proteins in each cluster go through a wavelet analysis program whereby conserved motifs will be identified. A hidden Markov model (HMM) profile will then be prepared for each identified motif. The allergens that do not appear to carry detectable allergen motifs will be saved in a small database. The allergenicity of an unknown protein may be predicted by comparing it against the HMM profiles, and, if no matching profiles are found, against the small allergen database by BLASTP. Over 70% of recall and over 90% of precision were observed using cross-validation experiments. Using the entire Swiss-Prot as the query, we predicted about 2000 potential allergens. AVAILABILITY: The software is available upon request from the authors.

Algorithms↗

PubMed2004

G-PRIMER: greedy algorithm for selecting minimal primer set.

UNLABELLED: G-PRIMER, a web-based primer design program, has been developed to compute a minimal primer set specifically annealed to all the open reading frames in a given microbial genome. This program has been successfully used in the microarray experiment for analyzing the expression of genes in the Xanthomonas campestris genome. AVAILABILITY: It is available at http://mammoth.bii.a-star.edu.sg/gprimer/. Its source code is available upon request.

Algorithms↗

PubMed2004

An adaptive and iterative algorithm for refining multiple sequence alignment.

Multiple sequence alignment is a basic tool in computational genomics. The art of multiple sequence alignment is about placing gaps. This paper presents a heuristic algorithm that improves multiple protein sequences alignment iteratively. A consistency-based objective function is used to evaluate the candidate moves. During the iterative optimization, well-aligned regions can be detected and kept intact. Columns of gaps will be inserted to assist the algorithm to escape from local optimal alignments. The algorithm has been evaluated using the BAliBASE benchmark alignment database. Results show that the performance of the algorithm does not depend on initial or seed alignments much. Given a perfect consistency library, the algorithm is able to produce alignments that are close to the global optimum. We demonstrate that the algorithm is able to refine alignments produced by other software, including ClustalW, SAGA and T-COFFEE. The program is available upon request.

Algorithms↗

PubMed2004

Rapid detection of conserved regions in protein sequences using wavelets.

We present an algorithm to detect protein sub-structural motifs from primary sequence. The input to the algorithm is a set of aligned multiple protein sequences. It uses wavelet transforms to decompose protein sequences represented numerically by different indices (such as polarity, accessible surface area or electron-ion integration potentials of the amino acids). The numerical representation of a protein sequence has significant correlation with its biological activity, thus common motifs are expected to be observable from the wavelet spectrum. The decomposed signals are then up-sampled and similarity search techniques are used to identify similar regions across all the proteins at multiple scales. Results indicate that wavelet transform techniques are a promising approach for rapid motif detection.

Algorithms↗

PubMed2003

ClustalW-MPI: ClustalW analysis using distributed and parallel computing.

ClustalW is a tool for aligning multiple protein or nucleotide sequences. The alignment is achieved via three steps: pairwise alignment, guide-tree generation and progressive alignment. ClustalW-MPI is a distributed and parallel implementation of ClustalW. All three steps have been parallelized to reduce the execution time. The software uses a message-passing library called MPI (Message Passing Interface) and runs on distributed workstation clusters as well as on traditional parallel computers.

Amino Acid Sequence↗

PubMed2002

Modern spectrum analysis in multidimensional NMR spectroscopy: comparison of linear-prediction extrapolation and maximum-entropy reconstruction.

NMR spectroscopy is an inherently insensitive technique, and many challenging applications such as biomolecular studies operate at the very limits of sensitivity and resolution. Advances in superconducting magnet, cryogenic probe, and pulse sequence technologies have resulted in dramatic improvements in both sensitivity and resolution in the past decade. Conversely, the signal-processing method used most widely in NMR spectroscopy, extrapolation of the time domain signal by linear prediction (LP) followed by discrete Fourier transformation (DFT), was developed in the early 1980s and has not been subjected to detailed scrutiny for its impact on sensitivity and resolution. Here we report the first systematic investigation of the accuracy and precision of spectra obtained by LP extrapolation followed by DFT. We compare the results to spectra obtained by maximum-entropy (MaxEnt) reconstruction, which was developed contemporaneously to LP extrapolation but is not widely employed in NMR spectroscopy. Although it reduces truncation artifacts and increases the amplitudes of strong peaks, we find that LP extrapolation generates false-positive peaks and introduces frequency errors. These defects of LP extrapolation become less pronounced for longer data records and higher signal-to-noise ratio. MaxEnt generally yields more detectable peaks for a given number of data samples, more accurate peak frequencies, and fewer false-positive peaks than LP extrapolation. MaxEnt also permits the use of nonlinear sampling, which can give dramatic improvements in resolution. These results show that the use of MaxEnt together with nonlinear sampling, rather than LP extrapolation, could reduce the amount of instrument time required for adequate sensitivity and resolution by a factor of 2 or more.

Fourier Analysis↗