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Studying and modelling dynamic biological processes using time-series gene expression data

Key Points

  • Studying time-series gene expression enables the identification of transient transcriptional changes, temporal patterns of a response and causal relationships between genes.

  • Experimental design and data processing methods — including sampling rates, synchronization, normalization and the identification of differentially expressed genes — should take into account the temporal aspects of the experiments.

  • Splines and other continuous representations of temporal gene expression support more sophisticated analyses as well as statistical methods for detecting differentially expressed genes.

  • Categories of time-series experiments include the response to an external signal, developmental processes and cyclic processes. Each type of experiment has a characteristic outcome: transient responses, fate switches and cyclic expression patterns, respectively.

  • Temporal data sets measuring transcriptional factor occupancy and histone modifications complement dynamic gene expression data, leading to improved biological models.

  • Computational techniques can integrate time-series gene expression data with other types of static and temporal high-throughput biological data to reconstruct dynamic regulatory networks.

  • Time-series gene expression studies in the clinical setting can be used to predict phenotypic outcome, but have a unique set of challenges regarding experimental design and computational analysis.

  • Several additional types of time-series expression data sets, including single-cell measurements and data from next-generation sequencing technologies, will provide new opportunities while also raising new computational analysis challenges.

Abstract

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.

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Figure 1: Computational workflow for time-series gene expression data.
Figure 2: Patterns of gene expression.
Figure 3: Identifying transcription factor dynamics with DREM.

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Acknowledgements

This work was supported in part by the US National Institutes of Health (NIH) grant 1RO1 GM085022 to Z.B.-J. Research in the laboratory of I.S. is supported by the Israel Science Foundation (grant 567/10), the German–Israeli Foundation (grant 998/2008), the Weinkselbaum family medical research fund and the European Research Council Starting Grant (281306).

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Correspondence to Ziv Bar-Joseph, Anthony Gitter or Itamar Simon.

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FURTHER INFORMATION

ModENCODE

Glossary

Spike controls

Special control transcripts that are mixed into a biological sample before microarray hybridization. Because the quantity added is known, the control probe signal can be used for accurate normalization.

dChip

A software for viewing and normalizing probe-level microarray data. It relies on the assumption that some genes are rank-invariant between different samples.

Approximating splines

A smooth piecewise polynomial function that can be fitted to a temporal gene expression profile.

Cubic splines

Splines composed of third-order polynomial functions.

Hierarchical clustering

A greedy clustering approach in which pairs of genes or clusters are sequentially connected until they form a tree-like structure.

K-means

A clustering approach that searches for a specific number of clusters (k) maximizing a global target function. Clusters are defined by their centre. Iteratively, genes are assigned to the best-matching cluster and then the clusters' centre values are updated.

Causal modelling

A causal model asserts that a gene controls its target genes and changes their expression levels. This is in contrast to a model that merely identifies genes for which expression is correlated over time.

Phorbol myristate acetate

(PMA). Phorbol 12-myristate 13-acetate is a diester of phorbol that is frequently used to activate the signal transduction enzyme protein kinase C (PKC).

Just-in-time

A temporal expression pattern that is characterized by a match between the temporal activation of a set of genes and the time in which their products are required.

NANOG

A transcription factor that is crucial for the self-renewal of undifferentiated embryonic stem cells.

Motif Activity Response Analysis

(MARA). A method for inferring DNA-binding-motif activity and for linking motifs to promoters. MARA models promoter expression as a linear function of motif activity and the number of functional binding sites.

Phosphoproteomic

The use of experimental technology such as mass spectrometry to identify and quantify protein or peptide phosphorylation.

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Bar-Joseph, Z., Gitter, A. & Simon, I. Studying and modelling dynamic biological processes using time-series gene expression data. Nat Rev Genet 13, 552–564 (2012). https://doi.org/10.1038/nrg3244

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