William Evan Johnson, PhD

Associate Professor, Medicine

William Johnson
(617) 638-2541
72 E. Concord St Evans Building


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.

Other Positions

  • 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 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.

    Read at: PubMed
  • 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.

    Read at: PubMed
  • 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.

    Read at: PubMed
  • 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.

    Read at: PubMed
  • 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.

    Read at: PubMed
  • 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.

    Read at: PubMed
  • 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.

    Read at: PubMed
  • Published on 6/26/2015

    MacNeil SM, Johnson WE, Li DY, Piccolo SR, Bild AH. Inferring pathway dysregulation in cancers from multiple types of omic data. Genome Med. 2015; 7(1):61. PMID: 26170901.

    Read at: PubMed
  • Published on 5/12/2015

    Hong C, Manimaran S, Johnson WE. PathoQC: Computationally Efficient Read Preprocessing and Quality Control for High-Throughput Sequencing Data Sets. Cancer Inform. 2014; 13(Suppl 1):167-76. PMID: 25983538.

    Read at: PubMed
  • Published on 3/24/2015

    Whipple JM, Youssef OA, Aruscavage PJ, Nix DA, Hong C, Johnson WE, Bass BL. Genome-wide profiling of the C. elegans dsRNAome. RNA. 2015 May; 21(5):786-800. PMID: 25805852.

    Read at: PubMed

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