Alan P. Boyle, Ph.D.

Principal Investigator since September 2014
Professor, Computational Biology & Bioinformatics and Human Genetics

Research areas

  • Non-coding variation
  • Gene Regulation
  • High-throughput genomics
  • Nanopore sequencing

Education

  • B.S.: Biochemistry, Mississippi State University (2005)
  • B.S.: Computer Science, Mississippi State University (2005)
  • Ph.D.: Computational Biology and Bioinformatics, Duke University (2009)
  • Postdoc: Stanford University (2014)

Background

Alan Boyle is Professor of Computational Medicine and Bioinformatics and Professor of Human Genetics at the University of Michigan Medical School. He earned his PhD in Computational Biology and Bioinformatics at Duke University, where he developed experimental and computational methods for mapping open chromatin, including DNase-seq. He completed his postdoctoral training in Genetics at Stanford University, where he developed methods for interpreting non-coding genetic variation and contributed to the ENCODE and modENCODE projects. His laboratory combines computational genomics, high-throughput functional assays, and long-read sequencing to understand how variation in regulatory and repetitive regions of the genome alters gene regulation and contributes to human disease. Current research includes interpreting regulatory variants through the IGVF Consortium, developing long-read approaches to study somatic mosaicism through the SMaHT Network, and investigating the effects of mobile elements, tandem repeats, and other structural variants on genome function.

Boyle lab papers

  1. Phanstiel DH, Boyle AP, Heidari N, Snyder MP. 2015. Mango: A bias correcting ChIA-PET analysis pipeline. Bioinformatics. DOI: 10.1093/bioinformatics/btv336.

  2. Diehl AG and Boyle AP. 2016. Deciphering ENCODE. Trends in Genetics. 32: 238-249. DOI: 10.1016/j.tig.2016.02.002.

  3. *Yang B, *Zhou W, *Jiao J, Nielsen JB, Mathis MR, Heydarpour M, Lettre G, Folkersen L, Prakash S, Schurmann C, Fritsche L, Farnum GA, Lin M, Othman M, Hornsby W, Driscoll A, Levasseur A, Thomas M, Farhat L, Dubé MP, Isselbacher EM, Franco-Cereceda A, Guo Dc, Bottinger EP, Deeb GM, Booher A, Kheterpal S, Chen YE, Kang HM, Kitzman J, Cordell HJ, Keavney BD, Goodship JA, Ganesh SK, Abecasis G, Eagle KA, Boyle AP, Loos RJF, †Eriksson P, †Tardif JC, †Brummett CM, †Milewicz DM, †Body SC, †Willer CJ. 2017. Protein-altering and regulatory genetic variants near GATA4 implicated in bicuspid aortic valve. Nature Communications. 8: 15481. DOI: 10.1038/ncomms15481.

  4. Spadafore M, Najarian K, Boyle AP. 2017. A proximity-based graph clustering method for the identification and application of transcription factor clusters. BMC Bioinformatics. 18: DOI: 10.1186/s12859-017-1935-y.

  5. Nishizaki SS and Boyle AP. 2017. Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms. Trends in Genetics. 33: 34-45. DOI: 10.1016/j.tig.2016.10.008.

  6. Nielsen JB, Fritsche LG, Zhou W, Teslovich TM, Holmen OL, Gustafsson S, Gabrielsen ME, Schmidt EM, Beaumont R, Wolford BN, Lin M, Brummett CM, Preuss MH, Refsgaard L, Bottinger EP, Graham SE, Surakka I, Chu Y, Skogholt AH, Dalen H, Boyle AP, Oral H, Herron TJ, Kitzman J, Jalife J, Svendsen JH, Olesen MS, Njølstad I, Løchen ML, Baras A, Gottesman O, Marcketta A, O'Dushlaine C, Ritchie MD, Wilsgaard T, Loos RJF, Frayling TM, Boehnke M, Ingelsson E, Carey DJ, Dewey FE, Kang HM, Abecasis GR, Hveem K, Willer CJ. 2017. Genome-wide Study of Atrial Fibrillation Identifies Seven Risk Loci and Highlights Biological Pathways and Regulatory Elements Involved in Cardiac Development. Human Genetics. 102: 103-115. DOI: 10.1016/j.ajhg.2017.12.003.

  7. Varshney A, VanRenterghem H, Orchard P, †Boyle AP, †Stitzel ML, †Ucar D, Parker SC. 2018. Cell specificity of regulatory annotations and their genetic effects on gene expression. Genetics. DOI: 10.1534/genetics.118.301525.

  8. Diehl AG and Boyle AP. 2018. Conserved and species-specific transcription factor co-binding patterns drive divergent gene regulation in human and mouse. Nucleic Acids Research. 46: 1878-1894. DOI: 10.1093/nar/gky018.

  9. Shigaki D, Adato O, Adhikar AN, Dong S, Hawkins-Hooker A, Inoue F, Juven-Gershon T, Kenlay H, Martin B, Patra A, Penar DP, Schubach M, Xiong C, Yan Z, Boyle AP, Kreimer A, Kulakovskiy IV, Reid J, Unger R, Yosef N, Shendure J, Ahituv N, Kircher M, and Beer MA. 2019. Integration of Multiple Epigenomic Marks Improves Prediction of Variant Impact in Saturation Mutagenesis Reporter Assay. Human Mutation. 40: 1280-1291. DOI: 10.1002/humu.23797.

  10. Nishizaki SS, Ng N, Dong S, Porter RS, Morterud C, Williams C, Asman C, Switzenberg JA, and Boyle AP. 2019. Predicting the effects of SNPs on transcription factor binding affinity. Bioinformatics. 50: 2434. DOI: 10.1093/bioinformatics/btz612.

  11. Dong S and Boyle AP. 2019. Predicting functional variants in enhancer and promoter elements using RegulomeDB. Human Mutation. 40: 1292-1298. DOI: 10.1002/humu.23791.

  12. Diehl AD and Boyle AP. 2019. CGIMP: Real-time exploration and covariate projection for self-organizing map datasets. Journal of Open Source Science. 4: 1520. DOI: 10.21105/joss.01520.

  13. Amemiya HM, Kundaje A, and Boyle AP. 2019. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Scientific Reports. 9: 9354.

  14. Tsuzuki M, Sethuraman S, Coke AN, Rothi MH, Boyle AP and Wierzbicki AT. 2020. Broad noncoding transcription suggests genome surveillance by RNA polymerase V. Proceedings of the National Academy of Sciences. 117: 30799-30804. DOI: 10.1073/pnas.2014419117.

  15. Ouyang N and Boyle AP. 2020. TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence. Genome Research. 30: 1040-1046. DOI: 10.1101/gr.258228.119.

  16. Nishizaki SS and Boyle AP. 2020. SEMplMe: A tool for integrating DNA methylation effects in transcription factor binding affinity predictions. bioRxiv. DOI: 10.1101/2020.08.13.250118v2.

  17. Lee CT, Cavalcante RG, Lee C, Qin T, Patil S, Wang S, Tsai ZT, Boyle AP, Sartor MA. 2020. Poly-Enrich: count-based methods for gene set enrichment testing with genomic regions. NAR Genomics and Bioinformatics. 2: lqaa006. DOI: 10.1093/nargab/lqaa006.

  18. Diehl AD and Boyle AP. 2020. MapGL: Inferring evolutionary gain and loss of short genomic sequence features by phylogenetic maximum parsimony. BMC Bioinformatics. 21: 416. DOI: 10.1186/s12859-020-03742-9.

  19. Diehl AD, Ouyang N, and Boyle AP. 2020. Transposable elements contribute to cell and species-specific chromatin looping and gene regulation in mammalian genomes.. Nature Communications. 1: 1796. DOI: 10.1038/s41467-020-15520-5.

  20. Zhao N and Boyle AP. 2021. F-Seq2: improving the feature density based peak caller with dynamic statistics. NAR Genomics and Bioinformatics. 3: lqab012. DOI: 10.1093/nargab/lqab012.

  21. Rothi MH, Sethuraman S, Dolata J, Boyle AP and Wierzbicki AT. 2021. DNA methylation directs nucleosome positioning in RNA-mediated transcriptional silencing. bioRxiv. DOI: 10.1101/2020.10.29.359794.

  22. Nishizaki SS, McDonald TL, Farnum GA, Holmes MJ, Drexel ML, Switzenberg JA, Boyle AP. 2021. The inducible lac operator-repressor system is functional in zebrafish cells. Frontiers in Genetics. 12: 994. DOI: 10.3389/fgene.2021.683394.

  23. McDonald TL, Zhou W, Castro CP, Mumm C, Switzenberg JA, Mills RE and Boyle AP. 2021. Cas9 targeted enrichment of mobile elements using nanopore sequencing. Nature Communications. 12: 3586. DOI: 10.1038/s41467-021-23918-y.

  24. Dong S and Boyle AP. 2021. Preprint: Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome. DOI: .