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Background: Data from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as "imputation", avoids apparent infinite fold-change values. However, the procedure comes at a cost: Imputing a large number of missing values has the potential to significantly impact the results of the subsequent differential expression analysis. Results: We propose a method that identifies differentially expressed proteins by ranking their observed changes with respect to the changes observed for other proteins. Missing values are taken into account by this method directly, without the need to impute them. We illustrate the performance of the new method on two distinct datasets and show that it is robust to missing values and, at the same time, provides results that are otherwise similar to those obtained with edgeR which is a state-of-art differential expression analysis method. Conclusions: The new method for the differential expression analysis of proteomic data is available as an easy to use Python package. [ABSTRACT FROM AUTHOR]

Background: MiRNAs (microRNA) are 18–24 nt endogenous noncoding RNAs that regulate gene expression at the post-transcriptional level, including tissue-specific, developmental timing and evolutionary conservation gene expression. Results: This study used high-throughput sequencing technology for the first time in Larix olgensis, predicted 78 miRNAs, including 12,229,003 reads sRNA, screened differentially expressed miRNAs. Predicting target genes was helpful for understanding the miRNA regulation function and obtained 333 corresponding target genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotation were analysed, mostly including nucleic acid binding, plant hormone signal transduction, pantothenate and CoA biosynthesis, and cellulose synthase. This study will lay the foundation for clarifying the complex miRNA-mediated regulatory network for growth and development. In view of this, spatio-temporal expression of miR396, miR950, miR164, miR166 and miR160 were analysed in Larix olgensis during the growth stages of not lignified, beginning of lignification, and completely lignified in different tissues (root, stem, and leaf) by quantitative real-time PCR (qRT-PCR). There were differences in the expression of miRNAs in roots, stems and leaves in the same growth period. At 60 days, miR160, miR166 and miR396–2 exhibited the highest expression in leaves. At 120 days, most miRNAs in roots and stems decreased significantly. At 180 days, miRNAs were abundantly expressed in roots and stems. Meanwhile, analysis of the expression of miRNAs in leaves revealed that miR396–2 was reduced as time went on, whereas other miRNAs increased initially and then decreased. On the other hand, in the stems, miR166–1 was increase, whereas other miRNAs, especially miR160, miR164, miR396 and miR950–1, first decreased and then increased. Similarly, in the roots, miR950–2 first decreased and then increased, whereas other miRNAs exhibited a trend of continuous increase. Conclusions: The present investigation included rapid isolation and identification of miRNAs in Larix olgensis through construction of a sRNA library using Solexa and predicted 78 novel miRNAs, which showed differential expression levels in different tissues and stages. These results provided a theoretical basis for further revealing the genetic regulation mechanism of miRNA in the growth and development of conifers and the verification of function in target genes. [ABSTRACT FROM AUTHOR]

Background: RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible., Results: Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis., Conclusions: High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

Background: RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking., Results: We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility., Conclusion: ideal is distributed as an R package in the Bioconductor project ( http://bioconductor.org/packages/ideal/ ), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

Background: Triple-Negative Breast Cancer (TNBC) is an aggressive and complex subtype of breast cancer. The current biomarkers used in the context of breast cancer treatment are highly dependent on the targeting of oestrogen receptor, progesterone receptor, or HER2, resulting in treatment failure and disease recurrence and creating clinical challenges. Thus, there is still a crucial need for the improvement of TNBC treatment; the discovery of effective biomarkers that can be easily translated to the clinics is essential., Methods: We report an approach for the discovery of biomarkers that can predict tumour relapse and pathologic complete response (pCR) in TNBC on the basis of mRNA expression quantified using the NanoString nCounter Immunology Panel. To overcome the limited sample size, prediction models based on random Forest were constructed using the differentially expressed genes (DEGs) as selected features. We also evaluated the differences between pre- and post-treatment groups aiming for the combinatorial assessment of pCR and relapse using additive models in edgeR., Results: We identify nine and 13 DEGs strongly associated with pCR and relapse, respectively, from 579 immune genes in a small number of samples (n = 55) using edgeR. An additive model for the comparison of pre- and post-treatment groups via the adjustment of the independent subject in the relapse group revealed associations for 41 genes. Comprehensive analysis indicated that our prediction models outperformed those constructed using features extracted from the existing feature selection model Elastic Net in terms of accuracy. The prediction models were assessed using a randomization test to validate the robustness (empirical P for the model of pCR = 0.015 and empirical P for the model of relapse = 0.018). Furthermore, three DEGs (FCER1A, EDNRB, and TGFBI) in the model of relapse showed prognostic significance for predicting the survival of patients with cancer through Cox proportional hazards regression model-based survival analysis., Conclusion: Gene expression quantified via the NanoString nCounter Immunology Panel can be seamlessly analysed using edgeR, even considering small sample sizes. Our approach provides a scalable framework that can easily be applied for the discovery of biomarkers based on the NanoString nCounter Immunology Panel., Data Availability: The source code will be available from github at https://github.com/sungheep/nanostring .

Eggs produced by bisexual infected mature female worms (MF) of Schistosoma japonicum are important in the transmission of the parasite and responsible for the pathogenesis of schistosomiasis. The single-sex infected female worms (SF) cannot mature and do not produce normal eggs; also they do not induce severe damage to the host. In this study, the microRNA (miRNA) expression profiles of 25d MF and 25d SF were investigated through Solexa deep-sequencing technology to explore the developmental mechanisms of schistosome female worms. There were 36 differentially expressed miRNA, 20 up-regulated and 16 down-regulated found in MF/SF worms, including some development related miRNA such as bantam (ban), let-7, miR-124, miR-8, miR-1, miR-7. There were 166 target genes of up-regulated miRNA and 201 target genes of down-regulated miRNA after comparing the target gene prediction software results with RNA-Seq transcriptome results. Analysis of the target genes shows that different ones are involved in MF and SF worms in Gene Ontology terms, with a similar situation in KEGG. This observation indicates that different genes regulated by differentially expressed miRNA take part in MF and SF and lead to differential sexual status. This means that the sexual status of female worms is regulated by miRNA.

Background: The predominant genotype of Toxoplasma in China is the Chinese 1 (ToxoDB#9) lineage. TgCtwh3 and TgCtwh6 are two representative strains of Chinese 1, exhibiting high and low virulence to mice, respectively. Little is known regarding the virulence mechanism of this non-classical genotype. Our previous RNA sequencing data revealed differential mRNA levels of TgMIC1 in TgCtwh3 and TgCtwh6. We aim to further confirm the differential expression of TgMIC1 and its significance in this atypical genotype., Methods: Quantitative real-time PCR was used to verify the RNA sequencing data; then, polyclonal antibodies against TgMIC1 were prepared and identified. Moreover, the invasion and proliferation of the parasite in HFF cells were observed after treatment with TgMIC1 polyclonal antibody or not., Results: The data showed that the protein level of TgMIC1 was significantly higher in high-virulence strain TgCtwh3 than in low-virulence strain TgCtwh6 and that the invasion and proliferation of TgCtwh3 were inhibited by TgMIC1 polyclonal antibody., Conclusion: Differential expression of TgMIC1 in TgCtwh3 and TgCtwh6 may explain, at least partly, the virulence mechanism of this atypical genotype.

Background: Study design is a critical aspect of any experiment, and sample size calculations for statistical power that are consistent with that study design are central to robust and reproducible results. However, the existing power calculators for tests of differential expression in single-cell RNA-seq data focus on the total number of cells and not the number of independent experimental units, the true unit of interest for power. Thus, current methods grossly overestimate the power., Results: Hierarchicell is the first single-cell power calculator to explicitly simulate and account for the hierarchical correlation structure (i.e., within sample correlation) that exists in single-cell RNA-seq data. Hierarchicell, an R-package available on GitHub, estimates the within sample correlation structure from real data to simulate hierarchical single-cell RNA-seq data and estimate power for tests of differential expression. This multi-stage approach models gene dropout rates, intra-individual dispersion, inter-individual variation, variable or fixed number of cells per individual, and the correlation among cells within an individual. Without modeling the within sample correlation structure and without properly accounting for the correlation in downstream analysis, we demonstrate that estimates of power are falsely inflated. Hierarchicell can be used to estimate power for binary and continuous phenotypes based on user-specified number of independent experimental units (e.g., individuals) and cells within the experimental unit., Conclusions: Hierarchicell is a user-friendly R-package that provides accurate estimates of power for testing hypotheses of differential expression in single-cell RNA-seq data. This R-package represents an important addition to single-cell RNA analytic tools and will help researchers design experiments with appropriate and accurate power, increasing discovery and improving robustness and reproducibility.

Background: The increasing number of transcriptomic datasets has allowed for meta-analyses, which can be valuable due to their increased statistical power. However, meta-analyses can be confounded by so-called “batch effects,” where technical variation across different batches of RNA-seq experiments can clearly produce spurious signals of differential expression and reduce our power to detect true differences. While batch effects can sometimes be accounted for, albeit with caveats, a better strategy is to understand their sources to better avoid them. In this study, we examined the effects of RNA isolation method as a possible source of batch effects in RNA-seq design. Results: Based on the different chemistries of “classic” hot phenol extraction of RNA compared to common commercial RNA isolation kits, we hypothesized that specific mRNAs may be preferentially extracted depending upon method, which could masquerade as differential expression in downstream RNA-seq analyses. We tested this hypothesis using the Saccharomyces cerevisiae heat shock response as a well-validated environmental response. Comparing technical replicates that only differed in RNA isolation method, we found over one thousand transcripts that appeared “differentially” expressed when comparing hot phenol extraction with the two kits. Strikingly, transcripts with higher abundance in the phenol-extracted samples were enriched for membrane proteins, suggesting that indeed the chemistry of hot phenol extraction better solubilizes those species of mRNA. Conclusions: Within a self-contained experimental batch (e.g. control versus treatment), the method of RNA isolation had little effect on the ability to identify differentially expressed transcripts. However, we suggest that researchers performing meta-analyses across different experimental batches strongly consider the RNA isolation methods for each experiment. [ABSTRACT FROM AUTHOR]

Objective: The class II transactivator (CIITA), encoded by the CIITA gene, controls expression of immune response regulators, which affect bone homeostasis. Previously, we investigated a functional CIITA polymorphism in elderly women. Women carrying the allele associated with lower CIITA levels displayed higher bone mineral density (BMD), but also higher bone loss. The present exploratory study in a rat model sought to investigate effects of differential expression of Ciita on bone structural integrity and strength. Two strains DA (normal-to-high expression) and DA.VRA4 (lower expression) underwent ovariectomy (OVX) or sham-surgery at ~ 14-weeks of age (DA OVX n = 8, sham n = 4; DA.VRA4 OVX n = 10, sham n = 2). After 16-weeks, femoral BMD and bone mineral content (BMC) were measured and morphometry and biomechanical testing performed. Results: In DA.VRA4 rats, BMD/BMC, cross-sectional area and biomechanical properties were lower. Ciita expression was accompanied by OVX-induced changes to cross-sectional area and femoral shaft strength; DA rats had lower maximum load-to-fracture. Thus, while lower Ciita expression associated with lower bone mass, OVX induced changes to structural and mechanical bone properties were less pronounced. Conclusion: The data tentatively suggests association between Ciita expression and structural and mechanical bone properties, and a possible role in bone changes resulting from estrogen deficiency. [ABSTRACT FROM AUTHOR]

Background: Various methods for differential expression analysis have been widely used to identify features which best distinguish between different categories of samples. Multiple hypothesis testing may leave out explanatory features, each of which may be composed of individually insignificant variables. Multivariate hypothesis testing holds a non-mainstream position, considering the large computation overhead of large-scale matrix operation. Random forest provides a classification strategy for calculation of variable importance. However, it may be unsuitable for different distributions of samples. Results: Based on the thought of using an ensemble classifier, we develop a feature selection tool for differential expression analysis on expression profiles (i.e., ECFS-DEA for short). Considering the differences in sample distribution, a graphical user interface is designed to allow the selection of different base classifiers. Inspired by random forest, a common measure which is applicable to any base classifier is proposed for calculation of variable importance. After an interactive selection of a feature on sorted individual variables, a projection heatmap is presented using k-means clustering. ROC curve is also provided, both of which can intuitively demonstrate the effectiveness of the selected feature. Conclusions: Feature selection through ensemble classifiers helps to select important variables and thus is applicable for different sample distributions. Experiments on simulation and realistic data demonstrate the effectiveness of ECFS-DEA for differential expression analysis on expression profiles. The software is available at http://bio-nefu.com/resource/ecfs-dea. [ABSTRACT FROM AUTHOR]

Background: A fundamental problem in RNA-seq data analysis is to identify genes or exons that are differentially expressed with varying experimental conditions based on the read counts. The relativeness of RNA-seq measurements makes the between-sample normalization of read counts an essential step in differential expression (DE) analysis. In most existing methods, the normalization step is performed prior to the DE analysis. Recently, Jiang and Zhan proposed a statistical method which introduces sample-specific normalization parameters into a joint model, which allows for simultaneous normalization and differential expression analysis from log-transformed RNA-seq data. Furthermore, an ℓ0 penalty is used to yield a sparse solution which selects a subset of DE genes. The experimental conditions are restricted to be categorical in their work. Results: In this paper, we generalize Jiang and Zhan’s method to handle experimental conditions that are measured in continuous variables. As a result, genes with expression levels associated with a single or multiple covariates can be detected. As the problem being high-dimensional, non-differentiable and non-convex, we develop an efficient algorithm for model fitting. Conclusions: Experiments on synthetic data demonstrate that the proposed method outperforms existing methods in terms of detection accuracy when a large fraction of genes are differentially expressed in an asymmetric manner, and the performance gain becomes more substantial for larger sample sizes. We also apply our method to a real prostate cancer RNA-seq dataset to identify genes associated with pre-operative prostate-specific antigen (PSA) levels in patients. [ABSTRACT FROM AUTHOR]

Background: Differential expression analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is important how to select features which best discriminate between different groups of patients. Despite the emergence of multivariate analysis approaches, prevailing feature selection methods primarily focus on multiple hypothesis testing on individual variables, and then combine them for an explanatory result. Besides, these methods, which are commonly based on hypothesis testing, view classification as a posterior validation of the selected variables. Results: Based on previously provided A5 feature selection strategy, we develop a joint covariate detection tool for differential expression analysis on tumor expression profiles. This software combines hypothesis testing with testing according to classification results. A model selection approach based on Gaussian mixture model is introduced in for automatic selection of features. Besides, a projection heatmap is proposed for the first time. Conclusions: Joint covariate detection strengthens the viewpoint for selecting variables which are not only individually but also jointly significant. Experiments on simulation and realistic data show the effectiveness of the developed software, which enhances the reliability of joint covariate detection for differential expression analysis on tumor expression profiles. The software is available at http://bio-nefu.com/resource/jcd-dea. [ABSTRACT FROM AUTHOR]

Background: RNA sequencing (RNA-seq) is a high throughput technology that profiles gene expression in a genomewide manner. RNA-seq has been mainly used for testing differential expression (DE) of transcripts between two conditions and has recently been used for testing differential alternative polyadenylation (APA). In the past, many algorithms have been developed for detecting differentially expressed genes (DEGs) from RNA-seq experiments, including the one we developed, XBSeq, which paid special attention to the context-specific background noise that is ignored in conventional gene expression quantification and DE analysis of RNA-seq data. Results: We present several major updates in XBSeq2, including alternative statistical testing and parameter estimation method for detecting DEGs, capacity to directly process alignment files and methods for testing differential APA usage. We evaluated the performance of XBSeq2 against several other methods by using simulated datasets in terms of area under the receiver operating characteristic (ROC) curve (AUC), number of false discoveries and statistical power. We also benchmarked different methods concerning execution time and computational memory consumed. Finally, we demonstrated the functionality of XBSeq2 by using a set of in-house generated clear cell renal carcinoma (ccRCC) samples. Conclusions: We present several major updates to XBSeq. By using simulated datasets, we demonstrated that, overall, XBSeq2 performs equally well as XBSeq in terms of several statistical metrics and both perform better than DESeq2 and edgeR. In addition, XBSeq2 is faster in speed and consumes much less computational memory compared to XBSeq, allowing users to evaluate differential expression and APA events in parallel. [ABSTRACT FROM AUTHOR]

Background: Extrachromosomal circular DNAs (eccDNAs) have been reported to play a key role in the occurrence and development of various diseases. However, the characterization and role of eccDNAs in pulmonary arterial hypertension (PAH) remain unclear. Methods: In the discovery cohort, we first explored eccDNA expression profiles by Circle-sequencing analysis. The candidate eccDNAs were validated by routine polymerase chain reaction (PCR), TOPO-TA cloning and Sanger sequencing. In the validation cohort, 30 patients with PAH and 10 healthy controls were recruited for qPCR amplification to detect the candidate eccDNAs. Datas at the baseline were collected, including clinical background, biochemical variables, echocardiography and hemodynamic factors. Receiver operating characteristic curve was used to investigate the diagnostic effect of the eccDNA. Results: We identified a total of 21,741 eccDNAs in plasma samples of 3 IPAH patients and 3 individuals in good health, and the expression frequency, GC content, length distribution, and genome distribution of the eccDNAs were thoroughly characterized and analyzed. In the validation cohort, 687 eccDNAs were differentially expressed in patients with IPAH compared with healthy controls (screening threshold: |FC|≥2 and P < 0.05). Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the specific eccDNAs in IPAH were significantly enriched in calcium channel activity, the mitogen-activated protein kinase pathway, and the wnt signaling pathway. Verification queue found that the expression of eccDNA-chr2:131208878–131,424,362 in PAH was considerably higher than that in healthy controls and exhibited a high level of accuracy in predicting PAH with a sensitivity of 86.67% and a specificity of 90%. Furthermore, correlation analysis disclosed a significant association between serum eccDNA-chr2:131208878–131,424,362 and mean pulmonary artery pressure (mPAP) (r = 0.396, P = 0.03), 6 min walking distance (6MWD) (r = -0.399, P = 0.029), N-terminal pro-B-type natriuretic peptide (NT-proBNP) (r = 0.685, P < 0.001) and cardiac index (CI) (r = − 0.419, P = 0.021). Conclusions: This is the first study to identify and characterize eccDNAs in patients with PAH. We revealed that serum eccDNA-chr2:131208878–131,424,362 is significantly overexpressed and can be used in the diagnosis of PAH, indicating its potential as a novel non-invasive biomarker. [ABSTRACT FROM AUTHOR]

Background: High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects., Results: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and by using the hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors., Conclusions: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN", which is freely available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html .

Objective: Differential expression (DE) is a fundamental step in the analysis of RNA-Seq count data. We had previously developed an R/Bioconductor package (called TCC) for this purpose. While this package has the unique feature of an in-built robust normalization method, its use has so far been limited to R users only. There is thus, a need for an alternative to DE analysis by TCC for non-R users., Results: Here, we present a graphical user interface for TCC (called TCC-GUI). Non-R users only need a web browser as the minimum requirement for its use ( https://infinityloop.shinyapps.io/TCC-GUI/ ). TCC-GUI is implemented in R and encapsulated in Shiny application. It contains all the major functionalities of TCC, including DE pipelines with robust normalization and simulation data generation under various conditions. It also contains (i) tools for exploratory analysis, including a useful score termed average silhouette that measures the degree of separation of compared groups, (ii) visualization tools such as volcano plot and heatmap with hierarchical clustering, and (iii) a reporting tool using R Markdown. By virtue of the Shiny-based GUI framework, users can obtain results simply by mouse navigation. The source code for TCC-GUI is available at https://github.com/swsoyee/TCC-GUI under MIT license.

Background: Existing tools for the aggregation and visualization of differential expression data have discrete functionality and require that end-users rely on multiple software packages with complex dependencies or manually manipulate data for analysis and interpretation. Furthermore, at present, data aggregation and visualization are laborious, time consuming, and subject to human error. This is a serious limitation on the current state of differential transcriptomic analysis, which makes it necessary to expend extensive time and resources to reach the point where biological meaning can be interpreted. Such an approach for analysis also leads to scattered and non-standardized code, unsystematic project management and non-reproducible result sets., Results: Here, we present a differential expression analysis toolkit, DEvis, that provides a powerful, integrated solution for the analysis of differential expression data with a rapid turnaround time. DEvis has simple installation requirements and provides a convenient, user-friendly R package that addresses the issues inherent to complex multi-factor experiments, such as multiple contrast aggregation and integration, result sorting and selection, visualization, project management, and reproducibility. This tool increases the capabilities of differential expression analysis while reducing workload and the potential for manual error. Furthermore, it provides a much-needed encapsulation of scattered functionality, making large and complex analysis more efficient and reproducible., Conclusion: DEvis provides a wide range of powerful visualization, data aggregation, and project management tools that provide flexibility and speed in analysis. The functionality provided by DEVis increases efficiency of analysis and supplies researchers with new and relevant means for the analysis of large and complicated transcriptomic experiments. DEvis furthermore incorporates automatic project management capabilities, which standardizes analysis and ensures the reproducibility of results. After the establishment of statistical frameworks that identify differentially expressed genes, this package is the next logical step for differential transcriptomic analysis, establishing the critical framework necessary to manipulate, explore, and extract biologically relevant meaning from differential expression data.