Cancer Informatics

We aim at developing a streamlined genome sequence compression algorithm to support alternative miniaturized sequencing devices, which have limited communication, storage, and computation power. Existing techniques that require heavy client (encoder side) cannot be applied. To tackle this challenge, we carefully examined distributed source coding theory and developed a customized reference-based genome compression protocol to meet the low-complexity need at the client side. Based on the variation between source and reference, our protocol will pick adaptively either syndrome coding or hash coding to compress subsequences of changing code length. Our experimental results showed promising performance of the proposed method when compared with the state-of-the-art algorithm (GRS).