From sequence to information

Abstract

Today massive amounts of sequenced metagenomic and metatranscriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding of biological systems analysed from different perspectives is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data are of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data have been successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.

Integrative research perspectives on marine conservation

“…But how are these 'big data' useful for marine conservation? To explore this question, the section closes with an article by Popa et al [54] on deriving information from sequences, where they show pathways to link molecular information to functional changes in the ecosystem. They advocate including this information into the analyses of temporal trends, and combining such monitoring with modelling and targeted experiments to develop a mechanistic understanding of processes.…”

Overview of the MOSAiC expedition: Ecosystem

“…A variety of bioinformatic and statistical modeling techniques aim at elucidating changes in composition and metabolic potential of Arctic marine microbial communities to improve our understanding of their influence on global biogeochemical cycles. The mechanistic understanding of ecological patterns is initially based on information from gene sequences combined with a descriptive approach of community members using co-occurrence networks that illustrate the occurrence of species at the same place and time (Popa et al, 2020). This graph approach, in which nodes are species and edges represent the correlation strength of their seasonality patterns, enables identification of i) central species (node hubs) and ii) species communities (network clusters) that are defined by several populations which are abundant in the same time period (Berry and Widder, 2014).…”