TY - JOUR AU - Baron, Daniel AU - Dubois, Emeric AU - Bihouée, Audrey AU - Teusan, Raluca AU - Steenman, Marja AU - Jourdon, Philippe AU - Magot, Armelle AU - Péréon, Yann AU - Veitia, Reiner AU - Savagner, Frédérique AU - Ramstein, Gérard AU - Houlgatte, Rémi PY - 2011 DA - 2011/02/16 TI - Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns JO - BMC Genomics SP - 113 VL - 12 IS - 1 AB - DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc. SN - 1471-2164 UR - https://doi.org/10.1186/1471-2164-12-113 DO - 10.1186/1471-2164-12-113 ID - Baron2011 ER -