Method For Gene Expression Analysis Research Articles

Identification of candidate miRNA biomarkers for pancreatic ductal adenocarcinoma by weighted gene co-expression network analysis.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a dismal prognosis which is, among others, due to a lack of suitable biomarkers and therapeutic targets. Previously, basic gene expression analysis methods have been used for their identification, but recently new algorithms have been developed allowing more comprehensive data analyses. Among them, weighted gene co-expression network analysis (WGCNA) has already been applied to several cancer types with promising results. We applied WGCNA to miRNA expression data from PDAC patients. Specifically, we processed microarray-based expression data of 2555 miRNAs in serum from 100 PDAC patients and 150 healthy subjects. We identified network modules of co-expressed miRNAs in the healthy subject dataset and verified their preservation in the PDAC dataset. In the non-preserved modules, we selected key miRNAs and carried out functional enrichment analyses of their experimentally known target genes. Finally, we tested their prognostic significance using overall survival analyses. Through WGCNA we identified several miRNAs that discriminate healthy subjects from PDAC patients and that, therefore, may play critical roles in PDAC development. At a functional level, we found that they regulate p53, FoxO and ErbB associated cellular signalling pathways, as well as cell cycle progression and various genes known to be involved in PDAC development. Some miRNAs were also found to serve as novel prognostic biomarkers, whereas others have previously already been proposed as such, thereby validating the WGCNA approach. In addition, we found that these novel data may explain at least some of our previous PDAC gene expression analysis results. We identified several miRNAs critical for PDAC development using WGCNA. These miRNAs may serve as biomarkers for PDAC diagnosis/prognosis and patient stratification, and as putative novel therapeutic targets.

Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO

BackgroundConventional differential gene expression analysis by methods such as student’s t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups.ResultsThrough simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method.ConclusionsThe proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies.

Using laser capture microdissection to study fiber specific signaling in locomotor muscle in COPD: A pilot study.

Quadriceps dysfunction is important in chronic obstructive pulmonary disease (COPD), with an associated increased proportion of type II fibers. Investigation of protein synthesis and degradation has yielded conflicting results, possibly due to study of whole biopsy samples, whereas signaling may be fiber-specific. Our objective was to develop a method for fiber-specific gene expression analysis. 12 COPD and 6 healthy subjects underwent quadriceps biopsy. Cryosections were immunostained for type II fibers, which were separated using laser capture microdissection (LCM). Whole muscle and different fiber populations were subject to quantitative polymerase chain reaction. Levels of muscle-RING-finger-protein-1 and Atrogin-1 were lower in type II fibers of COPD versus healthy subjects (P = 0.02 and P = 0.03, respectively), but differences were not apparent in whole muscle or type I fibers. We describe a novel method for studying fiber-specific gene expression in optimum cutting temperature compound-embedded muscle specimens. LCM offers a more sensitive way to identify molecular changes in COPD muscle. Muscle Nerve 55: 902-912, 2017.

Making the most of RNA-seq: Pre-processing sequencing data with Opossum for reliable SNP variant detection

RNA-seq (transcriptome sequencing) is primarily considered a method of gene expression analysis but it can also be used to detect DNA variants in expressed regions of the genome. However, current variant callers do not generally behave well with RNA-seq data due to reads encompassing intronic regions. We have developed a software programme called Opossum to address this problem. Opossum pre-processes RNA-seq reads prior to variant calling, and although it has been designed to work specifically with Platypus, it can be used equally well with other variant callers such as GATK HaplotypeCaller. In this work, we show that using Opossum in conjunction with either Platypus or GATK HaplotypeCaller maintains precision and improves the sensitivity for SNP detection compared to the GATK Best Practices pipeline. In addition, using it in combination with Platypus offers a substantial reduction in run times compared to the GATK pipeline so it is ideal when there are only limited time or computational resources available.

Selection of reference genes suitable for qRT-PCR expression profiling of biotic stress, nutrient deficiency and plant hormone responsive genes in bread wheat

Quantitative real-time PCR (qRT-PCR) is the most widely used and reliable method for gene expression analysis. The accuracy of qRT-PCR relies on normalization of gene expression based on reference genes, however recent studies have shown that none of the reference genes can be used universally across different experimental conditions. In the present study, four most commonly used reference genes, viz., ubiquitin, glyceraldehyde-3-phosphate dehydrogenase, actin (ACT) and 18S ribosomal RNA were considered. Using 32 wheat samples representing nutrient deficiency (deficiency of nitrogen, phosphorus and potassium), salinity, biotic stress (leaf rust, yellow rust, stem rust, wounding) hormone (salicylic acid, jasmonic acid, abscisic acid, auxin and gibberellin) treatments at two time points after treatment (24 and 48 h) were used to study the expression stability of reference genes. The four reference genes displayed huge variation in cycle threshold (Ct) values in the selected RNA samples. The expression stability was further quantified using three most widely used statistical software tools, viz., BestKeeper, NormFinder and geNorm. The most stable among the candidate reference genes was ACT, as indicated by the statistical analysis by different tools. Our results may assist in selecting most desirable reference gene for increasing precision of gene expression studies in bread wheat.

Polyadenylation Site-Based Analysis of Transcript Expression by 3'READS.

Deep sequencing of the 3' end region of poly(A)+ RNA identifies the cleavage and polyadenylation site (PAS) and measures transcript abundance. However, mispriming at internal A-rich regions by the oligo-dT oligo in reverse transcription can lead to falsely identified PASs. This problem can be resolved by direct ligation of an adapter to the 3' end of RNA. However, ligation-based methods are often inefficient. Here, we describe 3'READS+, an accurate and sensitive method for deep sequencing of the 3' end of poly(A)+ RNA. Through partial digestion by RNase H of the poly(A) tail bound to a locked nucleic acid (LNA)/DNA hybrid oligo, this method sequences an optimal number of terminal A's, which balances sequencing quality and accurate identification of PAS in A-rich regions. With efficient ligation steps, 3'READS+ is amenable to small amounts of input RNA. 3'READS+ can also be readily used as a cost-effective method for gene expression analysis.

Evaluation of candidate reference genes for RT-qPCR studies in three metabolism related tissues of mice after caloric restriction.

Reverse transcription quantitative-polymerase chain reaction (RT-qPCR) is a routine method for gene expression analysis, and reliable results depend on proper normalization by stable reference genes. Caloric restriction (CR) is a robust lifestyle intervention to slow aging and delay onset of age-associated diseases via inducing global changes in gene expression. Reliable normalization of RT-qPCR data becomes crucial in CR studies. In this study, the expression stability of 12 candidate reference genes were evaluated in inguinal white adipose tissue (iWAT), skeletal muscle (Sk.M) and liver of CR mice by using three algorithms, geNorm, NormFinder, and Bestkeeper. Our results showed β2m, Ppia and Hmbs as the most stable genes in iWAT, Sk.M and liver, respectively. Moreover, two reference genes were sufficient to normalize RT-qPCR data in each tissue and the suitable pair of reference genes was β2m-Hprt in iWAT, Ppia-Gusb in Sk.M and Hmbs-β2m in liver. By contrast, the least stable gene in iWAT or Sk.M was Gapdh, and in liver was Pgk1. Furthermore, the expression of Leptin and Ppar-γ were profiled in these tissues to validate the selected reference genes. Our data provided a basis for gene expression analysis in future CR studies.

The effects of Qi Teng Xiao Zhuo granules, traditional Chinese medicine, on the expression of genes in chronic glomerulonephritis rats

Background and aimChronic glomerulonephritis (CGN) is a primary glomerular disease that is related to immune-mediated inflammatory diseases. Qi Teng Xiao Zhuo granules have been proposed as a prescription of traditional Chinese medicine for treatment of CGN, but the comprehensive molecular mechanism underlying this therapeutic effect is not clear to date. The aim of this study was to evaluate and analyze the possible roles and molecular mechanisms of Qi Teng Xiao Zhuo granule-mediated treatment of CGN induced by adriamycin in rats. MethodsFor gene expression analysis, four samples of glomerular tissue from rats in the Qi Teng Xiao Zhuo granule group and four samples each from the adriamycin treated and control groups were hybridized with Agilent Rat 4×44K whole genome microarrays. KEGG and Gene Ontology (GO) analyses and LIMMA, String and Cytoscape software were used to analyze the functional microarray data and screen differentially expressed genes. Hub genes were identified using Pathway Studio software. Real-time PCR was performed to verify the selected genes. ResultsMicroarray gene expression analysis showed that Pnoc, Cacfd1, Fos, Igll1, Lcn2, and Syk were among the most downregulated genes in the Qi Teng Xiao Zhuo granule group compared with the adriamycin treated group, whereas Cyp2c7, Hsd3b6, Acsm5, and Ugt2b15 were significantly upregulated. Functional analysis demonstrated that metabolism of xenobiotics by cytochrome P450, the B cell receptor signaling pathway, and cytokine-cytokine receptor interaction pathways were significantly downregulated in the Qi Teng Xiao Zhuo granule group and that GO terms related to positive regulation of immune response, immune response-activating signal transduction, cell differentiation, cell cycle, proliferation, and adhesion were significantly affected. Fos and Syk were considered to be potential hub genes. ConclusionsIn the adriamycin-induced CGN rat model, comprehensive molecular mechanisms were involved with complex gene expression alterations containing many altered pathways and GO terms. However, how Qi Teng Xiao Zhuo granules regulate these events warrants further investigation.

Fast method for skeletal tissue gene expression analysis.

Several chronic diseases have been associated with bone alteration in the last few years. Despite the wealth of information provided by the analysis of the transcriptome in affected tissues, only a limited number of studies evaluated gene expression in bone tissue due to the difficulty to obtain high quality RNA. Therefore, skeletal pathologies have been often associated to a defective maturation process that occurs during recruitment of progenitor stem cells. In order to explore the possibility of analysing the gene expression during osteogenic differentiation in skeletal tissue, a single-step method to extract well-preserved RNA from bone specimens was performed. A comparison between this technique and a traditional method was made by analysing the quality and yield of RNA obtained. In addition, RNAs were assayed by reverse transcription-quantitative polymerase chain reaction to analyse the expression levels of the bone genes associated with the differentiation process in a mouse model. The present data showed that good quality RNA can be obtained from bone tissue by a simple single-step method allowing the expression analysis of the genes encoded by skeletal tissue. In conclusion, the present study allows the possibility to easily obtain good quality RNA from bone tissue that is suitable for gene expression studies of bone diseases.

Development of a metal-based detection method for simultaneous protein and gene expression analysis in single cells by mass cytometry

Abstract The Fluidigm® CyTOF system is a mass cytometer that uniquely enables high-dimensional single-cell analysis of complex populations. Mass cytometry is based on inductively coupled plasma time-of-flight mass spectrometry used for multiplex proteomic analysis. In this approach, metal-conjugated affinity reagents are used to tag the components of cells. The cells are nebulized and sent to an argon plasma, ionizing the multi-atom metal tags, which are then analyzed by a time-of-flight mass spectrometer. Gene expression can be finely tuned through the synthesis of RNA and through the control of its stability and location. Misregulation of gene expression can have severe consequences, such as developmental disorders, cancer and autoimmune diseases. The ability to acquire complete spatial-temporal profiles of gene expression is therefore critical for the understanding of disease pathophysiology, medical diagnostics and drug discovery. In this presentation, we discuss the development of a multiplex method for targeted RNA detection using the Fluidigm CyTOF® and Advanced Cell Diagnostics RNAscope® platforms. This novel assay includes the hybridization of RNA-specific target probes, followed by signal amplification and ending with the binding of amplifier-specific metal-labelled probes. Presently, we are able to detect four different mRNA probes in a single-cell analysis setting. The detection of RNA is compatible with Fluidigm products such as Maxpar® antibodies. Future developments will include high-dimensional detection that enables researchers to investigate the simultaneous expression profile of RNA and protein across millions of cells.

Evaluation of Methods for Gene Selection in Melanoma Cell Lines

A major objective in microarray experiments is to identify a panel of genes that are associated with a disease outcome or trait. Many statistical methods have been proposed for gene selection within the last fifteen years. While the comparison of some of these methods has been done, most of them concentrated on finding gene signatures based on two groups. This study evaluates four gene selection methods when the outcome of interested is continuous in nature. We provide a comparative review of four methods: the Statistical Analysis of Microarrays (SAM), the Linear Models for Microarray Analysis (LIMMA), the Lassoed Principal Components (LPC), and the Quantitative Trait Analysis (QTA). Comparison is based on the power to identify differentially expressed genes, the predictive ability of the genelists for a continuous outcome (G2 checkpoint function), and the prognostic properties of the genelists for distant metastasis-free survival. A simulated dataset and a publicly available melanoma cell lines dataset are used for simulations and validation, respectively. A primary melanoma dataset is used for assessment of prognosis. No common genes were found among the genelists from the four methods. While the SAM was generally the best in terms of power, the QTA genelist performed the best in the prediction of the G2 checkpoint function. Identification of genelists depends on the choice of the gene selection method. The QTA method would be preferred over the other approaches in predicting a quantitative outcome in melanoma research. We recommend the development of more robust statistical methods for differential gene expression analysis.

Evaluation of Suitable Internal Control Genes for RT-qPCR in Yak Mammary Tissue during the Lactation Cycle.

The yak is primarily found throughout the Tibetan high plateau and the surrounding mountainous area of south central Asia; among its others attributes, its milk is very important for the local population. A key concern in the field of yak research is the better understanding of which genes control the production and composition of milk. The most accurate and sensitive method for gene expression analysis is quantitative reverse transcription polymerase chain reaction (RT-qPCR). It is essential for reliable RT-qPCR to be able to the normalize the data using internal control genes (ICGs). However, it is critical to assess the reliability of the normalization by testing multiple ICGs. Our objective was to uncover a reliable normalization for RT-qPCR data obtained from yak mammary tissue during the lactation cycle. We assessed the reliability of 10 ICGs (ACTB, EIF6, GAPDH, LRP10, MRPL39, MRPS15, MTG1, RPS8, RPS23, and UXT) using geNorm. The analysis revealed that all of the tested ICGs can be considered to be reliable, but the use of the 6 most stable ICGs should be applied to yield a reliable normalization factor (NF). We compared the results of 3 target genes (CSN1S1, ESR1, and MYC) normalized using 6, 3, or 1 of the best ICGs. We did not observe overall differences between the 3 normalization strategies with the exception of 1 time point in MYC. The use of only a single ICG is not recommended; thus, we concluded that the calculation of the NF using the 3 best ICGs, MRPS15, RPS23, and UXT, is a reliable normalization strategy for RT-qPCR data obtained from yak mammary tissue during pregnancy and lactation. A dilution effect of the ICGs due to a large increase in the mRNA of abundantly expressed genes in bovine and porcine mammary tissue during the lactation cycle was previously observed. To test for the presence of a dilution effect in our study, we evaluated the pattern of non-normalized RT-qPCR data of ICGs from pregnancy to lactation and compared them with the total RNA concentration, milk yield, and non-normalized RT-qPCR data of 3 target genes. With a few exceptions, the non- normalized RT-qPCR data for the tested ICGs was significantly increased by lactation and had a positive correlation with total RNA and the non-normalized RT-qPCR data of CSN1S1. These data clearly indicated the presence of a “concentration effect” of single mRNA that remains unexplained but needs to be accounted for during the normalization of RT-qPCR data. Based on our findings, we recommend that the NF of the MRPS15, RPS23, and UXT genes should be used in the normalization of RT-qPCR data obtained from mammary tissue of lactating yaks during pregnancy and lactation.

VEGF-A mRNA measurement in meningiomas using a new simplified approach: branched DNA and chemiluminescence

For decades, the preferred and almost sole method for measurement of gene expression has been RT-qPCR. The method is robust, inexpensive, and well-studied; however, PCR is also quite laborious and vulnerable to contamination. As part of an investigation of VEGF-A gene expression in meningiomas, an alternative and less laborious method for gene expression analysis based on branched DNA hybridization and chemiluminescence (Lumistar) was tested. Albeit the two methods differ, in principle, cellular mRNA-concentration is measured with both. Because they both determine gene expression via the measurement of mRNA-concentration, they were expected to be comparable. The aim of the present study was to compare Lumistar to the traditional RT-qPCR approach in a routine laboratory setting, where there is emphasis on rapid analysis response. Meningioma (nÂ=Â10) and control brain tissue (nÂ=Â5) samples were collected and VEGF-A and GAPDH mRNA were quantified using both RT-qPCR and Lumistar. Furthermore, two dilution series of two of the meningioma samples were prepared in order to make quantitative analyses. Both Lumistar and RT-qPCR-results were found to follow concentration dependent linear paths when diluted (p<0.0001 and p<0.01). Finally, Lumistar and RT-qPCR analyses were performed with the inclusion of a reference gene (GAPDH), where similar results were obtained with the two methods (R2=0.48; p=0.01). It is intriguing that in spite of the vast difference in handling and assay principles, gene expression results are similar. The preferred method depends on the variability of the samples, budget, and time. Lumistar was less time consuming, while RT-qPCR was less expensive and best suited for data sets with large sample variability.

Stellaris® RNA Fluorescence In Situ Hybridization for the Simultaneous Detection of Immature and Mature Long Noncoding RNAs in Adherent Cells.

RNA fluorescence in situ hybridization (FISH), long an indispensable tool for the detection and localization of RNA, is becoming an increasingly important complement to other gene expression analysis methods. Especially important for long noncoding RNAs (lncRNAs), RNA FISH adds the ability to distinguish between primary and mature lncRNA transcripts and thus to segregate the site of synthesis from the site of action.We detail a streamlined RNA FISH protocol for the simultaneous imaging of multiple primary and mature mRNA and lncRNA gene products and RNA variants in fixed mammalian cells. The technique makes use of fluorescently pre-labeled, short DNA oligonucleotides (circa 20 nucleotides in length), pooled into sets of up to 48 individual probes. The overall binding of multiple oligonucleotides to the same RNA target results in fluorescent signals that reveal clusters of RNAs or single RNA molecules as punctate spots without the need for enzymatic signal amplification. Visualization of these punctate signals, through the use of wide-field fluorescence microscopy, enables the counting of single transcripts down to one copy per cell. Additionally, by using probe sets with spectrally distinct fluorophores, multiplex analysis of gene-specific RNAs, or RNA variants, can be achieved. The presented examples illustrate how this method can add temporospatial information between the transcription event and both the location and the endurance of the mature lncRNA. We also briefly discuss post-processing of images and spot counting to demonstrate the capabilities of this method for the statistical analysis of RNA molecules per cell. This information can be utilized to determine both overall gene expression levels and cell-to-cell gene expression variation.

Developmental Expression of Ecdysone-Related Genes Associated With Metamorphic Changes During Midgut Remodeling of Silkworm Bombyx mori (Lepidoptera:Bombycidae).

Steroid hormone 20-hydroxyecdysone is known as the systemic regulators of insect cells; however, how to impact the fate and function of mature and stem cells is unclear. For the first time, we report ecdysone regulatory cascades in both mature midgut cell and stem cell fractions related to developmental events by using histological, immunohistochemical, biochemical and gene expression analysis methods. Ecdysone receptor-B1 (EcR-B1) and ultraspiracle 1 (USP-1) mRNAs were detected mainly in mature cells during programmed cell death (PCD). Lowered E75A and probably BR-C Z4 in mature cells appear to provide a signal to the initiation of PCD. E74B, E75B and BR-C Z2 seem to be early response genes which are involved in preparatory phase of cell death. It is likely that βFTZ-F1, E74A and BR-C Z1 are probably associated with execution of death. EcR-A and USP2 mRNAs were found in stem cells during remodeling processes but EcR-B1, USP1 and E74B genes imply an important role during initial phase of metamorphic events in stem cells. BHR3 mRNAs were determined abundantly in stem cells suggesting its primary role in differentiation. All of these results showed the determination the cell fate in Bombyx mori (Linnaeus) midgut depends on type of ecdysone receptor isoforms and ecdysone-related transcription factors.

Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis.

BackgroundWhole transcriptome sequencing (RNA-seq) represents a powerful approach for whole transcriptome gene expression analysis. However, RNA-seq carries a few limitations, e.g., the requirement of a significant amount of input RNA and complications led by non-specific mapping of short reads. The Ion AmpliSeq™ Transcriptome Human Gene Expression Kit (AmpliSeq) was recently introduced by Life Technologies as a whole-transcriptome, targeted gene quantification kit to overcome these limitations of RNA-seq. To assess the performance of this new methodology, we performed a comprehensive comparison of AmpliSeq with RNA-seq using two well-established next-generation sequencing platforms (Illumina HiSeq and Ion Torrent Proton). We analyzed standard reference RNA samples and RNA samples obtained from human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs).ResultsUsing published data from two standard RNA reference samples, we observed a strong concordance of log2 fold change for all genes when comparing AmpliSeq to Illumina HiSeq (Pearson’s r = 0.92) and Ion Torrent Proton (Pearson’s r = 0.92). We used ROC, Matthew’s correlation coefficient and RMSD to determine the overall performance characteristics. All three statistical methods demonstrate AmpliSeq as a highly accurate method for differential gene expression analysis. Additionally, for genes with high abundance, AmpliSeq outperforms the two RNA-seq methods. When analyzing four closely related hiPSC-CM lines, we show that both AmpliSeq and RNA-seq capture similar global gene expression patterns consistent with known sources of variations.ConclusionsOur study indicates that AmpliSeq excels in the limiting areas of RNA-seq for gene expression quantification analysis. Thus, AmpliSeq stands as a very sensitive and cost-effective approach for very large scale gene expression analysis and mRNA marker screening with high accuracy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2270-1) contains supplementary material, which is available to authorized users.

Abstract 2172: Earth mover's distance for the identification of genes associated with drug resistance in cancer

Abstract Conventional methods for differential gene expression analysis perform well when intra-class heterogeneity is low and inter-class heterogeneity is high. However, many problems in biology and biomedicine, such as drug resistance in cancer, contain samples that show both significant intra- and inter-class heterogeneity. Conventional methods, which use means and variances to compute test statistics, lack power to identify genes differentially expressed between classes in these cases. To address this challenge, we developed EMDomics, a new method for differential gene expression analysis designed to perform well in the setting of intra-class heterogeneity. EMDomics uses the Earth mover's distance (EMD) to measure the overall difference between the distribution of a gene's expression in two classes of samples and uses permutations to estimate false discovery rates and obtain q-values for each gene. To evaluate the theoretical basis for EMDomics, we model heterogeneous mechanisms of drug resistance using simulated data and compare the performance of EMDomics to the commonly used conventional methods (SAM and Limma), in terms of sensitivity and specificity for identifying genes truly associated with drug resistance in the simulation. To test EMDomics on real biological data, we applied it to the challenging problem of identifying genes associated with drug response in ovarian cancer, using data from The Cancer Genome Atlas. In both the simulated and real biological data, EMDomics outperformed the competing approaches for the identification of differentially expressed genes. Using simulated data, EMDomics yielded higher sensitivity and precision for highly heterogeneous data. Using real data, EMDomics was able to identify genes that are highly relevant for ovarian cancer biology, which were not identified by the conventional methods. Also, applying gene set enrichment analysis showed that most highly enriched gene sets includes pathways known to play critical roles in ovarian cancer pathogenesis. The most enriched gene set identified by the EMDomics analysis is a set of genes down-regulated in cancer cell lines with mutated TP53. Other gene sets identified as highly enriched by the EMDomics analysis, include gene sets related to LEF1, BMI1, KRAS, EZH2, and PTEN, and pathways related to cell-cell junction organization, cell-cell communication, WNT signaling, and extracellular matrix organization. EMDomics represents a new approach for the identification of genes differentially expressed between heterogeneous classes. It is a robust non-parametric method, which does not make any assumptions about the distributions or differences between the two classes being compared, and thus has significantly more power than conventional approaches for identifying differential ‘Omics features between heterogeneous classes. The method can be applied in a wide variety of settings to compare distributions of ‘Omics data between two classes. Citation Format: Sheida Nabavi, Daniel Schmolze, Mayinuer Maitituoheti, Andrew H. Beck. Earth mover's distance for the identification of genes associated with drug resistance in cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2172. doi:10.1158/1538-7445.AM2015-2172

Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq.

BackgroundMassively 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.ResultsFalse-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.ConclusionsOur 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 materialThe online version of this article (doi:10.1186/s12864-015-1767-y) contains supplementary material, which is available to authorized users.

Selection of appropriate reference genes for quantitative real-time PCR in Oxytropis ochrocephala Bunge using transcriptome datasets under abiotic stress treatments.

Background: Oxytropis ochrocephala Bunge, an indigenous locoweed species in China, poses great threats to livestock on grasslands. There is a need for further genetic study in the plants per se, for understanding the basis of its acclimation mechanism in various unfavorable environmental conditions and to implement effective control measures. Quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) is the most commonly used method for gene expression analysis. To facilitate gene expression studies and obtain more accurate qRT-PCR data, normalization relative to stable reference genes is required. The aim of this study was to select the most stable reference genes for transcriptional analysis in O. ochrocephala.Results: We selected 12 candidate reference genes, 18S ribosomal RNA (18S RNA), actin2/7 (ACT7), β-actin (ACTB), actin101 (ACT101), actin11 (ACT11), β-tubulin (TUB), α-tubulin (TUA), glyceraldehyde-3-phosphate dehydrogenase-1 (GAPDH1), GAPDH2, metallothionein-like protein (MET), fructose-bisphosphate aldolase (FBA) and histone H3 (HIS), from the transcriptome datasets of O. ochrocephala and determined the suitability by analyzing their expression levels when exposed to a range of abiotic stress conditions. By employing software packages including geNorm, NormFinder and BestKeeper, HIS, ACT7, and ACT101 were assessed as the most suitable set for normalization in all samples. When normalized with the most stable reference genes, the expression patterns of the three target genes were in accordance with those in the transcriptome data, indicating that the reference genes selected in this study are suitable.Conclusions: The study provided appropriate reference genes for accurate normalization in qRT-PCR analysis in O. ochrocephala and emphasized the importance of validating reference genes for gene expression analysis under specific experimental condition. The usage of inappropriate reference gene would cause misinterpretation.

Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples.

Analysis of clinical trial specimens such as formalin-fixed paraffin-embedded (FFPE) tissue for molecular mechanisms of disease progression or drug response is often challenging and limited to a few markers at a time. This has led to the increasing importance of highly multiplexed assays that enable profiling of many biomarkers within a single assay. Methods for gene expression analysis have undergone major advances in biomedical research, but obtaining a robust dataset from low-quality RNA samples, such as those isolated from FFPE tissue, remains a challenge. Here, we provide a detailed evaluation of the NanoString Technologies nCounter platform, which provides a direct digital readout of up to 800 mRNA targets simultaneously. We tested this system by examining a broad set of human clinical tissues for a range of technical variables, including sensitivity and limit of detection to varying RNA quantity and quality, reagent performance over time, variability between instruments, the impact of the number of fields of view sampled, and differences between probe sequence locations and overlapping genes across CodeSets. This study demonstrates that Nanostring offers several key advantages, including sensitivity, reproducibility, technical robustness, and utility for clinical application.