['"The annotation of the Affymetrix HTA 2.0 array was updated to optimise the detection of RNA in human skeletal muscle biopsy samples by removing invalid and low signal-high-variance probes (as for CDF GPL24047). The probes were then summarized into groups (probe-sets) reflecting either an ensembl full transcript identifier (FL-ENST, GPL24047) or just the probes targeting the 3\' UTR or the 5\' UTR of that particular ENST. Therefore, 3 different CDF were used to process the HTA 2.0 arrays in this study. Note that each CEL file was GC adjusted using APT while our custom CDF pipeline removes any probe that has >80% or <20% GC content (~50,000). The analysis was carried out only on the pairs of probe-sets i.e. FL-ENST vs 3\'UTR or FL-ENST vs 5\'UTR or 3\'UTR vs 5\'UTR. Dynamic muscle loading alters tissue phenotype reflecting altered metabolic and functional demands. In humans, heterogeneous adaptation to loading complicates identification of the underpinning molecular regulators. We present a within-person analysis strategy that reduced heterogeneity for changes in muscle mass by ~40% and employed a genome-wide transcriptome method that modeled each mRNA from coding exons and 3’/5’ untranslated (UTR) regions. Our strategy detected ~3-4 times more regulated genes than similarly sized studies, including substantial UTR-selective regulation that other methods would not detect. We discovered a core of 141 genes correlated to muscle growth validated from newly analyzed independent samples (n=100). Further validating these identified genes, via RNAi in primary muscle cells, we demonstrate that members of the core genes were regulators of protein synthesis e.g. Molecular Transducers of Physical Activity in Humans MoTrPAC. Employing proteome-constrained networks and pathway analysis revealed notable relationships with the molecular characteristics of human muscle aging and insulin sensitivity, as well as potential drug-therapies."']