RefSeq Refinements of UniGene-Based Gene Matching Improve the Correlation of Expression Measurements Between Two Microarray Platforms

Abstract

Matching genes across microarray platforms is a critical step in meta-analysis. Standard practice uses UniGene to match genes. Numerous studies have found poor correlations between platforms when using UniGene matching. We profiled samples from 33 breast cancer patients on two different microarray platforms (Affymetrix and cDNA) and investigated gene matching. Our results confirmed that UniGene-based matching led to poor correlations of gene expression between platforms. Using RefSeq, a database maintained by the National Center for Biotechnology Information (NCBI), we developed and implemented a new method to refine gene matching. We found that the correlations between gene expression measurements were substantially higher after the RefSeq matching. Our approach differs from previously reported sequence-matching approaches and retains useful expression measurements. It is a sensible approach for matching probes across platforms. We conclude that UniGene alone is insufficient to match genes across platforms. Refined matching based on RefSeq significantly improves the quality of matches.

Optimized Cross-Study Analysis of Microarray-Based Predictors

“…Because genomic and transcript sequences are continuously updated, mappings become inaccurate with time. In this study we have found, as previously noted, that different annotation tools [29,30], as well as different annotation methods yield different results [34]. It has been reported that matching genes at the sequence level is more efficient than identifier-based mappings [35,36].…”

Mapping complex disease traits with global gene expression

“…The situation can be improved when matching of genes is sought using genomic sequence rather than sequences inferred from the Unigene database of transcripts (http://www.ncbi.nlm.nih.gov/unigene) 54 . Concordance between platforms is improved further when probes are compared only when they target overlapping transcript sequence regions on cDNA microarrays or gene-chips 55 .…”

Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration

“…This difference between UniGene and RefSeq results, albeit small, is likely due to the different methods of identifying and assigning transcripts used in the process, and has been observed in prior studies also [4], [5], [6], [7], [8], [9], [10]. Even though we did observe variability due to different laboratory protocols as seen by previous studies, a superior correlation between tissues with similar sources of cells was able to surpass this limitation and make the meta-analysis scientifically useful.…”

“…UniGene IDs have been used as the matching criterion to merge data across various platforms, but this has led to a substantial portion of the data remaining unmatched in previous studies [4], [5], [6], [7], [8]. Recent approaches have tried using Reference Sequence (RefSeq) IDs as the matching criterion [9]. RefSeq is a public access database, also maintained by NCBI.…”