Genetic Sequence Comparison is an important operation in Bioinformatics, executed routinely worldwide. Two relevant algorithms that compare genetic sequences are the Smith-Waterman (SW) algorithm and Sankoff’s algorithm. The Smith-Waterman algorithm is widely used for pairwise comparisons and it obtains the optimal result in quadratic time - O(n2), where n is the length of the sequences. The Sankoff algorithm is used to structurally align two sequences and it computes the optimal result in O(n4) time. In order to accelerate these algorithms, many parallel strategies were proposed in the literature. However, the alignment of whole chromosomes with hundreds of millions of characters with the SW algorithm is still a very challenging task, which requires extraordinary computing power. Likewise, obtaining the structural alignment of two sequences with the Sankoff algorithm requires parallel approaches. In this talk, we first present our MASA-CUDAlign tool, which was used to pairwise align real DNA sequences with up to 249 millions of characters in a cluster with 512 GPUs, achieving the best performance in the literature in 2021. We will present and discuss the innovative features of the most recent version of MASA-CUDAlign: parallelogram execution, incremental speculation, block pruning and score-share balancing strategies. We will also show performance and energy results in homogeneous and heterogeneous GPU clusters. Then, we will discuss the design of our CUDA-Sankoff tool and its innovative strategy to exploit multi-level wavefront parallelism. At the end, we will show a covid-19 case study, where we use the tools discussed in this talk to compare the SARS-CoV-2 genetic sequences, considering the reference sequence and its variants.