Population networks from DNA sequences: methodological developments

AbstractSeveral classes of methods have been proposed for inferring the history of populations from genetic polymorphism data. As connectivity is a key factor to explain the structure of populations, several graph-based methods have been developed to this aim, using population genetics data. Here we propose an original method based on graphical models that uses DNA sequences to provide relationships between populations. We tested our method on various simulated data sets, describing typical demographic scenarios, for different parameters values. We found that our method behaved noticeably well for realistic demographic evolutionary processes and recovered suitably the migration processes. Our method provides thus a complementary tool for investigating population history based on genetic material.

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Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽

10.3390/e22090949 ◽

2020 ◽

Vol 22 (9) ◽

pp. 949

Author(s):

Jiangyi Wang ◽

Min Liu ◽

Xinwu Zeng ◽

Xiaoqiang Hua

Keyword(s):

Neural Network ◽

Signal Detection ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Detection Method ◽

Learning Algorithm ◽

Simulated Data ◽

Data Sets ◽

Feature Maps ◽

Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

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A novel coalescent method insensitive to population structure

10.1101/2020.11.30.405431 ◽

2020 ◽

Author(s):

Zeqi Yao ◽

Kehui Liu ◽

Shanjun Deng ◽

Xionglei He

Keyword(s):

Population Structure ◽

Phylogenetic Tree ◽

Simulated Data ◽

Structured Populations ◽

Population History ◽

Cell Populations ◽

Developmental History ◽

Coalescent Analysis ◽

History Of ◽

Larger Sample

AbstractConventional coalescent inferences of population history make the critical assumption that the population under examination is panmictic. However, most populations are structured. This complicates the prevailing coalescent analyses and sometimes leads to inaccurate estimates. To develop a coalescent method unhampered by population structure, we perform two analyses. First, we demonstrate that the coalescent probability of two randomly sampled alleles from the immediate preceding generation (one generation back) is independent of population structure. Second, motivated by this finding, we propose a new coalescent method: i-coalescent analysis. i-coalescent analysis computes the instantaneous coalescent rate (iCR) by using a phylogenetic tree of sampled alleles. Using simulated data, we broadly demonstrate the capability of i-coalescent analysis to accurately reconstruct population size dynamics of highly structured populations, although we find this method often requires larger sample sizes for structured populations than for panmictic populations. Overall, our results indicate i-coalescent analysis to be a useful tool, especially for the inference of population histories with intractable structure such as the developmental history of cell populations in the organs of complex organisms.

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Benchmarking Statistical Multiple Sequence Alignment

10.1101/304659 ◽

2018 ◽

Cited By ~ 1

Author(s):

Michael Nute ◽

Ehsan Saleh ◽

Tandy Warnow

Keyword(s):

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Structural Alignment ◽

Estimation Method ◽

Simulated Data ◽

Protein Sequences ◽

Data Sets ◽

Sequence Alignments ◽

Multiple Sequence ◽

Simulated Data Sets

AbstractThe estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical co-estimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical co-estimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy is dramatically more accurate than the other alignment methods on the simulated data sets, but is among the least accurate on the biological benchmarks. There are several potential causes for this discordance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments; future research is needed to understand the most likely explanation for our observations. multiple sequence alignment, BAli-Phy, protein sequences, structural alignment, homology

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A Large-scale Analysis of Human Mitochondrial DNA Sequences with Special Reference to the Population History of East Eurasian.

Anthropological Science ◽

10.1537/ase.110.293 ◽

2002 ◽

Vol 110 (3) ◽

pp. 293-312 ◽

Cited By ~ 4

Author(s):

H. Oota ◽

N. Saitou ◽

S. Ueda

Keyword(s):

Mitochondrial Dna ◽

Special Reference ◽

Dna Sequences ◽

Large Scale ◽

Population History ◽

Scale Analysis ◽

Human Mitochondrial Dna ◽

Large Scale Analysis ◽

History Of

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Bayesian Planet Searches for the 10 cm/s Radial Velocity Era

Proceedings of the International Astronomical Union ◽

10.1017/s1743921316002817 ◽

2015 ◽

Vol 11 (A29A) ◽

pp. 205-207

Author(s):

Philip C. Gregory

Keyword(s):

Radial Velocity ◽

State Of The Art ◽

Simulated Data ◽

Model Parameters ◽

Data Sets ◽

Stellar Activity ◽

Bayesian Fusion ◽

Multiple State ◽

Simulated Data Sets ◽

Apodization Function

AbstractA new apodized Keplerian model is proposed for the analysis of precision radial velocity (RV) data to model both planetary and stellar activity (SA) induced RV signals. A symmetrical Gaussian apodization function with unknown width and center can distinguish planetary signals from SA signals on the basis of the width of the apodization function. The general model for m apodized Keplerian signals also includes a linear regression term between RV and the stellar activity diagnostic In (R'hk), as well as an extra Gaussian noise term with unknown standard deviation. The model parameters are explored using a Bayesian fusion MCMC code. A differential version of the Generalized Lomb-Scargle periodogram provides an additional way of distinguishing SA signals and helps guide the choice of new periods. Sample results are reported for a recent international RV blind challenge which included multiple state of the art simulated data sets supported by a variety of stellar activity diagnostics.

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Recurrent hybridization underlies the evolution of novelty in Gentiana (Gentianaceae) in the Qinghai-Tibetan Plateau

AoB Plants ◽

10.1093/aobpla/plaa068 ◽

2020 ◽

Author(s):

Peng-Cheng Fu ◽

Alex D Twyford ◽

Shan-Shan Sun ◽

Hong-Yu Wang ◽

Ming-Ze Xia ◽

...

Keyword(s):

Tibetan Plateau ◽

Sequence Data ◽

Evolutionary Process ◽

Source Area ◽

Nuclear Data ◽

Population History ◽

Plastid Genomes ◽

Key Factor ◽

History Of ◽

Postglacial Expansion

Abstract The Qinghai-Tibetan Plateau (QTP) and adjacent areas are centers of diversity for several alpine groups. Although the QTP acted as a source area for diversification of the alpine genus Gentiana, the evolutionary process underlying diversity in this genus, especially the formation of narrow endemics, is still poorly understood. Hybridization has been proposed as a driver of plant endemism in the QTP but few cases have been documented with genetic data. Here, we describe a new endemic species in Gentiana section Cruciata as G. hoae sp. nov., and explore its evolutionary history with complete plastid genomes and nuclear ribosomal ITS sequence data. Genetic divergence within G. hoae approximately 3 million years ago was followed by postglacial expansion on the QTP, suggesting Pleistocene glaciations as a key factor shaping the population history of G. hoae. Furthermore, a mismatch between plastid and nuclear data suggest that G. hoae participated in historical hybridization, while population sequencing show this species continues to hybridize with the co-occurring congener G. straminea in three locations. Our results indicate that hybridization may be a common process in the evolution of Gentiana and may be widespread among recently diverged taxa of the QTP.

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