Efficient parallel derivation of short distinguishing sequences for nondeterministic finite state machines using MapReduce

Distinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper, we address the scalability issue encountered while deriving distinguishing sequences from complete observable nondeterministic finite state machines by introducing a massively parallel MapReduce version of the well-known Exact Algorithm. To the best of our knowledge, this is the first study to tackle this task using the MapReduce approach. First, we give a concise overview of the well-known Exact Algorithm for deriving distinguishing sequences from nondeterministic finite state machines. Second, we propose a parallel algorithm for this problem using the MapReduce approach and analyze its communication cost using Afrati et al. model. Furthermore, we conduct a variety of intensive and comparative experiments on a wide range of finite state machine classes to demonstrate that our proposed solution is efficient and scalable.

COMPUTATIONAL IDENTIFICATION OF PROMOTER REGIONS IN PROKARYOTES AND EUKARYOTES

Promoters are modular DNA structures that contain complex regulatory elements required for the initiation of gene transcription. Therefore, the use of machine learning methods to identify promoters is very important for improving genome annotation and understanding transcriptional regulation. In recent years, many methods for predicting eukaryotic and prokaryotic promoters have been proposed. However, the performance of these methods is still far from satisfactory. In this article, we have developed a hybrid method (called IPMD) that combines a position correlation score function and diversity increment with modified Mahalanobis Discriminant to predict eukaryotic and prokaryotic promoters. The precise calculation and identification of promoters remains a challenge because these key DNA regulatory regions have variable structures composed of functional motifs that can provide gene-specific transcription initiation. The promoter is a regulatory DNA region, which is very important for gene transcription regulation. It is located near the transcription start site (TSS) upstream of the corresponding gene. In the post-genomics era, the availability of data makes it possible to build computational models to detect promoters robustly, because these models are expected to be helpful to academia and drug discovery. Until recently, the developed model only focused on distinguishing sequences into promoters and non-promoters. However, by considering the classification of weak and strong promoters, promoter predictors can be further improved. INDEX TERMS—: deep learning, DNA sequence analysis, Promoter prediction, Promoters, Promoter elements