Genomics data introduce a substantial computational burden as well as data privacy and ownership issues. Data sets generated by high-throughput sequencing platforms require immense amounts of computational resources to align to reference genomes and to call and annotate genomic variants. This problem is even more pronounced if reanalysis is needed for new versions of reference genomes, which may impose high loads to existing computational infrastructures. Additionally, after the compute-intensive analyses are completed, the results are either kept in centralized repositories with access control, or distributed among stakeholders using standard file transfer protocols. This imposes two main problems: (1) Centralized servers become gatekeepers of the data, essentially acting as an unnecessary mediator between the actual data owners and data users; and (2) servers may create single points of failure both in terms of service availability and data privacy. Therefore, there is a need for secure and decentralized platforms for data distribution with user-level data governance. A new technology, blockchain, may help ameliorate some of these problems. In broad terms, the blockchain technology enables decentralized, immutable, incorruptible public ledgers. In this Perspective, we aim to introduce current developments toward using blockchain to address several problems in omics, and to provide an outlook of possible future implications of the blockchain technology to life sciences.
Abstract. We revisit the problem of finding shortest unique substring (SUS) proposed recently by [6]. We propose an optimal O(n) time and space algorithm that can find an SUS for every location of a string of size n. Our algorithm significantly improves the O(n 2 ) time complexity needed by [6]. We also support finding all the SUSes covering every location, whereas the solution in [6] can find only one SUS for every location. Further, our solution is simpler and easier to implement and can also be more space efficient in practice, since we only use the inverse suffix array and longest common prefix array of the string, while the algorithm in [6] uses the suffix tree of the string and other auxiliary data structures. Our theoretical results are validated by an empirical study that shows our algorithm is much faster and more space-saving than the one in [6].
Repeat finding in strings has important applications in subfields such as computational biology. Surprisingly, all prior work on repeat finding did not consider the constraint on the locality of repeats. In this paper, we propose and study the problem of finding longest repetitive substrings covering particular string positions. We propose an O(n) time and space algorithm for finding the longest repeat covering every position of a string of size n. Our work is optimal since the reading and the storage of an input string of size n takes O(n) time and space. Because any substring of a repeat is also a repeat, our solution to longest repeat queries effectively provides a "stabbing" tool for practitioners for finding most of the repeats that cover particular string positions.
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