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Hongyu Zhao

Publications and source records attributed to Hongyu Zhao.

At least 55 records · Page 3Linked to original sources
PubMed2005

Detection of DNA copy number alterations using penalized least squares regression.

MOTIVATION: Genomic DNA copy number alterations are characteristic of many human diseases including cancer. Various techniques and platforms have been proposed to allow researchers to partition the whole genome into segments where copy numbers change between contiguous segments, and subsequently to quantify DNA copy number alterations. In this paper, we incorporate the spatial dependence of DNA copy number data into a regression model and formalize the detection of DNA copy number alterations as a penalized least squares regression problem. In addition, we use a stationary bootstrap approach to estimate the statistical significance and false discovery rate. RESULTS: The proposed method is studied by simulations and illustrated by an application to an extensively analyzed dataset in the literature. The results show that the proposed method can correctly detect the numbers and locations of the true breakpoints while appropriately controlling the false positives. AVAILABILITY: http://bioinformatics.med.yale.edu/DNACopyNumber CONTACT: hongyu.zhao@yale.edu SUPPLEMENTARY INFORMATION: http://bioinformatics.med.yale.edu/DNACopyNumber.

Algorithms↗
PubMed2005

Negative correlation between compositional symmetries and local recombination rates.

Although still not much understood, the universal reverse complement symmetry in genomes may contain much information about the genome. In this article, under the hypothesis that recombination rate variations may be related to the high order DNA structure, we studied the association between local recombination rates and local symmetry levels in mouse, rat and human. We found significant negative correlations between recombination rates and reverse complement compositional symmetries in these three organisms. This negative correlation pattern also held at individual chromosome levels when data only from each individual chromosome was analyzed.

Animals↗
PubMed2005

Inferring protein-protein interactions through high-throughput interaction data from diverse organisms.

MOTIVATION: Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities. RESULTS: We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism. AVAILABILITY: The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.

Binding Sites↗
PubMed2005

Are scale-free networks robust to measurement errors?

BACKGROUND: Many complex random networks have been found to be scale-free. Existing literature on scale-free networks has rarely considered potential false positive and false negative links in the observed networks, especially in biological networks inferred from high-throughput experiments. Therefore, it is important to study the impact of these measurement errors on the topology of the observed networks. RESULTS: This article addresses the impact of erroneous links on network topological inference and explores possible error mechanisms for scale-free networks with an emphasis on Saccharomyces cerevisiae protein interaction networks. We study this issue by both theoretical derivations and simulations. We show that the ignorance of erroneous links in network analysis may lead to biased estimates of the scale parameter and recommend robust estimators in such scenarios. Possible error mechanisms of yeast protein interaction networks are explored by comparisons between real data and simulated data. CONCLUSION: Our studies show that, in the presence of erroneous links, the connectivity distribution of scale-free networks is still scale-free for the middle range connectivities, but can be greatly distorted for low and high connecitivities. It is more appropriate to use robust estimators such as the least trimmed mean squares estimator to estimate the scale parameter gamma under such circumstances. Moreover, we show by simulation studies that the scale-free property is robust to some error mechanisms but untenable to others. The simulation results also suggest that different error mechanisms may be operating in the yeast protein interaction networks produced from different data sources. In the MIPS gold standard protein interaction data, there appears to be a high rate of false negative links, and the false negative and false positive rates are more or less constant across proteins with different connectivities. However, the error mechanism of yeast two-hybrid data may be very different, where the overall false negative rate is low and the false negative rates tend to be higher for links involving proteins with more interacting partners.

Algorithms↗
PubMed2005

Serum protein markers for early detection of ovarian cancer.

Early diagnosis of epithelial ovarian cancer (EOC) would significantly decrease the morbidity and mortality from this disease but is difficult in the absence of physical symptoms. Here, we report a blood test, based on the simultaneous quantization of four analytes (leptin, prolactin, osteopontin, and insulin-like growth factor-II), that can discriminate between disease-free and EOC patients, including patients diagnosed with stage I and II disease, with high efficiency (95%). Microarray analysis was used initially to determine the levels of 169 proteins in serum from 28 healthy women, 18 women newly diagnosed with EOC, and 40 women with recurrent disease. Evaluation of proteins that showed significant differences in expression between controls and cancer patients by ELISA assays yielded the four analytes. These four proteins then were evaluated in a blind cross-validation study by using an additional 106 healthy females and 100 patients with EOC (24 stage I/II and 76 stage III/IV). Upon sample decoding, the results were analyzed by using three different classification algorithms and a binary code methodology. The four-analyte test was further validated in a blind binary code study by using 40 additional serum samples from normal and EOC cancer patients. No single protein could completely distinguish the cancer group from the healthy controls. However, the combination of the four analytes exhibited the following: sensitivity 95%, positive predictive value (PPV) 95%, specificity 95%, and negative predictive value (NPV) 94%, a considerable improvement on current methodology.

Aged↗
PubMed2005

A tiling microarray expression analysis of rice chromosome 4 suggests a chromosome-level regulation of transcription.

The complete genome sequence of cultivated rice (Oryza sativa) provides an unprecedented opportunity to understand the biology of this model cereal. An essential and necessary step in this effort is the determination of the coding information and expression patterns of each sequenced chromosome. Here, we report an analysis of the transcriptional activity of rice chromosome 4 using a tiling path microarray based on PCR-generated genomic DNA fragments. Six representative rice organ types were examined using this microarray to catalog the transcribed regions of rice chromosome 4 and to reveal organ- and developmental stage-specific transcription patterns. This analysis provided expression support for 82% of the gene models in the chromosome. Transcriptional activities in 1643 nonannotated regions were also detected. Comparison with cytologically defined chromatin features indicated that in juvenile-stage rice the euchromatic region is more actively transcribed than is the transposon-rich heterochromatic portion of the chromosome. Interestingly, increased transcription of transposon-related gene models in certain heterochromatic regions was observed in mature-stage rice organs and in suspension-cultured cells. These results suggest a close correlation between transcriptional activity and chromosome organization and the developmental regulation of transcription activity at the chromosome level.

Chromosome Mapping↗
PubMed2005

Gene expression analysis reveals that histone deacetylation sites may serve as partitions of chromatin gene expression domains.

BACKGROUND: It has been a long-term puzzle whether chromatin can be further divided into distinct gene expression domains. Because histone deacetylation affects chromatin structure, that in turn may affect the expression of nearby genes, histone deacetylation sites may act to partition chromatin into different gene expression domains. In this article, we explore the relationship between histone deacetylation sites and gene expression patterns on the genome scale using different data sources, including microarray data measuring gene expression levels, microarray data measuring histone deacetylation sites, and information on regulatory targets of transcription factors. RESULTS: Using 269 Saccharomyces cerevisiae microarray datasets, histone deacetylation datasets, and regulatory targets of transcription factors assembled from the Yeast Proteome Database and ChIP-chip data, we found that histone deacetylation sites can reduce the level of co-expression of neighboring genes. CONCLUSION: Histone deacetylation sites may serve as possible partition sites for chromatin domains and affect gene expression.

Binding Sites↗
PubMed2005

Comprehensive in silico functional specification of mouse retina transcripts.

BACKGROUND: The retina is a well-defined portion of the central nervous system (CNS) that has been used as a model for CNS development and function studies. The full specification of transcripts in an individual tissue or cell type, like retina, can greatly aid the understanding of the control of cell differentiation and cell function. In this study, we have integrated computational bioinformatics and microarray experimental approaches to classify the tissue specificity and developmental distribution of mouse retina transcripts. RESULTS: We have classified a set of retina-specific genes using sequence-based screening integrated with computational and retina tissue-specific microarray approaches. 33,737 non-redundant sequences were identified as retina transcript clusters (RTCs) from more than 81,000 mouse retina ESTs. We estimate that about 19,000 to 20,000 genes might express in mouse retina from embryonic to adult stages. 39.1% of the RTCs are not covered by 60,770 RIKEN full-length cDNAs. Through comparison with 2 million mouse ESTs, spectra of neural, retinal, late-generated retinal, and photoreceptor -enriched RTCs have been generated. More than 70% of these RTCs have data from biological experiments confirming their tissue-specific expression pattern. The highest-grade retina-enriched pool covered almost all the known genes encoding proteins involved in photo-transduction. CONCLUSION: This study provides a comprehensive mouse retina transcript profile for further gene discovery in retina and suggests that tissue-specific transcripts contribute substantially to the whole transcriptome.

Animals↗
PubMed2005

Practical population group assignment with selected informative markers: characteristics and properties of Bayesian clustering via STRUCTURE.

Population stratification, which is caused by population genetic substructure (PGS), is a critical issue for the design and interpretation of genetic association studies. Methods to address this problem have been devised, but little is known at this point about practical genotyping requirements for resolving PGS based on different marker characteristics. In this report, we seek to (1) identify a small, practical marker set to differentiate African Americans (AAs) from European Americans (EAs), and (2) assess the impact of marker efficiency and sample size on clustering individuals into subgroups by the methods of STRUCTURE (Pritchard et al., [2000a] Genetics 155:945-959). A panel of 37 markers was genotyped for 865 individuals (640 EAs and 225 AAs) from the Northeastern United States. Among EAs, the assignment accuracy reached >99% using only the 4 most efficient markers. Among AAs, the assignment accuracy exceeded 95% when using the 6 most informative markers. Smaller sample size increased the variance in population differentiation, rather than degrading the results consistently. We conclude that the use of marker-efficiency measures for marker selection yielded a relatively small set of STR markers that were effective at differentiating EA and AA populations. The number of markers required is much lower than has been suggested in previous studies.

Black or African American↗
PubMed2005

Data mining.

Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of type I errors. The general motivations were to find the "story" in the data and to summarize information from a multitude of measures. Several methods illustrated strategies for better trait definition, using summarization of related traits. In the few studies that sought to identify genes for alcoholism, there was little agreement among the different strategies, likely reflecting the complexities of the disease. Nevertheless, Group 14 found that these methods offered strategies to gain a better understanding of the complex pathways by which disease develops.

Alcoholism↗
PubMed2005

A statistical method to detect chromosomal regions with DNA copy number alterations using SNP-array-based CGH data.

Single nucleotide polymorphism (SNP) arrays were used to detect chromosomal regions with DNA copy number alterations. Current statistical methods for microarray-based comparative genomic hybridization (array-CGH) analysis generally assume certain relationships among adjacent markers on the same chromosome, and these assumptions may be questionable. For an SNP-array-based CGH study, multiple normal reference SNP arrays were collected. In order to utilize these normal reference SNP arrays, we derived an empirical distribution of signal ratios for each SNP marker. With an assumed threshold value for the overall error rate control and the defined signal ratio ranges for chromosomal amplification and deletion, we proposed a procedure to identify chromosomal alteration regions based on several bootstrapped one-sample t-tests and the false discovery rate control. When we have multiple arrays for different individuals with the same disease, our method can also be used to detect SNP markers for chromosomal alteration regions that are common among these individuals. We applied our method to a published SNP array data set for breast carcinoma cell lines. For an individual with breast cancer, numerous chromosomal alteration regions were identified. Compared to results of previous studies, our method identified more chromosomal alteration regions, with some being implicated in the literature to harbor genes associated with breast cancer. For multiple cancer arrays, our results suggested the existence of common chromosomal alteration regions. However, a high proportion of false positives also indicated that genetic variations among different individuals with breast cancer can be present.

Breast Neoplasms↗
PubMed2005

Family-based association tests for different family structures using pooled DNA.

DNA pooling is a cost-effective strategy for genomewise association studies to identify disease genes. In the context of family-based association studies, Risch & Teng (1998) mainly considered families of identical structures to detect associations between genetic markers and disease, and suggested possible approaches to incorporating different family types without a thorough study of their properties. However, families collected in real genetic studies often have different structures and, more importantly, the informativeness of each family structure depends on the disease model which is generally unknown. So there is a need to develop and investigate statistical methods to combine information from diverse family types. In this article, we propose a general strategy to incorporate different family types by assigning each family an "optimal" weight in association tests. In addition, we consider measurement errors in our analysis. When we evaluate our approach under different disease models and measurement errors, we find that our weighting scheme may lead to a substantial reduction in sample size required over the approach suggested by Risch & Teng (1998), and measurement errors may have significant impact on the required sample size when the error rates are not negligible.

Computer Simulation↗
PubMed2005

A microarray analysis of the rice transcriptome and its comparison to Arabidopsis.

Arabidopsis and rice are the only two model plants whose finished phase genome sequence has been completed. Here we report the construction of an oligomer microarray based on the presently known and predicted gene models in the rice genome. This microarray was used to analyze the transcriptional activity of the gene models in representative rice organ types. Expression of 86% of the 41,754 known and predicted gene models was detected. A significant fraction of these expressed gene models are organized into chromosomal regions, about 100 kb in length, that exhibit a coexpression pattern. Compared with similar genome-wide surveys of the Arabidopsis transcriptome, our results indicate that similar proportions of the two genomes are expressed in their corresponding organ types. A large percentage of the rice gene models that lack significant Arabidopsis homologs are expressed. Furthermore, the expression patterns of rice and Arabidopsis best-matched homologous genes in distinct functional groups indicate dramatic differences in their degree of conservation between the two species. Thus, this initial comparative analysis reveals some basic similarities and differences between the Arabidopsis and rice transcriptomes.

Arabidopsis↗
PubMed2005

Organ-specific expression of Arabidopsis genome during development.

The development of complex eukaryotic organisms can be viewed as the selective expression of distinct fractions of the genome in different organs or tissue types in response to developmental and environmental cues. Here, we generated a genome expression atlas of 18 organ or tissue types representing the life cycle of Arabidopsis (Arabidopsis thaliana). We showed that each organ or tissue type had a defining genome expression pattern and that the degree to which organs share expression profiles is highly correlated with the biological relationship of organ types. Further, distinct fractions of the genome exhibited expression changes in response to environmental light among the three seedling organs, despite the fact that they share the same photo-perception and transduction systems. A significant fraction of the genes in the Arabidopsis genome is organized into chromatin domains exhibiting coregulated expression patterns in response to developmental or environmental signals. The knowledge of organ-specific expression patterns and their response to the changing environment provides a foundation for dissecting the molecular processes underlying development.

Arabidopsis↗
PubMed2005

[Roles of glial cell line-derived neurotropic factor in motoneuron development and motoneuron disease].

OBJECTIVE: To review research progress of the relation between glial cell line-derived neurotropic factor (GDNF) and motor neuron development and motor neuron disease. METHODS: The recent articles on GDNF and motor neurons were extensively reviewed. The molecular structure, the mode of action and the route of administration of GDNF were investigated. RESULTS: GDNF plays extensive roles in the development and disease of motor neuron. GDNF might regulate the development of the motor neurons of the spinal cord to some extent and also save the injured motor neurons. CONCLUSION: GDNF has a potential clinical value and inestimable future in the treatment of motor neuron diseases.

Animals↗