Scikit Bio

What it does

The Scikit Bio skill enables AI agents to analyze biological data using the scikit-bio Python library. It facilitates tasks such as manipulating DNA, RNA, and protein sequences; reading and writing various biological file formats (FASTA, FASTQ, GenBank); performing sequence alignments; constructing phylogenetic trees; and calculating diversity metrics for microbiome or community ecology data. This skill is particularly useful for bioinformatics research requiring pattern recognition and prediction within biological datasets.

When to use it

• You need to read and process biological sequences from files in formats like FASTA or FASTQ.
• You're performing sequence alignment or searching for specific motifs within DNA, RNA, or protein data.
• You want to construct or analyze phylogenetic trees representing evolutionary relationships.
• You are calculating diversity metrics (alpha/beta diversity, UniFrac distances) from microbiome data.

Key capabilities

• Sequence Manipulation: Reading, writing, slicing, concatenating, and searching biological sequences (DNA, RNA, protein).
• File Format Support: Handles FASTA, FASTQ, GenBank, Newick, BIOM formats for reading and writing biological data.
• Sequence Transformations: Performs reverse complement, transcription (DNA→RNA), and translation (RNA→protein) operations.
• Motif Finding: Identifies specific patterns within sequences using regular expressions.
• Distance Calculations: Calculates distances between sequences (e.g., Hamming distance, k-mer based).
• Metadata Handling: Manages sequence quality scores and associated metadata.

Example prompts

• "Read the DNA sequence from 'my_sequence.fasta' and find all occurrences of the motif 'ATG'."
• "Translate the RNA sequence in 'rna_data.txt' into a protein sequence."
• "Calculate the Hamming distance between these two DNA sequences: [Sequence 1] and [Sequence 2]."

Tips & gotchas

• Use the DNA, RNA, or Protein classes when working with sequences that require specific alphabet validation.
• The Sequence class is suitable for generic sequences without alphabet restrictions.
• Quality scores from FASTQ files are automatically loaded into positional metadata, which can be leveraged in subsequent analyses.