SpCLUST: Towards a fast and reliable clustering for potentially divergent biological sequences

Abstract

This paper presents SpCLUST, a new C++ package that takes a list of sequences as input, aligns them with MUSCLE, computes their similarity matrix in parallel and then performs the clustering. SpCLUST extends a previously released software by integrating additional scoring matrices which enables it to cover the clustering of amino-acid sequences. The similarity matrix is now computed in parallel according to the master/slave distributed architecture, using MPI. Performance analysis, realized on two real datasets of 100 nucleotide sequences and 1049 amino-acids ones, show that the resulting library substantially outperforms the original Python package. The proposed package was also intensively evaluated on simulated and real genomic and protein data sets. The clustering results were compared to the most known traditional tools, such as UCLUST, CD-HIT and DNACLUST. The comparison showed that SpCLUST outperforms the other tools when clustering divergent sequences, and contrary to the others, it does not require any user intervention or prior knowledge about the input sequences.

AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees

“…In comparison, AncestralClust took 5 minutes and UCLUST used < 1 second. Next, we analyzed 'genomic set 1' from Matar et al (2019), which consists of 39 sequences from 6 homologous genes (FCER1G, S100A1, S100A6, S100A8, S100A12, and SH3BGRL3 in Table 2). We expect these sequences to form 6 clusters.…”

“…Next, we analyzed ‘genomic set 1’ from Matar et al . (2019), which consists of 39 sequences from 6 homologous genes (FCER1G, S100A1, S100A6, S100A8, S100A12, and SH3BGRL3 in Table 2).…”

“…Table 2. Comparisons of clustering methods using 39 sequences from 6 homologous genes from Matar et al (2019). 'id' refers to the identity threshold used.…”

“…We compared AncestralClust to five other state-of-the-art clustering methods: UCLUST (Edgar, 2010), meshclust2 (James and Girgis, 2018), DNACLUST (Ghodsi et al, 2011), CD-HIT (Fu et al, 2012), and SpClust (Matar et al, 2019). We used a variety of measurements to assess the accuracy and evennness of the clustering.…”

“…Currently, there are no clustering methods that can accurately cluster large taxonomically divergent metabarcoding reference databases such as the Barcode of Life database (Ratnasingham and Hebert, 2007) in relatively even clusters. Only a few other methods, such as SpClust (Matar et al ., 2019) and TreeCluster (Balaban et al ., 2019), exist for clustering potentially divergent sequences. SpClust creates clusters based on the use of Laplacian Eigenmaps and a Gaussian Mixture Model based on a similarity matrix calculated on all input sequences.…”