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Estimation of evolutionary parameters using short, random and partial sequences from mixed samples of anonymous individuals.

Wu SH, Rodrigo AG - BMC Bioinformatics (2015)

Bottom Line: We used simulations to evaluate the performance of these algorithms, and our results demonstrate that LS performs poorly because bootstrap 95% Confidence Intervals (CIs) tend to under- or over-estimate the true values of the parameters.One major advantage of ABC-MCMC is that computational time scales linearly with the number of short-read sequences, and is independent of the number of full-length sequences in the original data.This allows us to perform the analysis on NGS datasets with large numbers of short read fragments.

View Article: PubMed Central - PubMed

Affiliation: Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA. stevenwu@asu.edu.

ABSTRACT

Background: Over the last decade, next generation sequencing (NGS) has become widely available, and is now the sequencing technology of choice for most researchers. Nonetheless, NGS presents a challenge for the evolutionary biologists who wish to estimate evolutionary genetic parameters from a mixed sample of unlabelled or untagged individuals, especially when the reconstruction of full length haplotypes can be unreliable. We propose two novel approaches, least squares estimation (LS) and Approximate Bayesian Computation Markov chain Monte Carlo estimation (ABC-MCMC), to infer evolutionary genetic parameters from a collection of short-read sequences obtained from a mixed sample of anonymous DNA using the frequencies of nucleotides at each site only without reconstructing the full-length alignment nor the phylogeny.

Results: We used simulations to evaluate the performance of these algorithms, and our results demonstrate that LS performs poorly because bootstrap 95% Confidence Intervals (CIs) tend to under- or over-estimate the true values of the parameters. In contrast, ABC-MCMC 95% Highest Posterior Density (HPD) intervals recovered from ABC-MCMC enclosed the true parameter values with a rate approximately equivalent to that obtained using BEAST, a program that implements a Bayesian MCMC estimation of evolutionary parameters using full-length sequences. Because there is a loss of information with the use of sitewise nucleotide frequencies alone, the ABC-MCMC 95% HPDs are larger than those obtained by BEAST.

Conclusion: We propose two novel algorithms to estimate evolutionary genetic parameters based on the proportion of each nucleotide. The LS method cannot be recommended as a standalone method for evolutionary parameter estimation. On the other hand, parameters recovered by ABC-MCMC are comparable to those obtained using BEAST, but with larger 95% HPDs. One major advantage of ABC-MCMC is that computational time scales linearly with the number of short-read sequences, and is independent of the number of full-length sequences in the original data. This allows us to perform the analysis on NGS datasets with large numbers of short read fragments. The source code for ABC-MCMC is available at https://github.com/stevenhwu/SF-ABC.

No MeSH data available.


Trace plot from ABC-MCMC for both effective population size and mutation rate after removing the first 10 % of the generations as burn-in. This demonstrates that the MCMC chain mixes well
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Fig4: Trace plot from ABC-MCMC for both effective population size and mutation rate after removing the first 10 % of the generations as burn-in. This demonstrates that the MCMC chain mixes well

Mentions: Some datasets failed to converge for ABC-MCMC and were reanalyzed with 10 million iterations. Two out of 100 datasets failed to converge even with 10 million iterations, and these are excluded from the analyses. Figure 4 is an example of the trace plot for both mutation rate and effective population size. The plot indicates that the MCMC chain mixes well. Figure 5 gives an example of the posterior density of the effective population size recovered, plotted against the prior distribution. Given the difference between the prior and posterior density, it is apparent that there is sufficient signal in the data to shift the posterior distribution of effective population size away from the prior distribution.Fig. 4


Estimation of evolutionary parameters using short, random and partial sequences from mixed samples of anonymous individuals.

Wu SH, Rodrigo AG - BMC Bioinformatics (2015)

Trace plot from ABC-MCMC for both effective population size and mutation rate after removing the first 10 % of the generations as burn-in. This demonstrates that the MCMC chain mixes well
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4634753&req=5

Fig4: Trace plot from ABC-MCMC for both effective population size and mutation rate after removing the first 10 % of the generations as burn-in. This demonstrates that the MCMC chain mixes well
Mentions: Some datasets failed to converge for ABC-MCMC and were reanalyzed with 10 million iterations. Two out of 100 datasets failed to converge even with 10 million iterations, and these are excluded from the analyses. Figure 4 is an example of the trace plot for both mutation rate and effective population size. The plot indicates that the MCMC chain mixes well. Figure 5 gives an example of the posterior density of the effective population size recovered, plotted against the prior distribution. Given the difference between the prior and posterior density, it is apparent that there is sufficient signal in the data to shift the posterior distribution of effective population size away from the prior distribution.Fig. 4

Bottom Line: We used simulations to evaluate the performance of these algorithms, and our results demonstrate that LS performs poorly because bootstrap 95% Confidence Intervals (CIs) tend to under- or over-estimate the true values of the parameters.One major advantage of ABC-MCMC is that computational time scales linearly with the number of short-read sequences, and is independent of the number of full-length sequences in the original data.This allows us to perform the analysis on NGS datasets with large numbers of short read fragments.

View Article: PubMed Central - PubMed

Affiliation: Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA. stevenwu@asu.edu.

ABSTRACT

Background: Over the last decade, next generation sequencing (NGS) has become widely available, and is now the sequencing technology of choice for most researchers. Nonetheless, NGS presents a challenge for the evolutionary biologists who wish to estimate evolutionary genetic parameters from a mixed sample of unlabelled or untagged individuals, especially when the reconstruction of full length haplotypes can be unreliable. We propose two novel approaches, least squares estimation (LS) and Approximate Bayesian Computation Markov chain Monte Carlo estimation (ABC-MCMC), to infer evolutionary genetic parameters from a collection of short-read sequences obtained from a mixed sample of anonymous DNA using the frequencies of nucleotides at each site only without reconstructing the full-length alignment nor the phylogeny.

Results: We used simulations to evaluate the performance of these algorithms, and our results demonstrate that LS performs poorly because bootstrap 95% Confidence Intervals (CIs) tend to under- or over-estimate the true values of the parameters. In contrast, ABC-MCMC 95% Highest Posterior Density (HPD) intervals recovered from ABC-MCMC enclosed the true parameter values with a rate approximately equivalent to that obtained using BEAST, a program that implements a Bayesian MCMC estimation of evolutionary parameters using full-length sequences. Because there is a loss of information with the use of sitewise nucleotide frequencies alone, the ABC-MCMC 95% HPDs are larger than those obtained by BEAST.

Conclusion: We propose two novel algorithms to estimate evolutionary genetic parameters based on the proportion of each nucleotide. The LS method cannot be recommended as a standalone method for evolutionary parameter estimation. On the other hand, parameters recovered by ABC-MCMC are comparable to those obtained using BEAST, but with larger 95% HPDs. One major advantage of ABC-MCMC is that computational time scales linearly with the number of short-read sequences, and is independent of the number of full-length sequences in the original data. This allows us to perform the analysis on NGS datasets with large numbers of short read fragments. The source code for ABC-MCMC is available at https://github.com/stevenhwu/SF-ABC.

No MeSH data available.