Problem Statement
In this portfolio, the challenge is to analyze a protein sequence dataset provided in the FASTA format. The dataset contains sequences with corresponding class labels, and the objective is to process this data, calculate alignment scores using the Smith-Waterman algorithm, embed sequences into a lower-dimensional space, and evaluate classification accuracy.
Solution Overview
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Problem: Importing protein sequence data from a FASTA file and extracting class labels from sequence descriptions.
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Solution:
- Utilize the Biopython package to parse the FASTA file and extract sequence data.
- Parse sequence descriptions to obtain class labels.
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Problem: Aligning protein sequences and embedding them into a lower-dimensional vector space.
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Solution:
- Implement the Smith-Waterman algorithm to calculate alignment scores for pairs of sequences.
- Construct pair-wise similarity matrices using different alignment approaches.
- Compute eigenvalues and eigenvectors of the similarity matrices.
- Perform embedding using the 100 largest eigenvalues to represent sequences in a 100-dimensional space.
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Problem: Evaluating the quality of the embedding and assessing classification accuracy.
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Solution:
- Evaluate classification algorithms such as GMLVQ, k-NN, SVM, and Random Forest.
- Tune hyperparameters to optimize classification accuracy.
- Analyze how accuracy varies with different alignment parameters and embedding dimensions.
- Visualize classification results and accuracy trends for better interpretation.
Conclusion
By systematically addressing the challenges of data import, alignment, embedding, and classification, the solution provides a robust framework for analyzing protein sequence data. The portfolio demonstrates the application of bioinformatics tools and machine learning techniques to extract insights and make informed decisions in biological research. Overall, the solution offers a comprehensive approach to learning from structured data with practical relevance in bioinformatics.