William Johnson specializes in computational biology and biostatistics, developing new tools to investigate disease prognoses and causes and to help determine effective regimens based on individual patients’ risk factors. He has published in the journals Cell, Proceedings of the National Academy of Sciences, Biometrics, Nature Reviews Genetics, Annals of Applied Statistics, and Biostatistics. His work has been funded by the NIH.
The focus of his group’s research is to develop computational and statistical tools to investigate core components that contribute to disease prognosis and etiology, and for the accurate determination of optimal diagnostic, prognostic, and therapeutic regimens for individual patients. They are actively developing methods and software tools for data preprocessing, integration, and downstream analysis, and applying these tools in a variety of clinical and biomedical applications. Their work includes a balance between statistical methods development, algorithm optimization, and clinical application. Statistical innovation focuses on the development of clinically motivated tools that integrate linear modeling, Bayesian methods, factor analysis and structural equations models, Hidden Markov models, mixture models, dynamic programming, and high-performance parallel computing. This work has resulted in widely used tools and algorithms for profiling transcription factors (MAT, MA2C), preprocessing and integrating of genomic data (ComBat, BatchQC, SCAN-UPC), aligning sequencing reads (GNUMAP), developing multi-gene biomarker signatures (ASSIGN), and metagenomic profiling (PathoScope). They have successfully applied their tools in several biomedical and clinical scenarios, ranging from mechanistic studies and to precision genomics.
- Associate Professor, Biostatistics, Boston University School of Public Health
- Member, Bioinformatics Graduate Program, Boston University
- Harvard University, PhD
- Harvard University, MA
- Brigham Young University, MS
- Southern Utah University, BS
- Published on 7/6/2017
Goldberg LR, Kirkpatrick SL, Yazdani N, Luttik KP, Lacki OA, Keith Babbs R, Jenkins DF, Evan Johnson W, Bryant CD. Casein kinase 1-epsilon deletion increases mu opioid receptor-dependent behaviors and binge eating1. Genes Brain Behav. 2017 Sep; 16(7):725-738. PMID: 28594147.
- Published on 10/25/2016
Kirkpatrick SL, Goldberg LR, Yazdani N, Babbs RK, Wu J, Reed ER, Jenkins DF, Bolgioni AF, Landaverde KI, Luttik KP, Mitchell KS, Kumar V, Johnson WE, Mulligan MK, Cottone P, Bryant CD. Cytoplasmic FMR1-Interacting Protein 2 Is a Major Genetic Factor Underlying Binge Eating. Biol Psychiatry. 2017 May 01; 81(9):757-769. PMID: 27914629.
- Published on 8/18/2016
Manimaran S, Selby HM, Okrah K, Ruberman C, Leek JT, Quackenbush J, Haibe-Kains B, Bravo HC, Johnson WE. BatchQC: interactive software for evaluating sample and batch effects in genomic data. Bioinformatics. 2016 Dec 15; 32(24):3836-3838. PMID: 27540268.
- Published on 4/11/2016
Yazdani N, Shen Y, Johnson WE, Bryant CD. Striatal transcriptome analysis of a congenic mouse line (chromosome 11: 50-60Mb) exhibiting reduced methamphetamine sensitivity. Genom Data. 2016 Jun; 8:77-80. PMID: 27222804.
- Published on 4/7/2016
Hilton SK, Castro-Nallar E, Pérez-Losada M, Toma I, McCaffrey TA, Hoffman EP, Siegel MO, Simon GL, Johnson WE, Crandall KA. Metataxonomic and Metagenomic Approaches vs. Culture-Based Techniques for Clinical Pathology. Front Microbiol. 2016; 7:484. PMID: 27092134.
- Published on 3/10/2016
Piccolo SR, Hoffman LM, Conner T, Shrestha G, Cohen AL, Marks JR, Neumayer LA, Agarwal CA, Beckerle MC, Andrulis IL, Spira AE, Moos PJ, Buys SS, Johnson WE, Bild AH. Integrative analyses reveal signaling pathways underlying familial breast cancer susceptibility. Mol Syst Biol. 2016 Mar 10; 12(3):860. PMID: 26969729.
- Published on 12/10/2015
Yazdani N, Parker CC, Shen Y, Reed ER, Guido MA, Kole LA, Kirkpatrick SL, Lim JE, Sokoloff G, Cheng R, Johnson WE, Palmer AA, Bryant CD. Hnrnph1 Is A Quantitative Trait Gene for Methamphetamine Sensitivity. PLoS Genet. 2015 Dec; 11(12):e1005713. PMID: 26658939.
- Published on 11/4/2015
Piccolo SR, Andrulis IL, Cohen AL, Conner T, Moos PJ, Spira AE, Buys SS, Johnson WE, Bild AH. Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility. BMC Med Genomics. 2015; 8:72. PMID: 26538066.
- Published on 8/16/2015
Castro-Nallar E, Shen Y, Freishtat RJ, Pérez-Losada M, Manimaran S, Liu G, Johnson WE, Crandall KA. Integrating microbial and host transcriptomics to characterize asthma-associated microbial communities. BMC Med Genomics. 2015; 8:50. PMID: 26277095.
- Published on 7/24/2015
Rahman M, Jackson LK, Johnson WE, Li DY, Bild AH, Piccolo SR. Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results. Bioinformatics. 2015 Nov 15; 31(22):3666-72. PMID: 26209429.
View 38 more publications: View full profile at BUMC