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Abstract Changes in telomere length are increasingly used to indicate species' response to environmental stress across diverse taxa. Despite this broad use, few studies have explored telomere length in plants. Thus, evaluation of new approaches for measuring telomeres in plants is needed. Rapid advances in sequencing approaches and bioinformatic tools now allow estimation of telomere content from whole‐genome sequencing (WGS) data, a proxy for telomere length. While telomere content has been quantified extensively using quantitative polymerase chain reaction (qPCR) and WGS in humans, no study to date has compared the effectiveness of WGS in estimating telomere length in plants relative to qPCR approaches. In this study, we use 100Populusclones re‐sequenced using short‐read Illumina sequencing to quantify telomere length comparing three different bioinformatic approaches (Computel, K‐seek and TRIP) in addition to qPCR. Overall, telomere length estimates varied across different bioinformatic approaches, but were highly correlated across methods for individual genotypes. A positive correlation was observed between WGS estimates and qPCR, however, Computel estimates exhibited the greatest correlation. Computel incorporates genome coverage into telomere length calculations, suggesting that genome coverage is likely important to telomere length quantification when using WGS data. Overall, telomere estimates from WGS provided greater precision and accuracy of telomere length estimates relative to qPCR. The findings suggest WGS is a promising approach for assessing telomere length and, as the field of telomere ecology evolves, may provide added value to assaying response to biotic and abiotic environments for plants needed to accelerate plant breeding and conservation management.
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Batch effects in population genomic studies with low‐coverage whole genome sequencing data: Causes, detection and mitigation
Abstract Over the past few decades, there has been an explosion in the amount of publicly available sequencing data. This opens new opportunities for combining data sets to achieve unprecedented sample sizes, spatial coverage or temporal replication in population genomic studies. However, a common concern is that nonbiological differences between data sets may generate patterns of variation in the data that can confound real biological patterns, a problem known as batch effects. In this paper, we compare two batches of low‐coverage whole genome sequencing (lcWGS) data generated from the same populations of Atlantic cod (Gadus morhua). First, we show that with a “batch‐effect‐naive” bioinformatic pipeline, batch effects systematically biased our genetic diversity estimates, population structure inference and selection scans. We then demonstrate that these batch effects resulted from multiple technical differences between our data sets, including the sequencing chemistry (four‐channel vs. two‐channel), sequencing run, read type (single‐end vs. paired‐end), read length (125 vs. 150 bp), DNA degradation level (degraded vs. well preserved) and sequencing depth (0.8× vs. 0.3× on average). Lastly, we illustrate that a set of simple bioinformatic strategies (such as different read trimming and single nucleotide polymorphism filtering) can be used to detect batch effects in our data and substantially mitigate their impact. We conclude that combining data sets remains a powerful approach as long as batch effects are explicitly accounted for. We focus on lcWGS data in this paper, which may be particularly vulnerable to certain causes of batch effects, but many of our conclusions also apply to other sequencing strategies.
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- Award ID(s):
- 1756316
- PAR ID:
- 10420666
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Molecular Ecology Resources
- Volume:
- 22
- Issue:
- 5
- ISSN:
- 1755-098X
- Page Range / eLocation ID:
- p. 1678-1692
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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