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The reduced cost of high‐throughput sequencing and the development of gene sets with wide phylogenetic applicability has led to the rise of sequence capture methods as a plausible platform for both phylogenomics and population genomics in plants. An important consideration in large targeted sequencing projects is the per‐sample cost, which can be inflated when using off‐the‐shelf kits or reagents not purchased in bulk. Here, we discuss methods to reduce per‐sample costs in high‐throughput targeted sequencing projects. We review the minimal equipment and consumable requirements for targeted sequencing while comparing several alternatives to reduce bulk costs inDNAextraction, library preparation, target enrichment, and sequencing. We consider how each of the workflow alterations may be affected byDNAquality (e.g., fresh vs. herbarium tissue), genome size, and the phylogenetic scale of the project. We provide a cost calculator for researchers considering targeted sequencing to use when designing projects, and identify challenges for future development of low‐cost sequencing in non‐model plant systems.
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Moving Just Enough Deep Sequencing Data to Get the Job Done
Motivation: As the size of high-throughput DNA sequence datasets continues to grow, the cost of transferring and storing the datasets may prevent their processing in all but the largest data centers or commercial cloud providers. To lower this cost, it should be possible to process only a subset of the original data while still preserving the biological information of interest. Results: Using 4 high-throughput DNA sequence datasets of differing sequencing depth from 2 species as use cases, we demonstrate the effect of processing partial datasets on the number of detected RNA transcripts using an RNA-Seq workflow. We used transcript detection to decide on a cutoff point. We then physically transferred the minimal partial dataset and compared with the transfer of the full dataset, which showed a reduction of approximately 25% in the total transfer time. These results suggest that as sequencing datasets get larger, one way to speed up analysis is to simply transfer the minimal amount of data that still sufficiently detects biological signal. Availability: All results were generated using public datasets from NCBI and publicly available open source software.
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- Award ID(s):
- 1659300
- PAR ID:
- 10132575
- Date Published:
- Journal Name:
- Bioinformatics and Biology Insights
- Volume:
- 13
- ISSN:
- 1177-9322
- Page Range / eLocation ID:
- 117793221985635
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/1177932219856359
