• Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes

Personal omics profiling reveals dynamic molecular and medical phenotypes.

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.

Pubmed ID: 22424236

Authors

  • Chen R
  • Mias GI
  • Li-Pook-Than J
  • Jiang L
  • Lam HY
  • Chen R
  • Miriami E
  • Karczewski KJ
  • Hariharan M
  • Dewey FE
  • Cheng Y
  • Clark MJ
  • Im H
  • Habegger L
  • Balasubramanian S
  • O'Huallachain M
  • Dudley JT
  • Hillenmeyer S
  • Haraksingh R
  • Sharon D
  • Euskirchen G
  • Lacroute P
  • Bettinger K
  • Boyle AP
  • Kasowski M
  • Grubert F
  • Seki S
  • Garcia M
  • Whirl-Carrillo M
  • Gallardo M
  • Blasco MA
  • Greenberg PL
  • Snyder P
  • Klein TE
  • Altman RB
  • Butte AJ
  • Ashley EA
  • Gerstein M
  • Nadeau KC
  • Tang H
  • Snyder M

Journal

Cell

Publication Data

March 16, 2012

Associated Grants

  • Agency: European Research Council, Id: 232854
  • Agency: NHLBI NIH HHS, Id: K08 HL083914
  • Agency: NIH HHS, Id: OD004613
  • Agency: NHGRI NIH HHS, Id: P50 HG002357
  • Agency: NHGRI NIH HHS, Id: P50 HG002357-06
  • Agency: NHGRI NIH HHS, Id: P50 HG002357-07
  • Agency: NHGRI NIH HHS, Id: P50 HG002357-08
  • Agency: NHGRI NIH HHS, Id: P50 HG002357-09
  • Agency: NHGRI NIH HHS, Id: P50 HG002357-10
  • Agency: NHGRI NIH HHS, Id: P50 HG002357-11
  • Agency: NIGMS NIH HHS, Id: R24-GM61374
  • Agency: NLM NIH HHS, Id: T15 LM007033
  • Agency: NLM NIH HHS, Id: T15-LM007033
  • Agency: NIGMS NIH HHS, Id: T32 GM007205
  • Agency: NHGRI NIH HHS, Id: T32 HG000044
  • Agency: NHGRI NIH HHS, Id: T32 HG000044-14
  • Agency: NHGRI NIH HHS, Id: T32 HG000044-15
  • Agency: NHLBI NIH HHS, Id: T32 HL094274
  • Agency: NHGRI NIH HHS, Id: U54 HG004558
  • Agency: NHGRI NIH HHS, Id: U54 HG004558-04
  • Agency: NHGRI NIH HHS, Id: U54 HG004558-05
  • Agency: NHGRI NIH HHS, Id: U54 HG004558-05S1

Mesh Terms

  • Diabetes Mellitus, Type 2
  • Female
  • Gene Expression Profiling
  • Genome, Human
  • Genomics
  • Humans
  • Individualized Medicine
  • Male
  • Metabolomics
  • Middle Aged
  • Mutation
  • Proteomics
  • Respiratory Syncytial Viruses
  • Rhinovirus