Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data

被引:35
|
作者
Yu, Hui [1 ]
Wang, Feng
Tu, Kang
Xie, Lu
Li, Yuan-Yuan
Li, Yi-Xue
Agrawal, Sunil
机构
[1] Shanghai Ctr Bioinformat Technol, Shanghai 200235, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Life Sci & Technol, Shanghai 200240, Peoples R China
[3] Chinese Acad Sci, Shanghai Inst Biol Sci, Key Lab Syst Biol, Shanghai 200031, Peoples R China
[4] Chinese Acad Sci, Shanghai Inst Biol Sci, Bioinformat Ctr, Shanghai 200031, Peoples R China
关键词
D O I
10.1186/1471-2105-8-194
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: The wide use of Affymetrix microarray in broadened fields of biological research has made the probeset annotation an important issue. Standard Affymetrix probeset annotation is at gene level, i.e. a probeset is precisely linked to a gene, and probeset intensity is interpreted as gene expression. The increased knowledge that one gene may have multiple transcript variants clearly brings up the necessity of updating this gene-level annotation to a refined transcript-level. Results: Through performing rigorous alignments of the Affymetrix probe sequences against a comprehensive pool of currently available transcript sequences, and further linking the probesets to the International Protein Index, we generated transcript-level or protein-level annotation tables for two popular Affymetrix expression arrays, Mouse Genome 430A 2.0 Array and Human Genome U133A Array. Application of our new annotations in re-examining existing expression data sets shows increased expression consistency among synonymous probesets and strengthened expression correlation between interacting proteins. Conclusion: By refining the standard Affymetrix annotation of microarray probesets from the gene level to the transcript level and protein level, one can achieve a more reliable interpretation of their experimental data, which may lead to discovery of more profound regulatory mechanism.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data
    Hui Yu
    Feng Wang
    Kang Tu
    Lu Xie
    Yuan-Yuan Li
    Yi-Xue Li
    BMC Bioinformatics, 8
  • [2] Advances and current limitations in transcript-level control of gene expression
    Leavitt, John M.
    Alper, Hal S.
    CURRENT OPINION IN BIOTECHNOLOGY, 2015, 34 : 98 - 104
  • [3] Transcript expression-aware annotation improves rare variant interpretation
    Cummings, Beryl B.
    Karczewski, Konrad J.
    Kosmicki, Jack A.
    Seaby, Eleanor G.
    Watts, Nicholas A.
    Singer-Berk, Moriel
    Mudge, Jonathan M.
    Karjalainen, Juha
    Satterstrom, F. Kyle
    O'Donnell-Luria, Anne H.
    Poterba, Timothy
    Seed, Cotton
    Solomonson, Matthew
    Alfoldi, Jessica
    Daly, Mark J.
    MacArthur, Daniel G.
    NATURE, 2020, 581 (7809) : 452 - +
  • [4] Transcript expression-aware annotation improves rare variant interpretation
    Beryl B. Cummings
    Konrad J. Karczewski
    Jack A. Kosmicki
    Eleanor G. Seaby
    Nicholas A. Watts
    Moriel Singer-Berk
    Jonathan M. Mudge
    Juha Karjalainen
    F. Kyle Satterstrom
    Anne H. O’Donnell-Luria
    Timothy Poterba
    Cotton Seed
    Matthew Solomonson
    Jessica Alföldi
    Mark J. Daly
    Daniel G. MacArthur
    Nature, 2020, 581 : 452 - 458
  • [5] Aggregating transcript-level analyses for single-cell differential gene expression
    Becht, Etienne
    Zhao, Edward
    Amezquita, Robert
    Gottardo, Raphael
    NATURE METHODS, 2020, 17 (06) : 583 - +
  • [6] Aggregating transcript-level analyses for single-cell differential gene expression
    Etienne Becht
    Edward Zhao
    Robert Amezquita
    Raphael Gottardo
    Nature Methods, 2020, 17 : 583 - 585
  • [7] Gene-level differential analysis at transcript-level resolution
    Lynn Yi
    Harold Pimentel
    Nicolas L. Bray
    Lior Pachter
    Genome Biology, 19
  • [8] Gene-level differential analysis at transcript-level resolution
    Yi, Lynn
    Pimentel, Harold
    Bray, Nicolas L.
    Pachter, Lior
    GENOME BIOLOGY, 2018, 19
  • [9] Transcript-level expression control of plant NLR genes
    Lai, Yan
    Eulgem, Thomas
    MOLECULAR PLANT PATHOLOGY, 2018, 19 (05) : 1267 - 1281
  • [10] Integrating multiple genome annotation databases improves the interpretation of microarray gene expression data
    Jun Yin
    Sarah McLoughlin
    Ian B Jeffery
    Antonino Glaviano
    Breandan Kennedy
    Desmond G Higgins
    BMC Genomics, 11