Jani, M. R. | Search bioRxiv

bioRxiv2025

Cluster Analysis for Protein Sequences

This paper presents a comprehensive analysis of MMseqs2 clusters and traditional machine learning (ML) clustering algorithms, including KMeans and Hierarchical clusterings, in terms of protein sequences. The analyses are validated experimentally. The cluster analyses have been performed in the AO_SCPLOWSTRALC_SCPLOW Compendium protein sequences dataset hosted in the SCOPe database. The dataset is embedded using two pre-trained transformer models using Evolutionary Scale Modeling (ESM) to perform KMeans and Hierarchical clustering algorithms. Afterward, those four clusters are compared with MMseqs2/Linclust and MMseqs2/easy-cluster methods. After performing the experiment, MMseqs2/Linclust and MMseqs2/easy-cluster outperform traditional machine learning cluster algorithms by a considerable margin. This analysis demonstrates the superiority of the MMseqs2 clustering techniques over conventional machine learning clustering algorithms. The source code of the experiment is publicly available and readily accessible through: https://github.com/mrzResearchArena/protein-clustering.

bioinformatics↗

bioRxiv2020

SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences

The information of a cell is primarily contained in Deoxyribonucleic Acid (DNA). There is a flow of information of DNA to protein sequences via Ribonucleic acids (RNA) through transcription and translation. These entities are vital for the genetic process. Recent developments in epigenetic also show the importance of the genetic material and knowledge of their attributes and functions. However, the growth in known attributes or functionalities of these entities are still in slow progression due to the time consuming and expensive in vitro experimental methods. In this paper, we have proposed an ensemble classification algorithm called SubFeat to predict the functionalities of biological entities from different types of datasets. Our model uses a feature subspace based novel ensemble method. It divides the feature space into sub-spaces which are then passed to learn individual classifier models and the ensemble is built on this base classifiers that uses a weighted majority voting mechanism. SubFeat tested on four datasets comprising two DNA, one RNA and one protein dataset and it outperformed all the existing single classifiers and as well as the ensemble classifiers. SubFeat is made availalbe as a Python-based tool. We have made the package SubFeat available online along with a user manual. It is freely accessible from here: https://github.com/fazlulhaquejony/SubFeat.

bioinformatics↗