Autoregressive spatial smoothing and temporal spline smoothing for mapping rates

This article proposes generalized additive mixed models for the analysis of geographic and temporal variability of mortality rates. This class of models accommodates random spatial effects and fixed and random temporal components. Spatiotemporal models that use autoregressive local smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are developed. The objective is the identification of temporal treads and the production of a series of smoothed maps from which spatial patterns of mortality risks can be monitored over time. Regions with consistently high rate estimates may be followed for further investigation. The methodology is illustrated by analysis of British Columbia infant mortality data.     

Defining genome maintenance pathways using functional genomic approaches

Genome maintenance activities including DNA repair, cell division cycle control, and checkpoint signaling pathways preserve genome integrity and prevent disease. Defects in these pathways cause birth defects, neurodegeneration, premature aging, and cancer. Recent technical advances in functional genomic approaches such as expression profiling, proteomics, and RNA interference (RNAi) technologies have rapidly expanded our knowledge of the proteins that work in these pathways. In this review, we examine the use of these high-throughput methodologies in higher eukaryotic organisms for the interrogation of genome maintenance activities.     

Penalized spline smoothing using Kaplan–Meier weights with censored data

In this paper, we consider the problem of nonparametric curve fitting in the specific context of censored data. We propose an extension of the penalized splines approach using Kaplan–Meier weights to take into account the effect of censorship and generalized cross‐validation techniques to choose the smoothing parameter adapted to the case of censored samples. Using various simulation studies, we analyze the effectiveness of the censored penalized splines method proposed and show that the performance is quite satisfactory. We have extended this proposal to a generalized additive models (GAM) framework introducing a correction of the censorship effect, thus enabling more complex models to be estimated immediately. A real dataset from Stanford Heart Transplant data is also used to illustrate the methodology proposed, which is shown to be a good alternative when the probability distribution for the response variable and the functional form are not known in censored regression models.     

Visualization of genomic changes by segmented smoothing using an L0 penalty

Copy number variations (CNV) and allelic imbalance in tumor tissue can show strong segmentation. Their graphical presentation can be enhanced by appropriate smoothing. Existing signal and scatterplot smoothers do not respect segmentation well. We present novel algorithms that use a penalty on the L(0) norm of differences of neighboring values. Visualization is our main goal, but we compare classification performance to that of VEGA.     

Utilization of spline functions for smoothing fermentation data and for estimation of specific rates

A response surface method of smoothing fermentation data with spline functions is presented. The available electron balance is used to optimally select the values of the smoothing parameters associated with the spline functions. The method is applied to six sets of anaerobic fermentation data in which pure and mixed cultures are grown in batch followed by fed batch culture. Lactobacillus bulgaricus and Streptococcus thermophilus are cultured on 3% dry milk. Measured concentrations of biomass, lactose, galactose, lactic acid, and other acid products are smoothed using spline functions. Values of specific growth rate, specific lactose consumption rate, specific galactose formation rate, and specific acid product formation rate are estimated and the consistency of the results is examined using the available electron balance. The results show that the method works reasonably well, but that an upper bound should be used for the smoothing parameters to obtain accurate estimates of the derivative quantities.     

Prostate cancer and the genomic revolution: Advances using microarray analyses

The emerging technology of microarray analysis allows the establishment of molecular portraits of prostate cancer and the discovery of novel genes involved in the carcinogenesis process. Many novel genes have already been identified using this technique, and functional analyses of these genes are currently being tested. The combination of microarray analysis with other recently developed high-throughput techniques, such as proteomics, tissue arrays, and gene promoter-methylation, especially using tissue microdissection methods, will provide us with more comprehensive insights into how prostate cancer develops and responds to gene-targeted therapies. Animal models of prostate cancer are being characterized by high throughput techniques to better define the similarities and differences between those models and the human disease, and to determine whether particular models may be useful for specific targeted therapies in pre-clinical studies. Although profiling of mRNA expression provides important information of gene expression, the development of proteomic technologies will allow for an even more precise global insight into cellular signaling and structural alterations during prostate carcinogenesis. Not only will the "omic" revolution change basic science, but it will lead to a new era of molecular medicine.     

Super-sparse principal component analyses for high-throughput genomic data

Background  

Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector loadings; for example, for gene expression data, biologically we expect only a portion of the genes to be expressed in any tissue, and an even smaller fraction to be involved in a particular process. Sparse PCA methods have recently been introduced for reducing the number of nonzero coefficients, but these existing methods are not satisfactory for high-dimensional data applications because they still give too many nonzero coefficients.     

Functional materials for microscale genomic and proteomic analyses

The design of functional materials for genomic and proteomic analyses in microscale systems has begun to mature, from materials designed for capillary-based electrophoresis systems to those tailored for microfluidic-based or 'chip-based' platforms. In particular, recent research has focused on evaluating different polymer chemistries for microchannel surface passivation and improved DNA separation matrix performance. Additionally, novel bioconjugate materials designed specifically for electrophoretic separations in microscale channels are facilitating new separation modalities.     

Mimotopes and proteome analyses using human genomic and cDNA epitope phage display

In the post-genomic era, validation of candidate gene targets frequently requires proteinbased strategies. Phage display is a powerful tool to define protein-protein interactions by generating peptide binders against target antigens. Epitope phage display libraries have the potential to enrich coding exon sequences from human genomic loci. We evaluated genomic and cDNA phage display strategies to identify genes in the 5q31 Interleukin gene cluster and to enrich cell surface receptor tyrosine kinase genes from a breast cancer cDNA library. A genomic display library containing 2 x 106 clones with exon-sized inserts was selected with antibodies specific for human Interleukin-4 (IL-4) and Interleukin-13. The library was enriched significantly after two selection rounds and DNA sequencing revealed unique clones. One clone matched a cognate IL-4 epitope; however, the majority of clone insert sequences corresponded to E. coli genomic DNA. These bacterial sequences act as 'mimotopes' (mimetic sequences of the true epitope), correspond to open reading frames, generate displayed peptides, and compete for binding during phage selection. The specificity of these mimotopes for IL-4 was confirmed by competition ELISA. Other E. coli mimotopes were generated using additional antibodies. Mimotopes for a receptor tyrosine kinase gene were also selected using a breast cancer SKBR-3 cDNA phage display library, screened against an anti-erbB2 monoclonal antibody. Identification of mimotopes in genomic and cDNA phage libraries is essential for phage display-based protein validation assays and two-hybrid phage approaches that examine protein-protein interactions. The predominance of E. coli mimotopes suggests that the E. coli genome may be useful to generate peptide diversity biased towards protein coding sequences.ABBREVIATIONS USED: IL, interleukin; ELISA, enzyme linked immunoabsorbant assay; PBS, phospho-buffered saline; cfu, colony forming units.     

Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses

Population-based genomic association analyses are more powerful than within-family analyses. However, population stratification (unknown or ignored origin of individuals from multiple source populations) and cryptic relatedness (unknown or ignored covariance between individuals because of their relatedness) are confounding factors in population-based genomic association analyses, which inflate the false-positive rate. As a consequence, false association signals may arise in genomic data association analyses for reasons other than true association between the tested genomic factor (marker genotype, gene or protein expression) and the study phenotype. It is therefore important to correct or account for these confounders in population-based genomic data association analyses. The common correction techniques for population stratification and cryptic relatedness problems are presented here in the phenotype–marker association analysis context, and comments on their suitability for other types of genomic association analyses (for example, phenotype–expression association) are also provided. Even though many of these techniques have originally been developed in the context of human genetics, most of them are also applicable to model organisms and breeding populations.     

Shape-invariant modeling of circadian rhythms with random effects and smoothing spline ANOVA decompositions

Medical studies often collect physiological and/or psychological measurements over time from multiple subjects, to study dynamics such as circadian rhythms. Under the assumption that the expected response functions of all subjects are the same after shift and scale transformations, shape-invariant models have been applied to analyze this kind of data. The shift and scale parameters provide efficient and interpretable data summaries, while the common shape function is usually modeled nonparametrically, to provide flexibility. However, due to the deterministic nature of the shift and scale parameters, potential correlations within a subject are ignored. Furthermore, the shape of the common function may depend on other factors, such as disease. In this article, we propose shape-invariant mixed effects models. A second-stage model with fixed and random effects is used to model individual shift and scale parameters. A second-stage smoothing spline ANOVA model is used to study potential covariate effects on the common shape function. We apply our methods to a real data set to investigate disease effects on circadian rhythms of cortisol, a hormone that is affected by stress. We find that patients with Cushing's syndrome lost circadian rhythms and their 24-hour means were elevated to very high levels. Patients with major depression had the same circadian shape and phases as normal subjects. However, their 24-hour mean levels were elevated and amplitudes were dampened for some patients.     

Functional analyses of human DNA repair proteins important for aging and genomic stability using yeast genetics

Model systems have been extremely useful for studying various theories of aging. Studies of yeast have been particularly helpful to explore the molecular mechanisms and pathways that affect aging at the cellular level in the simple eukaryote. Although genetic analysis has been useful to interrogate the aging process, there has been both interest and debate over how functionally conserved the mechanisms of aging are between yeast and higher eukaryotes, especially mammalian cells. One area of interest has been the importance of genomic stability for age-related processes, and the potential conservation of proteins and pathways between yeast and human. Translational genetics have been employed to examine the functional roles of mammalian proteins using yeast as a pliable model system. In the current review recent advancements made in this area are discussed, highlighting work which shows that the cellular functions of human proteins in DNA repair and maintenance of genomic stability can be elucidated by genetic rescue experiments performed in yeast.     

单细胞基因组学分析的技术前沿

基因组学已经深刻地改变了生命科学的诸多领域的面貌。目前它的主要内容是新的全基因组碱基序列的测定和在全基因组范围内鉴定那些在不同水平上影响生命活动的基因群的功能和相互作用。为达此要求,近年出现的第二代测序(深度测序)技术和基因芯片技术发挥了关键作用,但是两者都需要足够的高质量的核酸样品。所以,在只有或只能用单细胞或极少量细胞的情况下,如果没有特殊手段,上述分析往往不能常规、方便地进行。文章以DNA扩增为主线,综合阐述了目前在单细胞(特别是微生物)全基因组测序和大基因组的靶向重测序,以及对单细胞或微量细胞进行的基于深度测序或芯片杂交的功能基因组分析,如转录组、ChIP和DNA的CpG甲基化分析等的最新策略和技术,评价了单细胞基因组测序和功能基因组学各技术的特点并对发展前景进行了展望。     

Comparative genomic analyses in Asparagus.

Garden asparagus (Asparagus officinalis L.) belongs to the monocot family Asparagaceae in the order Asparagales. Onion (Allium cepa L.) and Asparagus officinalis are 2 of the most economically important plants of the core Asparagales, a well supported monophyletic group within the Asparagales. Coding regions in onion have lower GC contents than the grasses. We compared the GC content of 3374 unique expressed sequence tags (ESTs) from A. officinalis with Lycoris longituba and onion (both members of the core Asparagales), Acorus americanus (sister to all other monocots), the grasses, and Arabidopsis. Although ESTs in A. officinalis and Acorus had a higher average GC content than Arabidopsis, Lycoris, and onion, all were clearly lower than the grasses. The Asparagaceae have the smallest nuclear genomes among all plants in the core Asparagales, which typically have huge genomes. Within the Asparagaceae, European Asparagus species have approximately twice the nuclear DNA of that of southern African Asparagus species. We cloned and sequenced 20 genomic amplicons from European A. officinalis and the southern African species Asparagus plumosus and observed no clear evidence for a recent genome doubling in A. officinalis relative to A. plumosus. These results indicate that members of the genus Asparagus with smaller genomes may be useful genomic models for plants in the core Asparagales.     

Breakpoint identification and smoothing of array comparative genomic hybridization data

SUMMARY: We describe a tool, called aCGH-Smooth, for the automated identification of breakpoints and smoothing of microarray comparative genomic hybridization (array CGH) data. aCGH-Smooth is written in visual C++, has a user-friendly interface including a visualization of the results and user-defined parameters adapting the performance of data smoothing and breakpoint recognition. aCGH-Smooth can handle array-CGH data generated by all array-CGH platforms: BAC, PAC, cosmid, cDNA and oligo CGH arrays. The tool has been successfully applied to real-life data. AVAILABILITY: aCGH-Smooth is free for researchers at academic and non-profit institutions at http://www.few.vu.nl/~vumarray/.