refine.bio
  • Search
      • Normalized Compendia
      • RNA-seq Sample Compendia
  • Docs
  • About
  • My Dataset
github link
Showing 1 of 1 results
Sort by

Filters

Technology

Platform

accession-icon SRP171051
Small Sample-Big Data: Integrative Indexed Systems Biology Reveals Dramatic Molecular Ontogeny over the First Week of Human Life in Papua New Guinea
  • organism-icon Homo sapiens
  • sample-icon 50 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

This study examines the global transcriptomic profiles in peripheral blood of Papua New Guinea newborns at birth (D0) comparing with follow up at day 1 (D1), day 3 (D3), or day 7 (D7) post birth. Overall design: Systems biology provides a powerful approach to unravel complex biological processes yet it has not been applied systematically to samples from newborns, a group highly vulnerable to a wide range of diseases. Published methods rely on blood volumes that are not feasible to obtain from newborns. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1ml of blood, a volume readily obtained from newborns. Furthermore, indexing to baseline and applying innovative integrative computational methods that address the challenge of few data points with many features enabled identification of robust findings within a readily achievable sample size. This approach uncovered dramatic changes along a stable developmental trajectory over the first week of life. The ability to extract information from 'big data' and draw key insights from such small sample volumes will enable and accelerate characterization of the molecular ontogeny driving this crucial developmental period.

Publication Title

Dynamic molecular changes during the first week of human life follow a robust developmental trajectory.

Sample Metadata Fields

Sex, Subject

View Samples
Didn't see a related experiment?

refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

fund-icon Fund the CCDL

Developed by the Childhood Cancer Data Lab

Powered by Alex's Lemonade Stand Foundation

Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

BSD 3-Clause LicensePrivacyTerms of UseContact