CiteULike: Group: Bioinformatics - library [3487 articles]

Mapping the Nucleotide and Isoform-Dependent Structural and Dynamical Features of Ras Proteins

Alemayehu Gorfe — 2008-06-10T21:32:35-00:00

Structure, Vol. 16, No. 6. (11 June 2008), pp. 885-896.

Summary Ras GTPases are conformational switches controlling cell proliferation, differentiation, and development. Despite their prominent role in many forms of cancer, the mechanism of conformational transition between inactive GDP-bound and active GTP-bound states remains unclear. Here we describe a detailed analysis of available experimental structures and molecular dynamics simulations to quantitatively assess the structural and dynamical features of active and inactive states and their interconversion. We demonstrate that GTP-bound and nucleotide-free G12V H-ras sample a wide region of conformational space, and show that the inactive-to-active transition is a multiphase process defined by the relative rearrangement of the two switches and the orientation of Tyr32. We also modeled and simulated N- and K-ras proteins and found that K-ras is more flexible than N- and H-ras. We identified a number of isoform-specific, long-range side chain interactions that define unique pathways of communication between the nucleotide binding site and the C terminus.

Microarray learning with ABC.

Dhammika Amaratunga — 2007-08-02T13:13:29-00:00

Biostatistics (14 June 2007)

Standard clustering algorithms when applied to DNA microarray data often tend to produce erroneous clusters. A major contributor to this divergence is the feature characteristic of microarray data sets that the number of predictors (genes) in such data far exceeds the number of samples by many orders of magnitude, with only a small percentage of predictors being truly informative with regards to the clustering while the rest merely add noise. An additional complication is that the predictors exhibit an unknown complex correlational configuration embedded in a small subspace of the entire predictor space. Under these conditions, standard clustering algorithms fail to find the true clusters even when applied in tandem with some sort of gene filtering or dimension reduction to reduce the number of predictors. We propose, as an alternative, a novel method for unsupervised classification of DNA microarray data. The method, which is based on the idea of aggregating results obtained from an ensemble of randomly resampled data (where both samples and genes are resampled), introduces a way of tilting the procedure so that the ensemble includes minimal representation from less important areas of the gene predictor space. The method produces a measure of dissimilarity between each pair of samples that can be used in conjunction with (a) a method like Ward's procedure to generate a cluster analysis and (b) multidimensional scaling to generate useful visualizations of the data. We call the dissimilarity measures ABC dissimilarities since they are obtained by aggregating bundles of clusters. An extensive comparison of several clustering methods using actual DNA microarray data convincingly demonstrates that classification using ABC dissimilarities offers significantly superior performance.

VisANT: an integrative framework for networks in systems biology

Zhenjun Hu — 2008-05-09T02:17:03-00:00

Brief Bioinform (7 May 2008), bbn020.

The essence of a living cell is adaptation to a changing environment, and a central goal of modern cell biology is to understand adaptive change under normal and pathological conditions. Because the number of components is large, and processes and conditions are many, visual tools are useful in providing an overview of relations that would otherwise be far more difficult to assimilate. Historically, representations were static pictures, with genes and proteins represented as nodes, and known or inferred correlations between them (links) represented by various kinds of lines. The modern challenge is to capture functional hierarchies and adaptation to environmental change, and to discover pathways and processes embedded in known data, but not currently recognizable. Among the tools being developed to meet this challenge is VisANT (freely available at http://visant.bu.edu) which integrates, mines and displays hierarchical information. Challenges to integrating modeling (discrete or continuous) and simulation capabilities into such visual mining software are briefly discussed. 10.1093/bib/bbn020

A new pheromone trail-based genetic algorithm for comparative genome assembly

Fangqing Zhao — 2008-05-07T20:27:15-00:00

Nucl. Acids Res. (29 April 2008), gkn168.

Gap closing is considered one of the most challenging and time-consuming tasks in bacterial genome sequencing projects, especially with the emergence of new sequencing technologies, such as pyrosequencing, which may result in large amounts of data without the benefit of large insert libraries for contig scaffolding. We propose a novel algorithm to align contigs with more than one reference genome at a time. This approach can successfully overcome the limitations of low degrees of conserved gene order for the reference and target genomes. A pheromone trail-based genetic algorithm (PGA) was used to search globally for the optimal placement for each contig. Extensive testing on simulated and real data sets shows that PGA significantly outperforms previous methods, especially when assembling genomes that are only moderately related. An extended version of PGA can predict additional candidate connections for each contig and can thus increase the likelihood of identifying the correct arrangement of each contig. The software and test data sets can be accessed at http://sourceforge.net/projects/pga4genomics/. 10.1093/nar/gkn168

Darwinian Evolution on a Chip

Brian Paegel — 2008-04-08T18:05:48-00:00

PLoS Biology, Vol. 6, No. 4. (1 April 2008), e85.

Computer control of Darwinian evolution has been demonstrated by propagating a population of RNA enzymes in a microfluidic device. The RNA population was challenged to catalyze the ligation of an oligonucleotide substrate under conditions of progressively lower substrate concentrations. A microchip-based serial dilution circuit automated an exponential growth phase followed by a 10-fold dilution, which was repeated for 500 log-growth iterations. Evolution was observed in real time as the population adapted and achieved progressively faster growth rates over time. The final evolved enzyme contained a set of 11 mutations that conferred a 90-fold improvement in substrate utilization, coinciding with the applied selective pressure. This system reduces evolution to a microfluidic algorithm, allowing the experimenter to observe and manipulate adaptation.

A Microtubule Interactome: Complexes with Roles in Cell Cycle and Mitosis

Julian Hughes — 2008-04-23T13:18:47-00:00

PLoS Biology, Vol. 6, No. 4. (1 April 2008), e98.

The microtubule (MT) cytoskeleton is required for many aspects of cell function, including the transport of intracellular materials, the maintenance of cell polarity, and the regulation of mitosis. These functions are coordinated by MT-associated proteins (MAPs), which work in concert with each other, binding MTs and altering their properties. We have used a MT cosedimentation assay, combined with 1D and 2D PAGE and mass spectrometry, to identify over 250 MAPs from early Drosophila embryos. We have taken two complementary approaches to analyse the cellular function of novel MAPs isolated using this approach. First, we have carried out an RNA interference (RNAi) screen, identifying 21 previously uncharacterised genes involved in MT organisation. Second, we have undertaken a bioinformatics analysis based on binary protein interaction data to produce putative interaction networks of MAPs. By combining both approaches, we have identified and validated MAP complexes with potentially important roles in cell cycle regulation and mitosis. This study therefore demonstrates that biologically relevant data can be harvested using such a multidisciplinary approach, and identifies new MAPs, many of which appear to be important in cell division.

Very fast empirical prediction and rationalization of protein pKa values.

H Li — 2007-10-16T14:56:51-00:00

Proteins, Vol. 61, No. 4. (1 December 2005), pp. 704-721.

A very fast empirical method is presented for structure-based protein pKa prediction and rationalization. The desolvation effects and intra-protein interactions, which cause variations in pKa values of protein ionizable groups, are empirically related to the positions and chemical nature of the groups proximate to the pKa sites. A computer program is written to automatically predict pKa values based on these empirical relationships within a couple of seconds. Unusual pKa values at buried active sites, which are among the most interesting protein pKa values, are predicted very well with the empirical method. A test on 233 carboxyl, 12 cysteine, 45 histidine, and 24 lysine pKa values in various proteins shows a root-mean-square deviation (RMSD) of 0.89 from experimental values. Removal of the 29 pKa values that are upper or lower limits results in an RMSD = 0.79 for the remaining 285 pKa values.

Tracing evolutionary pressure

Kai Ye — 2008-03-26T14:55:55-00:00

Bioinformatics, Vol. 24, No. 7. (1 April 2008), pp. 908-915.

Motivation: Recent advances in sequencing techniques have yielded enormous amounts of protein sequence data from various species. This large dataset allows sequence comparison between paralogous and orthologous proteins to identify motifs or functional positions that account for the differences of functional subgroups ( specificity' positions). Algorithms such as SDPpred and the two-entropies analysis (TEA) have been developed to detect such specificity positions from a multiple sequence alignment (MSA) grouped into classes according to certain biological functions. Other algorithms such as TreeDet compute a classification and then predict specificity positions associated with it. However, there are still many unresolved questions: Was the optimal subdivision of a protein family achieved? Do the definitions at different levels of the phylogenetic tree affect the prediction of specificity positions? Can the whole phylogenetic tree be used instead of only one level in it to predict specificity positions? Results: Here we present a novel method, TEA-O (Two-entropies analysisObjective), to trace the evolutionary pressure from the root to the branches of the phylogenetic tree. At each level of the tree, a TEA plot is produced to capture the signal of the evolutionary pressure. A consensus TEA-O plot is composed from the whole series of plots to provide a condensed representation. Positions related to functions that evolved early (conserved) or later (specificity) are close to the lower-left or upper-left corner of the TEA-O plot, respectively. This novel approach allows an unbiased, user-independent, analysis of residue relevance in a protein family. We compared our TEA-O method with various algorithms using both synthetic and real protein sequences. The results show that our method is robust, sensitive to subtle differences in evolutionary pressure during evolution and comprehensive because all positions in the MSA are presented in the consensus plot. Availability: All computer programs and datasets used in this work are available at http://nava.liacs.nl/kye/TEA-O/ for academic use Contact: k.ye@lacdr.leidenuniv.nl 10.1093/bioinformatics/btn057

Integration of microarray and textual data improves the prognosis prediction of breast, lung and ovarian cancer patients.

O Gevaert — 2008-04-07T13:48:59-00:00

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (2008), pp. 279-290.

Microarray data are notoriously noisy such that models predicting clinically relevant outcomes often contain many false positive genes. Integration of other data sources can alleviate this problem and enhance gene selection and model building. Probabilistic models provide a natural solution to integrate information by using the prior over model space. We investigated if the use of text information from PUBMED abstracts in the structure prior of a Bayesian network could improve the prediction of the prognosis in cancer. Our results show that prediction of the outcome with the text prior was significantly better compared to not using a prior, both on a well known microarray data set and on three independent microarray data sets.

A Framework for Elucidating Regulatory Networks Based on Prior Information and Expression Data

Olivier Gevaert — 2007-11-19T21:29:55-00:00

Annals of the New York Academy of Sciences, Vol. 1115, No. 1. (December 2007), pp. 240-248.

Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation

Olivier Gevaert — 2008-01-22T15:53:51-00:00

BMC Cancer, Vol. 8 (22 January 2008), 18.

Prediction of ectopic pregnancy in women with a pregnancy of unknown location

Condous — 2007-06-02T15:36:44-00:00

Ultrasound in Obstetrics and Gynecology, Vol. 29, No. 6. (June 2007), pp. 680-687.

Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli

Robert Schuetz — 2007-07-11T18:26:55-00:00

Mol Syst Biol, Vol. 3 (10 July 2007)

Assessment of PLSDA cross validation

Johan Westerhuis — 2008-04-06T11:11:00-00:00

Metabolomics, Vol. 4, No. 1. (31 March 2008), pp. 81-89.

Abstract Classifying groups of individuals based on their metabolic profile is one of the main topics in metabolomics research. Due to the low number of individuals compared to the large number of variables, this is not an easy task. PLSDA is one of the data analysis methods used for the classification. Unfortunately this method eagerly overfits the data and rigorous validation is necessary. The validation however is far from straightforward. Is this paper we will discuss a strategy based on cross model validation and permutation testing to validate the classification models. It is also shown that too optimistic results are obtained when the validation is not done properly. Furthermore, we advocate against the use of PLSDA score plots for inference of class differences.

A simple method for assessing sample sizes in microarray experiments.

R Tibshirani — 2006-06-09T19:00:52-00:00

BMC Bioinformatics, Vol. 7 (2006)

BACKGROUND: In this short article, we discuss a simple method for assessing sample size requirements in microarray experiments. RESULTS: Our method starts with the output from a permutation-based analysis for a set of pilot data, e.g. from the SAM package. Then for a given hypothesized mean difference and various samples sizes, we estimate the false discovery rate and false negative rate of a list of genes; these are also interpretable as per gene power and type I error. We also discuss application of our method to other kinds of response variables, for example survival outcomes. CONCLUSION: Our method seems to be useful for sample size assessment in microarray experiments.

A second generation human haplotype map of over 3.1 million SNPs

2007-10-17T18:39:39-00:00

Nature, Vol. 449, No. 7164. (18 October 2007), pp. 851-861.

Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data

Manhong Dai — 2005-12-13T11:24:58-00:00

Nucleic Acids Research, Vol. 33, No. 20. (2005), pp. e175-e175.

Extending pathways based on gene lists using InterPro domain signatures

Florian Hahne — 2008-01-04T15:39:40-00:00

BMC Bioinformatics, Vol. 9 (04 January 2008), 3.

Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions.

Katleen De Preter — 2008-03-20T08:18:07-00:00

Nucleic Acids Res (16 March 2008)

The search for feature enrichment is a widely used method to characterize a set of genes. While several tools have been designed for nominal features such as Gene Ontology annotations or KEGG Pathways, very little has been proposed to tackle numerical features such as the chromosomal positions of genes. For instance, microarray studies typically generate gene lists that are differentially expressed in the sample subgroups under investigation, and when studying diseases caused by genome alterations, it is of great interest to delineate the chromosomal regions that are significantly enriched in these lists. In this article, we present a positional gene enrichment analysis method (PGE) for the identification of chromosomal regions that are significantly enriched in a given set of genes. The strength of our method relies on an original query optimization approach that allows to virtually consider all the possible chromosomal regions for enrichment, and on the multiple testing correction which discriminates truly enriched regions versus those that can occur by chance. We have developed a Web tool implementing this method applied to the human genome (http://www.esat.kuleuven.be/ approximately bioiuser/pge). We validated PGE on published lists of differentially expressed genes. These analyses showed significant overrepresentation of known aberrant chromosomal regions.

Computational methods for transcriptional regulation.

ED Siggia — 2006-08-25T21:25:29-00:00

Curr Opin Genet Dev, Vol. 15, No. 2. (April 2005), pp. 214-221.

How is the information from a thousand gene-expression arrays, the location of more than two hundred regulatory factors, and nine sequenced genomes to be integrated into a global view of the regulatory network in budding yeast? Computational methods that fit incomplete noisy data provide the outlines of regulatory pathways, but the errors are not quantified. In the fly, embryonic patterning has proved amenable to computational prediction, but only when the DNA-binding preferences of the relevant factors are taken into account. In both these model organisms, simply restricting attention to regulatory sequences that align with related species (i.e. "conserved") discards much information regarding what is functional.

Why we need a noninvasive diagnostic test for minimal to mild endometriosis with a high sensitivity.

TM D'Hooghe — 2008-04-01T12:25:40-00:00

Gynecol Obstet Invest, Vol. 62, No. 3. (2006), pp. 136-138.

ProteinChip technology is a useful method in the pathogenesis and diagnosis of endometriosis: a preliminary study.

CM Kyama — 2008-04-01T12:25:09-00:00

Fertil Steril, Vol. 86, No. 1. (July 2006), pp. 203-209.

OBJECTIVE: To test the feasibility of ProteinChip (Ciphergen Biosystems, Inc., Fremont, CA) technology as a proteomic tool in discovering and identifying proteins that are differentially expressed in endometrium, endometriotic tissue, and normal peritoneum from women with and without endometriosis. DESIGN: Differential analysis of protein expression in women with and without endometriosis. SETTING: University hospital. PATIENT(S): A total of nine patients during their secretory phase (days 20-22) were selected for this study on the basis of cycle phase and presence/or absence of endometriosis. INTERVENTION(S): Twelve tissues used in the study included six endometrial biopsies from women with mild endometriosis (n = 3) and a normal pelvis (n = 3) as well as paired samples of peritoneal endometriotic lesions (n = 3) and macroscopically normal peritoneum biopsies (n = 3) from three women with endometriosis. MAIN OUTCOME MEASURE(S): Numerous expression differences were observed in the above comparisons, representing both up-regulation and down-regulation in protein and peptide expression levels. RESULT(S): Endometrial expression for a number of proteins and peptides in the range of 2.8-12.3 kDa was 3-24 times lower in women with endometriosis than in those without endometriosis. When compared with normal peritoneum, endometriotic lesions showed an increased expression for a set of proteins and peptides in the range of 3-96 kDa, and especially an up-regulated cluster of proteins between 22 and 23 kDa, identified to be transgelin, a smooth muscle actin-binding protein. CONCLUSION(S): This preliminary study demonstrated that differential protein profiling by using ProteinChip array technology is feasible, reproducible, and may be developed into a powerful tool for endometriosis research.

Global gene analysis of late secretory phase, eutopic endometrium does not provide the basis for a minimally invasive test of endometriosis.

J R A Sherwin — 2008-04-01T12:22:47-00:00

Hum Reprod (19 March 2008)

BACKGROUND Endometriosis occurs in 10% of women and is currently diagnosed by invasive laparoscopic testing. We tested the hypothesis that endometrial gene expression in late secretory phase endometrium differs between patients with and without endometriosis. METHODS Ten patients with laparoscopically proven endometriosis (minimal/mild n = 5 and moderate/severe n = 5) and six controls, underwent endometrial biopsy in the late secretory phase (Day 23 onwards). Microarray interrogation of eutopic endometrial gene expression was performed. RESULTS Microarray data were obtained for all control samples and eight samples from the endometriosis patients (n = 4 minimal/mild, n = 4 moderate/severe disease). Eight genes were identified as up-regulated and one gene was down-regulated in all endometriotic samples (more than 1.75-fold, P < 0.01). Real-time PCR analysis of protocadherin-17 (PCDH17), protein tyrosine phosphatase, receptor type, R (PTPRR) and interleukin-6 signal transducer (IL6ST) expression validated the microarray findings. CONCLUSIONS Expression of very few transcripts differs, in late secretory eutopic endometrium, between controls and patients with endometriosis. The median fold changes of these genes are small. No transcripts were identified that could discriminate between minimal/mild and moderate/severe endometriosis. Therefore, interrogation of the late secretory endometrial transcriptome is not likely to form the basis of a minimally invasive diagnostic test for endometriosis.

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles.

E Edelman — 2006-11-07T10:35:36-00:00

Bioinformatics, Vol. 22, No. 14. (15 July 2006)

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.

Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants.

2007-11-01T06:11:56-00:00

Nat Genet, Vol. 39, No. 11. (November 2007), pp. 1329-1337.

We have genotyped 14,436 nonsynonymous SNPs (nsSNPs) and 897 major histocompatibility complex (MHC) tag SNPs from 1,000 independent cases of ankylosing spondylitis (AS), autoimmune thyroid disease (AITD), multiple sclerosis (MS) and breast cancer (BC). Comparing these data against a common control dataset derived from 1,500 randomly selected healthy British individuals, we report initial association and independent replication in a North American sample of two new loci related to ankylosing spondylitis, ARTS1 and IL23R, and confirmation of the previously reported association of AITD with TSHR and FCRL3. These findings, enabled in part by increased statistical power resulting from the expansion of the control reference group to include individuals from the other disease groups, highlight notable new possibilities for autoimmune regulation and suggest that IL23R may be a common susceptibility factor for the major 'seronegative' diseases.

Distribution modeling and simulation of gene expression data

Rudolph Parrish — 2008-03-31T07:10:58-00:00

Computational Statistics & Data Analysis, Vol. In Press, Accepted Manuscript

Pathological features of rectal cancer after preoperative radiochemotherapy.

O Dworak — 2008-03-28T17:14:01-00:00

Int J Colorectal Dis, Vol. 12, No. 1. (1997), pp. 19-23.

The standard therapy for rectal carcinoma is surgical, however, preoperative radiochemotherapy will play an increasing role especially in locally advanced disease. To estimate the prognosis and the effect of radiochemotherapy the postradiochemotherapeutical pathological features are important to assess. We examined the surgical specimens of 17 patients after preoperative radiochemotherapy to estimate and grade the histological reactions. A proposal for a grading system for tumor regression (not yet available in the literature) has also been described. All but one of the carcinomas showed different degrees of tumor regression. A total regression was not observed after standardised pathological work up. In only one case a locally curative resection was not possible. We think that preoperative radiochemotherapy is able to reduce tumor mass thus achieving operability in non-curatively operable cases. We recommend standards of pathological work up and regression grading for further studies comparing surgery and radiochemotherapy of rectal carcinoma.

Quantification of histologic regression of rectal cancer after irradiation: a proposal for a modified staging system.

JM Wheeler — 2008-03-28T17:13:19-00:00

Dis Colon Rectum, Vol. 45, No. 8. (August 2002), pp. 1051-1056.

PURPOSE: Long-course preoperative radiotherapy has been recommended for rectal carcinoma when there is concern about the ability to perform a curative resection, for example, in larger tethered tumors or those sited anteriorly or near the anal sphincter. "Downstaging" of the tumor may occur, and this is of importance when estimating the prognosis and selecting postoperative therapy for patients. We studied the effects of preoperative chemoradiotherapy on the pathology of rectal cancer, and we propose a simplified measurement of tumor regression, the Rectal Cancer Regression Grade. METHODS: We have reviewed those patients who received preoperative chemoradiotherapy followed by surgical resection for carcinomas of the mid or distal third of the rectum found to be Stage T3/4 on transrectal ultrasound or CT between January 1995 and December 1998. Patients received 45 to 50 Gy irradiation and an infusion of 5-fluorouracil. The surgical specimens were examined by one pathologist, and the Rectal Cancer Regression Grade was quantified. RESULTS: Forty-two patients, mean age 60 (range, 42-86) years, underwent chemoradiotherapy before surgery for rectal carcinoma. There were 28 anterior resections (67 percent; 9 with a colonic pouch), 12 abdominoperineal resections (27 percent), and 2 Hartmann's procedures (5 percent). Comparison of preoperative and pathologic staging revealed that the depth of invasion was downstaged in 17 patients (38 percent), and the status of involved lymph nodes was downstaged in 13 (50 percent) of 26 patients. Tumor regression was more than 50 percent (Rectal Cancer Regression Grades 1 and 2) in 36 patients (86 percent), with 7 patients (17 percent) having complete regression with absence of residual cancer cells. CONCLUSION: Significant tumor regression was seen in 86 percent of cases after chemoradiotherapy, with 19 patients showing a "good" responsiveness. We propose a modified pathologic staging system for irradiated rectal cancer, the Rectal Cancer Regression Grade, which includes a measurement of tumor regression. The utility of the proposed Rectal Cancer Regression Grade must be tested against long-term outcomes before its value in predicting prognosis and survival can be determined.

Epigenetics in cancer.

M Esteller — 2008-03-14T03:02:32-00:00

N Engl J Med, Vol. 358, No. 11. (13 March 2008), pp. 1148-1159.

MicroRNA epigenetic alterations in human cancer: one step forward in diagnosis and treatment.

N Yang — 2008-03-28T16:03:16-00:00

Int J Cancer, Vol. 122, No. 5. (1 March 2008), pp. 963-968.

MicroRNAs (miRNAs) are approximately 22 nt non-coding RNAs, which regulate gene expression in a sequence-specific manner via translational inhibition or messenger RNA (mRNA) degradation. Since the discovery of their fundamental mechanisms of action, the field of miRNAs has opened a new era in the understanding of small noncoding RNAs. By molecular cloning and bioinformatic approaches, miRNAs have been identified in viruses, plants and animals. miRNAs are predicted to negatively target up to one-third of human mRNAs. Cancer is a complex genetic disease caused by abnormalities in gene structure and expression. Previous studies have heavily focused on protein-coding genes; however, accumulating evidence is revealing an important role of miRNAs in cancer. Epigenetics is defined as mitotically and/or meiotically heritable changes in gene expression that are not accompanied by changes in DNA sequence. Given the critical roles of miRNAs and epigenetics in cancer, characterizing the epigenetic regulation of miRNAs will provide novel opportunities for the development of cancer biomarkers and/or the identification of new therapeutic targets in the foreseeable future.

Computational Epigenetics.

Christoph Bock — 2007-11-22T09:08:40-00:00

Bioinformatics (17 November 2007)

Epigenetic research aims to understand heritable gene regulation that is not directly encoded in the DNA sequence. Epigenetic mechanisms such as DNA methylation and histone modifications modulate the packaging of the DNA in the nucleus and thereby influence gene expression. Patterns of epigenetic information are faithfully propagated over multiple cell divisions, which makes epigenetic regulation a key mechanism for cellular differentiation and cell fate decisions. In addition, incomplete erasure of epigenetic information can lead to complex patterns of non-Mendelian inheritance. Stochastic and environment-induced epigenetic defects are known to play a major role in cancer and ageing, and they may also contribute to mental disorders and autoimmune diseases. Recent technical advances such as ChIP-on-chip and ChIP-seq have started to convert epigenetic research into a high-throughput endeavor, to which bioinformatics is expected to make significant contributions. Here, we review pioneering computational studies that have contributed to epigenetic research. In addition, we give a brief introduction into epigenetics - targeted at bioinformaticians who are new to the field - and we outline future challenges in computational epigenetics.

Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier

Pedro Domingos — 2008-02-09T21:52:16-00:00

(1996), pp. 105-112.

The simple Bayesian classifier (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been proposed so far. In this paper we show that the SBC does not in fact assume attribute independence, and can be optimal even when this assumption is violated by a wide margin. The key to this finding lies in...

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Pedro Domingos — 2006-11-18T20:26:55-00:00

Mach. Learn., Vol. 29, No. 2-3. (1997), pp. 103-130.

Idiot's Bayes—Not So Stupid After All?

David Hand — 2008-02-23T13:58:06-00:00

International Statistical Review, Vol. 69, No. 3. (2001), pp. 385-398.

Summary Folklore has it that a very simple supervised classification rule, based on the typically false assumption that the predictor variables are independent, can be highly effective, and often more effective than sophisticated rules. We examine the evidence for this, both empirical, as observed in real data applications, and theoretical, summarising explanations for why this simple rule might be effective. Resume La tradition veunt qu'une regle tres simple assumant l'independance des variables predictives. une hypothese fausse dans la plupart des cas, peut etre tres efficace, souvent meme plus efficace qu'une methode plus sophistiquee en ce qui concerne l'attribution de classes a un groupe d'objets. A ce sujet, nous examinons les preuves empiriques, et les preuves theoriques, e'est-a-dire les raisons pour lesquelles cette simple regle pourrait faciliter le processus de tri.

Outcome signature genes in breast cancer: is there a unique set?

Liat Ein-Dor — 2005-01-14T13:16:53-00:00

Bioinformatics, Vol. 21, No. 2., 171.

Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.

Y Wang — 2005-08-14T16:29:15-00:00

Lancet, Vol. 365, No. 9460. (5 2005), pp. 671-679.

BACKGROUND: Genome-wide measures of gene expression can identify patterns of gene activity that subclassify tumours and might provide a better means than is currently available for individual risk assessment in patients with lymph-node-negative breast cancer. METHODS: We analysed, with Affymetrix Human U133a GeneChips, the expression of 22000 transcripts from total RNA of frozen tumour samples from 286 lymph-node-negative patients who had not received adjuvant systemic treatment. FINDINGS: In a training set of 115 tumours, we identified a 76-gene signature consisting of 60 genes for patients positive for oestrogen receptors (ER) and 16 genes for ER-negative patients. This signature showed 93% sensitivity and 48% specificity in a subsequent independent testing set of 171 lymph-node-negative patients. The gene profile was highly informative in identifying patients who developed distant metastases within 5 years (hazard ratio 5.67 [95% CI 2.59-12.4]), even when corrected for traditional prognostic factors in multivariate analysis (5.55 [2.46-12.5]). The 76-gene profile also represented a strong prognostic factor for the development of metastasis in the subgroups of 84 premenopausal patients (9.60 [2.28-40.5]), 87 postmenopausal patients (4.04 [1.57-10.4]), and 79 patients with tumours of 10-20 mm (14.1 [3.34-59.2]), a group of patients for whom prediction of prognosis is especially difficult. INTERPRETATION: The identified signature provides a powerful tool for identification of patients at high risk of distant recurrence. The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.

An evaluation of human protein-protein interaction data in the public domain

Suresh Mathivanan — 2007-05-24T13:23:24-00:00

BMC Bioinformatics, Vol. 7, No. Suppl 5. (2006)

BACKGROUND:Protein-protein interaction (PPI) databases have become a major resource for investigating biological networks and pathways in cells. A number of publicly available repositories for human PPIs are currently available. Each of these databases has their own unique features with a large variation in the type and depth of their annotations.RESULTS:We analyzed the major publicly available primary databases that contain literature curated PPI information for human proteins. This included BIND, DIP, HPRD, IntAct, MINT, MIPS, PDZBase and Reactome databases. The number of binary non-redundant human PPIs ranged from 101 in PDZBase and 346 in MIPS to 11,367 in MINT and 36,617 in HPRD. The number of genes annotated with at least one interactor was 9,427 in HPRD, 4,975 in MINT, 4,614 in IntAct, 3,887 in BIND and <1,000 in the remaining databases. The number of literature citations for the PPIs included in the databases was 43,634 in HPRD, 11,480 in MINT, 10,331 in IntAct, 8,020 in BIND and <2,100 in the remaining databases.CONCLUSION:Given the importance of PPIs, we suggest that submission of PPIs to repositories be made mandatory by scientific journals at the time of manuscript submission as this will minimize annotation errors, promote standardization and help keep the information up to date. We hope that our analysis will help guide biomedical scientists in selecting the most appropriate database for their needs especially in light of the dramatic differences in their content.

A comparison of various methods for multivariate regression with highly collinear variables

Kiers — 2007-09-20T14:16:50-00:00

Statistical Methods and Applications, Vol. 16, No. 2. (August 2007), pp. 193-228.

The biology of cancer

RA Weinberg — 2008-03-26T18:38:15-00:00

(2007)

Hypoxia and cancer

Gregg Semenza — 2007-08-21T07:55:14-00:00

Cancer and Metastasis Reviews, Vol. 26, No. 2. (June 2007), pp. 223-224.

Familial cancer associated with a polymorphism in ARLTS1.

GA Calin — 2008-03-26T10:15:17-00:00

N Engl J Med, Vol. 352, No. 16. (21 April 2005), pp. 1667-1676.

BACKGROUND: The finding of hemizygous or homozygous deletions at band 14 on chromosome 13 in a variety of neoplasms suggests the presence of a tumor-suppressor locus telomeric to the RB1 gene. METHODS: We studied samples from 216 patients with various types of sporadic tumors or idiopathic pancytopenia, peripheral-blood samples from 109 patients with familial cancer or multiple cancers, and control blood samples from 475 healthy people or patients with diseases other than cancer. We performed functional studies of cell lines lacking ARLTS1 expression with the use of both the full-length ARLTS1 gene and a truncated variant. RESULTS: We found a gene at 13q14, ARLTS1, a member of the ADP-ribosylation factor family, with properties of a tumor-suppressor gene. We analyzed 800 DNA samples from tumors and blood cells from patients with sporadic or familial cancer and controls and found that the frequency of a nonsense polymorphism, G446A (Trp149Stop), was similar in controls and patients with sporadic tumors but was significantly more common among patients with familial cancer than among those in the other two groups (P=0.02; odds ratio, 5.7; 95 percent confidence interval, 1.3 to 24.8). ARLTS1 was down-regulated by promoter methylation in 25 percent of the primary tumors we analyzed. Transfection of wild-type ARLTS1 into A549 lung-cancer cells suppressed tumor formation in immunodeficient mice and induced apoptosis, whereas transfection of truncated ARLTS1 had a limited effect on apoptosis and tumor suppression. Microarray analysis revealed that the wild-type and Trp149Stop-transfected clones had different expression profiles. CONCLUSIONS: A genetic variant of ARLTS1 predisposes patients to familial cancer.

MicroRNA expression profiles classify human cancers

Jun Lu — 2005-06-08T20:26:28-00:00

Nature, Vol. 435, No. 7043., pp. 834-838.

Characterization of microRNA expression profiles in normal human tissues

Yu Liang — 2007-06-13T21:51:37-00:00

BMC Genomics, Vol. 8 (12 June 2007), 166.

Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)

Phillip Good — 2007-08-04T00:31:06-00:00

(01 December 2004)

This book provides a step-by-step manual on the application of permutation tests in biology, business, medicine, science, and engineering. Its intuitive and informal style will ideally suit it as a text for students and researchers whether experienced or coming to these resampling methods for the first time. The real-world problems of missing and censored data, multiple comparisons, nonresponders, after-the-fact covariates, and outliers are dealt with at length. The book's main features include: * detailed consideration of one-, two-, and k-sample tests, contingency tables, experimental design, clinical trials, cluster analysis, multiple comparisons, multivariate data, regression, and sample size reduction; * numerous practical applications in archeology, biology, climatology, economics, education, medicine, and the social sciences; * valuable techniques for reducing computation time; * practical advice on experimental design; * comparisons with bootstrap, parametric, and nonparametric techniques; * an extensive three-part bibliography featuring more than 1,000 articles. This new edition has more than 100 additional pages, and includes streamlined statistics for the k-sample comparison and analysis of variance plus expanded sections on computational techniques, multiple comparisons, multiple regression, comparing variances, and testing interactions in balanced designs. Comprehensive author and subject indexes, plus an expert-system guide to methods, provide for further ease of use. The invaluable exercises at the end of every chapter have been supplemented with drills and a number of graduate-level thesis problems.

A note on the calculation of empirical P values from Monte Carlo procedures.

BV North — 2008-03-25T15:51:55-00:00

Am J Hum Genet, Vol. 71, No. 2. (August 2002), pp. 439-441.

The meaning and use of the area under a receiver operating characteristic (ROC) curve.

JA Hanley — 2005-07-11T19:56:10-00:00

Radiology, Vol. 143, No. 1. (April 1982), pp. 29-36.

A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.

Predicting the clinical behavior of ovarian cancer from gene expression profiles.

F De Smet — 2008-03-25T13:37:03-00:00

Int J Gynecol Cancer, Vol. 16 Suppl 1 (b 2006), pp. 147-151.

We investigated whether prognostic information is reflected in the expression patterns of ovarian carcinoma samples. RNA obtained from seven FIGO stage I without recurrence, seven platin-sensitive advanced-stage (III or IV), and six platin-resistant advanced-stage ovarian tumors was hybridized on a complementary DNA microarray with 21,372 spotted clones. The results revealed that a considerable number of genes exhibit nonaccidental differential expression between the different tumor classes. Principal component analysis reflected the differences between the three tumor classes and their order of transition. Using a leave-one-out approach together with least squares support vector machines, we obtained an estimated classification test accuracy of 100% for the distinction between stage I and advanced-stage disease and 76.92% for the distinction between platin-resistant versus platin-sensitive disease in FIGO stage III/IV. These results indicate that gene expression patterns could be useful in clinical management of ovarian cancer.

Transcriptome classification of HCC is related to gene alterations and to new therapeutic targets.

S Boyault — 2007-07-05T08:24:53-00:00

Hepatology, Vol. 45, No. 1. (January 2007), pp. 42-52.

Hepatocellular carcinomas (HCCs) are a heterogeneous group of tumors that differ in risk factors and genetic alterations. We further investigated transcriptome-genotype-phenotype correlations in HCC. Global transcriptome analyses were performed on 57 HCCs and 3 hepatocellular adenomas and validated by quantitative RT-PCR using 63 additional HCCs. We determined loss of heterozygosity, gene mutations, promoter methylation of CDH1 and CDKN2A, and HBV DNA copy number for each tumor. Unsupervised transcriptome analysis identified 6 robust subgroups of HCC (G1-G6) associated with clinical and genetic characteristics. G1 tumors were associated with low copy number of HBV and overexpression of genes expressed in fetal liver and controlled by parental imprinting. G2 included HCCs infected with a high copy number of HBV and mutations in PIK3CA and TP53. In these first groups, we detected specific activation of the AKT pathway. G3 tumors were typified by mutation of TP53 and overexpression of genes controlling the cell cycle. G4 was a heterogeneous subgroup of tumors including TCF1-mutated hepatocellular adenomas and carcinomas. G5 and G6 were strongly related to beta-catenin mutations that lead to Wnt pathway activation; in particular, G6 tumors were characterized by satellite nodules, higher activation of the Wnt pathway, and E-cadherin underexpression. CONCLUSION: These results have furthered our understanding of the genetic diversity of human HCC and have provided specific identifiers for classifying tumors. In addition, our classification has potential therapeutic implications because 50% of the tumors were related to WNT or AKT pathway activation, which potentially could be targeted by specific inhibiting therapies.

From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Aravind Subramanian — 2006-03-01T14:37:52-00:00

PNAS, Vol. 102, No. 43. (25 October 2005), pp. 15545-15550.

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

What is principal component analysis?

Markus Ringnér — 2008-03-09T04:13:08-00:00

Nature Biotechnology, Vol. 26, No. 3., pp. 303-304.