SpCLUST

SpCLUST clusters potentially divergent biological sequences to group nucleotide and amino-acid sequences based on computed sequence similarity for genomic and proteomic analyses.

Key Features:

• Implementation: Implemented in C++ for efficient sequence clustering computations.
• Sequence Alignment: Aligns input sequences using MUSCLE.
• Scoring Matrices: Integrates additional scoring matrices to extend support for amino-acid sequence clustering.
• Parallel Similarity Computation: Computes the similarity matrix in parallel using a master/slave distributed architecture with MPI (Message Passing Interface).
• Supported Sequence Types: Processes both nucleotide and amino-acid sequences.
• Clustering Basis: Performs clustering based on the computed similarity matrix.

Scientific Applications:

• Divergent Sequence Clustering: Clusters potentially divergent biological sequences for genomic and proteomic research applications.
• Benchmarking and Performance Evaluation: Performance was evaluated against its original Python-based predecessor and benchmarked versus UCLUST, CD-HIT, and DNACLUST using datasets of 100 nucleotide sequences and 1049 amino-acid sequences, demonstrating superior performance on divergent sequences.

Methodology:

Accepts a list of biological sequences, aligns them with MUSCLE, computes the similarity matrix in parallel using MPI with a master/slave architecture, and performs clustering based on the computed similarity matrix.