Computational approaches for the systematic analysis of aging-associated molecular alterations

Advanced computational approaches are needed to interpret the data generated by high-throughput molecular profiling methods. This necessity is particularly pressing in the field of aging research because (i) it is challenging to separate causes and consequences of aging, (ii) samples are often very precious, thus reducing the number of biological replicates, and (iii) heterogeneity between replicates can be particularly high at old age. Here, we describe computational approaches ranging from basic data processing up to the integration of heterogeneous data types. For future studies, we suggest to sample more time points and -omics layers, and to determine to what extent reference information from young animals can be transferred to old ones in data integration approaches.