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 7/13/2018
Zhang Y, Jenkins DF, Manimaran S, Johnson WE. Alternative empirical Bayes models for adjusting for batch effects in genomic studies. BMC Bioinformatics. 2018 Jul 13; 19(1):262. PMID: 30001694.
- Published on 5/17/2018
Griffin PJ, Zhang Y, Johnson WE, Kolaczyk ED. Detection of multiple perturbations in multi-omics biological networks. Biometrics. 2018 May 17. PMID: 29772079.
- Published on 2/5/2018
Brady SW, McQuerry JA, Qiao Y, Piccolo SR, Shrestha G, Jenkins DF, Layer RM, Pedersen BS, Miller RH, Esch A, Selitsky SR, Parker JS, Anderson LA, Dalley BK, Factor RE, Reddy CB, Boltax JP, Li DY, Moos PJ, Gray JW, Heiser LM, Buys SS, Cohen AL, Johnson WE, Quinlan AR, Marth G, Werner TL, Bild AH. Publisher Correction: Combating subclonal evolution of resistant cancer phenotypes. Nat Commun. 2018 Feb 05; 9(1):572. PMID: 29402882.
- 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 1/1/2017
Rahman M, Macneil S, Jenkins DF, Johnson WE. Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes. Genome Medicine. 2017; 9(1).
- 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.
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