Robert Gentleman

Bio: Robert Gentleman is an academic researcher from Genentech. The author has contributed to research in topics: Bioconductor & Gene expression profiling. The author has an hindex of 52, co-authored 139 publications receiving 48510 citations. Previous affiliations of Robert Gentleman include Harvard University & Brigham and Women's Hospital.

Genome and transcriptome sequencing of lung cancers reveal diverse mutational and splicing events

Abstract: Lung cancer is a highly heterogeneous disease in terms of both underlying genetic lesions and response to therapeutic treatments. We performed deep whole-genome sequencing and transcriptome sequencing on 19 lung cancer cell lines and three lung tumor/normal pairs. Overall, our data show that cell line models exhibit similar mutation spectra to human tumor samples. Smoker and never-smoker cancer samples exhibit distinguishable patterns of mutations. A number of epigenetic regulators, including KDM6A, ASH1L, SMARCA4, and ATAD2, are frequently altered by mutations or copy number changes. A systematic survey of splice-site mutations identified 106 splice site mutations associated with cancer specific aberrant splicing, including mutations in several known cancer-related genes. RAC1b, an isoform of the RAC1 GTPase that includes one additional exon, was found to be preferentially up-regulated in lung cancer. We further show that its expression is significantly associated with sensitivity to a MAP2K (MEK) inhibitor PD-0325901. Taken together, these data present a comprehensive genomic landscape of a large number of lung cancer samples and further demonstrate that cancer-specific alternative splicing is a widespread phenomenon that has potential utility as therapeutic biomarkers. The detailed characterizations of the lung cancer cell lines also provide genomic context to the vast amount of experimental data gathered for these lines over the decades, and represent highly valuable resources for cancer biology.

Combined cDNA array comparative genomic hybridization and serial analysis of gene expression analysis of breast tumor progression

Abstract: To identify genetic changes involved in the progression of breast carcinoma, we did cDNA array comparative genomic hybridization (CGH) on a panel of breast tumors, including 10 ductal carcinoma in situ (DCIS), 18 invasive breast carcinomas, and two lymph node metastases. We identified 49 minimal commonly amplified regions (MCRs) that included known (1q, 8q24, 11q13, 17q21-q23, and 20q13) and several uncharacterized (12p13 and 16p13) regional copy number gains. With the exception of the 17q21 (ERBB2) amplicon, the overall frequency of copy number alterations was higher in invasive tumors than that in DCIS, with several of them present only in invasive cancer. Amplification of candidate loci was confirmed by quantitative PCR in breast carcinomas and cell lines. To identify putative targets of amplicons, we developed a method combining array CGH and serial analysis of gene expression (SAGE) data to correlate copy number and expression levels for each gene within MCRs. Using this approach, we were able to distinguish a few candidate targets from a set of coamplified genes. Analysis of the 12p13-p12 amplicon identified four putative targets: TEL/ETV6, H2AFJ, EPS8, and KRAS2. The amplification of all four candidates was confirmed by quantitative PCR and fluorescence in situ hybridization, but only H2AFJ and EPS8 were overexpressed in breast tumors with 12p13 amplification compared with a panel of normal mammary epithelial cells. These results show the power of combined array CGH and SAGE analysis for the identification of candidate amplicon targets and identify H2AFJ and EPS8 as novel putative oncogenes in breast cancer.

Maximum likelihood for interval censored data: Consistency and computation

Abstract: SUMMARY Standard convex optimization techniques are applied to the analysis of interval censored data. These methods provide easily verifiable conditions for the self-consistent estimator proposed by Turnbull (1976) to be a maximum likelihood estimator and for checking whether the maximum likelihood estimate is unique. A sufficient condition is given for the almost sure convergence of the maximum likelihood estimator to the true underlying distribution function.

Association of valproate-induced teratogenesis with histone deacetylase inhibition in vivo

Abstract: Chemically induced birth defects are an important public health and human problem. Here we use Xenopus and zebrafish as models to investigate the mechanism of action of a well-known teratogen, valproic acid (VPA). VPA is a drug used in treatment of epilepsy and bipolar disorder but causes spina bifida if taken during pregnancy. VPA has several biochemical activities, including inhibition of histone deacetylases (HDACs). To investigate the mechanism of action of VPA, we compared its effects in Xenopus and zebrafish embryos with those of known HDAC inhibitors and noninhibitory VPA analogs. We found that VPA and other HDAC inhibitors cause very similar and characteristic developmental defects whereas VPA analogs with poor inhibitory activity in vivo have little teratogenic effect. Unbiased microarray analysis revealed that the effects of VPA and trichostatin A (TSA), a structurally unrelated HDAC inhibitor, are strikingly concordant. The concordance is apparent both by en masse correlation of fold-changes and by detailed similarity of dose-response profiles of individual genes. Together, the results demonstrate that the teratogenic effects of VPA are very likely mediated specifically by inhibition of HDACs.

A Model Based Background Adjustment for Oligonucleotide Expression Arrays

Abstract: High density oligonucleotide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular. In the Affymetrix system, a fair amount of further pre-processing and data reduction occurs following the image processing step. Statistical procedures developed by academic groups have been successful at improving the default algorithms provided by the Affymetrix system. In this paper we present a solution to one of the pre-processing steps, background adjustment, based on a formal statistical framework. Our solution greatly improves the performance of the technology in various practical applications.Affymetrix GeneChip arrays use short oligonucleotides to probe for genes in an RNA sample. Typically each gene will be represented by 11-20 pairs of oligonucleotide probes. The first component of these pairs is referred to as a perfect match probe and is designed to hybridize only with transcripts from the intended gene (specific hybridization). However, hybridization by other sequences (non-specific hybridization) is unavoidable. Furthermore, hybridization strengths are measured by a scanner that introduces optical noise. Therefore, the observed intensities need to be adjusted to give accurate measurements of specific hybridization. One approach to adjusting is to pair each perfect match probe with a mismatch probe that is designed with the intention of measuring non-specific hybridization. The default adjustment, provided as part of the Affymetrix system, is based on the difference between perfect match and mismatch probe intensities. We have found that this approach can be improved via the use of estimators derived from a statistical model that use probe sequence information. The model is based on simple hybridization theory from molecular biology and experiments specifically designed to help develop it.A final step in the pre-processing of these arrays is to combine the 11-20 probe pair intensities,after background adjustment and normalization, for a given gene to define a measure of expression that represents the amount of the corresponding mRNA species. In this paper we illustrate the practical consequences of not adjusting appropriately for the presence of nonspecific hybridization and provide a solution based on our background adjustment procedure. Software that computes our adjustment is available as part of the Bioconductor project (http://www.bioconductor.

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Abstract: Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-Seq data, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data. DESeq2 uses shrinkage estimation for dispersions and fold changes to improve stability and interpretability of the estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression and facilitates downstream tasks such as gene ranking and visualization. DESeq2 is available as an R/Bioconductor package.