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Integrating physical and genetic maps: from genomes to interaction networks

Key Points

  • The integration of genetic and physical maps was a defining feature of the Human Genome Project. Mapping of the cell's regulatory and signalling networks is now proceeding along highly analogous lines.

  • A first step in sequencing the human genome was to assign quality scores to each sequenced nucleotide. In the case of physical and genetic interactions, the method of choice for improving quality is integration of data across a wide variety of measurement types.

  • Genome assembly was the process of putting sequence reads together to form contigs. In the context of molecular interactions, assembly refers to the integration of individual interactions into larger network structures that represent pathways, protein complexes and other components of cellular machinery.

  • Network assembly is aided by a classification system for molecular interactions. Towards this goal, recent studies have begun to place interactions into various categories beyond the initial division into genetic and physical.

  • Categories of interactions include ordered versus unordered, transient versus stable, between- versus within-pathway, alleviating versus aggravating, and interactions of the first versus second degree.

  • These types are being combined with one another in various combinations to assemble integrated network models. Examples include integration of protein–protein interactions with aggravating, alleviating or ordered genetic interactions, as well as integration of eQTLs with protein–DNA transcriptional interactions.

  • A final step is network annotation: inference of additional details such as interaction dynamics, strengths and condition-specificity onto the static network.

  • Integration of genetic and physical interaction mapping data will be particularly important to the current wave of genome-wide association studies, in which many genetic interactions are apparent with little physical or mechanistic explanation.

Abstract

Physical and genetic mapping data have become as important to network biology as they once were to the Human Genome Project. Integrating physical and genetic networks currently faces several challenges: increasing the coverage of each type of network; establishing methods to assemble individual interaction measurements into contiguous pathway models; and annotating these pathways with detailed functional information. A particular challenge involves reconciling the wide variety of interaction types that are currently available. For this purpose, recent studies have sought to classify genetic and physical interactions along several complementary dimensions, such as ordered versus unordered, alleviating versus aggravating, and first versus second degree.

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Figure 1: Genetic and physical mapping for networks and genomes.
Figure 2: Second-degree interactions imply first-degree relationships.
Figure 3: Examples of assembly across different interaction categories.
Figure 4: Network motifs assembled from different combinations of interaction measurements.

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Acknowledgements

This work was supported by the US National Institutes of Environmental Health Sciences grant ES014811. T.I. is a David and Lucille Packard Fellow.

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Cancer Genome Anatomy Project

CellCircuits DB

Database of Interacting Proteins (DIP)

General Respository for Interaction Datasets (GRID)

Human Protein Interaction Database (HPID)

Human Protein Reference Database (HPRD)

IntAct

MIPS Mammalian Protein–Protein Interaction Database

Molecular INTeractions Database (MINT)

Glossary

Radiation hybrid mapping

High-resolution mapping of human markers using X-ray exposure to fragment human chromosomes and fusing the irradiated cells with rodent cells. The frequency of co-occurrence of markers on the same fragment relates to their genomic distance.

Fluorescence in situ hybridization

Fluorescently labelled DNA probes are hybridized to chromosomal DNA. This allows genes (probes) to be assigned to chromosomes and provides a rough estimate of the chromosomal position of the cloned fragment.

Reverse-genetic screening

Identifying the mutant phenotype(s) associated with a known genetic mutation or a panel of known mutations, such as a gene-deletion library. This term contrasts with forward-genetic screening, which involves identifying the mutations that affect a given phenotype.

Regression

A statistical method for predicting a dependent variable on the basis of one or more independent variables.

Likelihood function

A statistical method for predicting the likelihood of an outcome that is conditional (dependent) on other evidence.

Petri network

A modelling approach that depicts a process on a bipartite graph. Nodes are either places or transitions that are connected by directed arcs. Tokens are transmitted from places to transitions or from transitions to places.

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Beyer, A., Bandyopadhyay, S. & Ideker, T. Integrating physical and genetic maps: from genomes to interaction networks. Nat Rev Genet 8, 699–710 (2007). https://doi.org/10.1038/nrg2144

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