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Computational challenges in genomics data mining and analysis are an impending roadblock in modern health-sciences research, where algorithms for DNA sequence processing grow alarmingly in computational needs as datasets from new instruments continue to dramatically expand. Reconfigurable computers featuring customizable processing devices (e.g. FPGAs) hold the key in addressing these challenges with high performance, productivity, and sustainability, where the architecture adapts to match the unique needs of each application instead of vice-versa. In this paper, we present the novel use of a method for incorporating control information into the data stream, limiting wasted cycles and increasing hardware utilization. This method is described in Section II, and then featured in Section III as device-level reconfigurable architectures for in-stream control with scalable systolic arrays to accelerate two leading genomics applications based upon wavefront algorithms, NeedlemanWunsch (NW) and Smith-Waterman (SW) [1], and a third application, Needle-Distance (ND) [2], an augmentation of NW. These architectures are experimentally evaluated on Novo-G, the reconfigurable supercomputer in the NSF CHREC Center at Florida, where results achieve unprecedented levels of sustained performance for these problems. Case-study results are reported in Section IV, followed by Section V with summary and conclusions.