Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Exon level integration of proteomics and microarray data.

Danny A Bitton1, Michał J Okoniewski, Yvonne Connolly

  • 1Cancer Research UK, Applied Computational Biology and Bioinformatics Group, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Wilmslow Road, Manchester, M20 4BX, UK. dbitton@picr.man.ac.uk

BMC Bioinformatics
|February 27, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ovo, an open-source ecosystem for de novo protein design.

Communications biology·2026
Same author

Runx1 and Runx2 act in concert to suppress Wnt/β-catenin-driven mammary tumourigenesis.

British journal of cancer·2026
Same author

Author Correction: A PP1-PP2A phosphatase relay controls mitotic progression.

Nature·2026
Same author

InCytokine, an Open-Source Software, Reveals a TREM2 Variant-Specific Cytokine Signature.

International journal of molecular sciences·2026
Same author

MAPK-driven epithelial cell plasticity drives colorectal cancer therapeutic resistance.

Nature·2025
Same author

Hepatic zonation determines tumorigenic potential of mutant β-catenin.

Nature·2025
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Integrating quantitative proteomics and microarray data at the exon level significantly improves correlation. This novel approach accounts for alternative splicing, enhancing the analysis of genomic and proteomic datasets.

Area of Science:

  • Genomics
  • Proteomics
  • Bioinformatics

Background:

  • Previous studies show poor correlation between quantitative proteomics and microarray data.
  • This discrepancy may stem from assays targeting different genomic regions and alternative splicing effects.

Purpose of the Study:

  • To improve the correlation between quantitative proteomics and microarray data.
  • To investigate the impact of alternative splicing on data integration.

Main Methods:

  • Integrated quantitative protein mass spectrometry with Affymetrix Exon array data.
  • Utilized a genome database for data integration at the individual exon level.
  • Performed the study using cell lines in equilibrium to minimize biological variation.

Related Experiment Videos

Main Results:

  • Achieved significantly higher correlation (r = 0.808) between proteomics and microarray data compared to previous studies.
  • Demonstrated the effectiveness of exon-level integration for improving data correspondence.
  • Validated the data integration methods by focusing on analysis rather than biological variation.

Conclusions:

  • Data analysis and limited granularity contribute to variations in integrated microarray and proteomics data.
  • The exon-level integration approach enables the combined analysis of microarray and proteomics datasets.
  • This method is crucial for studying alternative splicing in the human genome.