Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

Compression as an accelerant of computation is increasingly recognized as an important component in engineering fast real-world machine learning methods for big data; c.f., its impact on genome-scale approximate string matching. Previous work showed that compression can accelerate algorithms for Hidden Markov Models (HMM) with discrete observations, both for the classical frequentist HMM algorithms—Forward Filtering, Backward Smoothing and Viterbi—and Gibbs sampling for Bayesian HMM. For Bayesian HMM with continuous-valued observations, compression was shown to greatly accelerate computations for specific types of data. For instance, data from large-scale experiments interrogating structural genetic variation can be assumed to be piece-wise constant with noise, or, equivalently, data generated by HMM with dominant self-transition probabilities. Here we extend the compressive computation approach to the classical frequentist HMM algorithms on continuous-valued observations, providing the first compressive approach for this problem. In a large-scale simulation study, we demonstrate empirically that in many settings compressed HMM algorithms very clearly outperform the classical algorithms with no, or only an insignificant effect, on the computed probabilities and infered state paths of maximal likelihood. This provides an efficient approach to big data computations with HMM. An open-source implementation of the method is available from https://github.com/lucabello/wavelet-hmms.

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“…PLOS ONE [25]. Note that despite the fact that we can expect f ½t� � m q½t� , f cannot reliably be used to identify q directly.…”

Section: Wavelet Compressionmentioning

confidence: 99%

“…different compression levels, without re-estimating f for every given σ 2 . For this purpose, we have previously developed a highly efficient data structure called a breakpoint array with a linear time constructor [20,25] based on the lifting scheme [23,24].…”

Section: Wavelet Compressionmentioning

confidence: 99%

Compressed computations using wavelets for hidden Markov models with continuous observations

2023

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“…Newly available methods allow inference of CNV at high resolution with great accuracy (Wiedenhoeft, Brugel & Schliep, 2016). The frequency with which CNVs occur in animal and plant populations raises the question of how informative they would be at higher phylogenetic levels, and whether they would incur unwanted homoplasy that would obscure homology and phylogenetic relationships.…”

Section: Copy Number Variations (Cnv)mentioning

confidence: 99%

Peer Review #1 of "Embracing heterogeneity: coalescing the Tree of Life and the future of phylogenomics (v0.1)"

Pyron¹

2019

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Building the Tree of Life (ToL) is a major challenge of modern biology, requiring advances in cyberinfrastructure, data collection, theory, and more. Here, we argue that phylogenomics stands to benefit by embracing the many heterogeneous genomic signals emerging from the first decade of large-scale phylogenetic analysis spawned by highthroughput sequencing (HTS). Such signals include those most commonly encountered in phylogenomic datasets, such as incomplete lineage sorting, but also those reticulate processes emerging with greater frequency, such as recombination and introgression. Here we focus specifically on how phylogenetic methods can accommodate the heterogeneity incurred by such population genetic processes; we do not discuss phylogenetic methods that ignore such processes, such as concatenation or supermatrix approaches or supertrees. We suggest that methods of data acquisition and the types of markers used in phylogenomics will remain restricted until a posteriori methods of marker choice are made possible with routine whole-genome sequencing of taxa of interest. We

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“… Mahmud and Schliep (2011) tried to improve the computational demand of these modifications by integrating a KD-tree algorithm in CMCM sampling to approximate the data into compressed blocks. Wiedenhoeft et al (2016) noted that this enhancement imposes rigidness of the compression block sizes that was not CNVs’ nature and an inherent tendency for overfitting or weak clustering. Hence, they combined a Haar wavelet smoother with HMM segmenting, which helped shift the heavy computational effort from obvious CNVs to problematic ones.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy methods for the detection of copy number variations in comparative genomic hybridization arrays

AlShibli

Mathkour

2020

Saudi Journal of Biological Sciences

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Genomic copy number variations (CNVs) are considered as a significant source of genetic diversity and widely involved in gene expression and regulatory mechanism, genetic disorders and disease risk, susceptibility to certain diseases and conditions, and resistance to medical drugs. Many studies have targeted the identification, profiling, analysis, and associations of genetic CNVs. We propose herein two new fuzzy methods, taht is, one based on the fuzzy inference from the pre-processed input, and another based on fuzzy C-means clustering. Our solutions present a higher true positive rate and a lower false negative with no false positive, efficient performance and consumption of least resources.

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Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

Cited by 3 publications

References 92 publications

Compressed computations using wavelets for hidden Markov models with continuous observations

Compressed computations using wavelets for hidden Markov models with continuous observations

Peer Review #1 of "Embracing heterogeneity: coalescing the Tree of Life and the future of phylogenomics (v0.1)"

Fuzzy methods for the detection of copy number variations in comparative genomic hybridization arrays

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