ArticleGenetics2011
Inference of population mutation rate and detection of segregating sites from next-generation sequence data.
Article in Genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- X-linked hydrocephalus genes: Their proximity to telomeres and high A + T content compared to Parkinson's disease.Experimental neurology · 2023Review
- Review
- Characterizing bias in population genetic inferences from low-coverage sequencing data.Molecular biology and evolution · 2014Article
- Article
- Assessing the effect of sequencing depth and sample size in population genetics inferences.PloS one · 2013Article
- Neutrality tests for sequences with missing data.Genetics · 2012Article
- Exact coalescent simulation of new haplotype data from existing reference haplotypes.Bioinformatics (Oxford, England) · 2012Article
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.
Funding
Abstract
We live in an age in which our ability to collect large amounts of genome-wide genetic variation data offers the promise of providing the key to the understanding and treatment of genetic diseases. Over the next few years this effort will be spearheaded by so-called next-generation sequencing technologies, which provide vast amounts of short-read sequence data at relatively low cost. This technology is often used to detect unknown variation in regions that have been linked with a given disease or phenotype. However, error rates are significant, leading to some nontrivial issues when it comes to interpreting the data. In this article, we present a method with which to address questions of widespread interest: calling variants and estimating the population mutation rate. We show performance of the method using simulation studies before applying our approach to an analysis of data from the 1000 Genomes project.
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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.