Biology subjects

Susik, M.

Publications and source records attributed to Susik, M..

4 recordsLinked to original sources

bioRxiv2022

Variational inference accelerates accurate DNA mixture deconvolution

We investigate a class of DNA mixture deconvolution algorithms based on variational inference, and we show that this can significantly reduce computational runtimes with little or no effect on the accuracy and precision of the result. In particular, we consider Stein Variational Gradient Descent (SVGD) and Variational Inference (VI) with an evidence lower-bound objective. Both provide alternatives to the commonly used Markov-Chain Monte-Carlo methods for estimating the model posterior in Bayesian probabilistic genotyping. We demonstrate that both SVGD and VI significantly reduce computational costs over the current state of the art. Importantly, VI does so without sacrificing precision or accuracy, presenting an overall improvement over previously published methods.

bioinformatics↗

bioRxiv2022

Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark

We provide an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling [1]. We provide results for all 428 mixtures analysed by Riman et al. [2] and compare the results with two state-of-the-art software products: STRmix v2.6 and Euroformix v3.4.0. The comparison shows that the Hamiltonian Monte Carlo method provides reliable values of likelihood ratios (LRs) close to the other methods. We further propose a novel large-scale precision benchmark and quantify the precision of the Hamiltonian Monte Carlo method, indicating its improvements over existing solutions. Finally, we analyse the influence of the factors discussed by Buckleton et al. [3].

bioinformatics↗

bioRxiv2022

Hamiltonian Monte Carlo with strict convergence criteria reduces run-to-run variability in forensic DNA mixture deconvolution

MotivationAnalysing mixed DNA profiles is a common task in forensic genetics. Due to the complexity of the data, such analysis is often performed using Markov Chain Monte Carlo (MCMC)-based genotyping algorithms. These trade off precision against execution time. When the default settings are used, as large as a 10-fold changes in inferred likelihood ratios (LR) are observed when the software is run twice on the same case. So far, this uncertainty has been attributed to the stochasticity of MCMC algorithms. Since LRs translate directly to strength of the evidence in a criminal trial, forensic laboratories desire LR with small run-to-run variability. ResultsWe present a Hamiltonian Monte Carlo (HMC) algorithm that reduces run-to-run variability in forensic DNA mixture deconvolution by around an order of magnitude without increased runtime. We achieve this by enforcing strict convergence criteria. We show that the choice of convergence metric strongly influences precision. We validate our method by reproducing previously published results for benchmark DNA mixtures (MIX05, MIX13, and ProvedIt). We also present a complete software implementation of our algorithm that is able to leverage GPU acceleration, accelerating the inference process. In the benchmark mixtures, on consumer-grade hardware, the runtime is less than 7 minutes for 3 contributors, less than 35 minutes for 4 contributors, and less than an hour for 5 contributors with one known contributor.

genetics↗

bioRxiv2018

Forget Pixels: Adaptive Particle Representation of Fluorescence Microscopy Images

Modern microscopy modalities create a data deluge with gigabytes of data generated each second, and terabytes per day. Storing and processing these data is a severe bottleneck, not fully alleviated by data compression. We argue that this is because images are processed as regular grids of pixels. To address the root of the problem, we here propose a content-adaptive representation of fluorescence microscopy images called the Adaptive Particle Representation (APR). The APR replaces the regular grid of pixels with particles positioned according to image content. This overcomes storage bottlenecks, as data compression does, but additionally overcomes memory and processing bottlenecks, since the APR can directly be used in processing without going back to pixels. We present the ideas, concepts, and algorithms of the APR and validate them using noisy 3D image data. We show that the APR represents the content of an image while maintaining image quality. We then show that the adaptivity of the APR provides orders of magnitude benefits across a range of image processing tasks. Therefore, the APR provides a simple, extendable, and efficient content-aware representation of images that relaxes current data and processing bottlenecks.

bioinformatics↗