Bioinformatics & Computing

Projects in our lab revolve critically around bioinformatic analysis and integration of myriad datasets generated from state-of-the-art high-throughput technologies. This capability is shared across the experimental (“wet lab”) biologists and clinicians of our group, as well as with local and international collaborators. The bioinformatics team of 3 has substantial experience in analysing a wide range of ‘omics datasets including genome sequence, gene expression, protein expression, DNA methylation, ChIP-seq, QTL and Hi-C data. Our objective is to provide timely and accurate statistical analysis and interpretation, paying faithful attention to biology, the research question(s) and information generated by the multidisciplinary approach in our lab. We believe our work offers an indispensable link between “bench” and “bedside”, generating the necessary information to address current research topics in cardiovascular disease and personalized medicine, aimed at improving health outcomes directly translated into patient care and wellness.  

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Frequently applied computational expertise

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  • Transcriptomics: Microarray, bulk RNA-seq, single-cell RNA-seq and Cap Analysis of Gene Expression (CAGE) profiles.
  • Methylomics, Epigenomics: ChIP-seq, ATAC-seq and DNA methylation.
  • Clinical Genomics: Whole Genome Sequencing and Whole Exome Sequencing, Quantitative Trait Locus analysis.
  • Proteomics: Mass Spectrometry and Somalogic arrays.
  • Chromatin interactions: Hi-C, HiCChIP and ChIA-PET.

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Key research projects involving the bioinformatics team

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Next-Generation Sequencing in the Clinic is today’s leap forward in medicine. In the Heart Failure ATTRaCT BMRC/ASTAR-funded programme, we are analysing clinical samples for cardiac gene panels, Whole Exome, and Whole Genome Sequences. Our pipelines curate and interpret genetic variants, highlighting rare variants which may be causal to the underlying clinical phenotypes presented. An example of data output can be found at Wu et al. “Large-scale whole-genome sequencing of three diverse Asian populations in Singapore”. Link.
Identifying genetic markers for heterogeneous complex diseases such as heart failure has been challenging, and is likely to require prohibitively large cohort sizes in genome-wide association studies in order to identify variants that attain genome-wide statistical significance. On the other hand, chromatin quantitative trait loci elucidated by direct histone acetylation profiling (haQTL) of specific human tissues may help to prioritise sub-threshold variants for disease-association. In our most recent recent effort, we have performed enhancer H3K27ac ChIP-seq in human control and end-stage failing hearts and identified a large number of differentially acetylated peaks pointed to pathways altered in heart failure. Link. More information about the computational methodology is found at Sun et al. “Histone Acetylome-wide Association Study of Autism Spectrum Disorder”. Cell 2016, 167(5): 1385-1397. Link.
Heart failure is a leading cause of death in modern society. One of its underlying causes is the loss of cardiomyocytes, accompanied by functional derangement in contraction and relaxation. Studies on adult human hearts have found low rates of cardiomyocyte turnover mediated likely by the proliferation of pre-existing cardiomyocytes, suggesting that the development of pharmacological strategies to augment this process may represent revolutionary regenerative therapeutic approaches. We have developed a statistical pipeline to predict the cell cycle of human differentiated cardiomyocytes and integrate the predictions with their single-cell RNA-seq expression profiles to derive key cell cycle regulator targets. More information about the computational methodology is found at Motakis and Low. “CONFESS: fluorescence-based single-cell ordering in R”. Journal of Statistical Software (to appear) [/trx_ tab]
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Programming expertise

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R

Python

C++

Perl

Bash

“Large-scale whole-genome sequencing of three diverse Asian populations in Singapore”. Cell 2019 (link)

“Histone Acetylome-wide Association Study of Autism Spectrum Disorder”. Cell 2016, 167(5): 1385-1397. (link)

More information about the computational methodology is found at Motakis and Low. “CONFESS: fluorescence-based single-cell ordering in R”. Journal of Statistical Software (to appear). (link)

Wilson, Makis, Ah Jung

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