NullSeq

NullSeq generates random coding sequences with specified GC content and amino acid composition using a maximum-entropy approach to create null models for testing hypotheses about sequence-level evolutionary pressures.

Key Features:

• Customizable Constraints: Specify amino acid composition and GC content for generated coding sequences.
• Maximum Entropy Principle: Use the principle of maximum entropy to produce unbiased random sequences subject to specified constraints.
• Python Package: Implemented as a Python package.
• Expandable Constraints: Support additional constraints such as individual nucleotide usage and di-nucleotide frequencies.

Scientific Applications:

• Motif Enrichment Analysis: Generate null models to identify over- and under-represented sequence motifs in genomes.
• Evolutionary Pressure Testing: Test hypotheses about evolutionary pressures on transcription and translation.
• Biochemical Interaction Studies: Assess sequence-level effects relevant to ligand–substrate binding and host immunity.
• Synthetic Biology and Protein Design: Aid engineering of biological systems and the design of functional proteins with controlled sequence composition.

Methodology:

Generate coding sequences under explicit constraints (GC content, amino acid composition, optional nucleotide or di-nucleotide frequencies) using a maximum-entropy formulation; implementation provided as a Python package.