conjunction with the separation into modules on larger datasets.

Using module networks to identify causal regulatory mechanisms
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Module network inference is a statistically well-grounded method which uses probabilistic graphical models to reconstruct modules of co-regulated genes and their upstream regulatory programs, and which has been proven useful in many biological case studies [@add1; @segal2003; @friedman2004; @bonnet2015]. The module network model was originally introduced as a method to infer regulatory networks from large-scale gene expression compendia, as implemented in the **Genomica** software (<http://genomica.weizmann.ac.il>)[@segal2003]. Subsequently the method has been extended to integrate eQTL and gene expression data [@lee2006; @lee2009learning; @zhang2010bayesian]. The module network model starts from the same formula as Equation . It is then assumed that genes belonging to the same module share the same parents and conditional distributions; these conditional distributions are parameterized as decision trees, with the parental genes on the internal (decision) nodes and normal distributions on the leaf nodes [@segal2003]. Recent algorithmic innovations decouple the module assignment and tree structure learning from the parental gene assignment and use Gibbs sampling and ensemble methods for improved module network inference [@joshi2008; @joshi2009]. These algorithms are implemented in the **Lemon-Tree** software (<https://github.com/eb00/lemon-tree>), a command line software suite for module 