The DOI matches its Crossref record; 56 of the 57 references it cites were found in a registry or an index.
Genotype and SNP calling from next-generation sequencing data
Open accessAbstract
Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data. We review these methods and provide a guide for their use in NGS studies.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Similarly, NGS technology has been used to call SNP genotypes and structural variations and provides the ultimate coverage and resolution of genetic variations including private mutations of individuals.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Nielsen et al., 2011
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
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,457
Velvet: Algorithms for de novo short read assembly using de Bruijn graphsPreprint
Zerbino, D. R., & Birney, E. (2008) · Genome Research · doi:10.1101/gr.074492.107 · Cited by 9,575
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
dbSNP: the NCBI database of genetic variation
Sherry, S. T. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.308 · Cited by 7,502
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
Flagged first, then by citations
1 of the 57 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 801–900 of 1,000
Raffini, F., Jode, A. D., Johannesson, K., Faria, R., Zagrodzka, Z. B., Westram, A. M., Galindo, J., Rolán‐Alvarez, E., & Butlin, R. K. (2025) · Molecular Ecology · doi:10.1111/mec.70025 · Cited by 9
He, L., Pitkäniemi, J. M., Sarin, A., Salomaa, V. V., Sillanpää, M. J., & Ripatti, S. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21871 · Cited by 8
Mao, J., Ma, Y., & Zhou, R. (2017) · Biodiversity Science · doi:10.17520/biods.2017097 · Cited by 8
Noninvasive prenatal paternity determination using microhaplotypes: a pilot study
Wang, J. Y. T., Whittle, M. R., Puga, R., Yambartsev, A., Fujita, A., & Nakaya, H. I. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-00806-w · Cited by 8
Huy, H., Hung, H. V., Buckney, R. T., & Filippis, L. F. D. (2018) · Springer eBooks · doi:10.1007/978-3-319-93997-1_1 · Cited by 8
Fuchs, S. A., Harakaľová, M., van Haaften, G. W., van Hasselt, P. M., Cuppen, E. P. J. G., & Houwen, R. H. J. (2012) · Metallomics · doi:10.1039/c2mt20034a · Cited by 8
Yang, H., Dong, L., Wang, H., Liu, C., Liu, F., & Xie, C. (2018) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(18)62023-4 · Cited by 8
Berry, D. P., McHugh, N., Wall, E., McDermott, K., & O’Brien, A. C. (2019) · Irish Journal of Agricultural and Food Research · doi:10.2478/ijafr-2019-0001 · Cited by 8
Khanzadeh, H., Hossein‐Zadeh, N. G., & Ghovvati, S. (2020) · Iranian journal of applied animal science · Cited by 8
Universal probe-based intermediate primer-triggered qPCR (UPIP-qPCR) for SNP genotyping
Li, B., Liu, Y., Hao, X., Dong, J., Chen, L., Li, H., Wu, W., Liu, Y., Wang, J., Wang, Y., et al. (2021) · BMC Genomics · doi:10.1186/s12864-021-08148-2 · Cited by 8
High throughput sequencing approaches to mutation discovery in the mouse
Simon, M. M., Mallon, A., Howell, G. R., & Reinholdt, L. G. (2012) · Mammalian Genome · doi:10.1007/s00335-012-9424-0 · Cited by 8
Next Generation Sequencing Data and Proteogenomics
Ruggles, K. V., & Fenyö, D. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-42316-6_2 · Cited by 9
Donaldson, M. E., Rico, Y., Hueffer, K., Rando, H. M., Kukekova, A. V., & Kyle, C. J. (2017) · Ecology and Evolution · doi:10.1002/ece3.3583 · Cited by 8
Powerful Inference with the D-statistic on Low-Coverage Whole-Genome DataPreprint
Soraggi, S., Wiuf, C., & Albrechtsen, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/127852 · Cited by 8
Self-citationInference of human pigmentation from ancient DNA by genotype likelihoods
Perretti, S., Santos, P., Vizzari, M. T., Tassani, E., Benazzo, A., Ghirotto, S., & Barbujani, G. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2502158122 · Cited by 7
varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction
Nazaretyan, L., Rentzsch, P., & Kircher, M. (2025) · Genome Medicine · doi:10.1186/s13073-025-01517-6 · Cited by 7
Asatulloev, T., Cai, L., Yusupov, Z., Jia, K., Zhang, R., TOJIBAEV, K. S., & Sun, W. (2025) · Acta Botanica Yunnanica · doi:10.1016/j.pld.2025.11.001 · Cited by 7
Langille, B. L., Kess, T., Brachmann, M. K., Nugent, C. M., Messmer, A., Duffy, S., Holborn, M. K., Wyngaarden, M. V., Knutsen, T. M., Kent, M., et al. (2023) · Evolutionary Applications · doi:10.1111/eva.13590 · Cited by 7
Freitas, F. A. O., Brito, L. F., Fanalli, S. L., Gonçales, J. L., da Silva, B. P. M., Durval, M. C., Ciconello, F. N., de Oliveira, C. S., Nascimento, L. E., Gervásio, I. C., et al. (2024) · BMC Genomics · doi:10.1186/s12864-023-09863-8 · Cited by 7
Dennis, T. P. W., Essandoh, J., Mable, B. K., Viana, M., Yawson, A. E., & Weetman, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-58906-x · Cited by 7
Günther, T., Goldberg, A., & Schraiber, J. G. (2025) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkaf172 · Cited by 8
The Personalization of Therapy: Molecular Profiling Technologies and Their Application
Zerón-Medina-Cuairán, J., de Olza, M. O., Braña, I., & Rodon, J. A. (2015) · Seminars in Oncology · doi:10.1053/j.seminoncol.2015.09.026 · Cited by 7
Gießler, S., & Wolinska, J. (2013) · Molecular Ecology Resources · doi:10.1111/1755-0998.12144 · Cited by 7
Choudhury, O., Hazekamp, N., Thain, D., & Emrich, S. (2014) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2014.79 · Cited by 7
Considerations for clinical read alignment and mutational profiling using next-generation sequencing
Oliver, G. R. (2012) · F1000Research · doi:10.12688/f1000research.1-2.v2 · Cited by 7
Estimate of within population incremental selection through branch imbalance in lineage trees
Liberman, G., Benichou, J. I. C., Maman, Y., Glanville, J., Alter, I., & Louzoun, Y. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1198 · Cited by 7
Harrison, J. G., Forister, M. L., Mcknight, S. R., Nordin, E., & Parchman, T. L. (2019) · American Journal of Botany · doi:10.1002/ajb2.1235 · Cited by 7
An Overview of Bioinformatics Resources for SNP Analysis
Sukumar, S., Krishnan, A., & Banerjee, S. (2021) · Springer eBooks · doi:10.1007/978-981-33-6191-1_7 · Cited by 8
Evolution of an Amazonian Fish Is Driven by Allopatric Divergence Rather Than Ecological Divergence
Leroux, N., Sylvain, F., Normandeau, É., Holland, A., Val, A. L., & Derôme, N. (2022) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2022.875961 · Cited by 7
Next generation sequencing of CLU, PICALM and CR1: pitfalls and potential solutions.
Lord, J., Turton, J. C., Medway, C., Shi, H., Brown, K. S., Lowe, J. S., Mann, D. M. A., Pickering-Brown, S. M., Kalsheker, N. A., Passmore, P., et al. (2012) · PubMed Central · Cited by 7
Rogers, A., Beck, A. T., & Tintle, N. (2014) · BMC Proceedings · doi:10.1186/1753-6561-8-s1-s22 · Cited by 7
Donnard, E. R., Carpinetti, P. A., Navarro, F. C. P., Perez, R. O., Habr‐Gama, A., Parmigiani, R. B., Camargo, A. A., & Galante, P. A. F. (2015) · Genomics · doi:10.1016/j.ygeno.2015.01.009 · Cited by 7
Sasaki, E., Sugino, R. P., & Innan, H. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst103 · Cited by 7
SNPs Array Karyotyping in Non-Hodgkin Lymphoma
Etebari, M., Navari, M., & Piccaluga, P. P. (2015) · Microarrays · doi:10.3390/microarrays4040551 · Cited by 7
Genotyping-by-Sequencing to Predict Resistance to Lima Bean Downy Mildew in a Diversity Panel
Mhora, T. T., Ernest, E. G., Wisser, R. J., Evans, T. A., Patzoldt, M. E., Gregory, N., Westhafer, S. E., Polson, S. W., & Donofrio, N. M. (2016) · Phytopathology · doi:10.1094/phyto-02-16-0087-fi · Cited by 7
AD-LIBS: inferring ancestry across hybrid genomes using low-coverage sequence data
Schaefer, N. K., Shapiro, B., & Green, R. E. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1613-0 · Cited by 8
Concordance of mitochondrial DNA sequencing methods on bloodstains using Ion PGM™
Yao, L., Xu, Z., Zhao, H., Tu, Z., Liu, Z., Li, W., Hu, L., & Wan, L. (2018) · Legal Medicine · doi:10.1016/j.legalmed.2018.02.005 · Cited by 7
Ahmad, M. B., Leroy, T., Krigas, N., Temsch, E. M., Weiss‐Schneeweiss, H., Lexer, C., Sehr, E. M., & Paun, O. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.706574 · Cited by 7
Mas-Sandoval, À., Pope, N. S., Nielsen, K. N., Altinkaya, I., Fumagalli, M., & Korneliussen, T. S. (2022) · GigaScience · doi:10.1093/gigascience/giac032 · Cited by 7
Tuveri, S., Debackere, K., Marcelis, L., Dierckxsens, N., Demeulemeester, J., Dimitriadou, E., Dierickx, D., Lefesvre, P., Deraedt, K., Graux, C., et al. (2022) · Genes Chromosomes and Cancer · doi:10.1002/gcc.23069 · Cited by 7
Whole‐exome sequencing improves genetic testing accuracy in pulmonary artery hypertension
Zeng, X., Lian, T., Lin, J.-H., Li, S., Zheng, H., Cheng, C., Ye, J., Jing, Z., Wang, X., & Huang, W. (2018) · Pulmonary Circulation · doi:10.1177/2045894018763682 · Cited by 7
Trevoy, S. A. L., Janes, J. K., Muirhead, K., & Sperling, F. A. H. (2018) · Ecology and Evolution · doi:10.1002/ece3.4803 · Cited by 8
Tang, Y., Xia, Z., Ding, Z.-T., Ding, Y.-C., Liu, Z., Ma, X., & Liu, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-40489-7 · Cited by 7
Krusenbaum, L., & Wissuwa, M. (2025) · Theoretical and Applied Genetics · doi:10.1007/s00122-025-05080-x · Cited by 6
distAngsd: Fast and Accurate Inference of Genetic Distances for Next-Generation Sequencing Data
Zhao, L., Nielsen, R., & Korneliussen, T. S. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac119 · Cited by 6
Self-citationBlack, J. G., van Rooyen, A. V., Heinze, D. A., Gaffney, R., Hoffmann, A., Schmidt, T. L., & Weeks, A. (2023) · Molecular Ecology · doi:10.1111/mec.17224 · Cited by 5
Whole genome sequencing reveals population diversity and variation in HIV-1 specific host genes
Thami, P. K., Choga, W. T., Dandara, C., O’Brien, S. J., Essex, M., Gaseitsiwe, S., & Chimusa, E. R. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1290624 · Cited by 6
Aguirre, N. C., Villalba, P. V., García, M. N., Filippi, C. V., Rivas, J. G., Martinez, M. C., Acuña, C. V., López, A. J., López, J. A., Pathauer, P. S., et al. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1361418 · Cited by 6
Modeling Biases from Low-Pass Genome Sequencing to Enable Accurate Population Genetic Inferences
da Fonseca, E. M., Tran, L. N., Mendoza, H., & Gutenkunst, R. N. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf002 · Cited by 6
Joint Genotype Calling With Array and Sequence Data
O’Connell, J., & Marchini, J. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21657 · Cited by 6
Saliva as a comparable-quality source of DNA for Whole Exome Sequencing on Ion platforms
Ibrahim, O., Sutherland, H. G. E., Haupt, L. M., & Griffiths, L. R. (2019) · Genomics · doi:10.1016/j.ygeno.2019.08.014 · Cited by 6
Mendes, S. L., Machado, M. P., Coelho, M. M., & Sousa, V. C. (2021) · Heredity · doi:10.1038/s41437-021-00466-1 · Cited by 6
Variant Calling Using NGS Data in European Aspen (Populus tremula)
Wang, J., Scofield, D. G., Street, N. R., & Ingvarsson, P. K. (2015) · Springer eBooks · doi:10.1007/978-3-319-17157-9_4 · Cited by 6
Duan, J., Zhang, J., Deng, H., & Wang, Y. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346163 · Cited by 6
Minter, E. J. A., Lowe, C., Brockhurst, M. A., & Watts, P. C. (2014) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12321 · Cited by 6
A Bayes testing approach to metagenomic profiling in bacteria
Clarke, B. S., Valdes, C., Dobra, A., & Clarke, J. (2015) · Statistics and Its Interface · doi:10.4310/sii.2015.v8.n2.a5 · Cited by 6
Iterative Referencing for Improving the Interpretation of DNA Sequence Data
Ghanayim, A., & Geiger, D. (2013) · Cited by 6
Lees, C. J., Li, G., & Duncan, R. W. (2016) · Molecular Breeding · doi:10.1007/s11032-016-0576-6 · Cited by 6
Evaluation of MC1R high-throughput nucleotide sequencing data generated by the 1000 Genomes Project
Marano, L. A., Marcorin, L., Castelli, E. C., & Mendes‐Junior, C. T. (2017) · Genetics and Molecular Biology · doi:10.1590/1678-4685-gmb-2016-0180 · Cited by 6
Multiplex PCR with the blunt hairpin primers for next generation sequencing
Chen, K., Zhou, Y., Li, K., & Xiao, J. (2017) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-017-0133-0 · Cited by 6
Plant Genetic Engineering and GM Crops: Merits and Demerits
Parray, J. A., Mir, M. Y., & Shameem, N. (2019) · Springer eBooks · doi:10.1007/978-981-13-8840-8_4 · Cited by 6
An enrichment method for mapping ambiguous reads to the reference genome for NGS analysis
Liu, Y., Ma, Y. C., Salsman, E., Manthey, F. A., Elias, E. M., Li, X., & Yan, C. (2019) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720019400122 · Cited by 6
Gruenthal, K. M., & Larson, W. A. (2021) · Conservation Genetics Resources · doi:10.1007/s12686-020-01185-1 · Cited by 6
Ou, C., Sun, T., Liu, X., Li, C., Li, M., Wang, X., Ren, H., Zhao, Z., & Zhuang, F. (2022) · Plants · doi:10.3390/plants11030391 · Cited by 6
Gomez‐Raya, L., Izquierdo, E. G., de Mercado, E., García-Ruiz, F., & Rauw, W. M. (2022) · BMC Genomic Data · doi:10.1186/s12863-022-01025-1 · Cited by 6
Identification of novel SNP markers for kinship analysis in the Korean population
Youn, B. J., Cho, W.-C., Yoo, S., Lee, K., & Kim, C. H. (2022) · Forensic Science International · doi:10.1016/j.forsciint.2022.111541 · Cited by 6
Genomic Applications in Forensic Medicine
Børsting, C., & Morling, N. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-420196-5.00022-8 · Cited by 6
Dark period transcriptomic and metabolic profiling of two diverse Eutrema salsugineum accessions
Yin, J. L., Gosney, M. J., Dilkes, B. P., & Mickelbart, M. V. (2018) · Plant Direct · doi:10.1002/pld3.32 · Cited by 7
Dong, M., He, Q., Zhao, J., Zhang, Y., Yuan, D., & Zhang, J. (2019) · Genes · doi:10.3390/genes10080583 · Cited by 6
Inwood, S. N., McLaughlin, G. M., Buckley, T. R., Cox, M. P., Handley, K. M., Steeves, T. E., Strabala, T. J., McDougal, R. L., & Dearden, P. K. (2020) · New Zealand Journal of Ecology · doi:10.20417/nzjecol.44.22 · Cited by 6
Nürnberger, B., Baird, S. J. E., Čížková, D., Bryjová, A., Mudd, A. B., Blaxter, M., & Szymura, J. M. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab286 · Cited by 6
Kerksick, C. M., Tsatsakis, A. M., Hayes, A. W., Kafantaris, I., & Kouretas, D. (2014) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000000730 · Cited by 6
Molecular Mapping and Breeding with Microsatellite Markers
Lightfoot, D. A., & Iqbal, M. J. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-389-3_20 · Cited by 6
Bernhardsson, C., Wang, X., Eklöf, H., & Ingvarsson, P. K. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/805994 · Cited by 6
Bioinformatic processing of whole genome sequencing data with Tapir
Woerner, A. E., Stowe, B. N., King, J. L., Crysup, B., Huang, M., & Coble, M. D. (2025) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2025.103387 · Cited by 5
Rethinking DeepVariant: Efficient Neural Architectures for Intelligent Variant Calling
Gurianova, A., Pestruilova, A., Beliaeva, A., Kasianov, A. S., Mikhailova, L., Guguchkin, E., & Karpulevich, E. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27010513 · Cited by 5
Chen, Y., Zheng, Y., Hong, Y., Wen, J., Li, J., Huang, Y., Chen, Y., Zheng, X., Yang, T., Xu, Y., et al. (2022) · Journal of Leukocyte Biology · doi:10.1002/jlb.5a0721-361r · Cited by 5
Oury, N., & Magalon, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-57136-5 · Cited by 5
loco-pipe: an automated pipeline for population genomics with low-coverage whole-genome sequencing
Zhou, Z. T., Owens, G. L., Larson, W. A., Lou, R. N., & Sudmant, P. H. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae098 · Cited by 5
Bouchard, R., Babin, C., Normandeau, É., Xuereb, A. T. J., Boulanger, F., Coxon, A., Diamond, S., Fireman, R., Lameboy, J., Louttit, N., et al. (2025) · Molecular Ecology · doi:10.1111/mec.17739 · Cited by 6
Kim, W. (2015) · Human Heredity · doi:10.1159/000434645 · Cited by 5
Casellas, J., de Hijas-Villalba, M. M., Vázquez-Gómez, M., & Id‐Lahoucine, S. (2021) · Livestock Science · doi:10.1016/j.livsci.2021.104629 · Cited by 5
Application of Next-Generation Sequencing Technologies for Genetic Diversity Analysis in Cereals
Kiani, S. P., Akhunova, A. R., & Akhunov, E. (2013) · Springer eBooks · doi:10.1007/978-94-007-6401-9_4 · Cited by 5
Toward Efficient Variant Calling Inside Main-Memory Database Systems
Dorok, S., Breß, S., & Saake, G. (2014) · International Workshop on Database and Expert Systems Applications (DEXA) · doi:10.1109/dexa.2014.25 · Cited by 5
Zhi, D., Liu, N., & Zhang, K. (2015) · Methods · doi:10.1016/j.ymeth.2015.01.016 · Cited by 5
Targeted Next-Generation Sequencing: the Clinician’s Stethoscope for Genetic Disorders
Haas, J., Barb, I., Katus, H. A., & Meder, B. (2014) · Personalized Medicine · doi:10.2217/pme.14.40 · Cited by 5
Beyan, T., & Son, Y. A. (2014) · JMIR Medical Informatics · doi:10.2196/medinform.3169 · Cited by 5
Oleksiewicz, U., Tomczak, K. J., Woropaj, J., Markowska, M., Stępniak, P., & Shah, P. K. (2015) · Współczesna Onkologia · doi:10.5114/wo.2014.47137 · Cited by 5
Molecular Tools for Monitoring and Source-Tracking Salmonella in Wildlife and the Environment
Wright, A., Ginn, A., & Luo, Z. (2015) · Springer eBooks · doi:10.1007/978-3-319-24442-6_5 · Cited by 5
Lane-by-Lane Sequencing using Illumina's Genome Analyzer II
Gravina, M. T., Lin, J. H., & Levine, S. S. (2013) · BioTechniques · doi:10.2144/000114032 · Cited by 5
Pearson, S. K., Bradford, T. M., Ansari, M., Bull, C. M., & Gardner, M. (2016) · Transactions of the Royal Society of South Australia · doi:10.1080/03721426.2016.1216735 · Cited by 5
Cache Friendly Optimisation of de Bruijn Graph Based Local Re-Assembly in Variant Calling
Gamaarachchi, H., Bayat, A., Gaëta, B. A., & Parameswaran, S. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2881975 · Cited by 5
Medium-coverage DNA sequencing in the design of the genetic association study
Xu, C., Zhang, R., Shen, H., & Deng, H. (2020) · European Journal of Human Genetics · doi:10.1038/s41431-020-0656-2 · Cited by 5
A 14,000-year-old genome sheds light on the evolution and extinction of a Pleistocene vulture
Ericson, P. G. P., Irestedt, M., Zuccon, D., Larsson, P., Tison, J.-L., Emslie, S. D., Götherström, A., HUME, J. P., Werdelin, L., & Qu, Y. (2022) · Communications Biology · doi:10.1038/s42003-022-03811-0 · Cited by 5
Universal probe-based SNP genotyping with visual readout: a robust and versatile method
Wang, Z., Zhang, Z., Luo, W., Wang, L., Han, X., Zhao, R., Liu, X., Zhang, J., Yu, W. H., Li, J., et al. (2023) · Nanoscale · doi:10.1039/d3nr01950k · Cited by 5
Advances in Technologies and Study Design
Parnell, L. D. (2012) · Progress in molecular biology and translational science · doi:10.1016/b978-0-12-398397-8.00002-2 · Cited by 5
Comparing variant calling algorithms for target-exon sequencing in a large sample
Lo, Y., Kang, H. M., Nelson, M. R., Othman, M., Chissoe, S. L., Ehm, M. G., Abecasis, G. R., & Zöllner, S. K. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0489-0 · Cited by 5
Identification of Korean-specific SNP markers from whole-exome sequencing data
Kim, S., Yoo, S. Y., Nam, S. H., Lee, J. M., & Chung, K. W. (2016) · International Journal of Legal Medicine · doi:10.1007/s00414-015-1313-0 · Cited by 5
Clinical Next-Generation Sequencing
Dhawan, D. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809411-2.00002-7 · Cited by 5
VikNGS: a C++ variant integration kit for next generation sequencing association analysis
Baskurt, Z., Mastromatteo, S., Gong, J., Wintle, R. F., Scherer, S. W., & Strug, L. J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz716 · Cited by 6
Metadata
- Authors
- Nielsen, R., Paul, J. S., Albrechtsen, A., & Song, Y. S.
- Affiliations
- University of California, Berkeley (US)
- University of Copenhagen (DK)
- Corresponding author
- Nielsen, Rasmus
- Song, Yun S.
- Venue
- Nature Reviews Genetics
- Published
- 2011-05-18
- Volume
- 12
- Issue
- 6
- Pages
- 443-451
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3593722
- OpenAlex
- W2131016694
- Cited by
- 1,396
- References
- 57
- Citations by year
- 2026: 35 · 2025: 45 · 2024: 72 · 2023: 51 · 2022: 87 · 2021: 79 · 2020: 98 · 2019: 104 · 2018: 99 · 2017: 99 · 2016: 131 · 2015: 114 · 2014: 151 · 2013: 119 · 2012: 90
- FWCI
- 11.06
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- National Human Genome Research Institute (R01HG003229, R01-HG003229-0551, T32HG00047, T32HG000047, R01-HG003229-05)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- genotype calling, SNP calling, next-generation sequencing, low-pass sequencing, uncertainty quantification, genotyping accuracy
- MeSH terms
- Alleles, Chromosome Mapping, Data Interpretation, Statistical, Genotype, Humans, Probability, Linkage Disequilibrium, Likelihood Functions, Sequence Analysis, DNA, Polymorphism, Single Nucleotide, Genetic Diseases, Inborn
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