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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Inferring Rates and Length-Distributions of Indels Using Approximate Bayesian Computation
Eli Levy Karin1,2, Dafna Shkedy1, Haim Ashkenazy1
1Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Israel.
Genome Biology and Evolution
|April 29, 2017
Summary
This study introduces SpartaABC, a novel computational method for modeling DNA insertion and deletion (indel) dynamics. SpartaABC accurately infers indel parameters from sequence data without explicit likelihood functions.
Area of Science:
- Molecular Evolution
- Computational Biology
- Bioinformatics
Background:
- Single-base substitutions are common evolutionary events, with established models.
- Modeling DNA insertion and deletion (indel) dynamics is challenging due to the lack of explicit likelihood functions.
Purpose of the Study:
- To present SpartaABC, an approximate Bayesian computation (ABC) approach for inferring indel parameters.
- To provide a method for analyzing both aligned and unaligned sequence data.
Main Methods:
- SpartaABC extracts summary statistics from input sequence data.
- It simulates sequences using sampled indel parameters from a prior distribution.
- A distance metric between simulated and input statistics approximates posterior distributions.
Main Results:
- SpartaABC provides accurate estimates of indel parameters in simulations.
- The method was used to assess the impact of alignment errors on positive selection inference.
Conclusions:
- SpartaABC offers a robust framework for modeling indel dynamics.
- The approach is valuable for studying evolutionary processes and potential biases in sequence analysis.
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