02460nas a2200373   4500000000100000000000100001008004100002260001600043100001900059700001600078700002200094700002600116700001600142700002000158700001600178700001900194700002200213700002200235700001800257700001900275700001700294700001700311700002000328700001600348700001900364700001900383700001600402700001700418245010400435300000700539490000600546520152000552022001402072       2023                            d  c2023 Mar 201 aRichard Barker1 aColin Kruse1 aChristina Johnson1 aAmanda Saravia-Butler1 aHomer Fogle1 aHyun-Seok Chang1 aRalph Trane1 aNoah Kinscherf1 aAlicia Villacampa1 aAránzazu Manzano1 aRaúl Herranz1 aLaurence Davin1 aNorman Lewis1 aImara Perera1 aChris Wolverton1 aParul Gupta1 aPankaj Jaiswal1 aSigrid Reinsch1 aSarah Wyatt1 aSimon Gilroy00aMeta-analysis of the space flight and microgravity response of the Arabidopsis plant transcriptome.  a210 v93 a<p>Spaceflight presents a multifaceted environment for plants, combining the effects on growth of many stressors and factors including altered gravity, the influence of experiment hardware, and increased radiation exposure. To help understand the plant response to this complex suite of factors this study compared transcriptomic analysis of 15 Arabidopsis thaliana spaceflight experiments deposited in the National Aeronautics and Space Administration's GeneLab data repository. These data were reanalyzed for genes showing significant differential expression in spaceflight versus ground controls using a single common computational pipeline for either the microarray or the RNA-seq datasets. Such a standardized approach to analysis should greatly increase the robustness of comparisons made between datasets. This analysis was coupled with extensive cross-referencing to a curated matrix of metadata associated with these experiments. Our study reveals that factors such as analysis type (i.e., microarray versus RNA-seq) or environmental and hardware conditions have important confounding effects on comparisons seeking to define plant reactions to spaceflight. The metadata matrix allows selection of studies with high similarity scores, i.e., that share multiple elements of experimental design, such as plant age or flight hardware. Comparisons between these studies then helps reduce the complexity in drawing conclusions arising from comparisons made between experiments with very different designs.</p>
  a2373-8065