The Computational Biology team operates at the interface of the fields of biology, computer science, and statistics by developing and applying algorithms and models to understand biological systems and relationships.
The Computational Biology team at the Stowers Institute assists investigators with the analysis of biological data. The group combines software development and technical skills with biological insights to help find answers in complex and massive datasets. The bioinformatics expertise spans a wide range of topics, including but not limited to:
- Sequence QA/QC
- RNA-seq, single cell RNA-seq, and spatial RNA-seq
- Small RNA-seq
- ChIP-seq, Cut & Tag
- Ribosomal profiling
- Transcriptome/Genome assembly and annotation
- Variant Calling
- PacBio and Oxford Nanopore long read data analysis
- Custom code and pipeline creation
Open source software
- Bowtie, STAR, bwa are used for basic alignment.
- Trinity and StringTie are used for assembly.
- R and Bioconductor packages used for data analysis and visualization.
- GATK / DeepVariant for variant analysis.
Based on the research, Computational Biology can develop custom tools for specific needs.
Hua Li received an M.S. in Forestry Genetics from Beijing Forestry University in China and a Ph.D. in Bioinformatics from North Carolina State University. During postdoctoral studies at Virginia Bioinformatics Institute, she developed software (in C, SAS and R) for mass Affymetrix gene-chip data analysis and designed greenhouse and Affymetrix gene expression experiments. In 2006, she joined the Stowers Institute and completed her postdoctoral training in 2007. During that time, Li applied bi-variate analysis to improve the power of genome-wide association studies and constructed a Bayesian network using relaxed gene ordering. She became the Computational Biology group leader in 2009 and Head of Computational Biology in 2017. With over 10 years of bioinformatics experience, Li was appointed Director of Computational Biology Bioinformatics and Biostatistics in 2019.