BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues

Zou, Luli S.; Erdos, Michael R.; Taylor, D. Leland; Chines, Peter S.; Varshney, Arushi; Parker, Stephen C. J.; Collins, Francis S.; Didion, John P.

doi:10.1186/s12864-018-4766-y

Cited by 36 publications

(25 citation statements)

References 69 publications

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“…We showed that at the recommended sequencing depth of 30x, the technical variability for WGBS is two to three fold higher than that for the methylation array. This lower precision observed is likely to be due to insufficient reads/counts to generate reliable quantification 48 . The precision of WGBS improves with increased sequencing depth as observed from our downsampling analyses, and a minimum coverage of 100x is necessary to achieve a level of precision that is broadly similar to that observed in methylation array.…”

Section: Discussionmentioning

confidence: 99%

Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing

Zhou

Drautz–Moses

et al. 2019

Sci Rep

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Whole genome bisulfite sequencing (WGBS), with its ability to interrogate methylation status at single CpG site resolution epigenome-wide, is a powerful technique for use in molecular experiments. Here, we aim to advance strategies for accurate and efficient WGBS for application in future large-scale epidemiological studies. We systematically compared the performance of three WGBS library preparation methods with low DNA input requirement (Swift Biosciences Accel-NGS, Illumina TruSeq and QIAGEN QIAseq) on two state-of-the-art sequencing platforms (Illumina NovaSeq and HiSeq X), and also assessed concordance between data generated by WGBS and methylation arrays. Swift achieved the highest proportion of CpG sites assayed and effective coverage at 26x (P < 0.001). TruSeq suffered from the highest proportion of PCR duplicates, while QIAseq failed to deliver across all quality metrics. There was little difference in performance between NovaSeq and HiSeq X, with the exception of higher read duplication rate on the NovaSeq (P < 0.05), likely attributable to the higher cluster densities on its flow cells. Systematic biases exist between WGBS and methylation arrays, with lower precision observed for WGBS across the range of depths investigated. To achieve a level of precision broadly comparable to the methylation array, a minimum coverage of 100x is recommended.

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Section: Discussionmentioning

confidence: 99%

Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing

Zhou

Drautz–Moses

et al. 2019

Sci Rep

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“…XGBoost was successfully applied in hundreds of recent studies to predict, e.g. host-pathogen protein–protein interactions (16), microRNA disease association (17) and DNA methylation (18). Several studies including our own previous paper showed that XGBoost gives the best performance if compared with a number of known machine learning methods (see e.g.…”

Section: Description Of the Databasementioning

confidence: 99%

Translocatome: a novel resource for the analysis of protein translocation between cellular organelles

Mendik

Dobronyi

Hári

et al. 2018

Nucleic Acids Research

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Here we present Translocatome, the first dedicated database of human translocating proteins (URL: http://translocatome.linkgroup.hu). The core of the Translocatome database is the manually curated data set of 213 human translocating proteins listing the source of their experimental validation, several details of their translocation mechanism, their local compartmentalized interactome, as well as their involvement in signalling pathways and disease development. In addition, using the well-established and widely used gradient boosting machine learning tool, XGBoost, Translocatome provides translocation probability values for 13 066 human proteins identifying 1133 and 3268 high- and low-confidence translocating proteins, respectively. The database has user-friendly search options with a UniProt autocomplete quick search and advanced search for proteins filtered by their localization, UniProt identifiers, translocation likelihood or data complexity. Download options of search results, manually curated and predicted translocating protein sets are available on its website. The update of the database is helped by its manual curation framework and connection to the previously published ComPPI compartmentalized protein–protein interaction database (http://comppi.linkgroup.hu). As shown by the application examples of merlin (NF2) and tumor protein 63 (TP63) Translocatome allows a better comprehension of protein translocation as a systems biology phenomenon and can be used as a discovery-tool in the protein translocation field.

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“…The XGBoost model has achieved excellent performance in many fields of medical research. [23][24][25][26] Currently, no researchers have used the XGBoost model to predict the time series data of human brucellosis.…”

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confidence: 99%

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

Alim

Guan

et al. 2020

BMJ Open

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ObjectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China.DesignTime-series study.SettingMainland China.MethodsData on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction.ResultsThe number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 338.867, 450.223 and 10.323, respectively, and the MAE, RSME and MAPE of the XGBoost model were 189.332, 262.458 and 4.475, respectively. For the test set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 529.406, 586.059 and 17.676, respectively, and the MAE, RSME and MAPE of the XGBoost model were 249.307, 280.645 and 7.643, respectively.ConclusionsThe performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.

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BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues

Cited by 36 publications

References 69 publications

Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing

Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing

Translocatome: a novel resource for the analysis of protein translocation between cellular organelles

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

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