BioSeq-Analysis 2.0

BioSeq-Analysis 2.0 performs sequence-level and residue-level analysis of DNA, RNA, and protein sequences using machine learning to generate and evaluate predictive models for biological sequence interpretation.

Key Features:

• Dual-Level Analysis: Supports both sequence-level and residue-level analysis for DNA, RNA, and protein sequences.
• Comprehensive Feature Set: Utilizes 26 residue-level features and 90 sequence-level features to inform predictive models.
• Automated Predictor Generation: Automatically generates predictors from uploaded benchmark datasets for both residue- and sequence-level tasks.
• Performance Evaluation: Includes built-in mechanisms to evaluate predictor performance and has produced results comparable to or exceeding existing predictors.

Scientific Applications:

• Functional Annotation: Enables generation of predictors for annotating function in DNA, RNA, and protein sequences.
• Structural Prediction: Supports residue-level analyses relevant to structure-related predictive tasks.
• Evolutionary Studies: Facilitates predictive analyses applicable to evolutionary investigations of sequences.
• Genomics, Proteomics, and Systems Biology: Applies predictive modeling to problems in genomics, proteomics, and systems biology.

Methodology:

The method comprises feature extraction, predictor construction using machine learning, and performance evaluation.