Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants

Motivation Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown. Results We identify and systematically evaluate determinants of performance—including network properties, experimental design choices and data processing—by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets. Availability and implementation Code is available at http://github.com/bagherilab/networkinference/. Supplementary information Supplementary data are available at Bioinformatics online.

Colour, contours, shading and shape: flow interactions reveal anchor neighbourhoods

Two dilemmas arise in inferring shape information from shading. First, depending on the rendering physics, images can change significantly with (even) small changes in lighting or viewpoint, while the percept frequently does not. Second, brightness variations can be induced by material effects—such as pigmentation—as well as by shading effects. Improperly interpreted, material effects would confound shading effects. We show how these dilemmas are coupled by reviewing recent developments in shape inference together with a role for colour in separating material from shading effects. Aspects of both are represented in a common geometric (flow) framework, and novel displays of hue/shape interaction demonstrate a global effect with interactions limited to localized regions. Not all parts of an image are perceptually equal; shape percepts appear to be constructed from image anchor regions.

Correcting a SHAPE-directed RNA structure by a mutate-map-rescue approach

ABSTRACTThe three-dimensional conformations of non-coding RNAs underpin their biochemical functions but have largely eluded experimental characterization. Here, we report that integrating a classic mutation/rescue strategy with high-throughput chemical mapping enables rapid RNA structure inference with unusually strong validation. We revisit a paradigmatic 16S rRNA domain for which SHAPE (selective 2′-hydroxyl acylation with primer extension) suggested a conformational change between apo-and holo-ribosome conformations. Computational support estimates, data from alternative chemical probes, and mutate-and-map (M2) experiments expose limitations of prior methodology and instead give a near-crystallographic secondary structure. Systematic interrogation of single base pairs via a high-throughput mutation/rescue approach then permits incisive validation and refinement of the M2-based secondary structure and further uncovers the functional conformation as an excited state (25±5% population) accessible via a single-nucleotide register shift. These results correct an erroneous SHAPE inference of a ribosomal conformational change and suggest a general mutate-map-rescue approach for dissecting RNA dynamic structure landscapes.