Parallel Implementation of MotifBased Clustering for HTSELEX Dataset A clustering method for highthroughput sequencing with SELEX pools (HTSELEX) is crucial for selecting different types of aptamer candidates. The fast and accurate clustering method is indispensable for an enormous sequence data produced by HTSELSEX. We have already developed a fast motifbased clustering (FMBC) method for HTSELEX data implemented by R language. FMBC exhibited high accuracy of sequence clustering compared with conventional methods, while the processing time of FMBC is longer than AptaCluster. This paper proposes the parallel implementation of FMBC using Python with multithreading to improve the performance of FMBC. Experimental evaluation using the NCBI SRA data of SRR3279661 from BioProject PRJNA315881 demonstrated that parallel FMBC exhibited higher accuracy of clustering and shorter processing time than conventional methods.

Gclust: A Parallel Clustering Tool for Microbial Genomic Data Abstract   The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for nonredundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for noncommercial use at  https:github.comniulabgclust . We also introduce a web server for clustering useruploaded genomes at  http:niulab.scgrid.cngclust .