William Evan Johnson, PhD
Associate Professor
Boston University School of Medicine
Dept of Medicine
Computational Biomedicine Section

PhD, Harvard University
MA, Harvard University
MS, Brigham Young University



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
Boston University School of Public Health
Biostatistics


Member
Boston University
Bioinformatics Graduate Program




An interactive analysis toolkit for single cell RNA-seq in cancer research
08/01/2017 - 07/31/2018 (PI)
NIH/National Cancer Institute
1U01CA220413-01

DFCI Billing Agreement for Yuqing Zhang
09/01/2016 - 08/31/2017 (PI)
Dana-Farber Cancer Institute

Integrative analyses of reference epigenomic maps and applications
09/18/2014 - 08/31/2017 (PI)
NIH/National Institute of Environmental
5R01ES025002-02

Integrative signaling models to decipher complex cancer phenotypes
08/08/2012 - 07/31/2017 (PI)
University of Utah NIH NCI
U01CA164720

Preprocessing and Analysis Tools for High-Throughput Technologies
09/01/2016 - 06/30/2017 (PI)
Dana-Farber Cancer Institute NIH NIGMS
2R01GM083084-11

Billing Agreement for Heather Selby
07/01/2014 - 06/30/2017 (PI)
Dana-Farber Cancer Institute

Statistical Tools and Methods for Next-Generation Sequencing in Epigenetics
03/01/2012 - 02/29/2016 (PI)
NIH/National Human Genome Research Insti
5R01HG005692-05




Yr Title Project-Sub Proj Pubs
2016 Integrative signaling models to decipher complex cancer phenotypes 4U01CA164720-05 4
2015 Integrative analyses of reference epigenomic maps and applications 5R01ES025002-02
2015 Integrative signaling models to decipher complex cancer phenotypes 5U01CA164720-04 4
2014 Integrative analyses of reference epigenomic maps and applications 1R01ES025002-01
2014 Integrative signaling models to decipher complex cancer phenotypes 5U01CA164720-03 4
2014 Statistical Tools and Methods for Next-Generation Sequencing in Epigenetics 5R01HG005692-05 18
2013 Integrative signaling models to decipher complex cancer phenotypes 5U01CA164720-02 4
2013 Statistical Tools and Methods for Next-Generation Sequencing in Epigenetics 5R01HG005692-04 18
2012 Integrative signaling models to decipher complex cancer phenotypes 1U01CA164720-01A1 4
2012 Statistical Tools and Methods for Next-Generation Sequencing in Epigenetics 7R01HG005692-03 18
Showing 10 of 12 results. Show All Results
Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.

  1. 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.View Related Profiles. PMID: 27222804; DOI: 10.1016/j.gdata.2016.03.009;.
  2. 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; PMCID: PMC4823605; DOI: 10.3389/fmicb.2016.00484;.
  3. 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.View Related Profiles. PMID: 26969729; PMCID: PMC4812528; DOI: 10.15252/msb.20156506;.
  4. 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.View Related Profiles. PMID: 26658939; PMCID: PMC4675533; DOI: 10.1371/journal.pgen.1005713;.
  5. 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.View Related Profiles. PMID: 26538066; PMCID: PMC4634735; DOI: 10.1186/s12920-015-0145-6;.
  6. 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.View Related Profiles. PMID: 26277095; PMCID: PMC4537781; DOI: 10.1186/s12920-015-0121-1;.
  7. 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.View Related Profiles. PMID: 26209429; PMCID: PMC4804769; DOI: 10.1093/bioinformatics/btv377;.
  8. 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.View Related Profiles. PMID: 26170901; DOI: 10.1186/s13073-015-0189-4;.
  9. 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; DOI: 10.4137/CIN.S13890;.
  10. 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; PMCID: PMC4408787; DOI: 10.1261/rna.048801.114;.
Showing 10 of 44 results. Show More

This graph shows the total number of publications by year, by first, middle/unknown, or last author.

Bar chart showing 44 publications over 10 distinct years, with a maximum of 12 publications in 2015

YearPublications
20062
20072
20092
20103
20116
20123
20136
20145
201512
20163
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72 E. Concord St Evans Building
Boston MA 02118
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