scholarly journals Positional effects revealed in Illumina Methylation Array and the impact on analysis

2017 ◽  
Author(s):  
Chuan Jiao ◽  
Chunling Zhang ◽  
Rujia Dai ◽  
Yan Xia ◽  
Kangli Wang ◽  
...  
Keyword(s):  
Differential Methylation ◽  
Batch Effects ◽  
Laboratory Practice ◽  
Methylation Array ◽  
Cpg Sites ◽  
Positional Effects ◽  
Illumina Infinium Humanmethylation ◽  
Downstream Analysis ◽  
The Impact ◽  
Epigenetic Research

With the evolution of rapid epigenetic research, Illumina Infinium HumanMethylation BeadChips have been widely used to study DNA methylation. However, in evaluating the accuracy of this method, we found that the commonly used Illumina HumanMethylation BeadChips are substantially affected by positional effects; the DNA sample’s location in a chip affects the measured methylation levels. We analyzed three HumanMethylation450 and three HumanMethylation27 datasets by using four methods to prove the existence of positional effects. Three datasets were analyzed further for technical replicate analysis or differential methylation CpG sites analysis. The pre- and post-correction comparisons indicate that the positional effects could alter the measured methylation values and downstream analysis results. Nevertheless, ComBat, linear regression and functional normalization could all be used to minimize such artifact. We recommend performing ComBat to correct positional effects followed by the correction of batch effects in data preprocessing as this procedure slightly outperforms the others. In addition, randomizing the sample placement should be a critical laboratory practice for using such experimental platforms. Code for our method is freely available at: https://github.com/ChuanJ/posibatch.

scholarly journals MADA: a web service for analysing DNA methylation array data

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Xinyu Hu ◽  
Li Tang ◽  
Linconghua Wang ◽  
Fang-Xiang Wu ◽  
Min Li
Keyword(s):  
Dna Methylation ◽  
Data Analysis ◽  
Web Service ◽  
Differential Methylation ◽  
Methylation Analysis ◽  
Methylation Array ◽  
Array Data ◽  
Dna Methylation Array ◽  
Downstream Analysis ◽  
Differential Methylation Analysis

Abstract Background DNA methylation in the human genome is acknowledged to be widely associated with biological processes and complex diseases. The Illumina Infinium methylation arrays have been approved as one of the most efficient and universal technologies to investigate the whole genome changes of methylation patterns. As methylation arrays may still be the dominant method for detecting methylation in the anticipated future, it is crucial to develop a reliable workflow to analysis methylation array data. Results In this study, we develop a web service MADA for the whole process of methylation arrays data analysis, which includes the steps of a comprehensive differential methylation analysis pipeline: pre-processing (data loading, quality control, data filtering, and normalization), batch effect correction, differential methylation analysis, and downstream analysis. In addition, we provide the visualization of pre-processing, differentially methylated probes or regions, gene ontology, pathway and cluster analysis results. Moreover, a customization function for users to define their own workflow is also provided in MADA. Conclusions With the analysis of two case studies, we have shown that MADA can complete the whole procedure of methylation array data analysis. MADA provides a graphical user interface and enables users with no computational skills and limited bioinformatics background to carry on complicated methylation array data analysis. The web server is available at: http://120.24.94.89:8080/MADA

scholarly journals Positional effects revealed in Illumina methylation array and the impact on analysis

Epigenomics ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 643-659 ◽  
Author(s):  
Chuan Jiao ◽  
Chunling Zhang ◽  
Rujia Dai ◽  
Yan Xia ◽  
Kangli Wang ◽  
...  
Keyword(s):  
Methylation Array ◽  
Positional Effects ◽  
The Impact

EPIGENETIC ALTERATIONS ASSOCIATED WITH PREMATURE OVARIAN FAILURE

2008 ◽  
Vol 31 (4) ◽  
pp. 11
Author(s):  
Manda Ghahremani ◽  
Courtney W Hannah ◽  
Maria Peneherrera ◽  
Karla L Bretherick ◽  
Margo R Fluker ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Premature Ovarian Failure ◽  
Promoter Region ◽  
Gene Promoter ◽  
Ovarian Failure ◽  
P Value ◽  
Epigenetic Alterations ◽  
Methylation Array ◽  
Cpg Sites ◽  
Deficient Mice

Background/Purpose: Premature ovarian failure (POF) affects 1% of women with a largely idiopathic and poorly understood etiology. The objective of this study was to identify specific epigenetic alterations by measuring DNA methylation of gene regulatory regions in women with POF vs. controls. Methods: Blood samples were collected from idiopathic POFpatients (Amenorrhea for at least 3 months and 2 serum FSH levels of > 40mIU/ml obtained > 1 month apart prior to age 40) and control women (CW) (healthy pregnancy after age 37 with out a pregnancy loss). Genomic DNA was extracted from EDTA anticoagulated blood and bisulfite converted for analysis using the Illumina Golden Gate Methylation Panel which measures DNA methylation at 1506 CpG sites in the promoter regions of 807 genes in 10 POF and 12 CW. Candidate genes with altered epigenetic marks between POF and CW at a nominal P-value < 0.05 were identified using a t-testcomparison within the Illumina bead studio software. Genes of interest were further analyzed for quantitative methylation at specific CpG sites using pyrosequencing in 30 POF and 30 CW. Results: Comparison of DNA methylation profiles of our initial POF and CW groups identified several genes with statistically significanthyper- or hypo- methylation in the POF group (P < 0.05), including the Androgen Receptor (AR)promoter region, which was significantly hypermethylated. To further validate these results, DNA methylation of the AR gene promoter was quantified bypryosequencing in a larger group of POF and CW. Pyrosequencing further confirmed a significantly higher DNA methylation of the AR promoter region inPOF vs. CW (P=0.007). Conclusions: This is a novel study identifying epigenetic alterations in POF. The hypermethylation of the AR gene in POF patients may cause decreased level of the AR in these women. This is especially interesting given a recent report of induced POF in AR deficient mice^1. Specific epigenetic markers, as identified by DNA methylation array profiling in blood, may serve as useful biomarkers for POF and other fertility disorders. However, it will need to be determined if these methylation changes are present prior to diagnosis, or are a consequence of menopause itself. Reference: 1.Hiroko S. et al. Premature ovarian failure in androgenreceptor deficient mice. PNAS;103:224-9

scholarly journals Standards of laboratory practice: cardiac drug monitoring

1998 ◽  
Vol 44 (5) ◽  
pp. 1096-1109 ◽  
Author(s):  
Roland Valdes ◽  
Saeed A Jortani ◽  
Mihai Gheorghiade
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Drug Monitoring ◽  
Free Fraction ◽  
Therapeutic Index ◽  
Therapeutic Monitoring ◽  
Therapeutic Drug ◽  
Laboratory Practice ◽  
Clinical Complications ◽  
The Impact

Abstract In this Standard of Laboratory Practice we recommend guidelines for therapeutic monitoring of cardiac drugs. Cardiac drugs are primarily used for treatment of angina, arrhythmias, and congestive heart failure. Digoxin, used in congestive heart failure, is widely prescribed and therapeutically monitored. Monitoring and use of antiarrhythmics such as disopyramide and lidocaine have been steadily declining. Immunoassay techniques are currently the most popular methods for measuring cardiac drugs. Several reasons make measurement of cardiac drugs in serum important: their narrow therapeutic index, similarity in clinical complications and presentation of under- and overmedicated patients, need for dosage adjustments, and confirmation of patient compliance. Monitoring may also be necessary in other circumstances, such as assessment of acetylator phenotypes. We present recommendations for measuring digoxin, quinidine, procainamide (and N-acetylprocainamide), lidocaine, and flecainide. We discuss guidelines for measuring unbound digoxin in the presence of an antidote (Fab fragments), for characterizing the impact of digoxin-like immunoreactive factor (DLIF) and other cross-reactants on immunoassays, and for monitoring the unbound (free fraction) of drugs that bind to α1-acid glycoprotein. We also discuss logistic, clinical, hospital, and laboratory practice guidelines needed for implementation of a successful therapeutic drug monitoring service for cardiac drugs.

Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities

2019 ◽  
Vol 36 (7) ◽  
pp. 2017-2024
Author(s):  
Weiwei Zhang ◽  
Ziyi Li ◽  
Nana Wei ◽  
Hua-Jun Wu ◽  
Xiaoqi Zheng
Keyword(s):  
Real Data ◽  
R Package ◽  
Differential Methylation ◽  
Least Square ◽  
Epigenetic Mechanism ◽  
Supplementary Information ◽  
Cpg Sites ◽  
Tumor Purity ◽  
Different Sources ◽  
Normal Controls

Abstract Motivation Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for. Results We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose. Availability and implementation InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Batch Effect Removal via Batch-Free Encoding

2018 ◽  
Author(s):  
Uri Shaham
Keyword(s):  
Deep Learning ◽  
Biological Properties ◽  
Batch Effect ◽  
Training Data ◽  
Rna Seq ◽  
Batch Effects ◽  
Training Time ◽  
Learning Techniques ◽  
Downstream Analysis ◽  
Biological Patterns

AbstractBiological measurements often contain systematic errors, also known as “batch effects”, which may invalidate downstream analysis when not handled correctly. The problem of removing batch effects is of major importance in the biological community. Despite recent advances in this direction via deep learning techniques, most current methods may not fully preserve the true biological patterns the data contains. In this work we propose a deep learning approach for batch effect removal. The crux of our approach is learning a batch-free encoding of the data, representing its intrinsic biological properties, but not batch effects. In addition, we also encode the systematic factors through a decoding mechanism and require accurate reconstruction of the data. Altogether, this allows us to fully preserve the true biological patterns represented in the data. Experimental results are reported on data obtained from two high throughput technologies, mass cytometry and single-cell RNA-seq. Beyond good performance on training data, we also observe that our system performs well on test data obtained from new patients, which was not available at training time. Our method is easy to handle, a publicly available code can be found at https://github.com/ushaham/BatchEffectRemoval2018.

The impact of an integrated haematology screening system on laboratory practice

1996 ◽  
Vol 18 (4) ◽  
pp. 271-276 ◽  
Author(s):  
A.J. MACDONALD ◽  
A.E. BRADSHAW ◽  
W.A. HOLMES ◽  
S.M. LEWIS
Keyword(s):  
Screening System ◽  
Laboratory Practice ◽  
The Impact

scholarly journals Methylome and transcriptome signature of bronchoalveolar cells from multiple sclerosis patients in relation to smoking

2020 ◽  
pp. 135245852094376
Author(s):  
Mikael V Ringh ◽  
Michael Hagemann-Jensen ◽  
Maria Needhamsen ◽  
Susanna Kullberg ◽  
Jan Wahlström ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Lung Inflammation ◽  
Lung Cells ◽  
Gene Ontology Analysis ◽  
Cellular Motility ◽  
Cpg Sites ◽  
Neuronal Processes ◽  
Multiple Sclerosis Patients ◽  
The Impact ◽  
Transcriptome Signature

Background: Despite compelling evidence that cigarette smoking impacts the risk of developing multiple sclerosis (MS), little is known about smoking-associated changes in the primary exposed lung cells of patients. Objectives: We aimed to examine molecular changes occurring in bronchoalveolar lavage (BAL) cells from MS patients in relation to smoking and in comparison to healthy controls (HCs). Methods: We profiled DNA methylation in BAL cells from female MS ( n = 17) and HC ( n = 22) individuals, using Illumina Infinium EPIC and performed RNA-sequencing in non-smokers. Results: The most prominent changes were found in relation to smoking, with 1376 CpG sites (adjusted P < 0.05) differing between MS smokers and non-smokers. Approximately 30% of the affected genes overlapped with smoking-associated changes in HC, leading to a strong common smoking signature in both MS and HC after gene ontology analysis. Smoking in MS patients resulted in additional discrete changes related to neuronal processes. Methylome and transcriptome analyses in non-smokers suggest that BAL cells from MS patients display very subtle (not reaching adjusted P < 0.05) but concordant changes in genes connected to reduced transcriptional/translational processes and enhanced cellular motility. Conclusions: Our study provides insights into the impact of smoking on lung inflammation and immunopathogenesis of MS.

scholarly journals A comprehensive evaluation of long read error correction methods

BMC Genomics ◽  
2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Haowen Zhang ◽  
Chirag Jain ◽  
Srinivas Aluru
Keyword(s):  
Error Correction ◽  
Hybrid Methods ◽  
Comprehensive Evaluation ◽  
Data Sets ◽  
Short Reads ◽  
Long Reads ◽  
Long Read ◽  
Read Error Correction ◽  
Downstream Analysis ◽  
The Impact

Abstract Background Third-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. Results In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research. Conclusions Despite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE.

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