Biomedical subjects

Kun-Mao Chao

Publications and source records attributed to Kun-Mao Chao.

9 recordsLinked to original sources

PubMed2006

Distant HNF1 site as a master control for the human class I alcohol dehydrogenase gene expression.

Gene duplication and divergence have contributed to the biochemical diversity of the alcohol dehydrogenase (ADH) family. Class I ADH is the major enzyme that catalyzes alcohol to acetaldehyde in the liver. To investigate the mechanism(s) controlling tissue-specific and temporal regulation of the three human class I ADH genes (ADH1A, ADH1B, and ADH1C), we compared genomic sequences for the human and mouse ADH loci and analyzed human ADH gene expression in BAC transgenic mice carrying different lengths of the upstream sequences of the class I ADH. A conserved noncoding sequence, located between the class I and class IV ADH (ADH7) genes, was found to be essential for directing class I ADH gene expression in fetal and adult livers. Within this region, a 275-bp fragment displaying liver-specific DNase I hypersensitivity was bound by HNF1. The HNF1-containing upstream sequence enhanced all three class I ADH promoters in an orientation-dependent manner, and the transcriptional activation depended on binding to the HNF1 site. Deletion of the conserved HNF1 site in the BAC led to the shutdown of human class I ADH gene expression in the transgenic livers, leaving ADH1C gene expression in the stomach unchanged. Moreover, interaction between the upstream element and the class I ADH gene promoters was demonstrated by chromosome conformation capture, suggesting a DNA looping mechanism is involved in gene activation. Taken together, our data indicate that HNF1 binding, at approximately 51 kb upstream, plays a master role in controlling human class I ADH gene expression and may govern alcohol metabolism in the liver.

Alcohol Dehydrogenase↗

PubMed2006

A greedier approach for finding tag SNPs.

MOTIVATION: Recent studies have shown that a small subset of Single Nucleotide Polymorphisms (SNPs) (called tag SNPs) is sufficient to capture the haplotype patterns in a high linkage disequilibrium region. To find the minimum set of tag SNPs, exact algorithms for finding the optimal solution could take exponential time. On the other hand, approximation algorithms are more efficient but may fail to find the optimal solution. RESULTS: We propose a hybrid method that combines the ideas of the branch-and-bound method and the greedy algorithm. This method explores larger solution space to obtain a better solution than a traditional greedy algorithm. It also allows the user to adjust the efficiency of the program and quality of solutions. This algorithm has been implemented and tested on a variety of simulated and biological data. The experimental results indicate that our program can find better solutions than previous methods. This approach is quite general since it can be used to adapt other greedy algorithms to solve their corresponding problems. AVAILABILITY: The program is available upon request.

Algorithms↗

PubMed2005

Selecting additional tag SNPs for tolerating missing data in genotyping.

BACKGROUND: Recent studies have shown that the patterns of linkage disequilibrium observed in human populations have a block-like structure, and a small subset of SNPs (called tag SNPs) is sufficient to distinguish each pair of haplotype patterns in the block. In reality, some tag SNPs may be missing, and we may fail to distinguish two distinct haplotypes due to the ambiguity caused by missing data. RESULTS: We show there exists a subset of SNPs (referred to as robust tag SNPs) which can still distinguish all distinct haplotypes even when some SNPs are missing. The problem of finding minimum robust tag SNPs is shown to be NP-hard. To find robust tag SNPs efficiently, we propose two greedy algorithms and one linear programming relaxation algorithm. The experimental results indicate that (1) the solutions found by these algorithms are quite close to the optimal solution; (2) the genotyping cost saved by using tag SNPs can be as high as 80%; and (3) genotyping additional tag SNPs for tolerating missing data is still cost-effective. CONCLUSION: Genotyping robust tag SNPs is more practical than just genotyping the minimum tag SNPs if we can not avoid the occurrence of missing data. Our theoretical analysis and experimental results show that the performance of our algorithms is not only efficient but the solution found is also close to the optimal solution.

Algorithms↗

PubMed2005

An approximation algorithm for haplotype inference by maximum parsimony.

This paper studies haplotype inference by maximum parsimony using population data. We define the optimal haplotype inference (OHI) problem as given a set of genotypes and a set of related haplotypes, find a minimum subset of haplotypes that can resolve all the genotypes. We prove that OHI is NP-hard and can be formulated as an integer quadratic programming (IQP) problem. To solve the IQP problem, we propose an iterative semidefinite programming-based approximation algorithm, (called SDPHapInfer). We show that this algorithm finds a solution within a factor of O(log n) of the optimal solution, where n is the number of genotypes. This algorithm has been implemented and tested on a variety of simulated and biological data. In comparison with three other methods, (1) HAPAR, which was implemented based on the branching and bound algorithm, (2) HAPLOTYPER, which was implemented based on the expectation-maximization algorithm, and (3) PHASE, which combined the Gibbs sampling algorithm with an approximate coalescent prior, the experimental results indicate that SDPHapInfer and HAPLOTYPER have similar error rates. In addition, the results generated by PHASE have lower error rates on some data but higher error rates on others. The error rates of HAPAR are higher than the others on biological data. In terms of efficiency, SDPHapInfer, HAPLOTYPER, and PHASE output a solution in a stable and consistent way, and they run much faster than HAPAR when the number of genotypes becomes large.

Algorithms↗

PubMed2004

Efficient combination of multiple word models for improved sequence comparison.

MOTIVATION: Studies of efficient and sensitive sequence comparison methods are driven by a need to find homologous regions of weak similarity between large genomes. RESULTS: We describe an improved method for finding similar regions between two sets of DNA sequences. The new method generalizes existing methods by locating word matches between sequences under two or more word models and extending word matches into high-scoring segment pairs (HSPs). The method is implemented as a computer program named DDS2. Experimental results show that DDS2 can find more HSPs by using several word models than by using one word model. AVAILABILITY: The DDS2 program is freely available for academic use in binary code form at http://bioinformatics.iastate.edu/aat/align/align.html and in source code form from the corresponding author.

Algorithms↗

PubMed2003

A generalized global alignment algorithm.

MOTIVATION: Homologous sequences are sometimes similar over some regions but different over other regions. Homologous sequences have a much lower global similarity if the different regions are much longer than the similar regions. RESULTS: We present a generalized global alignment algorithm for comparing sequences with intermittent similarities, an ordered list of similar regions separated by different regions. A generalized global alignment model is defined to handle sequences with intermittent similarities. A dynamic programming algorithm is designed to compute an optimal general alignment in time proportional to the product of sequence lengths and in space proportional to the sum of sequence lengths. The algorithm is implemented as a computer program named GAP3 (Global Alignment Program Version 3). The generalized global alignment model is validated by experimental results produced with GAP3 on both DNA and protein sequences. The GAP3 program extends the ability of standard global alignment programs to recognize homologous sequences of lower similarity. AVAILABILITY: The GAP3 program is freely available for academic use at http://bioinformatics.iastate.edu/aat/align/align.html.

Algorithms↗

PubMed2003

MAVG: locating non-overlapping maximum average segments in a given sequence.

SUMMARY: MAVG is a software tool for finding k non-overlapping maximum-average segments that are sufficiently long in a given sequence of real numbers, for any k > 0. It has applications in several areas of biomolecular sequence analysis including locating GC-rich regions and CpG islands in a genomic sequence, and annotating multiple sequence alignments. AVAILABILITY: http://iubio.bio.indiana.edu/soft/molbio/pattern/cpg_islands/.

Algorithms↗

PubMed2003

Constrained multiple sequence alignment tool development and its application to RNase family alignment.

In this paper, we design a heuristic algorithm of computing a constrained multiple sequence alignment (CMSA for short) for guaranteeing that the generated alignment satisfies the user-specified constraints that some particular residues should be aligned together. If the number of residues needed to be aligned together is a constant alpha, then the time-complexity of our CMSA algorithm for aligning K sequences is O(alphaKn(4)), where n is the maximum of the lengths of sequences. In addition, we have built up such a CMSA software system and made several experiments on the RNase sequences, which mainly function in catalyzing the degradation of RNA molecules. The resulting alignments illustrate the practicability of our method.

Algorithms↗

PubMed2002

Constrained multiple sequence alignment tool development and its application to RNase family alignment.

In this paper, we design an algorithm of computing a constrained multiple sequence alignment (CMSA for short) for guaranteeing that the generated alignment satisfies the user-specified constraints that some particular residues should be aligned together. If the number of residues needed to be aligned together is a constant alpha, then the time-complexity of our CMSA algorithm for aligning K sequences is O (alphaKn4), where n is the maximum of the lengths of sequences. In addition, we have build up such a CMSA software system and made several experiments on the RNase sequences, which mainly function in catalyzing the degradation of RNA molecules. The resulting alignments illustrate the practicability of our method.

Algorithms↗