While unsupervised DNA pre-training has shown promise, we argue that supervised genomic profile prediction provides more effective DNA representations, since DNA functions are regulated by genomic profiles like chromatin accessibility. We propose Species-Profile Adaptive Collaborative Experts (SPACE), a model that uses Mixture of Experts (MoE) to capture cross-species and multi-profile relationships in genomic data. Through extensive evaluation, SPACE achieves state-of-the-art performance, demonstrating that supervised training with genomic profiles creates powerful DNA representations.