We utilize liquid chromatography, mass spectrometry, and bioinformatics methods in our research.

High-Resolution Mass Spectrometry

We operate our own Thermo Q-Exactive HF hybrid Quadrupole Orbitrap mass spectrometer, which was installed in the laboratory in September 2017. This instrument is capable of generating mass spectra with resolving power up to 240,000 and has excellent scan speed and performance for proteomics applications.

Multi-Dimensional Chromatography

We construct multi-dimensional liquid chromatography platforms, combining high/low pH reversed phase liquid chromatography and porous graphitic carbon chromatography for high-capacity peptide and glycan separation.


Ongoing projects include protein dynamics, protein isoform, and quantitative methods.


Proteins in the cell are finely balanced in a dynamic equilibrium of synthesis and degradation to maintain protein integrity and protein homeostasiss (“proteostasis”). When protein dynamics becomes disrupted, endoplasmic reticulum stress (ER stress) results and prompts potent response pathways that can either restore proteostasis or precipitate cell death. Mounting evidence now implicates ER stress as a central and conserved feature in multiple cardiac diseases, including cardiac hypertrophy and failure. Inhibition of ER stress shows therapeutic promises in reversing pathological aspects of cardiac remodeling in animals, suggesting proteostasis is causally linked to pathogenesis. However, relatively little is currently known on the quantitative parameters that describe the proteostasis of cardiac proteins, which encompasses proper protein synthesis, folding, glycosylation, and other dynamic processes that cannot be espied from steady-state expression. Without methods to quantify the temporal dynamics of protein networks, we lack a basic starting point from which to identify protein pathways targeted by proteostasis disruptions. Our goal is to better understand the proteostatic processes of the cardiac proteome and how they are regulated in health and disease.


Advances in large-scale “omics” approaches have led to an explosion of “gene list” data – lists of genes/proteins implicated in biological models resulting from discovery experiments. Connecting implicated molecules to useful knowledge is currently a major bottleneck in data interpretation. Gene Ontology (GO), KEGG, and other annotation sources are commonly used to analyze gene lists, but existing annotations are often better represented in lower-level functional concepts (e.g., whether a gene codes for a kinase or a cytosolic protein) while less complete in higher physiological concepts (e.g., how relevant is the gene to gastrointestinal processes). To help translate these molecular data from “omics” studies into biological knowledge, we are in the process of creating new annotation strategies to make sense of discovered genes and connect to known disease processes. Recently, our publications have demonstrated that public PubMed data can be mined to identify the semantic similarity between each gene/protein and higher physiological functions, and indeed any ontologies or concepts determined by the user that are searchable on PubMed (Lam et al. JACC 2015 and J Proteome Res 2016). We currently host a web app, “PubPular”, to allow users to specify a biomedical topic (e.g., cancer) and retrieve its popularly associated genes. PubPular is accessible via


By enabling multiple protein isoforms to be encoded in one gene, alternative pre-mRNA processing such as alternative splicing (AS) constitutes a major source of proteome complexity in eukaryotes. Differential isoform expression is a critical feature of human diseases from cancer to heart failure, as well as responses to environmental stress including alcohol and oxidative damage. Their importance notwithstanding, current knowledge remains poor on the functional consequences of alternative protein isoforms. In this project, our goal is to develop new multi-omics strategies by integrating RNA-seq and shotgun proteomics approaches, to determine the differential expression of protein alternative isoforms in normal physiology and pathophysiology.


The following software tools from our group are available online.

  • roboparse


      Dev Cycle:

    rOboPrase is a simpl parser for term attributes in OBO v.1.2 files such as found on Bioportal, used by OBO-edit for viewing and editing ontologies.

  • pymzid


      Dev Cycle:

    A python tool to read in mzIdentML v.1.2.0 proteomics search result files based on HUPO Human Proteome Standard Initiative specifications.

  • pubpular


      Dev Cycle:

    A bibliometrics analysis algorithm and R/Shiny web server to identify popular and relevant proteins in a topic of interest using literature data.