Gene Sequence Assembly Algorithm Model Based on the DBG Strategy and Its Application

With the continuous development of sequencing technology, the amount of bioinformatics data has increased geometrically, and the massive amount of bioinformatics data puts forward more stringent requirements for sequence assembly problems. The sequence assembly algorithm based on DBG (De Bruijn graph) strategy is a key algorithm in bioinformatics, which is widely used in the domain of gene sequence assembly. Current research on the domain of sequence assembly always focuses on optimization of specific steps to a specific algorithm and lack of research on domain-level high-abstract algorithm frameworks. To some extent, it leads to the redundancy of the sequence assembly algorithm, and some problems may be caused by the artificial selection algorithm. This paper analyzes the domain of DBGSA and establishes a feature model of this domain. Based on the production programming method, the DBGSA algorithm component is interactively designed. With the support of the PAR platform, the DBGSA algorithm component library is formally implemented, and furthermore, the DBGSA component library is used to assemble the specific algorithm. This research adds domain-level research to the domain of sequence assembly and implements the DBGSA component library, which can assemble specific sequence assembly algorithms, ensuring the efficiency of algorithm development and the reliability of assembly generation algorithms. At the same time, it also provides a valuable reference for solving problems in the domain of sequence assembly.

Facilitating the Verification of Diffusing Computations and Their Applications

We study a class of distributed algorithms, generally known by the name of diffusing computa- tions, that play an important role in all kinds distributed and/or database applications to perform tasks like termination detection, leader election, or propagation of information with feedback. We construct a highly parameterized abstract algorithm and shown that many existing algorithms and their applications can be obtained from this abstract algorithm by instantiating the parameters appropriately and/or refining some of its actions. Subsequently, we show that this use of param- eterization and re-usability of notation and proof leads to a reduction of the effort and cost of developing and verifying distributed diffusing computations. More specific, we show that proving the correctness of any application now boils down to verifying an application-specific safety prop- erty and reusing the termination and safety proofs of the underlying abstract algorithm.

An Assessment of Algorithms for Deriving Failure Deterministic Finite Automata

Failure deterministic finite automata (FDFAs) represent regular languages more compactly than deterministic finite automata (DFAs). Four algorithms that convert arbitrary DFAs to language-equivalent FDFAs are empirically investigated. Three are concrete variants of a previously published abstract algorithm, the DFA-Homomorphic Algorithm (DHA). The fourth builds a maximal spanning tree from the DFA to derive what it calls a delayed input DFA. A first suite of test data consists of DFAs that recognise randomised sets of finite length keywords. Since the classical Aho-Corasick algorithm builds an optimal FDFA from such a set (and only from such a set), it provides benchmark FDFAs against which the performance of the general algorithms can be compared. A second suite of test data consists of random DFAs generated by a specially designed algorithm that also builds language-equivalent FDFAs, some of which may have non-divergent cycles. These random FDFAs provide (not necessarily tight) lower bounds for assessing the effectiveness of the four general FDFA generating algorithms.

Principle-Based Engineering

This chapter discusses a new conception of computation. The conception is one of constraints rather than rules. In contrast to the rule-based approach of Turing machines, Post systems and lambda calculus, the constraint-based approach “models” the constraints in operation in the system, and between the system and the environment. There are similarities with Putnam’s idea that “everything is computation” because (1) computation must be “situated” in a profound way, embedded in its environment, but, there is also (2) a move away from the intuitive idea of “algorithm” as a step-by-step procedure, modellling the behaviour of the system in its environment, requiring a mapping of the abstract “algorithm” states to the physical states of “reality.”