The pitfalls of platform comparison: DNA copy number array technologies assessed
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE16400 | GEO | 2009/12/03
SECONDARY ACCESSION(S): PRJNA116291
REPOSITORIES: GEO
Similar Datasets
Project description:BACKGROUND:The accurate and high resolution mapping of DNA copy number aberrations has become an important tool by which to gain insight into the mechanisms of tumourigenesis. There are various commercially available platforms for such studies, but there remains no general consensus as to the optimal platform. There have been several previous platform comparison studies, but they have either described older technologies, used less-complex samples, or have not addressed the issue of the inherent biases in such comparisons. Here we describe a systematic comparison of data from four leading microarray technologies (the Affymetrix Genome-wide SNP 5.0 array, Agilent High-Density CGH Human 244A array, Illumina HumanCNV370-Duo DNA Analysis BeadChip, and the Nimblegen 385 K oligonucleotide array). We compare samples derived from primary breast tumours and their corresponding matched normals, well-established cancer cell lines, and HapMap individuals. By careful consideration and avoidance of potential sources of bias, we aim to provide a fair assessment of platform performance. RESULTS:By performing a theoretical assessment of the reproducibility, noise, and sensitivity of each platform, notable differences were revealed. Nimblegen exhibited between-replicate array variances an order of magnitude greater than the other three platforms, with Agilent slightly outperforming the others, and a comparison of self-self hybridizations revealed similar patterns. An assessment of the single probe power revealed that Agilent exhibits the highest sensitivity. Additionally, we performed an in-depth visual assessment of the ability of each platform to detect aberrations of varying sizes. As expected, all platforms were able to identify large aberrations in a robust manner. However, some focal amplifications and deletions were only detected in a subset of the platforms. CONCLUSION:Although there are substantial differences in the design, density, and number of replicate probes, the comparison indicates a generally high level of concordance between platforms, despite differences in the reproducibility, noise, and sensitivity. In general, Agilent tended to be the best aCGH platform and Affymetrix, the superior SNP-CGH platform, but for specific decisions the results described herein provide a guide for platform selection and study design, and the dataset a resource for more tailored comparisons.
| S-EPMC2797821 | biostudies-literature
Project description:BackgroundCopy number data are routinely being extracted from genome-wide association study chips using a variety of software. We empirically evaluated and compared four freely-available software packages designed for Affymetrix SNP chips to estimate copy number: Affymetrix Power Tools (APT), Aroma.Affymetrix, PennCNV and CRLMM. Our evaluation used 1,418 GENOA samples that were genotyped on the Affymetrix Genome-Wide Human SNP Array 6.0. We compared bias and variance in the locus-level copy number data, the concordance amongst regions of copy number gains/deletions and the false-positive rate amongst deleted segments.ResultsAPT had median locus-level copy numbers closest to a value of two, whereas PennCNV and Aroma.Affymetrix had the smallest variability associated with the median copy number. Of those evaluated, only PennCNV provides copy number specific quality-control metrics and identified 136 poor CNV samples. Regions of copy number variation (CNV) were detected using the hidden Markov models provided within PennCNV and CRLMM/VanillaIce. PennCNV detected more CNVs than CRLMM/VanillaIce; the median number of CNVs detected per sample was 39 and 30, respectively. PennCNV detected most of the regions that CRLMM/VanillaIce did as well as additional CNV regions. The median concordance between PennCNV and CRLMM/VanillaIce was 47.9% for duplications and 51.5% for deletions. The estimated false-positive rate associated with deletions was similar for PennCNV and CRLMM/VanillaIce.ConclusionsIf the objective is to perform statistical tests on the locus-level copy number data, our empirical results suggest that PennCNV or Aroma.Affymetrix is optimal. If the objective is to perform statistical tests on the summarized segmented data then PennCNV would be preferred over CRLMM/VanillaIce. Specifically, PennCNV allows the analyst to estimate locus-level copy number, perform segmentation and evaluate CNV-specific quality-control metrics within a single software package. PennCNV has relatively small bias, small variability and detects more regions while maintaining a similar estimated false-positive rate as CRLMM/VanillaIce. More generally, we advocate that software developers need to provide guidance with respect to evaluating and choosing optimal settings in order to obtain optimal results for an individual dataset. Until such guidance exists, we recommend trying multiple algorithms, evaluating concordance/discordance and subsequently consider the union of regions for downstream association tests.
| S-EPMC3146450 | biostudies-literature
Project description:BackgroundCopy Number Variations (CNVs) are usually inferred from Single Nucleotide Polymorphism (SNP) arrays by use of some software packages based on given algorithms. However, there is no clear understanding of the performance of these software packages; it is therefore difficult to select one or several software packages for CNV detection based on the SNP array platform.We selected four publicly available software packages designed for CNV calling from an Affymetrix SNP array, including Birdsuite, dChip, Genotyping Console (GTC) and PennCNV. The publicly available dataset generated by Array-based Comparative Genomic Hybridization (CGH), with a resolution of 24 million probes per sample, was considered to be the "gold standard". Compared with the CGH-based dataset, the success rate, average stability rate, sensitivity, consistence and reproducibility of these four software packages were assessed compared with the "gold standard". Specially, we also compared the efficiency of detecting CNVs simultaneously by two, three and all of the software packages with that by a single software package.ResultsSimply from the quantity of the detected CNVs, Birdsuite detected the most while GTC detected the least. We found that Birdsuite and dChip had obvious detecting bias. And GTC seemed to be inferior because of the least amount of CNVs it detected. Thereafter we investigated the detection consistency produced by one certain software package and the rest three software suits. We found that the consistency of dChip was the lowest while GTC was the highest. Compared with the CNVs detecting result of CGH, in the matching group, GTC called the most matching CNVs, PennCNV-Affy ranked second. In the non-overlapping group, GTC called the least CNVs. With regards to the reproducibility of CNV calling, larger CNVs were usually replicated better. PennCNV-Affy shows the best consistency while Birdsuite shows the poorest.ConclusionWe found that PennCNV outperformed the other three packages in the sensitivity and specificity of CNV calling. Obviously, each calling method had its own limitations and advantages for different data analysis. Therefore, the optimized calling methods might be identified using multiple algorithms to evaluate the concordance and discordance of SNP array-based CNV calling.
| S-EPMC4015297 | biostudies-literature
Project description:We sequenced two tumor/normal pairs obtained from two paediatric medulloblastoma patients (MB14 and MB24) with at least 30x coverage on all commonly used next-generation sequencing platforms for whole genome sequencing (SOLiD 4, 5500xl SOLiD, Illumina's HiSeq2000, and Complete Genomic' technology). The normal tissue samples came from venous blood. We compared their ability to call single nucleotide variations (SNVs) in whole-genome sequencing data with high confidence. As gold standard for SNV calling, we used genotypes determined by Affymetrix SNP 6.0 Array Technology (total of 907,551 SNPs after quality filtering).
2013-06-01 | E-MTAB-1159 | biostudies-arrayexpress
Project description:Two reference pools comprised of different miRNA content were hybridized to four different miRNA microarray platforms (Agilent, LC Sciences, Exiqon and a homemade chip (generated from an Inviteogen Ncode probeset)) This dataset includes the reference samples on the Agilent miRNA array. Keywords: miRNA Two reference pools were created from commercially available reference RNA (FirstChoice® mouse Total RNA including the small fraction (Catalogue # AM7800-AM7828, Ambion, Streetsville, ON)). Reference #1 contained equal parts of mouse testicle, ovary and 10-12 day embryo. Reference #2 contained liver, heart and lung. These references were hybridized to four different miRNA microarray platforms (two in two colour, two in one-colour, with 4 replicates each). Data from each platform were normalized by cyclic lowess and analysed by correlation analyses both within and between platforms.
2010-10-18 | E-GEOD-19669 | biostudies-arrayexpress
Project description:Copy number variation (CNV) is a common source of genetic variation that has been implicated in many genomic disorders, Mendelian diseases, and common/complex traits. Genomic microarrays are often employed for CNV detection. More recently, whole-exome sequencing (WES) has enabled detection of clinically relevant point mutations and small insertion-deletion exome wide. We evaluated (de Ligt et al. 2013) [1] the utility of short-read WES (SOLiD 5500xl) to detect clinically relevant CNVs in DNA from 10 patients with intellectual disability and compared these results to data from three independent high-resolution microarray platforms. Calls made by the different platforms and detection software are available at dbVar under nstd84.
| S-EPMC4526866 | biostudies-literature
Project description:A major challenge to the study of tumor DNA copy number (CN) in clinical specimens is the lack of appropriate fresh frozen samples and thus a dependence on Formalin-Fixed Paraffin Embedded (FFPE) banked samples, which typically have more extensive clinical follow up information. However, on most high density CN platforms, DNA from FFPE tissues generally underperforms or suffers high failure rates compared to fresh frozen samples because of DNA degradation and cross-linking. Molecular Inversion Probe (MIP) technology has been applied successfully to obtain high quality CN and genotype data from DNA isolated from cell lines and frozen tumor samples. Since the MIP probes require only a small (~40 bp) target binding site, we reasoned they may be well suited to assess FFPE samples. In this study, we successfully applied MIP technology with a panel of 50,000 markers to CN determination in FFPE samples. Using an input of 37 ng genomic DNA, we demonstrated high quality CN data with MIP technology from 93 FFPE samples from seven diverse collections. We found that the performance of FFPE DNA for CN determination was comparable to that of DNA obtained from matched frozen tumor, with only a modest loss in performance of DNA.
2009-02-18 | GSE14740 | GEO
Project description:: A major challenge to the study of tumor DNA copy number (CN) in clinical specimens is the lack of appropriate fresh frozen samples and thus a dependence on Formalin-Fixed Paraffin Embedded (FFPE) banked samples, which typically have more extensive clinical follow up information. However, on most high density CN platforms, DNA from FFPE tissues generally underperforms or suffers high failure rates compared to fresh frozen samples because of DNA degradation and cross-linking. Molecular Inversion Probe (MIP) technology has been applied successfully to obtain high quality CN and genotype data from DNA isolated from cell lines and frozen tumor samples. Since the MIP probes require only a small (~40 bp) target binding site, we reasoned they may be well suited to assess FFPE samples. In this study, we successfully applied MIP technology with a panel of 50,000 markers to CN determination in FFPE samples. Using an input of 37 ng genomic DNA, we demonstrated high quali ty CN data with MIP technology from 93 FFPE samples from seven diverse collections. We found that the performance of FFPE DNA for CN determination was comparable to that of DNA obtained from matched frozen tumor, with only a modest loss in performance of DNA.
2009-02-18 | GSE14745 | GEO
Project description:A major challenge to the study of tumor DNA copy number (CN) in clinical specimens is the lack of appropriate fresh frozen samples and thus a dependence on Formalin-Fixed Paraffin Embedded (FFPE) banked samples, which typically have more extensive clinical follow up information. However, on most high density CN platforms, DNA from FFPE tissues generally underperforms or suffers high failure rates compared to fresh frozen samples because of DNA degradation and cross-linking. Molecular Inversion Probe (MIP) technology has been applied successfully to obtain high quality CN and genotype data from DNA isolated from cell lines and frozen tumor samples. Since the MIP probes require only a small (~40 bp) target binding site, we reasoned they may be well suited to assess FFPE samples. In this study, we successfully applied MIP technology with a panel of 50,000 markers to CN determination in FFPE samples. Using an input of 37 ng genomic DNA, we demonstrated high quali ty CN data with MIP technology from 93 FFPE samples from seven diverse collections. We found that the performance of FFPE DNA for CN determination was comparable to that of DNA obtained from matched frozen tumor, with only a modest loss in performance of DNA.
2009-02-18 | GSE14742 | GEO
Project description:Two reference pools comprised of different miRNA content were hybridized to four different miRNA microarray platforms (Agilent, LC Sciences, Exiqon and a homemade chip (generated from an Inviteogen Ncode probeset)) This dataset includes the reference samples on the Agilent miRNA array. Keywords: miRNA
2010-10-18 | GSE19669 | GEO