Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies.

Abstract:

:Generating a comprehensive map of molecular interactions in living cells is difficult and great efforts are undertaken to infer molecular interactions from large-scale perturbation experiments. Here, we develop the analytical and numerical tools to quantify the fundamental limits for inferring transcriptional networks from gene knockout screens and introduce a network inference method that is unbiased with respect to measurement noise and scalable to large network sizes. We show that network asymmetry, knockout coverage and measurement noise are central determinants that limit prediction accuracy, whereas the knowledge about gene-specific variability among biological replicates can be used to eliminate noise-sensitive nodes and thereby boost the performance of network inference algorithms.

journal_name

Nat Commun

journal_title

Nature communications

authors

Blum CF,Heramvand N,Khonsari AS,Kollmann M

doi

10.1038/s41467-017-02489-x

subject

Has Abstract

pub_date

2018-01-09 00:00:00

pages

133

issue

1

issn

2041-1723

pii

10.1038/s41467-017-02489-x

journal_volume

9

pub_type

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