Core D: Quantitative Biology: Biostatistics, Bioinformatics and Computation

Summary

The purpose of the Quantitative Biology Core is to provide investigators with consultative support in biostatistics/computational biology and bioinformatics, and to support web-based dissemination of bioinformatic solutions and database access. Most specific aims with the projects produce high-dimensional biological and exposure data, and often involve complicated questions addressing the possible interaction of environmental exposures and high-dimensional measures of the genome, proteome, and other high throughput technologies. These high-dimensional data sets are characterized by many thousands of measurements made on each unit (e.g. person, yeast culture, soil community).  Core D reflects an evolution in the field of biostatistics and bioinformatics towards developing methodologies that can both find patterns in high dimensional data sets as well as providing proper statistical inference for these patterns. A consensus among our project researches and the methodological experts has formed around a set of core principles regarding optimal estimation and inference in the context of complicated questions and high-dimensional data.  Specifically, the consensus favors using (when possible): semi-parametric locally efficient estimation with robust inference and the development of optimal methods used to integrate the statistical results into existing metadata to suggest relevant biological pathways and networks.  Applying this approach will enable analyses to incorporate diverse data to query similar patterns/pathways in both related toxins and possible related diseases thus substantially leveraging data generated by the Program.  To implement this methodology, the Quantitative Biology Core will provide access to a computational environment that lends itself to the computationally intensive methods developed for data mining and re-sampling based inference.  Because of the scale of the data collection as well as the desirability of converging to a general methodology, our Program requires a more centralized system that can both archive data for, provide sharing to this Core, guidance on the access of metadata/annotation and routines for leveraging such data to find overprinting of our results on existing hypothesized regulatory networks. The Core will also develop tools to find and compares pathway, and create and maintain a web-based system that will allow for both efficient sharing of our methodological expertise with the project researchers and ultimately serve as a tool for outreach among the general scientific community.

This is relevant because despite improvements in technology, the lack of statistical rigor among the proliferating methods used to discover disease etiology and develop effective interventions are producing large numbers of false positive claims. However, by using methods that optimally balance the complexity of models with the need to provide inferences consistent with the amount of data available, one can avoid wild goose chases engendered by false discoveries.

Core Leadership

Mark van der Laan, PhD

Professor of Biostatistics and Statistics

Biostatistics, School of Public Health
University of California, Berkeley

Alan Hubbard, PhD

Associate Professor, Biostatistics

Biostatistics, School of Public Health
University of California, Berkeley

Core Update

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Core Update Archive

Core News

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News Archive

Selected Publications

2014

Thomas R, Hubbard AE, McHale CM, Zhang L, Rappaport SM, Lan Q, et al. (2014) Characterization of changes in gene expression and biochemical pathways at low levels of benzene exposure. PLoS One. 9(5):e91828. PMCID: PMC4006721. [PDF]

McHale CM, Zhang L, Lan Q, Vermeulen R, Li G, Hubbard AE, et al. (2011) Global gene expression profiling of a population exposed to a range of benzene levels. Environ Health Perspect. 119(5):628-34. PMCID: PMC3074412. [PDF]

2013

Thomas R, McHale CM, Lan Q, Hubbard AE, Zhang L, Vermeulen R, et al. Global gene expression response of a population exposed to benzene: a pilot study exploring the use of RNA-sequencing technology. Environ Mol Mutagen. 2013;54(7):566-73. PMCID: PMC4353497. [PDF]

Rappaport SM, Kim S, Thomas R*, Johnson BA, Bois FY, Kupper LL (2013) Low-dose Metabolism of Benzene in Humans: Science and Obfuscation. Carcinogenesis. Jan;34(1):2-9. PMCID: PMC3584950.

Rappaport SM, Johnson BA, Bois FY, Kupper LL, Kim S, Thomas R (2013) Ignoring and adding errors do not improve the science. Carcinogenesis. Jul;34(7):1689-91. PMCID: PMC3697890. [PDF]

2012

Zhang L, Lan Q, Ji Z, Li G, Shen M, Vermeulen R, Guo W, Hubbard A, McHale CM, Rappaport SM, Hayes RB, Linet B, Yin S, Fraumeni JF, Rothman N, Smith MT (2012). Leukemia-related chromosomal loss detected in hematopoietic progenitor cells of benzene-exposed workers.Leukemia. Dec;26(12):2494-2498. PMCID: PMC3472034. [PDF]

Phuong J, Kim S, Thomas R, Zhang L (2012) Predicted toxicity of the biofuel candidate 2,5-dimethylfuran in environmental and biological systems. Environ Mol Mutagen. Jul;53(6):478-87. PMID: 22730190. (PMC Journal – In Process). [PDF]

Thomas R, Phuong J, McHale CM, Zhang L (2012) Using bioinformatic approaches to identify pathways targeted by human leukemogens. Int J Environ Res Public Health. Jul;9(7):2479-503. PMCID: PMC3407916. [PDF]

Godderis L, Thomas R, Hubbard AE, Tabish AM, Hoet P, Zhang L, Smith MT, Veulemans H,  McHale CM (2012) Effect of Chemical Mutagens and Carcinogens on Gene Expression Profiles in Human TK6 Cells. PLoS One. 7(6):e39205. PMCID: PMC3377624. [PDF]

2011

Zhang L, Lan Q, Guo W, Hubbard AE, Li G, Rappaport SM, McHale CM, Shen M, Ji Z, Vermeulen R, Yin S, Rothman N, Smith MT (2011) Chromosome-Wide Aneuploidy Study (CWAS) in Workers Exposed to an Established Leukemogen, Benzene. Carcinogenesis. Apr;32(4):605-12. PMCID: PMC3066415. [PDF]

2010

Zhang L, Tang X, Rothman N, Vermeulen R, Ji Z, Shen M, Qiu C, Guo W, Liu S, Reiss B, Freeman LB, Ge Y, Hubbard AE, Hua M, Blair A, Galvan N, Ruan X, Alter BP, Xin KX, Li S, Moore LE, Kim S, Xie Y, Hayes RB, Azuma M, Hauptmann M, Xiong J, Stewart P, Li L, Rappaport SM, Huang H, Fraumeni JF Jr, Smith MT, Lan Q (2010) Occupational Exposure to Formaldehyde, Hematotoxicity, and Leukemia-Specific Chromosome Changes in Cultured Myeloid Progenitor Cells. Cancer Epidemiol Biomarkers Prev. Jan;19(1):80-88. PMID: 20056626. PMC Journal – In Process. [PDF]

Zhang L, McHale CM, Rothman N, Li G, Ji Z, Vermeulen R, Hubbard AE, Ren X, Shen M, Rappaport SM, North M, Skibola CF, Yin S, Vulpe C, Chanock SJ, Smith MT, Lan Q (2010) Systems biology of human benzene exposure. Chem Biol Interact. Mar 19;184(1-2):86-93. PMID: 20026094. PMCID: PMC2846187. [PDF]

2009

McHale CM, Zhang L, Lan Q, Li G, Hubbard AE, Forrest MS, Vermeulen R, Chen J, Shen M, Rappaport SM, Yin S, Smith MT, Rothman N (2009) Changes in the peripheral blood transcriptome associated with occupational benzene exposure identified by cross-comparison on two microarray platforms. Genomics. Apr; 93(4):343-9. PMID: 19162166. PMCID: PMC2693268. [PDF]

2008

Johnson DR, Brodie EL, Hubbard AE, Andersen GL, Zinder SH, Alvarez-Cohen L (2008) Temporal transcriptomic microarray analysis of “Dehalococcoides ethenogenes” strain 195 during the transition into stationary phase. Appl Environ Microbiol. May; 74(9):2864-72. PMID: 18310438. PMCID: PMC2394897. [PDF]

Hubbard AE, Laan MJ (2008) Population intervention models in causal inference. Biometrika. 95(1):35-47. PMID: 18629347. PMCID: PMC2464276. [PDF]

2007

McHale CM, Zhang L, Hubbard AE, Zhao X, Baccarelli A, Pesatori AC, Smith MT, Landi MT (2007) Microarray analysis of gene expression in peripheral blood mononuclear cells from dioxin-exposed human subjects. Toxicology. Jan 5;229(1-2):101-13. PMID: 17101203. [PDF]

Escobar PA, Smith MT, Vasishta A, Hubbard AE, Zhang L (2007) Leukaemia-specific chromosome damage detected by comet with fluorescence in situ hybridization (comet-FISH). Mutagenesis. Sep; 22(5):321-7. PMID: 17575318. [PDF]

Zhang L, Rothman N, Li G, Guo W, Yang W, Hubbard AE, Hayes RB, Yin S, Lu W, Smith MT (2007) Aberrations in chromosomes associated with lymphoma and therapy-related leukemia in benzene-exposed workers. Environ Mol Mutagen. Jul; 48(6):467-74. PMID: 17584886. [PDF]

Chen J, van der Laan MJ, Smith MT, Hubbard AE (2007) A comparison of methods to control type I errors in microarray studies. Stat Appl Genet Mol Biol. 6:Article28. PMID: 18052911. [PDF]

2006

Birkner MD, Hubbard AE, van der Laan MJ, Skibola CF, Hegedus CM, Smith MT (2006) Issues of processing and multiple testing of SELDI-TOF MS proteomic data. Stat Appl Genet Mol Biol. 5:Article11. PMID: 16646865. [PDF]

van der Laan MJ, Hubbard AE (2006) Quantile-function based null distribution in resampling based multiple testing. Stat Appl Genet Mol Biol. 5:Article14. PMID: 17049025. [PDF]

2005

Zhang L, Lan Q, Guo W, Li G, Yang W, Hubbard AE, Vermeulen R, Rappaport SM, Yin S, Rothman N, Smith MT (2005) Use of OctoChrome fluorescence in situ hybridization to detect specific aneuploidy among all 24 chromosomes in benzene-exposed workers. Chem Biol Interact. May 30; 153-154(117-22. PMID: 15935807. [PDF]

Zhang L, Yang W, Hubbard AE, Smith MT (2005) Nonrandom aneuploidy of chromosomes 1, 5, 6, 7, 8, 9, 11, 12, and 21 induced by the benzene metabolites hydroquinone and benzenetriol. Environ Mol Mutagen. May; 45(4):388-96. PMID: 15662717. [PDF]

Forrest MS, Lan Q, Hubbard AE, Zhang L, Vermeulen R, Zhao X, Li G, Wu YY, Shen M, Yin S, Chanock SJ, Rothman N, Smith MT (2005) Discovery of novel biomarkers by microarray analysis of peripheral blood mononuclear cell gene expression in benzene-exposed workers.Environ Health Perspect. Jun; 113(6):801-7. PMID: 15929907. [PDF]