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Zhangjium Fei

Adjunct Associate Professor

Boyce Thompson Institute, Room 227
607-254-3234
Research Overview

The development of high throughput technologies has given rise to a wealth of information at system level including genome, transcriptome, proteome and metabolome. However, it remains a major challenge to digest the massive amounts of information and use it in an intelligent and comprehensive manner. To address this question, Dr. Fei’s group has focused on developing computational tools and resources to analyze and integrate large scale “omics” datasets,” which help researchers to understand how genes work together to comprise functioning cells and organisms.

Development of online databases to facilitate data distribution, analysis, mining and integration

Development of computational tools for omics data analysis

  • Plant MetGenMAP – a web-based tool for comprehensive mining and integration of gene expression and metabolite changes in the context of biochemical pathways.
  • iAssembler – A de novo assembly package for transcriptome sequences generated using 454 or Sanger platforms
  • iTAK – A package to identify and classify plant transcription factors and protein kinases.
  • VirusDetect – An automated pipeline for efficient virus discovery using deep sequencing of small RNAs.

Application of NGS technologies and bioinformatics in crop improvement

During the past several years, significant progresses have been made regarding the DNA sequencing technologies. As a result, several next-generation sequencing (NGS) platforms, such Illumina HiSeq, have received wide applications due to their high throughput and low cost. We are interested in using NGS technologies to investigate genomes, epigenomes and transcriptomes of several economically important crops including tomato, cucurbits, sweetpotato, and fruit tree crops, to facilitate the understanding of the evolution and regulatory networks of important agronomical traits. We are also using NGS technologies to perform large-scale virus survey for crops like sweet potato and tomato, in an effort to understand global virus diversity, distribution and evolution in important food crops.

Inferring gene regulatory networks

Living cells are the product of gene expression programs involving regulated transcription of thousands of genes. How a collection of transcriptional regulatory factors associates with genes during specific biological processes or under specific environmental conditions can be described as a gene regulatory network. We are interested in developing new algorithms to infer gene regulatory networks by integrating datasets from various different sources, including gene expression data, metabolomics data, promoter sequences, and microRNA information.