We present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability.

/pdf/bayesian-inference-of-ancestral-recombination-graphs-2a9s2pkj.pdf

Bayesian inference of ancestral recombination graphs

Building ancestral recombination graphs (ARG) with minimum number of recombination events for large datasets is a challenging problem. We have proposed ARG4WG and REARG heuristic algorithm for constructing ARGs with thousands of whole genome sequences. However, these algorithms do not result in ARGs with minimal number of recombination events. In this work, we propose GAMARG algorithm, an improvement of ARG4WG, to optimize the number of recombination events in ARG building process. Experiment with different datasets showed that GAMARG algorithm outperforms other heuristic algorithms in building ARGs for large datasets. It also is much better than other heuristic algorithms and comparable to exhaustive search methods for small datasets.

A Hybrid Approach to Optimize the Number of Recombinations in Ancestral Recombination Graphs

Building ancestral recombination graphs (ARGs) with the minimum number of recombination events is an essential problem in genomic population studies. Exhaustive search methods have been proposed to solve the problem with few sequences and markers. Recently, we introduced ARG4WG algorithm [1] to build plausible ARGs for whole genomes of thousand samples. However, the ARG4WG algorithm was not designed to optimize the number of recombination events. In this work, we propose a new design of the ARG4WG algorithm to optimize the number of recombination events. The key idea is to analyze and combine different criteria (i.e., the longest shared ends, the maximum similarity between sequences, and the length of sequences) to select the best candidates for recombinations. The experiments on different data sets showed that our new algorithms produced ARGs with smaller number of recombination events in comparison to other methods. The methods allow us to build minimum recombination ARGs from whole genome data sets.

Building minimum recombination ancestral recombination graphs for whole genomes

Photoplethysmography (PPG) is a non-invasive optical technique used to measure cardiovascular activity in both clinical and consumer applications. PPG signals are notoriously susceptible to noise and motion artefacts and in applications where high-frequency PPG signals are required, these issues are even more pronounced. In this work, we propose a two-stage algorithm to process these PPG waveforms without prior knowledge of their noise characteristics. To evaluate its performance, we utilized a set of PPG signal quality indices used to classify waveform quality. We found that using a Chebyshev type-II band-pass filter with cutoff frequencies of 0.35 Hz for high pass and 10 Hz for low pass performs best across six measured Signal Quality Indices (SQIs), achieving a 12% improvement in the dynamic time-warping (DTW) SQI. Furthermore, using a modified Savitzky-Golay smoothing algorithm with a frequency domain-derived window parameter leads to additional improvements in SQI-derived features of 14.09% and 12.22% in DTW and skewness respectively. Our proposed approach provides a universal solution for processing high-frequency raw PPG recordings and prepares them for the extraction of morphological features.

Optimal filtering and smoothing thresholds for high-frequency photoplethysmography signals

Inference under the coalescent with recombination

Electrocardiogram (ECG) and photoplethysmogram (PPG) are commonly used to determine the vital signs of heart rate, respiratory rate, and oxygen saturation in patient monitoring. In addition to simple observation of those summarized indexes, waveform signals can be analyzed to provide deeper insights into disease pathophysiology and support clinical decisions. Such data, generated from continuous patient monitoring from both conventional bedside and low-cost wearable monitors, are increasingly accessible. However, the recorded waveforms suffer from considerable noise and artifacts and, hence, are not necessarily used prior to certain quality control (QC) measures, especially by those with limited programming experience. Various signal quality indices (SQIs) have been proposed to indicate signal quality. To facilitate and harmonize a wider usage of SQIs in practice, we present a Python package, named vital_sqi, which provides a unified interface to the state-of-the-art SQIs for ECG and PPG signals. The vital_sqi package provides with seven different peak detectors and access to more than 70 SQIs by using different settings. The vital_sqi package is designed with pipelines and graphical user interfaces to enable users of various programming fluency to use the package. Multiple SQI extraction pipelines can take the PPG and ECG waveforms and generate a bespoke SQI table. As these SQI scores represent the signal features, they can be input in any quality classifier. The package provides functions to build simple rule-based decision systems for signal segment quality classification using user-defined SQI thresholds. An experiment with a carefully annotated PPG dataset suggests thresholds for relevant PPG SQIs.

vital_sqi: A Python package for physiological signal quality control

We propose a heuristic algorithm, called ARG4WG, to build plausible ancestral recombination graphs (ARGs) from thousands of whole genome samples. By using the longest shared end for recombination inference, ARG4WG constructs ARGs with small numbers of recombination events that perform well in association mapping on genome-wide association studies.

Building Ancestral Recombination Graphs for Whole Genomes

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hai Bich Ho

Author Tools

Chat about Author

Papers

vital_sqi: A Python package for physiological signal quality control

Building Ancestral Recombination Graphs for Whole Genomes