The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes
Open accessAbstract
The advent of modern DNA sequencing technology is the driving force in obtaining complete intra-specific genomes that can be used to detect loci that have been subject to positive selection in the recent past. Based on selective sweep theory, beneficial loci can be detected by examining the single nucleotide polymorphism patterns in intraspecific genome alignments. In the last decade, a plethora of algorithms for identifying selective sweeps have been developed. However, the majority of these algorithms have not been designed for analyzing whole-genome data. We present SweeD (Sweep Detector), an open-source tool for the rapid detection of selective sweeps in whole genomes. It analyzes site frequency spectra and represents a substantial extension of the widely used SweepFinder program. The sequential version of SweeD is up to 22 times faster than SweepFinder and, more importantly, is able to analyze thousands of sequences. We also provide a parallel implementation of SweeD for multi-core processors. Furthermore, we implemented a checkpointing mechanism that allows to deploy SweeD on cluster systems with queue execution time restrictions, as well as to resume long-running analyses after processor failures. In addition, the user can specify various demographic models via the command-line to calculate their theoretically expected site frequency spectra. Therefore, (in contrast to SweepFinder) the neutral site frequencies can optionally be directly calculated from a given demographic model. We show that an increase of sample size results in more precise detection of positive selection. Thus, the ability to analyze substantially larger sample sizes by using SweeD leads to more accurate sweep detection. We validate SweeD via simulations and by scanning the first chromosome from the 1000 human Genomes project for selective sweeps. We compare SweeD results with results from a linkage-disequilibrium-based approach and identify common outliers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
Practical Methods of Optimization
Fletcher, R. (2000) · Wiley eBooks · doi:10.1002/9781118723203 · Cited by 7,605
Practical methods of optimization
(1988) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(88)90129-2 · Cited by 5,818
The hitch-hiking effect of a favourable gene
Smith, J. M., & Haigh, J. (1974) · Genetical Research · doi:10.1017/s0016672300014634 · Cited by 3,415
The hitch-hiking effect of a favourable gene
MAYNARD, J., & HAIGH, J. (2007) · Genetics Research · doi:10.1017/s0016672308009579 · Cited by 2,643
Generating samples under a Wright–Fisher neutral model of genetic variation
Hudson, R. R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.2.337 · Cited by 2,364
Hitchhiking Under Positive Darwinian Selection
Fay, J. C., & Wu, C.-I. (2000) · Genetics · doi:10.1093/genetics/155.3.1405 · Cited by 1,819
Genotype and SNP calling from next-generation sequencing data
Nielsen, R., Paul, J. S., Albrechtsen, A., & Song, Y. S. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2986 · Cited by 1,396
Genomic scans for selective sweeps using SNP data
Nielsen, R., Williamson, S., Kim, Y., Hubisz, M. J., Clark, A. G., & Bustamante, C. (2005) · Genome Research · doi:10.1101/gr.4252305 · Cited by 1,022
MPFR: A multiple-precision binary floating-point library with correct rounding
Fousse, L., Hanrot, G., Lefèvre, V., Pélissier, P., & Zimmermann, P. (2007) · ACM Transactions on Mathematical Software · doi:10.1145/1236463.1236468 · Cited by 952
Cited by
PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses in R
Pfeifer, B., Wittelsbürger, U., Ramos-Onsins, S. E., & Lercher, M. J. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu136 · Cited by 939
High-depth African genomes inform human migration and health
Choudhury, A., Aron, S., Botigué, L. R., Sengupta, D., Botha, G., Bensellak, T., Wells, G., Kumuthini, J., Shriner, D., Fakim, Y. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2859-7 · Cited by 355
The population genetics of structural variants in grapevine domestication
Zhou, Y., Minio, A., Massonnet, M., Solares, E., Lv, Y., Beridze, T., Cantu, D., & Gaut, B. S. (2019) · Nature Plants · doi:10.1038/s41477-019-0507-8 · Cited by 339
Adaptive introgression underlies polymorphic seasonal camouflage in snowshoe hares
Jones, M. R., Mills, L. S., Alves, P. C., Callahan, C. M., Alves, J. M., Lafferty, D. J. R., Jiggins, F. M., Jensen, J. D., Melo-Ferreira, J., & Good, J. M. (2018) · Science · doi:10.1126/science.aar5273 · Cited by 339
Evolutionary genomics of grape ( Vitis vinifera ssp. vinifera ) domestication
Zhou, Y., Massonnet, M., Sanjak, J. S., Cantu, D., & Gaut, B. S. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1709257114 · Cited by 316
Metadata
- Authors
- Pavlidis, P., Živković, D., Stamatakis, A., & Alachiotis, N.
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06-18
- Volume
- 30
- Issue
- 9
- Pages
- 2224-2234
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- OpenAlex
- W2102951980
- Cited by
- 473
- References
- 28
- FWCI
- 10.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes. https://app.cochat.ai/papers/10.1093/molbev/mst112
- The paper
- Pavlidis, P., Živković, D., Stamatakis, A., & Alachiotis, N. (2013). SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes. Molecular Biology and Evolution. https://doi.org/10.1093/molbev/mst112
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
