Team

Allison received her B.S. in Biological Sciences from University of California, Davis. After graduating, she joined a lab at the UC Davis School of Veterinary Medicine where she first learned about genome sequencing through researching the population genomics of disease-transmitting mosquitoes. She was introduced to liquid handlers and high-throughput NGS techniques during her time at an immuno-oncology startup, then further developed her skill set at Verily Life Sciences as a member of the Debug team, where she was responsible for troubleshooting and scaling up a bacterium detection pipeline that she used to process thousands of mosquitoes each week. She joined the Stanford Microbiome Therapies Initiative (MITI) in 2019, where she is applying her molecular biology experience to the human gut microbiome, and generates 16S, short-read, and long-read sequence data (Illumina and Oxford Nanopore) for strain purity assessment, genome assembly, and elucidation of bacterial community dynamics. In addition to MITI, Allison collaborates with investigators from CZ Biohub’s Microbiome Initiative to consult on and carry out NGS sample processing needs. She is always happy to discuss genomic sequencing, mosquitoes, human microbiomes, mosquito microbiomes, how mosquitoes are differentially drawn to certain human microbiomes, and how ironic she finds it that Stanford Biohub has a liquid handler called “the mosquito” that she uses to process microbiome samples.

Brian Yu received his Ph.D. in Electrical Engineering and carried out his thesis research in Stephen Quake’s lab in Bioengineering at Stanford University, where he developed high-throughput microfluidic and sequencing technologies to study microbes. He also created Mini-Metagenomics, a microfluidic method to sequence and constructing genomes of novel bacterial organisms from mixed communities. At CZ Biohub, Brian collaborates with faculties from Stanford, UCSF, and Berkeley who make up the Biohub Microbiome Intercampus Initiative. His research focuses at the intersection of multi-omics and the human gut microbiome, inventing high-throughput experimental and data analysis platforms with the aim of understanding strain-strain and strain-nutrient interactions. As a member of the Genomics Platform, Brian actively incorporates new sequencing technologies that benefit Biohub’s central research aims. In addition, he builds, maintains, and updates user-facing computational infrastructure, enabling user interactions with genomic platform capabilities.

Sunit graduated in 2011 with a Master of Science degree in Bioinformatics from University of Michigan (Ann Arbor, USA). While working as a Bioinformatics Specialist at the Michigan Geomicrobiology Lab, he was first introduced to the concept of prospecting complex microbial communities for natural therapeutic products. In 2016, he joined Second Genome where he contributed to the discovery of other biotherapeutic small molecules for therapeutic areas such as Immuno-oncology, Irritable Bowel Syndrome (IBS) and Diabetes. At Second Genome, he also led the development of their cloud based multi-modal-omics analysis pipelines and data infrastructure. In 2019, he joined CZ Biohub as a part of the Microbiome Initiative and the Stanford Microbiome Therapies Initiative (MITI). Here, he collaborates with a number of labs at Stanford to develop custom algorithms, data solutions and cloud infrastructure for the analysis of microbiome data.

Xiandong Meng is a bioinformatics scientist at Stanford, who supports the Michael Fischbach research group from raw sequences to complete genomes. He develops data processing pipelines to automate microbiome data analytics processes, proposes data solutions, and provides a deeper understanding of the human microbiome used in the Stanford Microbiome Therapies Initiative project. At Stanford, he works closely with the CZ Biohub team and contributes to the multiple key research projects. Xiandong holds a Ph.D. in Computer Engineering and spent years developing the bioinformatics software at Lawrence Berkeley National Laboratory.