Senior Group Leader,
Laboratory of Systems Biology and Data Analytics
Associate Director,
Spatial and Single Cell Systems




Prabhakar Lab webpage

Transcriptional Regulation
Disease Mechanisms, Therapeutics, Diagnostics, Omics, Algorithms, Data Analysis

The majority of genetic mutations responsible for common diseases reside within gene-regulatory sequences such as enhancers, promoters and insulators. In addition, transcriptional and epigenetic dysregulation are known to drive tumorigenesis, tumor progression and drug resistance. Thus, gene regulation lies at the heart of disease mechanisms and treatment response.

The Prabhakar Lab uses a combination of high-throughput omics assays (wet-lab) and data analytics (dry-lab) to study gene-regulatory mechanisms of human diseases. In particular, we use single-cell RNA-seq, cohort-scale histone ChIP-seq and other NGS technologies to understand autism, psychiatric drug response, lung and colon cancer, chronic myeloid leukemia, autoimmune disorders and host response to infection.

We also develop cutting-edge algorithms and pipelines for deriving biological insights from large datasets. This involves statistics, machine learning and extensive benchmarking for performance and scalability.

In addition to curiosity-driven science, we pursue inventions and discoveries that will (hopefully) make a difference in the world. For example, we are engaged in team science to discover markers of immunotherapy response and develop new imaging-based diagnostic technologies. The methods we develop have spawned research collaborations with multiple industry partners spanning biotech, IT and pharma.

Major achievements include the first single-cell transcriptomic analysis of colorectal tumors (Li, Courtois et al., Nat Genet 2017), the first study of histone acetylation changes in autism spectrum disorder (Sun, Poschmann et al., Cell 2016), the first large-scale study of variants that alter histone acetylation and contribute to disease susceptibility (del Rosario, Poschmann et al., Nat Methods 2015) and the first unified signal-processing method for peak detection in whole-genome profiling data (Kumar et al., Nat Biotechnol 2013). We have also uncovered fundamental properties of transcription factor binding to genomic DNA (Jankowski et al., Genome Res 2013) and demonstrated that H2BK20ac is a distinctive signature of enhancers and cell-type-specific promoters (Kumar, Rayan, Muratani et al., Genome Res 2016). Earlier work explored the contribution of gene regulatory elements to human origins (Prabhakar, Noonan et al., Science 2006; Prabhakar et al., Science 2008).

Citations: Google Scholar

Centre for Big Data and Integrative Genomics (c-BIG)

Straits Times interview: Histone acetylation changes in autism
Channel News Asia interview: Histone acetylation changes in autism
Labroots webinar: Single cell algorithms, application to colorectal cancer
Human Cell Atlas, Asian Immune Diversity Atlas (AIDA): HCA Barcelona 2019, HCA Equity Addis Ababa 2019
3rd Annual HCA Asia Meeting, Singapore: Single Cell Algorithms

Job Openings
Postdoctoral fellows: Machine Learning and Mathematical Analysis of Spatial Transcriptomics Data
Bioinformatics Specialists: Machine Learning and Genome Data Analytic

Singapore Single-Cell Network
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Selected Publications

  • Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Li H, Courtois ET, Sengupta D, Tan Y, Chen KH, Goh JJL, Kong SL, Chua C, Hon LK, Tan WS, Wong M, Choi PJ, Wee LJK, Hillmer AM, Tan IB, Robson P, Prabhakar S. Nat Genet 2017 May; 49(5):708-18. Abstract
  • Histone Acetylome-wide Association Study of Autism Spectrum Disorder. Sun W, Poschmann JF, del Rosario RC, Parikshak NN, Hajan HS, Kumar V, Ramaswamy R, Belgard TG, Elanggovan B, Wong CCY, Mill J, Geschwind DH, Prabhakar S. Cell 2016 Nov; 167:1385-1397. Abstract
  • Comprehensive benchmarking reveals H2BK20 acetylation as a distinctive signature of cell-state-specific enhancers and promoters. Kumar V, Rayan NA, Muratani M, Lim S, Elanggovan B, Lixia X, Lu T, Makhija H, Poschmann J, Lufkin T, Ng HH, Prabhakar S. Genome Res 2016 May; 26(5):612-23. Abstract
  • Sensitive detection of chromatin-altering polymorphisms reveals autoimmune disease mechanisms. Del Rosario RC, Poschmann J, Rouam SL, Png E, Khor CC, Hibberd ML, Prabhakar S. Nat Methods 2015 May; 12:458-64. Abstract
  • Noncoding Origins of Anthropoid Traits and a New Null Model of Transposon Functionalization. del Rosario RC, Rayan NA, Prabhakar S. Genome Research 2014 Sep; 24(9):1469-84. Abstract
  • Uniform, optimal signal processing of mapped deep-sequencing data. Kumar V, Muratani M, Rayan NA, Kraus P, Lufkin T, Ng HH, Prabhakar S. Nat Biotechnol 2013 July; 31(7):615-22. Abstract
  • TherMos: Estimating protein-DNA binding energies from in vivo binding profiles. Sun W, Hu X, Lim MH, Ng CK, Choo S, Castro D, Drechsel D, Guillemot F, Kolatkar PR, Jauch R, Prabhakar S. Nucleic Acids Res 2013 Jun 1; 41(11):5555-68. Abstract
  • Comprehensive prediction in 78 human cell lines reveals rigidity and compactness of transcription factor dimers. Jankowski A, Szczurek E, Jauch R, Tiuryn J, Prabhakar S. Genome Res 2013 Aug;23(8):1307-18. Abstract
  • In Vivo epigenomic profiling of germ cells reveals germ cell molecular signatures. Ng JH, Kumar V, Muratani M, Kraus P, Yeo JC, Yaw LP, Xue K, Lufkin T, Prabhakar S#, Ng HH#. (#: corresponding author) Dev Cell 2013 Feb 11; 24(3):324-33. Abstract
  • Human-specific gain of function in a developmental enhancer. Prabhakar S, Visel A, Akiyama JA, Shoukry M, Lewis KD, Holt A, Plajzer-Frick I, Morrison H, FitzPatrick DR, Afzal V, Rubin EM, Noonan JP. Science. 2008 Sep;321:1346-50. Abstract