Method For Expression Analysis Research Articles

A comparison of methods for differential expression analysis of RNA-seq data

BackgroundFinding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data.ResultsWe conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data.ConclusionsVery small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.

AUREA: an open-source software system for accurate and user-friendly identification of relative expression molecular signatures

BackgroundPublic databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)—a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery.ResultsHerein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets.ConclusionsWe have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis ‘point-and-click’ simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both ‘in silico’ and ‘bench’ scientists. AUREA can be found at http://price.systemsbiology.net/AUREA/.

A novel method for cross-species gene expression analysis

BackgroundAnalysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.ResultsIn this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.ConclusionsThe method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.

Selection of Reference Genes for Quantitative Real-Time PCR in Bamboo (Phyllostachys edulis)

BackgroundThe Moso bamboo (Phyllostachys edulis) is one of the most important forestry resources and plays essential ecological roles in southern China. A draft nuclear genome sequence is expected to be publicly available in the near future; an explosion of gene expression data related to the unique traits of Moso bamboo will undoubtedly follow. Reverse transcription quantitative real-time PCR ((RT-)qPCR) is a widely used method for gene expression analysis. A necessary prerequisite of exact and reliable data is the accurate choice of reference genes.ResultIn this study, 14 candidate reference genes were chosen, and their expression levels were assessed by (RT-)qPCR in a set of six tissue samples (root, stem, mature stem, leaf, flower, and leaf sheath) and at two developmental stages (before and after flowering) in bamboo specimens obtained in three locations. The stability and suitability of the candidate reference genes were validated using the geNorm, NormFinder and BestKeeper programs. The results showed that TIP41 and NTB were suitable reference genes across all the tissues and at the different developmental stages examined in this study. While the expression of the NTB, TIP41 and UBQ were the mostly stable in different plant tissues samples, the expression of the TIP41, NTB and CAC were ranked the most stable in bamboo plants at various developmental stages. AP2-like gene was further assessed by using the reference genes TIP41 and NTB in comparison to ACT. Significant difference of the expression profile of AP2-like demonstrated the importance of choosing adequate reference genes in bamboo.Conclusion TIP41 and NTB were found to be homogeneously expressed and were adequate for normalization purposes, showing equivalent transcript levels in different samples. They are therefore the recommended reference genes for measuring gene expression in P. edulis.

Learning realistic facial expressions from web images

A large amount of labeled training data is required to develop effective and robust facial expression analysis methods. However, obtaining such data is typically a tedious and time-consuming task. With a rapid advance of the Internet and Web technologies, it has been feasible to collect a large number of images with label information at a low cost of human efforts. In this paper, we propose a search based framework to collect realistic facial expression images from the Web so as to further advance research on robust facial expression recognition. Due to the limitation of current commercial web search engines, a large fraction of returned images is not related to a given query keyword. We present a Support Vector Machine (SVM) based active learning approach for selecting relevant images from noisy image search results. The resulting dataset is more diverse with more sample images per expression compared to other well established facial expression datasets such as CK and JAFFE. In addition, a novel facial expression feature based on the state-of-the-art Weber Local Descriptor (WLD) and histogram contextualization is proposed to handle such a challenging dataset. Comprehensive experimental results demonstrate that our web based dataset is capable of resembling more closely to the real world conditions compared to the CK and JAFFE datasets, and our proposed feature is more effective than the existing widely used features.

Puma 3.0: improved uncertainty propagation methods for gene and transcript expression analysis

BackgroundMicroarrays have been a popular tool for gene expression profiling at genome-scale for over a decade due to the low cost, short turn-around time, excellent quantitative accuracy and ease of data generation. The Bioconductor package puma incorporates a suite of analysis methods for determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analysis. As isoform level expression profiling receives more and more interest within genomics in recent years, exon microarray technology offers an important tool to quantify expression level of the majority of exons and enables the possibility of measuring isoform level expression. However, puma does not include methods for the analysis of exon array data. Moreover, the current expression summarisation method for Affymetrix 3’ GeneChip data suffers from instability for low expression genes. For the downstream analysis, the method for differential expression detection is computationally intensive and the original expression clustering method does not consider the variance across the replicated technical and biological measurements. It is therefore necessary to develop improved uncertainty propagation methods for gene and transcript expression analysis.ResultsWe extend the previously developed Bioconductor package puma with a new method especially designed for GeneChip Exon arrays and a set of improved downstream approaches. The improvements include: (i) a new gamma model for exon arrays which calculates isoform and gene expression measurements and a level of uncertainty associated with the estimates, using the multi-mappings between probes, isoforms and genes, (ii) a variant of the existing approach for the probe-level analysis of Affymetrix 3’ GeneChip data to produce more stable gene expression estimates, (iii) an improved method for detecting differential expression which is computationally more efficient than the existing approach in the package and (iv) an improved method for robust model-based clustering of gene expression, which takes technical and biological replicate information into consideration.ConclusionsWith the extensions and improvements, the puma package is now applicable to the analysis of both Affymetrix 3’ GeneChips and Exon arrays for gene and isoform expression estimation. It propagates the uncertainty of expression measurements into more efficient and comprehensive downstream analysis at both gene and isoform level. Downstream methods are also applicable to other expression quantification platforms, such as RNA-Seq, when uncertainty information is available from expression measurements. puma is available through Bioconductor and can be found at http://www.bioconductor.org.

Troubleshooting methods for microarray gene expression analysis in the onset of diabetic kidney disease

Introduction: Microarrays have become the standard technique for discovering new genes involved in the development of (kidney) disease. Diabetic nephropathy is a frequent complication of diabetes and is characterized by renal fibrosis. As the pathways leading to fibrosis are initiated early in diabetes and in the current study, we aimed at identifying genes associated with renal fibrosis in the first week after induction of diabetes in the rat streptozotocin (STZ) model. Methods: Conventional microarray analysis methods comparing gene expression to a common reference are not very suitable for time series as gene lists for all time point are very heterogeneous. We therefore sought an analysis technique that would allow us to easily find genes that we either substantially up or down regulated during the first week of diabetes. In the new method, the normalized expression of individual genes was plotted in time. Subsequently, the area under the curve (AUC) was calculated to quantify the overall level of changes in expression of individual genes. Results: AUCs for all genes were plotted in a histogram showing a normal distribution with a mean of close to 0, indicating no change in expression for the majority of genes. Genes with AUCs outside 3 standard deviations of the mean were considered significantly different from control. Discussion: Using this technique, a total of 290 genes were found to be significantly changed in the first week of diabetes. Data on a subset of genes were confirmed by real-time PCR, indicating the validity of the employed new analysis method.

High-level diterpene production by transient expression in Nicotiana benthamiana.

BackgroundCharacterization of plant terpene synthases is typically done by production of recombinant enzymes in Escherichia coli. This is often difficult due to solubility and codon usage issues. Furthermore, plant terpene synthases which are targeted to the plastids, such as diterpene synthases, have to be shortened in a more or less empirical approach to improve expression. We report here an optimized Agrobacterium-mediated transient expression assay in Nicotiana benthamiana for plant diterpene synthase expression and product analysis.ResultsAgrobacterium-mediated transient expression of plant diterpene synthases in N. benthamiana led to the accumulation of diterpenes within 3 days of infiltration and with a maximum at 5 days. Over 50% of the products were exported onto the leaf surface, thus considerably facilitating the analysis by reducing the complexity of the extracts. The robustness of the method was tested by expressing three different plant enzymes, cembratrien-ol synthase from Nicotiana sylvestris, casbene synthase from Ricinus communis and levopimaradiene synthase from Gingko biloba. Furthermore, co-expression of a 1-deoxy-D-xylulose-5-phosphate synthase from tomato and a geranylgeranyl diphosphate synthase from tobacco led to a 3.5-fold increase in the amount of cembratrien-ol produced, with maximum yields reaching 2500 ng/cm2.ConclusionWith this optimized method for diterpene synthase expression and product analysis, a single infiltrated leaf of N. benthamiana would be sufficient to produce quantities required for the structure elucidation of unknown diterpenes. The method will also be of general use for gene function discovery, pathway reconstitution and metabolic engineering of diterpenoid biosynthesis in plants.

Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine

BackgroundPatient-specific aberrant expression patterns in conjunction with functional screening assays can guide elucidation of the cancer genome architecture and identification of therapeutic targets. Since most statistical methods for expression analysis are focused on differences between experimental groups, the performance of approaches for patient-specific expression analyses are currently less well characterized. A comparison of methods for the identification of genes that are dysregulated relative to a single sample in a given set of experimental samples, to our knowledge, has not been performed.MethodsWe systematically evaluated several methods including variations on the nearest neighbor based outlying degree method, as well as the Zscore and a robust variant for their suitability to detect patient-specific events. The methods were assessed using both simulations and expression data from a cohort of pediatric acute B lymphoblastic leukemia patients.ResultsWe first assessed power and false discovery rates using simulations and found that even under optimal conditions, high effect sizes (>4 unit differences) were necessary to have acceptable power for any method (>0.9) though high false discovery rates (>0.1) were pervasive across simulation conditions. Next we introduced a technical factor into the simulation and found that performance was reduced for all methods and that using weights with the outlying degree could provide performance gains depending on the number of samples and genes affected by the technical factor. In our use case that highlights the integration of functional assays and aberrant expression in a patient cohort (the identification of gene dysregulation events associated with the targets from a siRNA screen), we demonstrated that both the outlying degree and the Zscore can successfully identify genes dysregulated in one patient sample. However, only the outlying degree can identify genes dysregulated across several patient samples.ConclusionOur results show that outlying degree methods may be a useful alternative to the Zscore or Rscore in a personalized medicine context especially in small to medium sized (between 10 and 50 samples) expression datasets with moderate to high sample-to-sample variability. From these results we provide guidelines for detection of aberrant expression in a precision medicine context.

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

Chapter 7: Pharmacogenomics

There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.

Stable Internal Reference Genes for the Normalization of Real-Time PCR in Different Sweetpotato Cultivars Subjected to Abiotic Stress Conditions

Reverse transcription quantitative real-time PCR (RT-qPCR) has become one of the most widely used methods for gene expression analysis, but its successful application depends on the stability of suitable reference genes used for data normalization. In plant studies, the choice and optimal number of reference genes must be experimentally determined for the specific conditions, plant species, and cultivars. In this study, ten candidate reference genes of sweetpotato (Ipomoea batatas) were isolated and the stability of their expression was analyzed using two algorithms, geNorm and NormFinder. The samples consisted of tissues from four sweetpotato cultivars subjected to four different environmental stress treatments, i.e., cold, drought, salt and oxidative stress. The results showed that, for sweetpotato, individual reference genes or combinations thereof should be selected for use in data normalization depending on the experimental conditions and the particular cultivar. In general, the genes ARF, UBI, COX, GAP and RPL were validated as the most suitable reference gene set for every cultivar across total tested samples. Interestingly, the genes ACT and TUB, although widely used, were not the most suitable reference genes in different sweetpotato sample sets. Taken together, these results provide guidelines for reference gene(s) selection under different experimental conditions. In addition, they serve as a foundation for the more accurate and widespread use of RT-qPCR in various sweetpotato cultivars.

A strategy to reduce technical variability and bias in RNA sequencing data

A strategy to reduce technical variability and bias in RNA sequencing data

Statistical modeling for differential transcriptome analysis using RNA-Seq technology

RNA-Seq is a recently developed technology for transcriptome profiling. Numerous advantages of RNA-Seq suggest that it will be the platform of choice for genome-wide expression studies. RNA-Seq generates large volumes of data which require statistical methods for data processing and accurate inference. This article reviews the RNA-Seq technologies followed by a detailed discussion of current statistical methods for normalization and differential expression analysis.

Differential Expression Analysis for RNA-Seq Data.

RNA-Seq is increasingly being used for gene expression profiling. In this approach, next-generation sequencing (NGS) platforms are used for sequencing. Due to highly parallel nature, millions of reads are generated in a short time and at low cost. Therefore analysis of the data is a major challenge and development of statistical and computational methods is essential for drawing meaningful conclusions from this huge data. In here, we assessed three different types of normalization (transcript parts per million, trimmed mean of M values, quantile normalization) and evaluated if normalized data reduces technical variability across replicates. In addition, we also proposed two novel methods for detecting differentially expressed genes between two biological conditions: (i) likelihood ratio method, and (ii) Bayesian method. Our proposed methods for finding differentially expressed genes were tested on three real datasets. Our methods performed at least as well as, and often better than, the existing methods for analysis of differential expression.

Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

BackgroundCurrent experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions. In this way, the cell transcriptional machinery can respond optimally to internal or external stimuli. This provides a research opportunity to identify and study co-expressed gene modules whose transcription is controlled by shared gene regulatory networks.ResultsWe developed and integrated a set of computational methods of differential gene expression analysis, gene clustering, gene network inference, gene function prediction, and DNA motif identification to automatically identify differentially co-expressed gene modules, reconstruct their regulatory networks, and validate their correctness. We tested the methods using microarray data derived from soybean cells grown under various stress conditions. Our methods were able to identify 42 coherent gene modules within which average gene expression correlation coefficients are greater than 0.8 and reconstruct their putative regulatory networks. A total of 32 modules and their regulatory networks were further validated by the coherence of predicted gene functions and the consistency of putative transcription factor binding motifs. Approximately half of the 32 modules were partially supported by the literature, which demonstrates that the bioinformatic methods used can help elucidate the molecular responses of soybean cells upon various environmental stresses.ConclusionsThe bioinformatics methods and genome-wide data sources for gene expression, clustering, regulation, and function analysis were integrated seamlessly into one modular protocol to systematically analyze and infer modules and networks from only differential expression genes in soybean cells grown under stress conditions. Our approach appears to effectively reduce the complexity of the problem, and is sufficiently robust and accurate to generate a rather complete and detailed view of putative soybean gene transcription logic potentially underlying the responses to the various environmental challenges. The same automated method can also be applied to reconstruct differentially co-expressed gene modules and their regulatory networks from gene expression data of any other transcriptome.

A novel method for RNA extraction from Andosols using casein and its application to amoA gene expression study in soil

The lack of a universal method to extract RNA from soil hinders the progress of studies related to nitrification in soil, which is an important step in the nitrogen cycle. It is particularly difficult to extract RNA from certain types of soils such as Andosols (volcanic ash soils), which is the dominant agricultural soil in Japan, because of RNA adsorption by soil. To obtain RNA from these challenging soils to study the bacteria involved in nitrification, we developed a soil RNA extraction method for gene expression analysis. Autoclaved casein was added to an RNA extraction buffer to recover RNA from soil, and high-quality RNA was successfully extracted from eight types of agricultural soils that were significantly different in their physicochemical characteristics. To detect bacterial ammonia monooxygenase subunit A gene (amoA) transcripts, bacterial genomic DNA and messenger RNA were co-extracted from two different types of Andosols during incubation with ammonium sulfate. Polymerase chain reaction-denaturing gradient gel electrophoresis and reverse transcription polymerase chain reaction-denaturing gradient gel electrophoresis analyses of amoA in soil microcosms revealed that only few amoA, which had the highest similarities to those in Nitrosospira multiformis, were expressed in these soils after treatment with ammonium sulfate, although multiple amoA genes were present in the soil microcosms examined.

The functional and structural characterization of a novel oncogene GIG47 involved in the breast tumorigenesis

BackgroundA candidate oncogene GIG47, previously known as a neudesin with a neurotrophic activity, was identified by applying the differential expression analysis method.MethodsAs a first step to understand the molecular role of GIG47, we analyzed the expression profile of GIG47 in multiple human cancers including the breast cancer and characterized its function related to human carcinogenesis. Based on this oncogenic role of GIG47, we then embarked on determining the high-resolution structure of GIG47. We have applied multidimensional heteronuclear NMR methods to GIG47.ResultsGIG47 was over-expressed in primary breast tumors as well as other human tumors including carcinomas of the uterine cervix, malignant lymphoma, colon, lung, skin, and leukemia. To establish its role in the pathogenesis of breast cancer in humans, we generated stable transfectants of MCF7 cells. The ectopic expression of GIG47 in MCF7 cells promoted the invasiveness in the presence of 50% serum. In addition, it also resulted in the increased tumorigenicity in in vivo tumor formation assay. The tumorigenesis mechanism involving GIG47 might be mediated by the activation of MAPK and PI3K pathways. These results indicate that GIG47 plays a role in the breast tumorigenesis, thus representing a novel target for the treatment of breast cancer. To facilitate the development of GIG47-targeted therapeutics, we determined the structural configuration of GIG47. The high-resolution structure of GIG47 was obtained by combination of NMR and homology modeling. The overall structure of GIG47 has four α-helices and 6 β-strands, arranged in a β1-α1-β2-β3-α2-β4-α3-α4-β5-β6 topology. There is a potential heme/steroid binding pocket formed between two helices α2 and α3.ConclusionThe determined three-dimensional structure of GIG47 may facilitate the development of potential anti-cancer agents.

Protein expression can be monitored in yeast by peptide-mediated induction of TetR-controlled gene expression

The rapidly increasing number of completed genome sequences urgently calls for convenient and efficient methods for analysis of gene function and expression. TetR-inducing peptides (TIP) can induce reporter gene expression controlled by Tet repressor (TetR) when fused to a protein of choice which makes them a highly valuable tool for monitoring expression in vivo. However, TIP functionality has only been demonstrated in bacteria so far. Here, we report that TIP is also functional in yeast. An mCherry-TIP fusion that locates to the nucleus induces TetR-controlled gfp+ expression in a dose-dependent manner. This opens up potential applications in proteome research in which the expression of proteins can be analyzed in vivo by fusing TIP to proteins of choice in conjunction with a Tet-controlled reporter system.

Comparative Analysis of Gene Expression Level by Quantitative Real-Time PCR Has Limited Application in Objects with Different Morphology

qRT-PCR is a generally acknowledged method for gene expression analysis due to its precision and reproducibility. However, it is well known that the accuracy of qRT-PCR data varies greatly depending on the experimental design and data analysis. Recently, a set of guidelines has been proposed that aims to improve the reliability of qRT-PCR. However, there are additional factors that have not been taken into consideration in these guidelines that can seriously affect the data obtained using this method. In this study, we report the influence that object morphology can have on qRT-PCR data. We have used a number of Arabidopsis thaliana mutants with altered floral morphology as models for this study. These mutants have been well characterised (including in terms of gene expression levels and patterns) by other techniques. This allows us to compare the results from the qRT-PCR with the results inferred from other methods. We demonstrate that the comparison of gene expression levels in objects that differ greatly in their morphology can lead to erroneous results.