Simulation-Based Statistical Alignment Data

Data from a study presenting SimBa-SAl, a simulation-based approach to statistical alignment inference. The method uses an explicit continuous Markov process for insertion-deletion and substitution events, developed by author Eli Levy Karin and published in 2020. It enables the computation of maximum-likelihood alignments and sampling of alternative alignments according to their probability.

Use Cases

• Benchmark the SimBa-SAl alignment algorithm against classic scoring-based methods using simulated sequence data.
• Estimate parameters for the long-indel model of sequence evolution as described in the study.
• Compute the probability of a given pair of sequences integrated over all possible alignments using the provided statistical framework.
• Sample alternative sequence alignments according to their probability to assess alignment uncertainty.
• Study the goodness-of-fit of evolutionary models like long-indel and TKF91 on empirical sequence datasets.

Strengths

• Data supports a method that decouples event probability estimation from alignment inference, introducing potential accelerations.
• The approach is general and can accommodate various stochastic models of indel formation.
• Associated with a peer-reviewed research publication and shared under a permissive CC0 1.0 license.
• Last updated in June 2020, providing a contemporary snapshot of research in statistical alignment.

Limitations

• The specific data content, format, size, and structure (e.g., rows, columns) are unknown from the input.
• The dataset's primary application is methodological research, limiting direct utility for applied biological analyses without sequence data.
• Recency is from 2020, which may not reflect the latest advancements in the rapidly evolving field of bioinformatics.

Provenance

Source

Dryad digital repository.

Collection Method

Generated as part of a research study on a simulation-based statistical alignment approach.

Freshness

Last updated 2020-06-24.