Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Strategies for processing and quality control of Illumina genotyping arrays.

ABSTRACT: Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on?>?100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies.

SUBMITTER: Zhao S

PROVIDER: S-EPMC6171493 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Strategies for processing and quality control of Illumina genotyping arrays.

Zhao Shilin S Jing Wang W Samuels David C DC Sheng Quanghu Q Shyr Yu Y Guo Yan Y

Briefings in bioinformatics 20180901 5

Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 sub ...[more]

PMID: 28334151

Similar Datasets

Illumina human exome genotyping array clustering and quality control.

Project description:With the rise of high-throughput sequencing technology, traditional genotyping arrays are gradually being replaced by sequencing technology. Against this trend, Illumina has introduced an exome genotyping array that provides an alternative approach to sequencing, especially suited to large-scale genome-wide association studies (GWASs). The exome genotyping array targets the exome plus rare single-nucleotide polymorphisms (SNPs), a feature that makes it substantially more challenging to process than previous genotyping arrays that targeted common SNPs. Researchers have struggled to generate a reliable protocol for processing exome genotyping array data. The Vanderbilt Epidemiology Center, in cooperation with Vanderbilt Technologies for Advanced Genomics Analysis and Research Design (VANGARD), has developed a thorough exome chip-processing protocol. The protocol was developed during the processing of several large exome genotyping array-based studies, which included over 60,000 participants combined. The protocol described herein contains detailed clustering techniques and robust quality control procedures, and it can benefit future exome genotyping array-based GWASs.

| S-EPMC4441213 | biostudies-literature

shinyMethyl: interactive quality control of Illumina 450k DNA methylation arrays in R.

Project description:We present shinyMethyl, a Bioconductor package for interactive quality control of DNA methylation data from Illumina 450k arrays. The package summarizes 450k experiments into small exportable R objects from which an interactive interface is launched. Reactive plots allow fast and intuitive quality control assessment of the samples. In addition, exploration of the phenotypic associations is possible through coloring and principal component analysis. Altogether, the package makes it easy to perform quality assessment of large-scale methylation datasets, such as epigenome-wide association studies or the datasets available through The Cancer Genome Atlas portal. The shinyMethyl package is implemented in R and available via Bioconductor. Its development repository is at https://github.com/jfortin1/shinyMethyl.

| S-EPMC4176427 | biostudies-literature

argyle: An R Package for Analysis of Illumina Genotyping Arrays.

Project description:Genotyping microarrays are an important and widely-used tool in genetics. I present argyle, an R package for analysis of genotyping array data tailored to Illumina arrays. The goal of the argyle package is to provide simple, expressive tools for nonexpert users to perform quality checks and exploratory analyses of genotyping data. To these ends, the package consists of a suite of quality-control functions, normalization procedures, and utilities for visually and statistically summarizing such data. Format-conversion tools allow interoperability with popular software packages for analysis of genetic data including PLINK, R/qtl and DOQTL. Detailed vignettes demonstrating common use cases are included as supporting information. argyle bridges the gap between the low-level tasks of quality control and high-level tasks of genetic analysis. It is freely available at https://github.com/andrewparkermorgan/argyle and has been submitted to Bioconductor.

| S-EPMC4751548 | biostudies-literature

Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray.

Project description:The decreasing cost of performing genome-wide association studies has made genomics widely accessible. However, there is a paucity of guidance for best practice in conducting such analyses. For the results of a study to be valid and replicable, multiple biases must be addressed in the course of data preparation and analysis. In addition, standardizing methods across small, independent studies would increase comparability and the potential for effective meta-analysis. This article provides a discussion of important aspects of quality control, imputation and analysis of genome-wide data from a low-coverage microarray, as well as a straight-forward guide to performing a genome-wide association study. A detailed protocol is provided online, with example scripts available at https://github.com/JoniColeman/gwas_scripts.

| S-EPMC5863770 | biostudies-literature

Preprocessing and Quality Control Strategies for Illumina DASL Assay-Based Brain Gene Expression Studies with Semi-Degraded Samples.

Project description:Available statistical preprocessing or quality control analysis tools for gene expression microarray datasets are known to greatly affect downstream data analysis, especially when degraded samples, unique tissue samples, or novel expression assays are used. It is therefore important to assess the validity and impact of the assumptions built in to preprocessing schemes for a dataset. We developed and assessed a data preprocessing strategy for use with the Illumina DASL-based gene expression assay with partially degraded postmortem prefrontal cortex samples. The samples were obtained from individuals with autism as part of an investigation of the pathogenic factors contributing to autism. Using statistical analysis methods and metrics such as those associated with multivariate distance matrix regression and mean inter-array correlation, we developed a DASL-based assay gene expression preprocessing pipeline to accommodate and detect problems with microarray-based gene expression values obtained with degraded brain samples. Key steps in the pipeline included outlier exclusion, data transformation and normalization, and batch effect and covariate corrections. Our goal was to produce a clean dataset for subsequent downstream differential expression analysis. We ultimately settled on available transformation and normalization algorithms in the R/Bioconductor package lumi based on an assessment of their use in various combinations. A log2-transformed, quantile-normalized, and batch and seizure-corrected procedure was likely the most appropriate for our data. We empirically tested different components of our proposed preprocessing strategy and believe that our results suggest that a preprocessing strategy that effectively identifies outliers, normalizes the data, and corrects for batch effects can be applied to all studies, even those pursued with degraded samples.

| S-EPMC3286152 | biostudies-literature

Human telomerase RNA processing and quality control

Project description:RNA sequencing of HeLa cells treated with siRNA against the RNA exosome components hRRP40, hRRP6, hDIS3, and hRRP6/hDIS3 or the splicing inhibitors Isoginkgetin and spliceostatin A, respectively. Stranded, ribo-depleted RNA seq profiles of HeLa cells treated with exosome targeting siRNAs or splicing inhibitors using Illumina HiSeq. All experiments were carried out in triplicate starting with independent cell cultures

2015-11-25 | E-GEOD-72055 | biostudies-arrayexpress

Human telomerase RNA processing and quality control

Project description:Primary telomerase RNA transcripts are processed into shorter mature forms that assemble into a complex with the catalytic subunit and provide the template for telomerase activity. In diverse fungi telomerase RNA 3â end processing involves a single cleavage reaction by the spliceosome akin to the first step of splicing. Longer forms of human telomerase RNA (hTR) have been reported, but how the mature form of precisely 451 nucleotides is generated is still unknown. We now show that the splicing inhibitor isoginkgetin causes accumulation of long hTR transcripts, but find no evidence for a direct role for splicing in hTR processing. Instead, isoginkgetin mimics the effects of inhibiting the RNA exosome. Depletion of exosome components and accessory factors reveals functions for the cap binding complex (CBC) and the nuclear exosome targeting (NEXT) complex in hTR turnover. Whereas longer transcripts are predominantly degraded, shorter precursor RNAs are oligo-adenylated by TRF4-2 and either processed by poly (A) specific ribonuclease (PARN) or degraded by the exosome. Our results reveal that hTR biogenesis involves a kinetic competition between RNA processing and quality control pathways and suggest new treatment options for dyskeratosis congenita caused by mutations in RNA processing factors. We cloned and sequenced 3â ends by RLM-RACE coupled with high-throughput sequencing to gain further insights into hTR processing.

2015-11-25 | E-GEOD-73776 | biostudies-arrayexpress

Human telomerase RNA processing and quality control

Project description:Primary telomerase RNA transcripts are processed into shorter mature forms that assemble into a complex with the catalytic subunit and provide the template for telomerase activity. In diverse fungi telomerase RNA 3’ end processing involves a single cleavage reaction by the spliceosome akin to the first step of splicing. Longer forms of human telomerase RNA (hTR) have been reported, but how the mature form of precisely 451 nucleotides is generated is still unknown. We now show that the splicing inhibitor isoginkgetin causes accumulation of long hTR transcripts, but find no evidence for a direct role for splicing in hTR processing. Instead, isoginkgetin mimics the effects of inhibiting the RNA exosome. Depletion of exosome components and accessory factors reveals functions for the cap binding complex (CBC) and the nuclear exosome targeting (NEXT) complex in hTR turnover. Whereas longer transcripts are predominantly degraded, shorter precursor RNAs are oligo-adenylated by TRF4-2 and either processed by poly (A) specific ribonuclease (PARN) or degraded by the exosome. Our results reveal that hTR biogenesis involves a kinetic competition between RNA processing and quality control pathways and suggest new treatment options for dyskeratosis congenita caused by mutations in RNA processing factors.

2015-11-25 | GSE73776 | GEO

Human telomerase RNA processing and quality control

2015-11-25 | GSE72055 | GEO

Redundancy in genotyping arrays.

Project description:Despite their unprecedented density, current SNP genotyping arrays contain large amounts of redundancy, with up to 40 oligonucleotide features used to query each SNP. By using publicly available reference genotype data from the International HapMap, we show that 93.6% sensitivity at <5% false positive rate can be obtained with only four probes per SNP, compared with 98.3% with the full data set. Removal of this redundancy will allow for more comprehensive whole-genome association studies with increased SNP density and larger sample sizes.

| S-EPMC1805688 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data