POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Evolutionary information in the form of a Position-Specific Scoring Matrix (PSSM) is a widely used and highly informative representation of protein sequences. Accordingly, PSSM-based feature descriptors have been successfully applied to improve the performance of various predictors of protein attributes. Even though a number of algorithms have been proposed in previous studies, there is currently no universal web server or toolkit available for generating this wide variety of descriptors. Here, we present POSSUM ( Po sition- S pecific S coring matrix-based feat u re generator for m achine learning), a versatile toolkit with an online web server that can generate 21 types of PSSM-based feature descriptors, thereby addressing a crucial need for bioinformaticians and computational biologists. We envisage that this comprehensive toolkit will be widely used as a powerful tool to facilitate feature extraction, selection, and benchmarking of machine learning-based models, thereby contributing to a more effective analysis and modeling pipeline for bioinformatics research. Availability and implementation: http://possum.erc.monash.edu/ .

References in zbMATH (referenced in 8 articles )

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  1. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  2. Pan, Yi; Wang, Shiyuan; Zhang, Qi; Lu, Qianzi; Su, Dongqing; Zuo, Yongchun; Yang, Lei: Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions (2019)
  3. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  4. Liang, Yunyun; Zhang, Shengli: Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence (2018)
  5. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  6. Zhang, Shengli; Duan, Xin: Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC (2018)
  7. Khan, Muslim; Hayat, Maqsood; Khan, Sher Afzal; Ahmad, Saeed; Iqbal, Nadeem: Bi-PSSM: position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins (2017)
  8. Shatabda, Swakkhar; Saha, Sanjay; Sharma, Alok; Dehzangi, Abdollah: iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features (2017)