Names of group members are in bold.


S. Mangiola, A. Schulze*, M. Trussart*, E. Zozaya*, M. Ma, Z. Gao, A. F. Rubin, T. P. Speed#, H. Shim#, A. T. Papenfuss#, Robust differential composition and variability analysis for multisample cell omics (*: These authors contributed equally; #: These authors contributed equally)[link][R package sccomp]

R. Lyu, V. Tsui, W. Crismani, R. Liu, H. Shim, D.J. McCarthy, sgcocaller and comapr: personalised haplotype assembly and comparative crossover map analysis using single gametes [link][software sgcocaller][software comapr in github][software comapr in Bioconductor][code for analysis 1][code for analysis 2]

Q. Feng, K. Tiedje, S. Ruybal-Pesántez, G. Tonkin-Hill, M. Duffy, K. Day, H. Shim, Y. Chan, An accurate method for identifying recent recombinants from unaligned sequences. Bioinformatics. 2022 Jan 13, doi:10.1093/bioinformatics/btac012 [link][software]


Y. S. Foo and H. Shim, A Comparison of Bayesian Inference Techniques for Sparse Factor Analysis.[link][Implementation of proposed algorithms]

Y. You, M. B. Clark, H. Shim, NanoSplicer: Accurate identification of splice junctions using Oxford Nanopore sequencing.[link][supplementary material] [software NanoSplicer]

H. Shim, Z. Xing, E. Pantaleo, F. Luca, R. Pique-Regi, M. Stephens, Multi-scale Poisson process approaches for differential expression analysis of high-throughput sequencing data.[link][supplementary material] [software multiseq][code for analysis]

I. Alqassem, Y. Sonthalia, E. Klitzke, H. Shim*, S. Canzar*, McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data. Bioinformatics. 2021 Jan 30, doi: 10.1093/bioinformatics/btab050 (*: joint corresponding authors) [link][software McSplicer]


A. G. Shanku, A. Findley, C. A. Kalita, H. Shim, F. Luca, R. Pique-Regi, circuitSNPs: Predicting genetic effects using a Neural Network to model regulatory modules of DNase-seq footprints [link]


I. E. Schor, J. F. Degner, D. Harnett, E. Cannavo, F. P. Casale, H. Shim, D. Garfield, E. Birney, M. Stephens, O. Stegle, E. E. Furlong, Promoter shape varies across populations and affects promoter evolution and expression noise, Nature Genetics, 2017, February 13, doi:10.1038/ng.3791 [link]

H. Shim and B. Larget, BayesCAT: Bayesian Co-estimation of Alignment and Tree, Biometrics. 2017 Jan 18. doi: 10.1111/biom.12640 [link][supplementary materials][software BayesCAT]


A. Raj*, S. Wang*, H. Shim*, A. Harpak, Y. I. Li, B. Englemann, M. Stephens, Y. Gilad, J. K. Pritchard, Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling, eLife 2016;10.7554/eLife.13328. (*: co-first authors) [link][software riboHMM]


A. Raj*, H. Shim*, Y. Gilad, J. K. Pritchard and M. Stephens, msCentipede: Modeling heterogeneity across genomic sites improves accuracy in the inference of transcription factor binding, PLoS ONE 10(9): e0138030, 2015. (*: co-first authors)[link][software msCentipede]

H. Shim and M. Stephens, Wavelet-based genetic association analysis of functional phenotypes arising from high-throughput sequencing assays, Ann. Appl. Stat. 9 (2015), no. 2, 665–686.[pdf][link][software WaveQTL][supplementary materials][supplementary figures]

H. Shim, D. I. Chasman, J. D. Smith, S. Mora, P. M. Ridker, D. A. Nickerson, R. M. Krauss, M. Stephens,  A multivariate genome-wide association analysis of 10 LDLsubfractions, and their response to statin treatment, in 1868 Caucasians, PLoS ONE 10(4): e0120758, 2015. [link][software mvBIMBAM]


L. M. Mangravite, B. E. Engelhardt, M. W. Medina, J. D. Smith, C. D. Brown, D. I. Chasman, B. H. Mecham, B. Howie, H. Shim, D. Naidoo, Q. Feng, M. J. Rieder, Y. D. Chen, J. I. Rotter, P. M. Ridker, J. C. Hopewell, S. Parish, J. Armitage, R. Collins, R. A. Wilke, D. A. Nickerson, M. Stephens, R. M. Krauss.  A statin-dependent QTL for GATM expression is associated with statin-induced myopathy, Nature, 502:377–380, 2013. [link]

H. Shim*, H. Chun*, C. D. Engelman, and B. A. Payseur. Genome-wide association studies using SNPs vs. haplotypes:  An empirical comparison with data from the North American Rheumatoid Arthritis Consortium, BMC Proceedings, 3 (Suppl 7):S35, 2009 (*: co-first authors) [link]

H. Shim and S. Keles. Integrating quantitative information from ChIP-chip experiments into motif finding, Biostatistics, 9(1):51-65, 2007. [link][software SUCcESS]