Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1134909 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Comparison of false positive and false negative errors in targeted next generation sequencing results from certified NGS service providers
| PRJNA1125125 | ENA
Project description:Targeted genomic enrichment followed by next-generation sequencing dramatically increased the efficiency of mutation discovery in human genomes. Here we demonstrate that these techniques also revolutionize traditional genetic approaches in model systems. We developed a two-step protocol utilizing a traditional bulk-segregant analysis (BSA) approach for positional cloning mutants in phenotype-driven forward genetic screens. First, BSA pools are 'light' sequenced for rough mapping, followed by targeted enrichment and deep-sequencing of the mutant BSA pool for the linked genomic region to fine-map and discover candidate mutations. We applied this method successfully to three Arabidopsis mutants and show that it can be scaled by multiplexing. Similarly, we applied these techniques to a gene-driven reverse genetics method (chemical driven target-selected mutagenesis or TILLING) that is used for generating gene knockouts in a wide range of organisms, including plants, invertebrates and vertebrates. We developed an efficient multiplexed genomic enrichment protocol for pre-barcoded samples. As a proof-of-principle, 770 genes were screened for induced mutations in 30 rats, which identified all but one known variants (30) as well as a large series of novel knockout and missense alleles. Mutations were retrieved at the expected frequency with a the false-positive rate of less than 1 in 6 million basepairs, which is much lower as compared to traditional mutation discovery approaches. Both methods are largely independent of the genome size due to the targeted enrichment and can thus be applied to any genetic model system of interest. Targeted genomic enrichment followed by next-generation sequencing dramatically increased the efficiency of mutation discovery in human genomes. Here we demonstrate that these techniques also revolutionize traditional genetic approaches in model systems. We developed a two-step protocol utilizing a traditional bulk-segregant analysis (BSA) approach for positional cloning mutants in phenotype-driven forward genetic screens. First, BSA pools are 'light' sequenced for rough mapping, followed by targeted enrichment and deep-sequencing of the mutant BSA pool for the linked genomic region to fine-map and discover candidate mutations. We applied this method successfully to three Arabidopsis mutants and show that it can be scaled by multiplexing. Similarly, we applied these techniques to a gene-driven reverse genetics method (chemical driven target-selected mutagenesis or TILLING) that is used for generating gene knockouts in a wide range of organisms, including plants, invertebrates and vertebrates. We developed an efficient multiplexed genomic enrichment protocol for pre-barcoded samples. As a proof-of-principle, 770 genes were screened for induced mutations in 30 rats, which identified all but one known variants (30) as well as a large series of novel knockout and missense alleles. Mutations were retrieved at the expected frequency with a the false-positive rate of less than 1 in 6 million basepairs, which is much lower as compared to traditional mutation discovery approaches. Both methods are largely independent of the genome size due to the targeted enrichment and can thus be applied to any genetic model system of interest.
2010-09-12 | E-GEOD-22024 | biostudies-arrayexpress
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
2013-10-03 | PXD000320 | Pride
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
2013-10-03 | PXD000321 | Pride
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
2013-10-04 | PXD000324 | Pride
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
2013-10-04 | PXD000323 | Pride
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
2013-10-04 | PXD000322 | Pride
Project description:Microarray-based enrichment of selected genomic loci is a powerful method for genome complexity reduction. Since the vast majority of exons in vertebrate genomes are smaller than 150 nt, we have explored the use of short fragment libraries (85-110bp) to achieve higher enrichment specificity by reducing carryover and adverse effects of flanking intronic sequences. These short fragment libraries were enriched for 1.69 Mb of exonic sequences, using custom 244K microarrays, and sequenced using AB/SOLiD. High enrichment specificity (60 M-bM-^@M-^S 75%) was obtained at 67-213x average coverage, with 77-92% and 90-98% of targeted regions covered with more than 25% and 10% of the average coverage, respectively. As a more appropriate measure of the evenness of coverage, which is relatively independent of sequencing depth, we introduce the evenness of coverage parameter E. E values up to 75% were achieved. To verify the accuracy of SNP/mutation detection we evaluated 384 known non-reference SNPs in the targeted regions. At ~ 200x average sequence coverage, we were able to survey 96.4% of 1.69 Mb of genomic sequence with only 4.2% false negative calls while 3.6% of targeted regions were marked as unsurveyed. A total of 1197 new variants were detected. Verification revealed only 8 false positive calls, resulting in an overall false positive rate of less than 1 per ~200,000 bp (0.0005%, equivalent to an overall phred score of 55). 4 samples + capture design file
2010-02-01 | E-GEOD-18542 | biostudies-arrayexpress
Project description:In recent years, Next Generation Sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the allele balance distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state-of-the-art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies (RVAS).
| EGAS00001003027 | EGA
Project description:The application of RNA-seq profiling of cell lines following compound treatment has been highlighted as a line of evidence for Next Generation Risk Assessment of ingredients. We demonstrate as part of a use case the evaluation of a targeted RNAseq using Tempo-Seq platform as a general unbiased approach to complement more targeted approaches. We show impact of the use of different cell lines to the point of departure.
2020-06-30 | E-MTAB-8985 | biostudies-arrayexpress