Silvia (Shuchang) Liu, PhD

Silvia (Shuchang) Liu, PhD*

(412) 648-4288

S406 Biomedical Science Tower
200 Lothrop Street
Pittsburgh, PA 15261

Liver-Related Work

I have been working in the area of biotechnology, biostatistics and bioinformatics. I have been one of the key members in the team to analyze whole genome sequencing, methylation sequencing, ChIP sequencing and transcriptome sequencing. We developed several models in making prognostic or diagnostic predictions of human cancers (Hepatocellular Carcinoma, Prostate Cancer, Breast Cancer, etc). During the process, I have developed some novel methodologies to improve the fusion transcript detection in transcriptome sequencing. I am also a key member in developing the CNV models to predict prostate cancer recurrence based on the Affymetrix SNP6.0 analysis of the genomic DNA from the circulating leukocytes. All these studies contribute to the novel biomarker discovery and disease status/subtype prediction.
My research effort also focused on whole genome sequencing analysis for structural variation detection. We applied and developed new bioinformatics pipelines to solve both research and clinical problems. We introduced machine learning models to optimally combine the structural variations called by multiple algorithms. Our analyses also show that the knowledge from the long-read sequencing can guide the short-read discovery. We developed a new bioinformatics tool to detect large copy number variations from WGS data with high sensitivity using Hidden Markov Model and k-mer signals. Due to the high resolution and accuracy of the whole genome sequencing, it may potentially replace the traditional methods such as karyotyping as a first-tier cytogenetic diagnosis.
My major research interests include:
  1. High throughput genomic data analysis by machine learning and statistical methods
    Clustering, classification, bi-clustering, meta-analysis, omics data analysis, linear models, regression, probability theory, gene regulation, data preprocessing, feature selection, dimension reduction, biomarker detection, pathway analysis, differential expression analysis, microarray (mRNA, SNP, etc) data analysis, clinical/survival data analysis
  2. Next Generation Sequencing (NGS) and Long-read Sequencing data analysis 
    Transcriptome sequencing (RNA-Seq), whole genome sequencing (WGS), whole exome sequencing (WES), Chip-Seq, Oxford Nanopore sequencing, quality control (QC), trimming, alignment, fusion gene detection, gene expression, structural variation (SV) detection, copy number variation (CNV) analysis, SNP calling, peak calling, long-read sequencing (third-generation) analysis


PLRC Genomics and Systems Biology Core Manager

Assistant Professor
Department of Pathology