Time-course data analysis software tools | Gene expression microarray

Assists biologists in understanding the temporal progression of genetic events and biological processes following a stimulus, based on gene expression microarray data. StepMiner includes features for identifying genes which undergo one or more binary transitions. This program permits users to extract three types of binary temporal patterns. Moreover, this tool is useful for researchers interested in binary models of gene expression time courses.

A software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology.

Allows the analysis of gene expression data. EXPANDER gives the user access to a range of microarray analysis algorithms covering the complete analysis process: preprocessing (2) visualizing (3) clustering (4) biclustering and (5) performing downstream analysis of clusters and biclusters such as functional enrichment and promoter analysis. The software incorporates several conventional gene expression analysis algorithms.

Allows the analysis of multiple time course transcriptomics data. maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. The software incorporates a clustering function to visualize genes with similar profiles. maSigPro was initially developed for microarrays and later updated to model count data. It includes Iso-maSigPro, a functionality to study differential isoform usage in time course RNA-seq experiments.

Enables chromatin states prediction based on time-course epigenetic marks data. GATE is a hierarchical model with two layers: (1) a FMM, in which each component of the mixture represents a cluster of genomic segments that share temporal epigenomic patterns and (2) a bottom layer that models the time-course epigenomic data in each cluster. The software models epigenomic data as a dynamic continuum and directly utilizes temporal information to annotate epigenomic states. It was tested with simulated cell differentiation processes.

Allows detection of expanding, contracting and steady peaks from time course ChIP-seq of single chromatin marks. ChromTime separates the genome into fixed length bins and counts the number of ChIP-seq. This tool allows researchers to study directionality of spatial dynamics of peaks. It is based on a probabilistic graphical model that directly represents changes in the genomic territory occupied by single chromatin marks over time.

Provides a Boltzmann time warping of gene expression time series. BTW is a web server that allows users to upload two tab-separated text files, A and B of gene expression data, each possibly having a different number of time intervals of different durations. It then computes time warping distance between each gene of A with each gene of B, using a symmetric algorithm which additionally computes the Boltzmann partition function and outputs Boltzmann pair probabilities.

Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models.

A computational tool for analysing multiple gene expression time series data sets for the same organism.

Compares temporal gene expression patterns. NACEP compares the temporal patterns of a gene between two experimental conditions, taking into consideration all of the possible co-expression modules that this gene may participate in. It utilizes the clustering information to assist the detection of different time-course expression patterns in a soft way. The results can be used to reveal and compare gene expression differences between different conditions.

Captures single impulse-like progression patterns in high throughput time series datasets. ImpulseDE can be applied on any kind of high throughput gene expression data. It reports differentially expressed genes across time points from a single or between two time courses from two experiments, by fitting a representative impulse model to each gene. To optimize running time, the code uses clustering and multi-threading.

Analyzes the association of gene expression indices with interval times. GAIT works in three steps: it (i) calculates the joint probability density distribution of the event times based on the multivariate survival analysis, (ii) calculates the conditional expected interval time given the observed censored data for each sample, and (iii) calculates the statistical significances for the association between gene expression indices and the expected interval times using simple linear regression models.

Identifies gene clusters that exhibit distinctly similar or different gene expression patterns among the comparing sample conditions. TimesVector is a triclustering algorithm which is designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. This tool identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters.

Measures genomic features such as gene expression levels over time. DPGP is a statistical model for clustering time series data that identifies disjoint clusters of time series gene expression observations using extensive simulations. It assumes that (i) cluster trajectories are stationary, (ii) cluster trajectories are exchangeable, (iii) each gene belongs to only one cluster, (iv) expression levels are sampled at the same time points across all genes and (v) the time point-specific residuals have a Gaussian distribution.

A package to identify differentially expressed genes in microarray time-course data. BETR explicitly uses the time-dependent structure of the data, employing an empirical Bayes procedure to stabilize estimates derived from the small sample sizes typical in microarray experiments. It is applicable to one- or two-color replicated microarray data, and can be used to detect differences between two conditions or changes from baseline in a single condition. BETR outperforms three commonly used techniques in the analysis of time-course data. This advantage is particularly noticeable for genes with a small but sustained differential expression signal. When the magnitude of differential expression is of similar magnitude to background noise, it is difficult to identify by examining each time point in isolation. These patterns of differential expression become easier to identify when the time series structure of the data is taken into account; a small, noisy signal becomes identifiable if it is sustained across several adjoining time points.

A package for ranking differentially expressed gene expression time courses through Gaussian process regression. gprege fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS.

Allows users to determine the best number of clusters for a given data set. OLYMPUS is an algorithm integrating the Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS). This approach supplies sets of genes enabling the deciphering of distinct phases in dynamic cellular processes. It is able to recover the optimal cluster against known unsupervised clustering methods.

A filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria. TMRMR incorporates temporal information by combining relevance, which is calculated as an average F-statistic value across different time steps, with redundancy, which is calculated by employing dynamical time warping approach. The incorporation of the temporal information into the feature selection process leads to selection of more discriminative features.

Detects dependencies between genes at different time delays in order to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data.

Analyses sparse and heterogeneous time course data with high detection sensitivity and transparency. TTCA 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 recognize 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.

Treats the interaction strengths as random variables which are modulated by suitable priors. NETGEM is a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. It represents an optimal trade-off between model complexity and data requirement. NETGEM was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks.

It was developed using an iterative software development cycle that involved a high level of user feedback and evaluation. MaTSE is an exploratory analysis tool that allows users to find unexpected patterns of temporal activity in gene expression time-series data.

Implements a wavelet-based model for analyzing transcriptome data and extends it towards more complex experimental designs. With waveTiling, the user is able to discover group-wise expressed regions, differentially expressed regions between any two groups in single-factor studies and in multifactorial designs. Moreover, for time-course experiments, it is also possible to detect linear time effects and a circadian rhythm of transcripts. By considering the expression values of the individual tiling probes as a function of genomic position, waveTiling allows to dectect effect regions regardless of existing annotation.

Permits to processing and analysing both gene or protein expression time series data sets. ReTrOS is based on a differential equation model to account for transcription and degradation of mRNA molecules and translation and degradation of protein molecules. It permits to obtain high-resolution back-calculated transcriptional profiles and can be combined into computational or analysis pipelines. The tool can extract distributions of transcriptional ‘switching’ activity and degrade rate estimates.

Identifies time-delayed regulatory interactions of genes. HRNN is a method that surmounts the interpretation difficulties of recurrent neural network (RNN) for application of gene regulatory networks (GRNs) modeling. It facilitates capturing paths with different lengths from leaf nodes in the network to the target node. This model was evaluated on a real biological system and linear/nonlinear synthetic generated data for different sizes of networks and variances of noise.

Serves to parse metric binary outputs by Illumina sequencers. Illuminate is a Python module that parses metric binaries from Illumina sequencer runs and provides usable data in the form of python dictionaries and dataframes. Users can print sequencing run metrics to the command line and work with the data programmatically at the same time. The reading of active sequencing runs for tile, index and quality metrics is also supported.

Provides an analytical framework to dissect such datasets and ultimately accelerate knowledge discovery through visualization and data-mining. MAPT is a user-friendly tool for both time-series and single-timepoint dataset analysis, with features such as clustering, visualization and annotation-upload. With PAICE, MAPT provides an easy to use analysis suite to facilitate time series and single time point transcriptomics analysis. In unison, MAPT and PAICE serve as a visual workbench for transcriptomics knowledge discovery, data-mining and functional annotation.

Utilizes the proven and successful KEGG web-service to map numerical expression onto biochemical pathways. PAICE is a rapid bioinformatics pathway visualization tool for KEGG-compatible accessions derived from Illumina Solexa next-gen and Affymetrix datasets. With MAPT, PAICE provides an easy to use analysis suite to facilitate time series and single time point transcriptomics analysis. In unison, MAPT and PAICE serve as a visual workbench for transcriptomics knowledge discovery, data-mining and functional annotation.

Measures the delayed responses and the similarity of the early and late genes. LPWC determines the similarity score with weighted correlation and penalizes the correlation of lagged temporal profiles using a Gaussian kernel. It can deal with irregular time sampling. This tool detects clusters with unique temporal patterns. It implements lags to retrieve common but unsynchronized temporal patterns.

Consists of a “low-rank plus sparse” framework for building time-point specific gene co-expression networks (GCNs) from time-course gene expression data. TGCN is a network model that jointly models the temporal transcriptomic data when the samples at different time points are from distinct subjects. This algorithm generates time-specific gene-gene correlation matrices which could serve as input for gene co-expression network analysis procedures, such as weighted correlation network analysis (WGCNA).

Provides data structures and algorithms for basic and extended hidden Markov models (HMMs) with discrete and continous emissions. GHMM is a library that implements standard algorithms for computing with HMMs and several extensions, both to the model class and algorithms. A graphical application for editing HMMs is also available. The software can be used from interactive languages such as Python and R.

Offers functions for data analysis and graphical displays for developmental microarray time course data.