Heejung Shim Lab

heejung.shim@unimelb.edu.au

School of Mathematics and Statistics

University of Melbourne

We are a research group at the School of Mathematics and Statistics and Melbourne Integrative Genomics (MIG) at the University of Melbourne. We develop statistical methods and computational tools for applications to a wide range of biological questions, with particular emphasis on genomic data analysis and understanding how genetic variants influence cellular processes and organism-level traits.

Our research spans several interconnected areas: multi-scale methods (wavelets) for functional data analysis, genome-wide association analysis, transcription factor binding inference, ribosome profiling, phylogenetics, and alternative splicing. A key emphasis of our group is close communication with biologists to ensure that our statistical approaches properly address the biological questions at hand and respect the properties of the data.

We are affiliated with the ARC Centre of Excellence for Mathematical Analysis of Cellular Systems (MACSYS).

We are looking for passionate new PhD students, Postdocs, and undergraduate/Master students to join the team (more info)! Multiple PhD scholarships are available. We also support postdoctoral fellowship applications. Please contact Heejung if you are interested.

news

Mar 15, 2025	Postdoctoral Research Fellow position open! Application deadline: May 15, 2025, 11:55 PM AEST. See openings for details.
Feb 25, 2025	Our paper ‘Spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis’ is published in Nature Genetics!
Feb 25, 2025	Heejung spoke at the WEHI Bioinformatics seminar series in Melbourne.
Feb 10, 2025	Welcome Ngoc Minh Vu, who is working on CRISPR-modified single-cell isoform expression using nanopore RNA sequencing!
Feb 05, 2025	Heejung spoke at the 21st KOGO Winter Symposium in Korea.

selected publications

Spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis

A. Vannan, R. Lyu, A.L. Williams, and 21 more authors

Nature Genetics, 2025

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We apply spatial transcriptomics to identify molecular niche dysregulation in pulmonary fibrosis, revealing key changes associated with distal lung remodeling.
Multi-scale Poisson process approaches for differential expression analysis of high-throughput sequencing data

H. Shim, Z. Xing, E. Pantaleo, and 3 more authors

Annals of Applied Statistics. Software: multiseq , 2024

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We develop multiseq, a multi-scale Bayesian method for differential expression analysis of high-throughput sequencing data that models count data directly and substantially outperforms existing approaches.
Robust differential composition and variability analysis for multisample cell omics

S. Mangiola, A. Schulze, M. Trussart, and 7 more authors

PNAS. Software: sccomp , 2023

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We develop sccomp, a method for differential composition and variability analysis for multisample cell omics data that is robust to outliers and handles multiple covariates.
Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Y. You, Y.D.J. Prawer, R. De Paoli-Iseppi, and 4 more authors

Genome Biology. Software: BLAZE , 2023

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We develop BLAZE, a tool for identifying cell barcodes from long-read single-cell RNA-seq data without reference to short-read data.
sgcocaller and comapr: personalised haplotype assembly and comparative crossover map analysis using single-gamete sequencing data

R. Lyu, V. Tsui, W. Crismani, and 3 more authors

Nucleic Acids Research. Software: comapr , 2022

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We develop sgcocaller and comapr, tools for personalised haplotype assembly and comparative crossover map analysis using single-gamete sequencing data.
Promoter shape varies across populations and affects promoter evolution and expression noise

I. E. Schor, J. F. Degner, D. Harnett, and 8 more authors

Nature Genetics, 2017

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We find that promoter shape — the distribution of transcription start sites — varies across populations and affects both promoter evolution and expression noise.
Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling

A. Raj, S. Wang, H. Shim, and 6 more authors

eLife. Software: riboHMM , 2016

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Using riboHMM, a hidden Markov model leveraging ribosome profiling data, we identify thousands of novel translated open reading frames in the human genome.
Wavelet-based genetic association analysis of functional phenotypes arising from high-throughput sequencing assays

H. Shim, and M. Stephens

Annals of Applied Statistics. Software: WaveQTL , 2015

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We develop WaveQTL, a wavelet-based method for genetic association analysis of functional phenotypes from high-throughput sequencing assays. WaveQTL identified 50% more dsQTLs than typical window-based analyses.