de novo repeat detection based on the third generation sequencing reads Repetitive sequences refer to fragments that appear at more than one location in a genome. Numerous studies have shown that the repetitive sequences in genomes play indispensable roles in the evolution, inheritance, variation, gene expression, transcriptional regulation, chromosome construction, and physiological metabolism of organisms. In many sequence and genome analyses such as read alignment, de novo assembly and genome annotation, repetitive sequences can pose major challenges. Detection and classification of repeats is one of the main steps for genome sequence analysis in bioinformatics. However, most existing de novo detection methods are difficult to achieve satisfactory results for marking repetitive regions in both size and accuracy due to the NGS reads are too short to identify long repeats and the raw SMS long reads are with the high error rates. In this study, we present a new de novo repeat detection method called DLR (Detection of Long Repeats) based on PacBio long reads. DLR first converts all long reads into unique  kmers  of a certain length, and screens out the  kmers  with the high frequency. Then, these high frequency  kmers  are aligned to long reads by using multiple sequence alignment, and the high frequency regions on long reads that are covered by those high frequency  kmers  are recorded. Finally, the recorded high frequency regions with inclusion relations are merged and the final repetitive sequences are obtained. The experimental results show that DLR achieves optimal results in terms of effective size and accuracy compared with other existing algorithms.

DGCF: A Distributed Greedy Clustering Framework for Largescale Genomic Sequences Clustering is a very fundamental while timeconsuming compute operation in biological sequence analysis. New sequencing technologies such as NGS and 3GS have dramatically increased both the dataset size and the length of a single read sequence. However, existing tools lack scalability for handling largescale datasets as well as long sequences. A feasible solution to this problem is to use parallel and distributed systems. The efficient deployment of such systems, however, requires high parallelism in both software implementations as well as algorithmic optimizations. In this paper, we propose DGCF, a Distributed Greedy Clustering Framework which is capable to handle largescale datasets and long sequences. Our framework adopts a greedy clustering strategy which overlaps communication with computation among many distributed computing nodes. We also design and implement a sparse suffix array (SSA)based alignment algorithm that can support long sequences. Experiments show that our framework achieves nearlinear speedups on a distributed memory cluster.