Microarray Time Research Articles

Bayesian analysis of longitudinal data via empirical likelihood

Longitudinal data consists of repeated observations that are typically correlated, which makes the likelihood-based inference challenging. This limits the use of Bayesian methods for longitudinal data in many general situations. To address this issue, empirical likelihood is used to develop a fully Bayesian method for analyzing longitudinal data based on a set of moment equations parallel to the form of generalized estimating equations. It is demonstrated in the context of two popular priors for Bayesian inference and regularization, the Laplace prior and the horseshoe prior. The proposed Bayesian shrinkage method performs well in both estimation accuracy and variable selection, while also providing a full quantification of uncertainty. The method is illustrated using a yeast cell-cycle microarray time course gene expression data set.

TimesVector-Web: A Web Service for Analysing Time Course Transcriptome Data with Multiple Conditions.

From time course gene expression data, we may identify genes that modulate in a certain pattern across time. Such patterns are advantageous to investigate the transcriptomic response to a certain condition. Especially, it is of interest to compare two or more conditions to detect gene expression patterns that significantly differ between them. Time course analysis can become difficult using traditional differentially expressed gene (DEG) analysis methods since they are based on pair-wise sample comparison instead of a series of time points. Most importantly, the related tools are mostly available as local Software, requiring technical expertise. Here, we present TimesVector-web, which is an easy to use web service for analysing time course gene expression data with multiple conditions. The web-service was developed to (1) alleviate the burden for analyzing multi-class time course data and (2) provide downstream analysis on the results for biological interpretation including TF, miRNA target, gene ontology and pathway analysis. TimesVector-web was validated using three case studies that use both microarray and RNA-seq time course data and showed that the results captured important biological findings from the original studies.

Metabolic Pathway of Hereditary Cancer Disease from PPI-network of DEGS Detected using Mean-of-Mean Method

Uncontrolled growth of cells often results Cancer disease in human body. When it is eventually transmitted throughout the generations in a family, it is referred to as hereditary diseases. Metabolic pathway explains several chemical reactions occurred for growth of a disease and KEGG pathway analysis identifies key genes involved with that disease. At first, differentially expressed genes (DEGs) are detected from cancer gene microarray time series datasets using a new and simple method, Mean of Mean (MoM). The MoM concept is developed from Gregor Johann Mendel's First Law of Heredity or Segregation rule of heredity. Highly expressed (HG) and lowly expressed (LG) genes in two different groups of microarray dataset are identified first using MoM. Then DEGs are found by implementing intersection operation between HG and LG genes. Performance of MoM method is analyzed by Support Vector Machine classifiers (SVMs) on some binary class cancer microarray data samples. Then all results are compared with the performance of other statistical parametric and nonparametric hypothetic tests. It is noticed that performance ofMoMis better than other statistical methods in almost all data sets. Finally, Protein-Protein Interaction Networks (PPINs) are constructed within identified DEGs using web based tool. Lastly, KEGG pathway analysis is performed for all proteins involved in PPINs to obtain list of key genes for growth of cancer disease.

KCNK3 Mutation Causes Altered Immune Function in Pulmonary Arterial Hypertension Patients and Mouse Models.

Loss of function KCNK3 mutation is one of the gene variants driving hereditary pulmonary arterial hypertension (PAH). KCNK3 is expressed in several cell and tissue types on both membrane and endoplasmic reticulum and potentially plays a role in multiple pathological process associated with PAH. However, the role of various stressors driving the susceptibility of KCNK3 mutation to PAH is unknown. Hence, we exposed kcnk3fl/fl animals to hypoxia, metabolic diet and low dose lipopolysaccharide (LPS) and performed molecular characterization of their tissue. We also used tissue samples from KCNK3 patients (skin fibroblast derived inducible pluripotent stem cells, blood, lungs, peripheral blood mononuclear cells) and performed microarray, immunohistochemistry (IHC) and mass cytometry time of flight (CyTOF) experiments. Although a hypoxic insult did not alter vascular tone in kcnk3fl/fl mice, RNASeq study of these lungs implied that inflammatory and metabolic factors were altered, and the follow-up diet study demonstrated a dysregulation of bone marrow cells in kcnk3fl/fl mice. Finally, a low dose LPS study clearly showed that inflammation could be a possible second hit driving PAH in kcnk3fl/fl mice. Multiplex, IHC and CyTOF immunophenotyping studies on human samples confirmed the mouse data and strongly indicated that cell mediated, and innate immune responses may drive PAH susceptibility in these patients. In conclusion, loss of function KCNK3 mutation alters various physiological processes from vascular tone to metabolic diet through inflammation. Our data suggests that altered circulating immune cells may drive PAH susceptibility in patients with KCNK3 mutation.

MiR‐203a‐3p promotes loureirin A‐induced hair follicle stem cells differentiation by targeting Smad1

MicroRNAs (miRNAs) participate in the repair of skin trauma. Our previous study indicated that loureirin A promoted hair follicle stem cells (HFSCs) to repair skin epidermis. However, the mechanism of miRNA-mediated regulation of loureirin A-induced HFSC differentiation remained to be explored. In the present study, HFSCs from rat vibrissa were identified by immunofluorescence in vitro. Microarray and quantitative real time polymerase chain reaction analyses demonstrated that miR-203a-3p was upregulated in differentiated HFSCs induced by loureirin A. The expression of cytoskeletal keratin (CK) 5 and involucrin was promoted by miR-203a-3p mimics while repressed by a miR-203a-3p inhibitor. Smad1 was identified as a key target of miR-203a-3p using target prediction tools. Luciferase reporter gene test confirmed a special target association between miR-203a-3p and Smad1. Short interfering Smad1 was transfected into HFSCs, and the expression levels of CK5 and involucrin were upregulated. Thus, it can be inferred that miR-203a-3p negatively regulated the expression of Smad1 and promoted the differentiation of loureirin A-induced HFSCs. Bone morphogenetic protein (BMP) signal inhibition and Wnt activation coregulate skin injury repair. BMP/Smad1 signaling is involved in maintaining the characteristics of HFSCs and inhibiting their differentiation. Our results showed that miR-203a-3p reduces Smad1 to release BMP inhibition. Taken together, miR-203a-3p/Smad1 is a potential therapeutic molecular target in skin wound healing, and may play an active role in wound repair and regenerative medicine.

MicroRNA in inflammatory bowel disease at a glance.

Inflammatory bowel disease (IBD) as a chronic inflammation in colon and small intestine has two subtypes: ulcerative colitis (UC) and Crohn's disease (CD). Genome studies have shown that UC and CD are related to microRNAs (miRNAs) expression in addition to environmental factors. This article reviews important researches that have recently been done on miRNAs roles in CD and UC disease. First, miRNA is introduced and its biogenesis and function are discussed. Afterward, roles of miRNAs in inflammatory processes involved in IBD are showed. Finally, this review proposes some circulating and tissue-specific miRNAs, which are useful for CD and UC fast diagnosis and grade prediction. As a conclusion, miRNAs are efficient diagnostic molecules especially in IBD subtypes discrimination and can be used by microarray and real time PCR methods for disease detection and classification.

Analysis of the Circadian Regulation of Cancer Hallmarks by a Cross-Platform Study of Colorectal Cancer Time-Series Data Reveals an Association with Genes Involved in Huntington's Disease.

Accumulating evidence points to a link between circadian clock dysfunction and the molecular events that drive tumorigenesis. Here, we investigated the connection between the circadian clock and the hallmarks of cancer in an in vitro model of colorectal cancer (CRC). We used a cross-platform data normalization method to concatenate and compare available microarray and RNA-sequencing time series data of CRC cell lines derived from the same patient at different disease stages. Our data analysis suggests differential regulation of molecular pathways between the CRC cells and identifies several of the circadian and likely clock-controlled genes (CCGs) as cancer hallmarks and circadian drug targets. Notably, we found links of the CCGs to Huntington’s disease (HD) in the metastasis-derived cells. We then investigated the impact of perturbations of our candidate genes in a cohort of 439 patients with colon adenocarcinoma retrieved from the Cancer Genome Atlas (TCGA). The analysis revealed a correlation of the differential expression levels of the candidate genes with the survival of patients. Thus, our study provides a bioinformatics workflow that allows for a comprehensive analysis of circadian properties at different stages of colorectal cancer, and identifies a new association between cancer and HD.

Pulmonary expression of MYCN mRNA following exposure to 2,4-D with or without endotoxin challenge

The present study aimed to observe the expression of MYCN in lungs of mice following chronic exposure of 2,4-D with or without lipopolysaccharide (LPS). 2,4-D was administered orally dissolved in corn oil at high and low dose (1/10th and 1/20th of LD50) for 90 days. After 90 days of exposure, animals from each group were challenged with LPS/normal saline solution at 80 μg/animal. The lung tissues were processed for microarray and real time studies. LPS resulted decrease (–0.173 fold) in m-RNA expression level of MYCN as compared to control, while High dose of 2,4-D alone and in combination with LPS resulted 0.949-fold change and 1.656-fold change increase in expression of MYCN m-RNA, respectively, as compared to control. Similarly, Low dose of 2,4-D alone or in combination with LPS also altered MYCN expression. The microarray data when validated by Real Time PCR was found to be in concordance with the Real Time PCR data. The data taken together suggest that, high and low exposure of 2,4-D alone or in combination with LPS alters expression of MYCN at m-RNA level.

Adaptive Sparse Estimation With Side Information

ABSTRACT The article considers the problem of estimating a high-dimensional sparse parameter in the presence of side information that encodes the sparsity structure. We develop a general framework that involves first using an auxiliary sequence to capture the side information, and then incorporating the auxiliary sequence in inference to reduce the estimation risk. The proposed method, which carries out adaptive Stein’s unbiased risk estimate-thresholding using side information (ASUS), is shown to have robust performance and enjoy optimality properties. We develop new theories to characterize regimes in which ASUS far outperforms competitive shrinkage estimators, and establish precise conditions under which ASUS is asymptotically optimal. Simulation studies are conducted to show that ASUS substantially improves the performance of existing methods in many settings. The methodology is applied for analysis of data from single cell virology studies and microarray time course experiments. Supplementary materials for this article are available online.

Boolean function network analysis of time course liver transcriptome data to reveal novel circadian transcriptional regulators in mammals.

Many biological processes in mammals are subject to circadian control at the molecular level. Disruption of circadian rhythms has been demonstrated to be associated with a wide range of diseases, such as diabetes mellitus, mental disorders, and cancer. Although the core circadian genes are well established, there are multiple reports of novel peripheral circadian regulators. The goal of this study was to provide a comprehensive computational analysis to identify novel potential circadian transcriptional regulators. To fulfill the aforementioned goal, we applied a Boolean function network method to analyze the microarray time course mouse and rat liver datasets available in the literature. The inferred direct pairwise relations were further investigated using the functional annotation tool. This approach generated a list of transcription factors (TFs) and cofactors, which were associated with significantly enriched circadian gene ontology (GO) categories. As a result, we identified 93 transcriptional circadian regulators in mouse and 95 transcriptional circadian regulators in rat. Of these, 19 regulators in mouse and 21 regulators in rat were known, whereas the rest were novel. Furthermore, we validated novel circadian TFs with bioinformatics databases, previous large-scale circadian studies, and related small-scale studies. Moreover, according to predictions inferred from ChIP-Seq experiments reported in the database, 40 of our candidate circadian regulators were confirmed to have circadian genes as direct regulatory targets. In addition, we annotated candidate circadian regulators with disorders that were often associated with disruptions of circadian rhythm in the literature. In summary, our computational analysis, which was followed by an extensive verification by means of a literature review, can contribute to translational study from endocrinology to cancer research and provide insights for future investigation.

MiR-96 and autophagy are involved in the beneficial effect of grape seed proanthocyanidins against high-fat-diet-induced dyslipidemia in mice.

We aimed to investigate the possible signaling pathways underlying the regulation of grape seed proanthocyanidins extracts (GSPE) on lipid metabolism. One hundred male C57BL/6 mice were divided into four groups: control group (normal diet), GSPE group (normal diet + GSPE), high-fat diet group (HFD), and high-fat diet plus GSPE (200mg/kg/day) group (HFD+GSPE). Mice received the diets for 180days. Body weight and serum lipid levels were measured. Autophagic flux characteristics, such as accumulation of lipids, mitochondria, and autophagosomes in the liver, were detected using transmission electron microscopy. Expression profile of microRNAs (miRNAs) in the liver was determined using RNA microarray and quantitative real time polymerase chain reaction (qRt-PCR). GSPE significantly decreased the weight gain, serum levels of triglycerides, total cholesterol, and low-density lipoprotein cholesterol but increased high-density lipoprotein cholesterol in the HFD mice. Autophagic flux was significantly increased by HFD but decreased by GSPE treatment. GSPE significantly attenuated HFD-induced miR-96 upregulation, which in turn reduced the expressions of miR-96 downstream molecules, FOXO1, mTOR, p-mTOR, and LC3A/B. These results suggested that the miR-96 is involved in the protective effect of GSPE against HFD-induced dyslipidemia. Possible mechanisms might be through mTOR and FOXO1, which facilitate autophagic flux for clearance of lipid accumulation.

ArgR of Streptomyces coelicolor Is a Pleiotropic Transcriptional Regulator: Effect on the Transcriptome, Antibiotic Production, and Differentiation in Liquid Cultures.

ArgR is a well-characterized transcriptional repressor controlling the expression of arginine and pyrimidine biosynthetic genes in bacteria. In this work, the biological role of Streptomyces coelicolor ArgR was analyzed by comparing the transcriptomes of S. coelicolor ΔargR and its parental strain, S. coelicolor M145, at five different times over a 66-h period. The effect of S. coelicolor ArgR was more widespread than that of the orthologous protein of Escherichia coli, affecting the expression of 1544 genes along the microarray time series. This S. coelicolor regulator repressed the expression of arginine and pyrimidine biosynthetic genes, but it also modulated the expression of genes not previously described to be regulated by ArgR: genes involved in nitrogen metabolism and nitrate utilization; the act, red, and cpk genes for antibiotic production; genes for the synthesis of the osmotic stress protector ectoine; genes related to hydrophobic cover formation and sporulation (chaplins, rodlins, ramR, and whi genes); all the cwg genes encoding proteins for glycan cell wall biosynthesis; and genes involved in gas vesicle formation. Many of these genes contain ARG boxes for ArgR binding. ArgR binding to seven new ARG boxes, located upstream or near the ectA-ectB, afsS, afsR, glnR, and redH genes, was tested by DNA band-shift assays. These data and those of previously assayed fragments permitted the construction of an improved model of the ArgR binding site. Interestingly, the overexpression of sporulation genes observed in the ΔargR mutant in our culture conditions correlated with a sporulation-like process, an uncommon phenotype.

Verification of animal species in ham and salami by DNA microarray and Real time PCR methods

Consumer protection and detecting of adulteration is very important and has a wide societal impact in the economic sphere. Detection of animal species in meat products and the use of combining different methods is one of the means to achieve relevant product status. The aim of this study was to reveal whether or not the products label clearly meets the content declared by producer. In our study, 29 samples of meat products such as salami and ham obtained from stores and supermarkets in Slovakia were analyzed to detect the existing animal species according to the product label the use of Chipron LCD Array Analysis System, Meat 5.0. Products in which the presence of non-declared animal species has been detected were subjected to testing by the innuDETECT PCR Real-Time Kit, repeatedly. The results showed that 20 (68.96%) samples were improperly labeled. From in total 14 tested ham samples 11 (78.57%) products exhibited non-conformity with declared composition. Tested salami samples (15) revealed 9 (60%) incorrectly labelled products. The results obtained by DNA Microarray and Real Time PCR methods were identical, and both methods should be extensively promoted for the detection of animal species in the meat and meat products.

Identification of Effective Genes from HIV-1 VPR Microarray Datasets after Constructing GA-Optimized Genetic Network

Identification of effective genes from microarray dataset is important for obtaining relationship between that gene and a particular disease. For this, Gene Regulatory Network (GRN) construction is important. In this paper, GA-optimized GRNs are constructed using radial basis neural network (RBN) approach within cluster center matrix of human HIV-1 Vpr mutant microarray time series gene expression data. After this, effective genes from each microarray dataset are found by calculating minimum Euclidean distance between optimized output value and corresponding dataset.

MicroRNA-182 targets protein phosphatase 1 regulatory inhibitor subunit 1C in glioblastoma.

Glioblastoma (GBM) is an incurable cancer, with mean post-diagnosis survival time of 14-16 months. Metagenomic analysis by The Cancer Genome Atlas (TCGA) program has identified microRNA-182-5p (miR-182-5p or miR-182) as the only miRNA associated with favorable disease prognosis and temozolomide (TMZ) susceptibility. Previous reports have indicated that miR-182 down regulates expression of BCL2L12, c-MET, and HIF2A. However, other messenger RNA (mRNA) targets of miR-182 have not been validated which would explain its association with a favorable disease prognosis. In situ analysis revealed that protein phosphatase 1 regulatory inhibitor subunit 1C (PPP1R1C) is a putative target of miR-182. PPP1R1C protein and RNA expression as assessed by tissue microarray and quantitative real time PCR, respectively, was inversely correlated to miR-182 expression in glioblastoma patients and in the metastatic glioblastoma cell line U87-MG. Reporter assays using PPP1R1C 3′ untranslated region (UTR) showed that miR-182 can interact with the wild-type but not a miR-182-5-seed mutant. Ectopic expression of miR-182 mimic in the U87-MG cell line significantly decreased proliferation as well as suppressed in vitro migration and invasion. Opposite observations were made when the non-malignant neuronal cell line HCN-2 was transfected with anti-miR-182 antagomir. The miR-182 mimic or siRNA targeting PPP1R1C induced TMZ susceptibility indicating that decreased susceptibility to TMZ in GBM patients might be attributed to high expression of PPP1R1C. Inverse correlation of PPP1R1C mRNA and miR-182 levels in 20 GBM patients confirmed the same. Cumulatively, our results indicate that loss of miR-182 leads to increased expression of PPP1R1C which in part explain disease progression and resistance to TMZ therapy.

Abstract 4324: Estrogen promotes proliferation and apoptosis of endometrial carcinoma cell lines Ishikawa and HEC1A via endometrial stromal cells

Abstract Endometrial carcinoma is the leading malignancies of women in Western countries and the second leading cancer of female in China. As endometrial stromal cells (ESCs) are sharply decreased in endometrial carcinoma (EC) compared to normal endometrium, it is interested to explore the roll of ESCs in the tumorigenesis of endometrium. Unopposed estrogen is considered as the etiology of endometrioid carcinoma, but the underlying mechanism is needed to further explore. This study is to determine the biological effects of primary cultured ESCs on two endometrial cancer cell lines-Ishikawa and HEC1A. ESCs from normal, atypical hyperplastic and malignant endometrial specimens were collected, validated by immunohistochemistry and cultured in vitro, which were maintained in a medium containing 10 nM estradiol (E2) for three months to simulate the unopposed estrogen stimulation of EC patients. Microarray were performed to detect the alteration of gene expression, which were then confirmed by real time RT-PCR. Co-culture of ESC and EC cell lines were performed by Transwell plate. Cell proliferation was analyzed by CCK8 assay. Apoptosis was analyzed by flow cytometry based on PI/Anexin V staining. Our results showed E2 promoted the proliferation of three types of ESCs. E2 has no direct effect on cancer cell lines Ishikawa and HEC1A, but when they co-cultured with ESCs, which were stimulated by E2, the proliferation of cancer cells was promoted by all three type of ESCs. Moor interestedly, we found that the much obvious promoted effects were occurred by the ESCs that extracted from malignant samples. Estrogen could also reduce the apoptosis of both cancer cell lines when co-culturing with ESCs from normal specimen. Gene microarray and real time RT-PCR indicated that collagen 9 were up-regulated 2.3 times when ESCs cells treated by E2. In conclusion, Estrogen altered the microenvironment of stromal cells, which could lead to the proliferation of endometrial carcinoma cells via paracrine function. The alteration of collagen synthesis by ESCs might contribute to this communication between the epithelial and mesenchymal cells. Citation Format: Xiang Tao, Jian Wu, Yinhua Yu. Estrogen promotes proliferation and apoptosis of endometrial carcinoma cell lines Ishikawa and HEC1A via endometrial stromal cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4324. doi:10.1158/1538-7445.AM2017-4324

Inferring transcriptional logic from multiple dynamic experiments.

MotivationThe availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable.ResultsWe propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcription factors (TFs) regulate the transcriptional process of a gene of interest. Our dynamic model links the mRNA transcription rate of the target gene to the activation states of the TFs assuming that these interactions are consistent across multiple experiments and over time. A trans-dimensional Markov Chain Monte Carlo (MCMC) algorithm is used to efficiently sample the regulatory logic under different combinations of parents and rank the estimated models by their posterior probabilities. We demonstrate and compare our methodology with other methods using simulation examples and apply it to a study of transcriptional regulation of selected target genes of Arabidopsis Thaliana from microarray time series data obtained under multiple biotic stresses. We show that our method is able to detect complex regulatory interactions that are consistent under multiple experimental conditions.Availability and implementationPrograms are written in MATLAB and Statistics Toolbox Release 2016b, The MathWorks, Inc., Natick, Massachusetts, United States and are available on GitHub https://github.com/giorgosminas/TRS and at http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software.Supplementary information Supplementary data are available at Bioinformatics online.

APOBEC gene family expression and hallmarks in chronic lymphocytic leukemia

Abstract The hallmark activity of APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide) family of cytidine deaminases, including activation-induced deaminase (AID) and APOBEC3 genes, has been detected in somatic mutation signatures by ultra-deep sequencing of the genomes of many cancers, including chronic lymphocytic leukemia (CLL). The acquisition of these mutations is hypothesized to lead to the progression towards aggressive disease in cancer. To examine this in CLL, we tested if increased APOBEC family member gene expression in CLL cells, as measured by microarray and quantitative real time PCR, correlated with worse patient outcome. Higher levels of AID, APOBEC3B, APOBEC3F and APOBEC3H in CLL cells correlated with worse patient outcome, whereas APOBEC3G did not. Interestingly, higher levels of a truncated splice variant of APOBEC3F tended to correlate with better patient outcome. The expression of truncated APOBEC3F may possibly interfere with APOBEC family member mutation activity. To test mutation activity, CLL cells were activated by transfer into NOD-scid IL2Rγnull mice, a xenograft model of human CLL, and hallmark mutation signatures in the expressed immunoglobulin variable region (IGHV) of CLL cells were analyzed by targeted ultra-deep sequencing. Induced IGHV mutation hallmarks consistent with AID were found. These data support the hypothesis that expression and mutation activity of APOBEC family members, such as AID, in CLL cells could lead to adverse patient consequences.

Functional Prediction: Gene Filtering Based on Multivariate Techniques

Background: Gene expression data is available on several organisms of interest in publicly available data-bases and knowledge on gene functions can be extracted from gene expression profiles by pattern comparison between genes when analyzed with multivariate techniques. Nevertheless, gene expression data is very noisy and those patterns are often difficult to detect with classical multivariate analysis. Keywords: Microarray time course data, plant pathogen, multivariate techniques, clustering, gene filtering.

TTCA: an R package for the identification of differentially expressed genes in time course microarray data

BackgroundThe analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements.ResultsThe method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF).ConclusionHere we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1.