SeqEntropy

SeqEntropy quantifies the impact of sequence repeats on genome assembly from short-read sequencing by computing an entropy-based, read-length-dependent metric to evaluate limitations imposed by repetitive sequences during genome reconstruction.

Key Features:

• Entropy-Based Metric (H(k)): Computes H(k), the entropy of sequencing reads at read length k, to quantify sequence complexity affecting assembly fidelity.
• Relative Entropy Loss (ΔH(k)): Calculates ΔH(k), the relative loss in entropy due to repetitive sequences, where ΔH(k) greater than 1% indicates a significant reduction in assembly accuracy.
• Read Length Optimization: Determines minimal read lengths required to reduce ΔH(k) below 1%, a requirement that varies across organisms and is independent of genome size.
• Rapid Quantitative Evaluation: Performs entropy-based measurements rapidly (typically seconds to minutes), enabling analysis of large eukaryotic genomes.
• Parallelization Capability: Supports parallel computation to scale analyses across extensive genomic datasets.

Scientific Applications:

• Genome assembly optimization: Identifies limitations imposed by sequence repeats to inform de-novo genome assembly strategies.
• Sequencing parameter selection: Guides choice of read lengths and sequencing strategies to achieve target assembly fidelity.
• Reference-guided assessment: Assesses entropy metrics in the context of a closely related reference genome to refine assembly expectations.
• Comparative repeat analysis: Evaluates repeat structures across prokaryotic and eukaryotic genomes to compare expected assembly challenges.

Methodology:

Applies an information-theoretic approach by analyzing idealized, error-free reads at varying lengths to quantify H(k), compute ΔH(k), and correlate entropy loss with genome assembly coverage.