Evidence map›Paper›PMID 26157642›Full record

ArticlePeerJ2015

A reproducible approach to high-throughput biological data acquisition and integration.

Daniela Börnigen, Yo Sup Moon, Gholamali Rahnavard, Levi Waldron, Lauren McIver, Afrah Shafquat, Eric A Franzosa, Larissa Miropolsky, Christopher Sweeney, Xochitl C Morgan and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Low Tristetraprolin Expression Is Associated with Lethal Prostate Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019
    Article
  3. bioBakery: a meta'omic analysis environment.Bioinformatics (Oxford, England) · 2018
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Systems Medicine as an Emerging Tool for Cardiovascular Genetics.Frontiers in cardiovascular medicine · 2016
    Review
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Daniela Börnigen *Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.
Yo Sup Moon *Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.
Gholamali RahnavardBiostatistics Department, Harvard School of Public Health, Boston, MA, USA.
Levi WaldronBiostatistics Department, Harvard School of Public Health, Boston, MA, USA.
Lauren McIverBiostatistics Department, Harvard School of Public Health, Boston, MA, USA.
Afrah ShafquatBiostatistics Department, Harvard School of Public Health, Boston, MA, USA.
Eric A FranzosaBiostatistics Department, Harvard School of Public Health, Boston, MA, USA.
Larissa MiropolskyBiostatistics Department, Harvard School of Public Health, Boston, MA, USA.
Christopher SweeneyDana-Farber Cancer Institute, Boston, MA, USA.
Xochitl C Morgan *Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.
Wendy S Garrett *The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Curtis HuttenhowerBiostatistics Department, Harvard School of Public Health, Boston, MA, USA.

Funding

Gut microbiota and inflammatory monocytes in colorectal cancerR01CA154426 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI GARRETT, WENDY S. · 2011 to 2025
$5.2M
NCI NIH HHS R01 CA154426
6 · The paper itself

Abstract

Modern biological research requires rapid, complex, and reproducible integration of multiple experimental results generated both internally and externally (e.g., from public repositories). Although large systematic meta-analyses are among the most effective approaches both for clinical biomarker discovery and for computational inference of biomolecular mechanisms, identifying, acquiring, and integrating relevant experimental results from multiple sources for a given study can be time-consuming and error-prone. To enable efficient and reproducible integration of diverse experimental results, we developed a novel approach for standardized acquisition and analysis of high-throughput and heterogeneous biological data. This allowed, first, novel biomolecular network reconstruction in human prostate cancer, which correctly recovered and extended the NFκB signaling pathway. Next, we investigated host-microbiome interactions. In less than an hour of analysis time, the system retrieved data and integrated six germ-free murine intestinal gene expression datasets to identify the genes most influenced by the gut microbiota, which comprised a set of immune-response and carbohydrate metabolism processes. Finally, we constructed integrated functional interaction networks to compare connectivity of peptide secretion pathways in the model organisms Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa.

Indexed as

Data acquisitionData integrationHeterogeneous dataHigh-throughput dataMeta-analysisReproducibility

Identifiers

PMID26157642
PMCPMC4493686

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.