共查询到20条相似文献,搜索用时 15 毫秒
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Zhenqiang Su Hong Fang Huixiao Hong Leming Shi Wenqian Zhang Wenwei Zhang Yanyan Zhang Zirui Dong Lee J Lancashire Marina Bessarabova Xi Yang Baitang Ning Binsheng Gong Joe Meehan Joshua Xu Weigong Ge Roger Perkins Matthias Fischer Weida Tong 《Genome biology》2014,15(12)
Background
Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment?Results
We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined.Conclusions
Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.Electronic supplementary material
The online version of this article (doi:10.1186/s13059-014-0523-y) contains supplementary material, which is available to authorized users. 相似文献3.
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Ikuko N Motoike Mitsuyo Matsumoto Inaho Danjoh Fumiki Katsuoka Kaname Kojima Naoki Nariai Yukuto Sato Yumi Yamaguchi-Kabata Shin Ito Hisaaki Kudo Ichiko Nishijima Satoshi Nishikawa Xiaoqing Pan Rumiko Saito Sakae Saito Tomo Saito Matsuyuki Shirota Kaoru Tsuda Junji Yokozawa Kazuhiko Igarashi Naoko Minegishi Osamu Tanabe Nobuo Fuse Masao Nagasaki Kengo Kinoshita Jun Yasuda Masayuki Yamamoto 《BMC genomics》2014,15(1)
Background
Validation of single nucleotide variations in whole-genome sequencing is critical for studying disease-related variations in large populations. A combination of different types of next-generation sequencers for analyzing individual genomes may be an efficient means of validating multiple single nucleotide variations calls simultaneously.Results
Here, we analyzed 12 independent Japanese genomes using two next-generation sequencing platforms: the Illumina HiSeq 2500 platform for whole-genome sequencing (average depth 32.4×), and the Ion Proton semiconductor sequencer for whole exome sequencing (average depth 109×). Single nucleotide polymorphism (SNP) calls based on the Illumina Human Omni 2.5-8 SNP chip data were used as the reference. We compared the variant calls for the 12 samples, and found that the concordance between the two next-generation sequencing platforms varied between 83% and 97%.Conclusions
Our results show the versatility and usefulness of the combination of exome sequencing with whole-genome sequencing in studies of human population genetics and demonstrate that combining data from multiple sequencing platforms is an efficient approach to validate and supplement SNP calls.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-673) contains supplementary material, which is available to authorized users. 相似文献13.
Arianne C Richard Paul A Lyons James E Peters Daniele Biasci Shaun M Flint James C Lee Eoin F McKinney Richard M Siegel Kenneth GC Smith 《BMC genomics》2014,15(1)
Background
Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study.Results
Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this “gold-standard” comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues.Conclusions
Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-649) contains supplementary material, which is available to authorized users. 相似文献14.
Maurizio Callari Matteo Dugo Valeria Musella Edoardo Marchesi Giovanna Chiorino Maurizia Mello Grand Marco Alessandro Pierotti Maria Grazia Daidone Silvana Canevari Loris De Cecco 《PloS one》2012,7(9)
Background
Microarray technology applied to microRNA (miRNA) profiling is a promising tool in many research fields; nevertheless, independent studies characterizing the same pathology have often reported poorly overlapping results. miRNA analysis methods have only recently been systematically compared but only in few cases using clinical samples.Methodology/Principal Findings
We investigated the inter-platform reproducibility of four miRNA microarray platforms (Agilent, Exiqon, Illumina, and Miltenyi), comparing nine paired tumor/normal colon tissues. The most concordant and selected discordant miRNAs were further studied by quantitative RT-PCR. Globally, a poor overlap among differentially expressed miRNAs identified by each platform was found. Nevertheless, for eight miRNAs high agreement in differential expression among the four platforms and comparability to qRT-PCR was observed. Furthermore, most of the miRNA sets identified by each platform are coherently enriched in data from the other platforms and the great majority of colon cancer associated miRNA sets derived from the literature were validated in our data, independently from the platform. Computational integration of miRNA and gene expression profiles suggested that anti-correlated predicted target genes of differentially expressed miRNAs are commonly enriched in cancer-related pathways and in genes involved in glycolysis and nutrient transport.Conclusions
Technical and analytical challenges in measuring miRNAs still remain and further research is required in order to increase consistency between different microarray-based methodologies. However, a better inter-platform agreement was found by looking at miRNA sets instead of single miRNAs and through a miRNAs – gene expression integration approach. 相似文献15.
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Anto P. Rajkumar Per Qvist Ross Lazarus Francesco Lescai Jia Ju Mette Nyegaard Ole Mors Anders D. B?rglum Qibin Li Jane H. Christensen 《BMC genomics》2015,16(1)
Background
Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples.Results
False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values.Conclusions
Our results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-1767-y) contains supplementary material, which is available to authorized users. 相似文献17.
Huang J Morehouse C Streicher K Higgs BW Gao J Czapiga M Boutrin A Zhu W Brohawn P Chang Y Viner J LaVallee T Richman L Jallal B Yao Y 《PloS one》2011,6(10):e26177
Purpose
Insulin-like growth factor (IGF) signaling through human insulin receptor isoform A (IR-A) contributes to tumorigenesis and intrinsic resistance to anti-IGF1R therapy. In the present study, we (a) developed quantitative TaqMan real time-PCR-based assays (qRT-PCR) to measure human insulin receptor isoforms with high specificity, (b) evaluated isoform expression levels in molecularly-defined breast cancer subtypes, and (c) identified the IR-A:IR-B mRNA ratio as a potential biomarker guiding patient stratification for anti-IGF therapies.Experimental Design
mRNA expression levels of IR-A and IR-B were measured in 42 primary breast cancers and 19 matched adjacent normal tissues with TaqMan qRT-PCR assays. The results were further confirmed in 165 breast cancers. The tumor samples were profiled using whole genome microarrays and subsequently subtyped using the PAM50 breast cancer gene signature. The relationship between the IR-A:IR-B ratio and cancer subtype, as well as markers of proliferation were characterized.Results
The mRNA expression levels of IR-A in the breast tumors were similar to those observed in the adjacent normal tissues, while the mRNA levels of IR-B were significantly decreased in tumors. The IR-A:IR-B ratio was significantly higher in luminal B breast cancer than in luminal A. Strong concordance between the IR-A:IR-B ratio and the composite Oncotype DX proliferation score was observed for stratifying the latter two breast cancer subtypes.Conclusions
The reduction in IR-B expression is the key to the altered IR-A:IR-B ratio observed in breast cancer. The IR-A:IR-B ratio may have biomarker utility in guiding a patient stratification strategy for an anti-IGF therapeutic. 相似文献18.
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