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Susan R Wilson

Publications and source records attributed to Susan R Wilson.

14 recordsLinked to original sources
PubMed2006

SeqVis: visualization of compositional heterogeneity in large alignments of nucleotides.

UNLABELLED: Most phylogenetic methods assume that the sequences evolved under homogeneous, stationary and reversible conditions. Compositional heterogeneity in data intended for studies of phylogeny suggests that the data did not evolve under these conditions. SeqVis, a Java application for analysis of nucleotide content, reads sequence alignments in several formats and plots the nucleotide content in a tetrahedron. Once plotted, outliers can be identified, thus allowing for decisions on the applicability of the data for phylogenetic analysis. AVAILABILITY: http://www.bio.usyd.edu.au/jermiin/programs.htm.

Algorithms↗
PubMed2006

Proteomic classification of pancreatic adenocarcinoma tissue using protein chip technology.

BACKGROUND & AIMS: Pancreatic adenocarcinoma is a most devastating cancer that presents late and is rapidly progressive. This study aimed to identify unique, tissue-specific protein biomarkers capable of differentiating pancreatic adenocarcinoma (PC) from adjacent uninvolved pancreatic tissue (AP), benign pancreatic disease (B), and nonmalignant tumor tissue (NM). METHODS: Tissue samples representing PC (n = 31), AP (n = 44), and B (n = 19) tissue were analyzed on hydrophobic protein chip arrays by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Training models were developed using logistic regression and validated using the 10-fold cross-validation approach. RESULTS: The hydrophobic protein chip array revealed 13 protein peaks differentially expressed between PC and AP (receiver operating characteristic [ROC] area under the curve [AUC], 0.64-0.85), 8 between PC and B (ROC AUC, 0.67-0.78), and 12 between PC and NM tissue (ROC AUC, 0.63-0.81). Logistic regression and cross-validation identified overlapping panels of peaks to develop a training model that distinguished PC from AP (77.4% sensitivity, 84.1% specificity), B (83.9% sensitivity, 78.9% specificity), and NM tissue (58.1% sensitivity, 90.5% specificity). The final panels selected correctly classified 80.6% of PC and 88.6% of AP samples (ROC AUC, 0.92), 93.5% of PC and 89.5% of B samples (ROC AUC, 0.99), and 71.0% of PC and 92.1% of NM samples (ROC AUC, 0.91). CONCLUSIONS: This study used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to discover a number of protein panels that can distinguish effectively between pancreatic adenocarcinoma, benign, and adjacent pancreatic tissue. Identification of these proteins will add to our understanding of the biology of pancreatic cancer. Furthermore, these protein panels may have important diagnostic implications.

Adenocarcinoma↗
PubMed2005

Discussion of "A Bayesian approach to DNA sequence segmentation".

This article discusses the results in Boys and Henderson (2004, Biometrics 60, 573-581) in which the authors propose a new approach to the classification of genomic DNA into a number of hidden Markov states with a variable order of dependency, potentially allowing for the high-throughput detection of structure within genomic DNA. This article is likely to be an important point of departure for further modeling of this type. We question whether the genome of the bacteriophage lambda is the most appropriate example with which to demonstrate the method's effectiveness, whether it can be expected that the method will carry over to genomes where there is only one direction of transcription and no operon structure, and suggest a graphical display that seems to offer insight into the results. It would be interesting to see an analysis that uses the codon alphabet.

Algorithms↗
PubMed2005

Optimizing microarray in experimental hypertension.

BACKGROUND: Genetic noise between outbred animals can potentially be a major confounder in the use of microarray technology for gene expression profiling. The study of paired organs from the same animal offers an alternative approach (e.g., for studies of the kidney in experimental hypertension). The present study was undertaken to determine the level of genetic noise between outbred adult Sprague-Dawley (SD) rats, and to determine the effects of unilateral nephrectomy on changes in gene expression as a basis for the design of microarray studies in experimental hypertension. METHODS: Male SD rats (approximately 130 g) were acclimatized before measurement of tail-cuff systolic blood pressure (SBP) for 6 control days and 4 days of saline treatment. Left kidney nephrectomy was performed, and the tissue snap-frozen in liquid nitrogen for subsequent RNA extraction. Two weeks later, SBP was measured over 4 control and 8 saline treatment days, and the remaining right kidney removed and frozen. Total RNA purification, preparation of cRNA, hybridization, and scanning of the Rat U34A Affymetrix arrays were performed, and data analyzed using MAS5 software Affymetrix Suite (v5), Bioconductor, as well as statistical methods motivated by relevant simulations. RESULTS: Gene expression profiles in the left control kidney were extremely consistent across animals. The expression profiles of pairs of kidneys from the same animal were, however, more similar than those of kidneys from different animals. Nephrectomy had little effect on the gene expression profiles in the time frame examined. CONCLUSION: Despite the outbred nature of the rats used in this study, they are useful for gene expression profiling comparisons. The use of paired organs from an individual animal ensures even further genetic identity, allowing determination of genes modified by the treatment of interest.

Animals↗
PubMed2005

Use of principal component analysis and the GE-biplot for the graphical exploration of gene expression data.

This note is in response to Wouters et al. (2003, Biometrics 59, 1131-1139) who compared three methods for exploring gene expression data. Contrary to their summary that principal component analysis is not very informative, we show that it is possible to determine principal component analyses that are useful for exploratory analysis of microarray data. We also present another biplot representation, the GE-biplot (Gene Expression biplot), that is a useful method for exploring gene expression data with the major advantage of being able to aid interpretation of both the samples and the genes relative to each other.

Algorithms↗
PubMed2004

Statistical analysis of adsorption models for oligonucleotide microarrays.

Recent analyses have shown that the relationship between intensity measurements from high density oligonucleotide microarrays and known concentration is non linear. Thus many measurements of so-called gene expression are neither measures of transcript nor mRNA concentration as might be expected. Intensity as measured in such microarrays is a measurement of fluorescent dye attached to probe-target duplexes formed during hybridization of a sample to the probes on the microarray. We develop several dynamic adsorption models relating fluorescent dye intensity to target RNA concentration, the simplest of which is the equilibrium Langmuir isotherm, or hyperbolic response function. Using data from the Affymerix HG-U95A Latin Square experiment, we evaluate various physical models, including equilibrium and non-equilibrium models, by applying maximum likelihood methods. We show that for these data, equilibrium Langmuir isotherms with probe dependent parameters are appropriate. We describe how probe sequence information may then be used to estimate the parameters of the Langmuir isotherm in order to provide an improved measure of absolute target concentration.

Journal Article↗
PubMed2004

Amplification of MMP-2 and MMP-9 production by prostate cancer cell lines via activation of protease-activated receptors.

BACKGROUND: The matrix metalloproteinases (MMP) are a family of proteolytic enzymes involved in facilitating cancer metastasis. Protease-activated receptors (PARs) have previously been shown to be involved in pathways of MMP upregulation by tumor cells. METHODS: Two androgen independent prostate cancer cell lines, PC3 and DU-145, and one androgen dependent prostate cancer line LNCaP, were investigated. PAR expression was detected using RT-PCR and immunofluorochemistry (IFC) techniques. MMP activity assays were used to quantify the levels of MMP-2 and -9 on all three prostate cell lines after PAR activation. RESULTS: RT-PCR and IFC showed the presence of PAR-1 and PAR-2 in all cell lines investigated, only LNCaP showed PAR-3 and PAR-4 expression. Increased levels of MMP-2 and MMP-9 activity, up to sevenfold depending on prostate cancer cell line, following PAR activation by specific PAR peptides was shown. CONCLUSION: Preliminary studies show the activation of PAR-1 or PAR-2 produced increased levels of MMP-2 and MMP-9 activity in prostate cancer cell lines, indicating their potential role in the metastasis of prostate cancer cells.

Androgens↗
PubMed2004

Generalized resistance to thymic deletion in the NOD mouse; a polygenic trait characterized by defective induction of Bim.

The cause of common polygenic autoimmune diseases is not understood because of genetic and cellular complexity. Here, we pinpoint the action of a subset of autoimmune susceptibility loci in the NOD mouse strain linked to D1mit181, D2mit490, D7mit101, and D15mit229, which cause a generalized resistance to thymic deletion in vivo that applies equally to Aire-induced organ-specific gene products in the thymic medulla and to systemic antigens expressed at high levels throughout the thymus and affects CD4(+), CD4(+)8(+), and CD4(+)25(+) thymocytes. Resistance to thymic deletion does not reflect a general deficit in TCR signaling to calcineurin- or ERK-induced genes, imbalance in constitutive regulators of apoptosis, nor excessive signaling to prosurvival genes but is distinguished by failure to induce the proapoptotic gene and protein, Bim, during in vivo encounter with high-avidity autoantigen. These findings establish defects in thymic deletion and Bim induction as a key mechanism in the pathogenesis of autoimmunity.

Animals↗
PubMed2004

An efficient Z-score algorithm for assessing sequence alignments.

We describe an alternative method for scoring of the pairwise alignment of two biological sequences. Designed to overcome the bias due to the composition of the alignment, it measures the distance (in standard deviations) between the given alignment and the mean value of all other alignments that can be obtained by a permutation of either sequence. We demonstrate that the standard deviation can be calculated efficiently. By concentrating upon the ungapped case, the mean and standard deviation can be calculated exactly and in two steps, the first being O(N) time, where N is the length of the sequence, the second in a fixed number of calculations, i.e., in O(1) time. We argue that this statistic is a more consistent measure than a similarity score based upon a standard scoring matrix. Even in the ungapped case, the statistic proves in many cases to be more accurate than the commonly used (FASTA) (Pearson and Lipman, 1988) gapped Z-score in which the sequence is matched against a random sample of the database. We demonstrate the use of the POZ-score as a secondary filter which screens out several well-known types of false positive, reducing the amount of manual screening to be done by the biologist.

Algorithms↗
PubMed2004

Objective determination of appropriate reporting intervals.

BACKGROUND: The reporting interval is the incremental value chosen in reporting analyte concentration. Reporting intervals for different analytes are often inappropriately narrow, when analytical imprecision and biological variability are considered. METHODS: We have used statistical techniques to determine intervals for individual analytes at which there is 50% or 95% confidence that two results are analytically different, and compared these with the reporting intervals in use for a range of general chemistry analytes and analytes usually measured by immunoassay. RESULTS: No analytes met the criteria for 95% confidence that the results are analytically different. Even at the 50% confidence level, 24 of 46 analytes failed at all concentrations examined. For some analytes, particularly hormones at high concentration, the reporting interval increment should be increased by a factor of at least ten. CONCLUSIONS: The majority of analytes are inappropriately reported when analytical precision alone is considered. The concept of the 'uncertainty of measurement' has not been adequately addressed. A consensus should be reached and implemented on appropriate reporting intervals for all analytes.

Chemistry, Clinical↗
PubMed2003

Visualisation of gene expression data - the GE-biplot, the Chip-plot and the Gene-plot.

Visualisation methods for exploring microarray data are particularly important for gaining insight into data from gene expression experiments, such as those concerned with the development of an understanding of gene function and interactions. Further, good visualisation techniques are useful for outlier detection in microarray data and for aiding biological interpretation of results, as well as for presentation of overall summaries of the data. The biplot is particularly useful for the display of microarray data as both the genes and the chips can be simultaneously plotted. In this paper we describe several ordination techniques suitable for exploring microarray data, and we call these the GE-biplot, the Chip-plot and the Gene-plot. The general method is first evaluated on synthetic data simulated in accord with current biological interpretation of microarray data. Then it is applied to two well-known data sets, namely the colon data of Alon et al. (1999) and the leukaemia data of Golub et al. (1999). The usefulness of the approach for interpreting and comparing different analyses of the same data is demonstrated.

Journal Article↗
PubMed2002

Genetic testing for HFE hemochromatosis in Australia: the value of testing relatives of simple heterozygotes.

BACKGROUND: It is unclear whether screening of relatives of C282Y and H63D heterozygotes (other than compound heterozygotes) for hemochromatosis will detect sufficient numbers of cases to justify introduction of this screening strategy. METHODS: Conditional probabilities were determined using published Australian allele frequencies and penetrance data to determine the detection rate of hemochromatosis by testing the siblings and offspring of heterozygotes (subjects with only one HFE mutation). RESULTS: The number of individuals who are at risk of developing increased body iron stores because of HFE mutations is substantially higher (1 in 80) than previously estimated. In addition, 33% of the Australian population are heterozygous for either C282Y or H63D. Based on population estimates, the relative risk to the offspring of C282Y and H63D heterozygotes of developing increased iron stores is 4.1 and 1.5, respectively, while the relative risk to each sibling is 2.3 and 1, respectively. The risk of developing clinical features of hemochromatosis or hepatic fibrosis is likely to be substantially lower. CONCLUSIONS: Although the detection rate from testing the families of unaffected heterozygotes is low, this can be justified as a clinically useful screening strategy. At the present time this strategy should be restricted to first-degree relatives of heterozygotes. Further studies are recommended to determine if cascade genetic screening is a cost-effective alternative to general population screening.

Australia↗
PubMed2002

Hypothesis testing for data from different family study designs.

A test statistic that is valid for data collected according to a particular type of family study design is not necessarily valid when applied to data obtained from a different type of family study design. When this can occur, a different test that usually is valid is developed for each type of family study design. However, investigators might find that their data come from two (or more) different family study designs, each requiring a different test, yet they want an overall conclusion, essentially a valid hypothesis test that is as powerful as possible. When the underlying genetic model is unknown, it is not clear how to proceed, as several alternative approaches might appear feasible. By using as an example the development of a test of association for data concerning affected singletons and their parents and affected sib pairs and their parents, it is shown that it may not be possible to develop a universally optimal approach without knowledge of the underlying genetic model.

Data Interpretation, Statistical↗