scholarly journals Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data

2019 ◽  
Author(s):  
Thibaut Sellinger ◽  
Diala Abu Awad ◽  
Markus Möst ◽  
Aurélien Tellier

AbstractSeveral methods based on the Sequential Markovian Coalescent (SMC) have been developed to use full genome sequence data to uncover population demographic history, which is of interest in its own right and a key requirement to generate a null model for selection tests. While these methods can be applied to all possible species, the underlying assumptions are sexual reproduction at each generation and no overlap of generations. However, in many plant, invertebrate, fungi and other species, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates of seed/egg-bank and of self-fertilization, and 2) the populations’ past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.8 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations on the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation.

PLoS Genetics ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. e1009504
Author(s):  
Thibaut Paul Patrick Sellinger ◽  
Diala Abu Awad ◽  
Markus Moest ◽  
Aurélien Tellier

PLoS Genetics ◽  
2020 ◽  
Vol 16 (4) ◽  
pp. e1008698 ◽  
Author(s):  
Thibaut Paul Patrick Sellinger ◽  
Diala Abu Awad ◽  
Markus Moest ◽  
Aurélien Tellier

Author(s):  
Amnon Koren ◽  
Dashiell J Massey ◽  
Alexa N Bracci

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online


Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106784
Author(s):  
Chinda Chhe ◽  
Ayaka Uke ◽  
Sirilak Baramee ◽  
Umbhorn Ungkulpasvich ◽  
Chakrit Tachaapaikoon ◽  
...  

Data in Brief ◽  
2020 ◽  
Vol 33 ◽  
pp. 106416
Author(s):  
Asset Daniyarov ◽  
Askhat Molkenov ◽  
Saule Rakhimova ◽  
Ainur Akhmetova ◽  
Zhannur Nurkina ◽  
...  

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Lynsey K. Whitacre ◽  
Jesse L. Hoff ◽  
Robert D. Schnabel ◽  
Sara Albarella ◽  
Francesca Ciotola ◽  
...  

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