A Bioinformatics Approach for the Identification of Developmental QTL Candidate Genes

Alejandro Q Nato Jr¹, Bo Li², Fang Chen¹,
James H Millonig^1,2,3, and Tara C Matise¹

¹Department of Genetics, Rutgers University, NJ
²Center for Advanced Biotechnology and Medicine, NJ
³UMDNJ-Robert Wood Johnson Medical School, NJ

A Collaborative Study of Millonig and Matise Labs

To start using devQTL, click here or click the "devQTL" link above.



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