Pocock, M. | Search bioRxiv

bioRxiv2021

Integration of probabilistic functional networks without an external Gold Standard

BackgroundProbabilistic functional integrated networks (PFINs) are designed to aid our understanding of cellular biology and can be used to generate testable hypotheses about protein function. PFINs are generally created by scoring the quality of interaction datasets against a Gold Standard dataset, usually chosen from a separate high-quality data source, prior to their integration. Use of an external Gold Standard has several drawbacks, including data redundancy, data loss and the need for identifier mapping, which can complicate the network build and impact on PFIN performance. ResultsWe describe the development of an integration technique, ssNet, that scores and integrates both high-throughput and low-throughout data from a single source database in a consistent manner without the need for an external Gold Standard dataset. Using data from Saccharomyces cerevisiae we show that ssNet is easier and faster, overcoming the challenges of data redundancy, Gold Standard bias and ID mapping, while producing comparable performance. In addition ssNet results in less loss of data and produces a more complete network. ConclusionsThe ssNet method allows PFINs to be built successfully from a single database, while producing comparable network performance to networks scored using an external Gold Standard source.

bioinformatics↗

bioRxiv2020

Inferring species interactions from ecological survey data: a mechanistic approach to predict quantitative food webs of seed-feeding by carabid beetles

O_LIEcological networks are valuable for ecosystem analysis but their use is often limited by a lack of data because many types of ecological interaction, e.g. predation, are short-lived and difficult to observe or detect. There are different methods for inferring the presence of interactions, which we lack methods to predict interaction strengths and so use weighted network analysis. C_LIO_LIHere, we develop a trait-based approach suitable for creating quantitative networks, i.e. with varying interaction strengths. We developed the method for seed-feeding carabid ground beetles (Coleoptera: Carabidae) although the principles can be applied to other interactions. C_LIO_LIWe used existing literature data from experimental seed-feeding trials to predict a per-individual interaction cost index based on carabid and seed size with frequency-dependent prey selection and assuming bottom up control. This was scaled up to the population level to create predicted inferred weighted networks using the abundance of carabids and seeds in samples from arable fields and energetic intake rates of carabids from the literature. From these weighted networks, we also calculated a novel measure of predation pressure. C_LIO_LIWe applied it to existing ecological survey data from 255 arable fields with carabid data from pitfall traps and plant seeds from seed rain traps. Analysis of these inferred networks led to testable hypotheses about how networks and predation pressure varied amongst fields. C_LIO_LIInferred networks are valuable because (i) they provide null models for the structuring of food webs to test against empirical species interaction data, e.g. DNA analysis of carabid gut regurgitates, and (ii) they allow weighted inferred networks to be constructed whenever we can estimate interactions between species and have ecological census data available. This would permit network analysis even at times and in places when interactions were not directly assessed. C_LI

ecology↗