ArticleMolecular biology and evolution2010
A population genetic hidden Markov model for detecting genomic regions under selection.
Article in Molecular biology and evolution, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Genomewide architecture of adaptation in experimentally evolvedJournal of genetics · 2024Article
- Identification of natural selection in genomic data with deep convolutional neural network.BioData mining · 2021Article
- Detecting Selection from Linked Sites Using anGenetics · 2020Article
- The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference.Molecular biology and evolution · 2019Article
- Supervised Machine Learning for Population Genetics: A New Paradigm.Trends in genetics : TIG · 2018Review
- zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm.BMC bioinformatics · 2013Article
- Evolutionary forces shaping genomic islands of population differentiation in humans.BMC genomics · 2012Article
- Genomics of isolation in hybrids.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2012Article
- Methods to detect selection on noncoding DNA.Methods in molecular biology (Clifton, N.J.) · 2012Review
- A population genetics-phylogenetics approach to inferring natural selection in coding sequences.PLoS genetics · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, hidden Markov models have been applied to numerous problems in genomics. Here, we introduce an explicit population genetics hidden Markov model (popGenHMM) that uses single nucleotide polymorphism (SNP) frequency data to identify genomic regions that have experienced recent selection. Our popGenHMM assumes that SNP frequencies are emitted independently following diffusion approximation expectations but that neighboring SNP frequencies are partially correlated by selective state. We give results from the training and application of our popGenHMM to a set of early release data from the Drosophila Population Genomics Project (dpgp.org) that consists of approximately 7.8 Mb of resequencing from 32 North American Drosophila melanogaster lines. These results demonstrate the potential utility of our model, making predictions based on the site frequency spectrum (SFS) for regions of the genome that represent selected elements.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.