Evidence map›Paper›PMID 27153682›Full record

ArticleBioinformatics (Oxford, England)2016

RIPPER: a framework for MS1 only metabolomics and proteomics label-free relative quantification.

Susan K Van Riper, LeeAnn Higgins, John V Carlis, Timothy J Griffin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Software Options for the Analysis of MS-Proteomic Data.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  5. Article
  6. Article
  7. 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

4 authors.

Susan K Van RiperDepartment of Biomedical Informatics and Computational Biology, University of Minnesota, Rochester University of Minnesota Informatics Institute, University of Minnesota, St Paul.
LeeAnn HigginsDepartment of Biochemistry, Molecular Biology, and Biophysics.
John V CarlisDepartment of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Timothy J GriffinDepartment of Biochemistry, Molecular Biology, and Biophysics.

Funding

Characterizing Oral Cancer Progression via Saliva ProteomicsR01DE017734 · NIDCR · UNIVERSITY OF MINNESOTA · PI GRIFFIN, TIMOTHY J. · 2007 to 2010
$1.4M
NIDCR NIH HHS R01 DE017734
6 · The paper itself

Abstract

unlabelledRIPPER is a framework for mass-spectrometry-based label-free relative quantification for proteomics and metabolomics studies. RIPPER combines a series of previously described algorithms for pre-processing, analyte quantification, retention time alignment, and analyte grouping across runs. It is also the first software framework to implement proximity-based intensity normalization. RIPPER produces lists of analyte signals with their unnormalized and normalized intensities that can serve as input to statistical and directed mass spectrometry (MS) methods for detecting quantitative differences between biological samples using MS. AVAILABILITY AND IMPLEMENTATION: http://www.z.umn.edu/ripper CONTACT: vanr0014@umn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Mass SpectrometryMetabolomicsProteomicsSoftwareAlgorithmsComputational BiologyHumans

Identifiers

PMID27153682
PMCPMC4920113

What OpenQuestion holds

Textmetadata
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.