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A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data
Open accessAbstract
Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. Availability: http://samtools.sourceforge.net Contact: hengli@broadinstitute.org
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Human pluripotent stem-cell-derived islets ameliorate diabetes in non-human primates
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Improved plant cytosine base editors with high editing activity, purity, and specificity
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MethodInfluentialCathepsin F mutations cause Type B Kufs disease, an adult-onset neuronal ceroid lipofuscinosis
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Human origins in a southern African palaeo-wetland and first migrations
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Origins and genetic legacies of the Caribbean Taino
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Natural hybridization reveals incompatible alleles that cause melanoma in swordtail fish
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Accuracy and efficiency of germline variant calling pipelines for human genome data
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An ancestral recombination graph of human, Neanderthal, and Denisovan genomes
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ATLAS: Analysis Tools for Low-depth and Ancient Samples
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A catalog of single nucleotide changes distinguishing modern humans from archaic hominins
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Common germline variants of the human APOE gene modulate melanoma progression and survival
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Germline genetic determination of cancer progression and survival
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Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing Data
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Ancient and modern genomes unravel the evolutionary history of the rhinoceros family
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Characterizing bias in population genetic inferences from low-coverage sequencing data
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The Genomic History of the Bronze Age Southern Levant
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Haplotype-aware pantranscriptome analyses using spliced pangenome graphs
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Nuclear DNA from two early Neandertals reveals 80,000 years of genetic continuity in Europe
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A 3,000-year-old Egyptian emmer wheat genome reveals dispersal and domestication history
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SORL1 mutations in early- and late-onset Alzheimer disease
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Metadata
- Authors
- Li, H.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-09-08
- Volume
- 27
- Issue
- 21
- Pages
- 2987-2993
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2150131459
- Cited by
- 6,486
- References
- 13
- FWCI
- 15.64
- Type
- journal article
- Language
- en
- Open access
- green
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.
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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.