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1 m patterns compared with approaches based on genotype calling.
2 e-phasing WGS reference panels after initial genotype calling.
3 out association testing without intermediate genotype calling.
4 ments in read mapping, variant discovery and genotype calling.
5 supports development of improved methods for genotype calling.
6 containing subsets of the entire dataset for genotype calling.
7  postmortem damage, which inhibits confident genotype calling.
8 enomes have low depth of coverage, hindering genotype calling.
9 timated and used for filtering loci prior to genotype calling.
10 ing population stratification, and improving genotype calling.
11 mulations, we show that trios provide higher genotype calling accuracy across the frequency spectrum,
12 multiple offspring can dramatically increase genotype calling accuracy and reduce phasing and Mendeli
13 igh Density (SNiPer-HD), for highly accurate genotype calling across hundreds of thousands of SNPs.
14 ize and batch composition for effects on the genotype calling algorithm BRLMM using raw data of 270 H
15 iva (domestic rice), we have developed a new genotype calling algorithm called 'ALCHEMY' based on sta
16 veloped an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MA
17         Here, we present a fast and accurate genotype calling algorithm for the Illumina BeadArray ge
18             We have introduced a model-based genotype calling algorithm which does not rely on having
19                    We have developed a novel genotype-calling algorithm for the Illumina platform, op
20                     Here, we propose a novel genotype-calling algorithm that, in contrast to the othe
21                                     Standard genotype calling algorithms are less likely to call rare
22                                         Most genotype calling algorithms currently used for GWAS are
23                          Existing microarray genotype-calling algorithms adopt either SNP-by-SNP (SNP
24 ot rely on single-nucleotide polymorphism or genotype calling and are particularly suitable for low s
25 ads affect the downstream variant discovery, genotype calling and association analysis.
26 inD) study, we identify a source of error in genotype calling and demonstrate that a standard battery
27  the development of methods capable of joint genotype calling and haplotype assembly.
28     We anticipate our method will facilitate genotype calling and haplotype inference for many ongoin
29     We develop a new probabilistic model for genotype calling and haplotype phasing from NGS data tha
30 Bayesian Markov model (DBM) for simultaneous genotype calling and haplotype phasing in low-coverage N
31  Although our model improves the accuracy of genotype calling and haplotype phasing, haplotype inform
32 eta-analysis of European ancestry by refined genotype calling and imputation and by the addition of 5
33                                  Accuracy of genotype calling and imputation were high with both simu
34  been underexplored, partly due to a lack of genotype calling and quality-control procedures.
35 g data, read mapping, inference of RAD loci, genotype calling, and filtering of the output data, as w
36                   Some simple approaches for genotype calling apply fixed filters, such as calling a
37 and accurate cell lineage tree inference and genotype calling approach based on the infinite-sites mo
38  model-based cell lineage tree inference and genotype calling approach that is capable of handling da
39 eqEM offers an improved, robust and flexible genotype-calling approach that can be widely applied in
40             Highly accurate and reproducible genotype calling are paramount since errors introduced b
41 ropose methods to model contamination during genotype calling as an alternative to removal of contami
42 ods, that can account for the uncertainty in genotype-calling associated with Next Generation Sequenc
43        When sequencing a set of individuals, genotype calling can be challenging due to low sequencin
44 oaches, RefEditor can significantly increase genotype calling consistency (from 43% to 61% at 4X cove
45 formed a comprehensive analysis to study how genotype calling errors affect type I error and statisti
46              We concluded that non-symmetric genotype calling errors need careful consideration in th
47  In simulation studies, we found that biased genotype calling errors yielded not only an inflation of
48  haplotype construction and imputation-based genotype calling, even without the availability of large
49              We used these data to establish genotype-calling filters that dramatically increase accu
50 netic studies, researchers typically perform genotype calling first and then apply standard genotype-
51                                     Accurate genotype calling for high throughput Illumina data is an
52 perior performance for haplotype phasing and genotype calling for population NGS data over existing m
53                                              Genotype calling from high-throughput platforms such as
54                                 Simultaneous genotype calling from low-coverage sequence data and imp
55 xisting approaches for haplotype phasing and genotype-calling from short read data.
56 everal tools have been developed for SNP and genotype calling in NGS data, haplotype phasing is often
57 10, well below the depth needed for accurate genotype calling in polyploid and outcrossing species.
58 ng genotype cluster plots to verify sensible genotype calling in putatively associated single nucleot
59               Here, we describe a method for genotype calling in settings where sequence data are ava
60  and thereby it takes the uncertainty of the genotype calling into account.
61                                     Accurate genotype calling is a pre-requisite of a successful Geno
62 kage disequilibrium (LD) based refinement of genotyping calling is essential to improve the accuracy.
63                                     However, genotype-calling methods for family-based sequence data,
64                                        Other genotype-calling methods, such as MAQ and SOAPsnp, are i
65 using external panels can greatly facilitate genotype calling of sequencing data with a small number
66 oach is demonstrated through applications to genotype calling on a set of HapMap samples used for qua
67                           Here, we develop a genotype calling pipeline named WEScall to analyse both
68 t take advantage of specific features of the genotype calling problem.
69 us, which induces uncertainty in the SNP and genotype calling procedures and, ultimately, adversely a
70                We have developed a novel SNP genotype calling program, SNiPer-High Density (SNiPer-HD
71    In order to improve variant detection and genotype calling, raw sequence data are typically examin
72 hat both batch size and composition affected genotype calling results and significantly associated SN
73        Batch size and composition affect the genotype calling results in GWAS using BRLMM.
74                          Microarrays are the genotype calling technology of choice in GWAS as they pe
75    However, such a two-step approach ignores genotype calling uncertainty in the association testing
76                           These methods take genotype calling uncertainty into account by directly in
77 nd identify problematic individuals prior to genotype calling will save researchers considerable comp