Efficient and Accurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram Cutting PROJECT TITLE :Efficient and Accurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram CuttingABSTRACT:De novo clustering may be a widespread technique to perform taxonomic profiling of a microbial community by grouping 16S rRNA amplicon reads into operational taxonomic units (OTUs). In this work, we have a tendency to introduce a replacement dendrogram-primarily based OTU clustering pipeline referred to as CRiSPy. The key idea employed in CRiSPy to enhance clustering accuracy is the applying of an anomaly detection technique to obtain a dynamic distance cutoff rather than using the de facto worth of 97 % sequence similarity as in most existing OTU clustering pipelines. This technique works by detecting an abrupt modification in the merging heights of a dendrogram. To produce the output dendrograms, CRiSPy employs the OTU hierarchical clustering approach that's computed on a genetic distance matrix derived from an all-against-all scan comparison by pairwise sequence alignment. However, most existing dendrogram-primarily based tools have problem processing datasets larger than 10,00zero unique reads thanks to high computational complexity. We have a tendency to address this difficulty by developing two efficient algorithms for CRiSPy: a compute-economical GPU-accelerated parallel algorithm for pairwise distance matrix computation and a memory-efficient hierarchical clustering algorithm. Our experiments on numerous datasets with distinct attributes show that CRiSPy is in a position to produce additional correct OTU groupings than most OTU clustering applications. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Clustering Game Behavior Data Inferring Time-Delayed Causal Gene Network Using Time-Series Expression Data