Key Points
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Predicting cis-regulatory modules (CRMs) can both expand our understanding of biology and have applications in medicine and other fields. For example, most of the genetic variants that are significantly associated with susceptibility to disease are not in protein-coding regions, and we surmise that many affect regulation of gene expression.
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Clusters of transcription factor binding sites (TFBSs) do not provide sufficient specificity to be used for effective prediction of CRMs in large-scale investigations. However, when applied to identified or likely CRMs, they provide important insights into potentially cooperating transcription factors and help to build a regulatory code.
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Predicting CRMs on the basis of strong constraint in non-coding sequences finds an important subset of the CRMs: that is, those that control developmental regulatory genes. However, they miss a large number of, possibly most, transcription-factor-occupied segments.
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High-quality data sets of epigenetic features associated with gene regulation, such as DNase-hypersensitive sites, transcription factor occupancy and diagnostic histone modifications, provide a reasonably unbiased, sensitive view of the regulatory landscape. The integrative analysis of these features is currently the best approach for predicting CRMs.
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Evolutionary and motif patterns in predicted CRMs should be used to partition the identified regions into categories that could have different functions in regulation.
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Predicting and testing CRMs is essential for deciphering a regulatory code. Synthetic biology approaches, in which DNA sequences inferred for a particular function are synthesized and tested for that function, can assess the accuracy of models for regulatory codes and point to needed improvements.
Abstract
Differential gene expression is the fundamental mechanism underlying animal development and cell differentiation. However, it is a challenge to identify comprehensively and accurately the DNA sequences that are required to regulate gene expression: namely, cis-regulatory modules (CRMs). Three major features, either singly or in combination, are used to predict CRMs: clusters of transcription factor binding site motifs, non-coding DNA that is under evolutionary constraint and biochemical marks associated with CRMs, such as histone modifications and protein occupancy. The validation rates for predictions indicate that identifying diagnostic biochemical marks is the most reliable method, and understanding is enhanced by the analysis of motifs and conservation patterns within those predicted CRMs.
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Acknowledgements
Support is from US National Institutes of Health grants R01 DK065806, RC2 HG005573 and U54 HG004695 and funds from Emory University to J.T.
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FURTHER INFORMATION
CCGB: Miller lab (for BLASTZ and LASTZ)
LAGAN Toolkit Website (for MLAGAN and CHAOS)
Nature Reviews Genetics Series on Regulatory elements
Regulatory sequence analysis tools
Siggia lab Rockefeller Univesrity software list (for Ahab)
Software for computational molecular biology (from Miller lab; for yama)
Glossary
- Purifying selection
-
The evolutionary process by which deleterious mutations are removed from a population or genome. Also referred to as negative selection or evolutionary constraint.
- Enhancers
-
DNA sequences that cause increased expression of their target gene (or genes).
- Epigenetic features
-
Molecules and chemical modifications that are associated with genomic DNA, including covalent modifications of DNA and histones, RNA transcribed from the DNA, occupancy of DNA by transcription factors and accessibility of DNA in chromatin to DNases.
- Transcription factor binding site
-
(TFBS). A short segment of DNA that is bound by a particular transcription factor in vivo.
- TFBS motif
-
A short string of DNA base pairs (often 6–10 bp long) constituting the sequence recognized by the DNA-binding domain of the transcription factor.
- Promoter
-
The DNA sequence that directs RNA polymerase to initiate transcription at the correct place.
- TFBS consensus
-
A string of DNA nucleotides describing the most frequently occurring short sequences in a collection of TFBSs, usually including ambiguous positions (for example, R refers to G or A nucleotides).
- Position-specific weight matrix
-
(PWM). A matrix providing the frequency at which each nucleotide is found at the positions of the TFBS consensus.
- Silencers
-
DNA sequences that cause reduced expression of their target gene (or genes).
- Insulators
-
DNA sequences that control the ability of an enhancer to regulate a promoter by an enhancer blocking activity or a domain barrier function or both.
- Position effects
-
The observation that the level of expression of some genes is affected by their position on chromosomes, with normal level of expression in one location but altered expression when translocated. For example, proximity to centromeres is associated with lowered expression for many genes.
- False positive
-
In a prediction experiment, a case in which the prediction is positive, but the true class is negative.
- Sensitivity
-
In a prediction experiment, the proportion of the true class that is predicted by the method: that is, (number of true positives)/(number of true positives + number of false negatives).
- Positive predictive value
-
In a prediction experiment, the proportion of positive predictions that are true positives.
- Position-specific scoring matrix
-
(PSSM). A matrix providing the log ratio of frequency at which each nucleotide is found at the positions of the TFBS consensus relative to a background model.
- TFBS motif instances
-
A match to a TFBS consensus or motif matrix within a longer DNA sequence (for example, a genome or chromosome).
- Specificity
-
In a prediction experiment, the proportion of the false class that is not predicted by the method: that is, (number of true negatives)/(number of true negatives + number of false positives).
- Logistic regression
-
A form of regression used when the output is binary. The predictor is a linear combination of the input variables transformed with the logistic function to form a probability. For classification, the coefficients are learned to maximize the (log) conditional likelihood of the training data.
- Homotypic clusters
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Clusters of similar transcription factor binding sites that often bind the same transcription factor.
- Chromatin immunoprecipitation
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(ChIP). A method for purifying the DNA segments that are in close contact with a transcription factor in living cells. After crosslinking DNA to native proteins in cells and preparing sheared chromatin, antibodies that specifically react with one transcription factor are used to isolate the DNA bound to that transcription factor.
- ChIP followed by high-throughput sequencing
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(ChIP–seq). A technique for mapping the particular segments of DNA purified by chromatin immunoprecipitation (ChIP): it involves massively parallel short-read (second generation) sequencing and then aligning the reads to a reference genome. ChIP–seq is often highly accurate and has close to whole-genome coverage.
- Hidden Markov model
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A statistical model in which internal states are not visible but the outputs of these states are, and the outputs can therefore be used to infer the internal states. This model can be used to determine biologically relevant states from chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–seq) data sets.
- Morpholino oligonucleotides
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Synthetic oligonucleotides in which the ribose portion of the nucleotide is replaced a morpholino compound; these are more stable than RNA and can be used to interfere with gene activity in transgenic zebrafish.
- ChIP–exo
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An extension of ChIP–seq that includes exonuclease trimming after immunoprecipitation to increase the resolution of the mapped transcription-factor-bound sites.
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Hardison, R., Taylor, J. Genomic approaches towards finding cis-regulatory modules in animals. Nat Rev Genet 13, 469–483 (2012). https://doi.org/10.1038/nrg3242
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DOI: https://doi.org/10.1038/nrg3242
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