Mayer, B. | bioRxiv Science

bioRxiv2018

DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis.

A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code and primary data be made available with publications. There has been little practical advice on implementing reproducible research work-flows for large omics or systems biology data sets used by teams of analysts working in collaboration. In such instances it is important to ensure all analysts use the same version of a data set for their analyses. Yet, instantiating relational databases and standard operating procedures can be unwieldy, with high \"startup\" costs and poor adherence to procedures when they deviate substantially from an analysts usual work-flow. Ideally a reproducible research work-flow should fit naturally into an individuals existing work-flow, with minimal disruption. Here, we provide an overview of how we have leveraged popular open source tools, including Bioconductor, Rmarkdown, git version control, R, and specifically Rs package system combined with a new tool DataPackageR, to implement a lightweight reproducible research work-flow for preprocessing large data sets, suitable for sharing among small-to-medium sized teams of computational scientists. Our primary contribution is the DataPackageR tool, which decouples time-consuming data processing from data analysis while leaving a traceable record of how raw data is processed into analysis-ready data sets. The software ensures packaged data objects are properly documented and performs checksum verification of these along with basic package version management, and importantly, leaves a record of data processing code in the form of package vignettes. Our group has implemented this work-flow to manage, analyze and report on pre-clinical immunological trial data from multi-center, multi-assay studies for the past three years.

bioinformatics↗

bioRxiv2018

A metaproteomics method to determine carbon sources and assimilation pathways of species in microbial communities

Measurements of the carbon stable isotope ratio ({delta}13C) are widely used in biology to address major questions regarding food sources and metabolic pathways used by organisms. Measurement of these so called stable carbon isotope fingerprints (SIFs) for microbes involved in biogeochemical cycling and microbiota of plants and animals have led to major discoveries in environmental microbiology. Currently, obtaining SIFs for microbial communities is challenging as the available methods either only provide limited taxonomic resolution, such as with the use of lipid biomarkers, or are limited in throughput, such as NanoSIMS imaging of single cells.\n\nHere we present \"direct Protein-SIF\" and the Calis-p software package (https://sourceforge.net/projects/calis-p/), which enable high-throughput measurements of accurate {delta}13C values for individual species within a microbial community. We benchmark the method using 20 pure culture microorganisms and show that the method reproducibly provides SIF values consistent with gold standard bulk measurements performed with an isotope ratio mass spectrometer. Using mock community samples, we show that SIF values can also be obtained for individual species within a microbial community. Finally, a case study of an obligate bacteria-animal symbiosis showed that direct Protein-SIF confirms previous physiological hypotheses and can provide unexpected new insights into the symbionts metabolism. This confirms the usefulness of this new approach to accurately determine {delta}13C values for different species in microbial community samples.\n\nSignificanceTo understand the roles that microorganisms play in diverse environments such as the open ocean and the human intestinal tract, we need an understanding of their metabolism and physiology. A variety of methods such as metagenomics and metaproteomics exist to assess the metabolism of environmental microorganisms based on gene content and gene expression. These methods often only provide indirect evidence for which substrates are used by a microorganism in a community. The direct Protein-SIF method that we developed allows linking microbial species in communities to the environmental carbon sources they consume by determining their stable carbon isotope signature. Direct Protein-SIF also allows assessing which carbon fixation pathway is used by autotrophic microorganisms that directly assimilate CO2.

microbiology↗