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Additional file 1: Table S1. Total number of sequencing reads before and after QC filtering and coverage after QC filtering. Median DNA yield per sample: 4.76 µg/ml (interquartile range 2.53 µg/ml – 11.50 µg/ml). Table S2. Czech cohort demographics. Table S3. Cox proportional hazards analysis showing univariable associations of variables with outcome (death or recurrence of CRC) in 127 UK patients. Table S4. Demographics of patients included in full tumour exome sequencing. Fig. S1. Czech data set. Y-axis labels are species, or higher taxonomic rank if species data is not known, X-axis labels show the taxonomic order. Clustering from UK data is applied. Fig. S2. Scatter plots of matched 16S rRNA amplicon sequencing read counts with bacteria-specific qPCR for four target bacteria (a) Ruminococcus gnavus, (b) Faecalibacterium prausnitzii, (c) Fusobacterium nucleatum, (d) Granulicatella adiacens. r and p values derived from Spearman correlation. Fig. S3. Czech data set for metabolites with clustering from UK data applied. Fig. S4. Network analysis linking bacterial taxa with metabolites based on identification of metabolites which are involved in enzymatic reactions encoded by genes present in the microbial clusters, with reference to the KEGG database. 13 Green circle: the microbe is found in the list of organisms with enzymatic link(s) to the metabolite. Magenta cross: the microbe is not be found in the list of organisms with enzymatic link(s) to the metabolite. Fig. S5. Results of full exome sequencing on 9 tumour samples. Each sample is denoted by its dominant microbiota (micro) and metabolomic (met) cluster subtypes. Mutation loads are shown. Mutations to key driver genes are listed within sub-groups. The type of mutation is shown adjacent (right side) of each gene. FS: frameshift deletion; NS: non synonymous mutation; SS: splice site; PS: premature stop; non-FS del: non-frameshift deletion.

A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately, the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol), and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved 1H NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g., 2D-NMR spectroscopy, solid-phase extraction, liquid chromatography prefaced mass spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D 1H NMR as part of routine metabolic profiling in large data sets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resources.

Motivation Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra. Results The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building Availability and implementation The algorithm is implemented using the MWASTools R/Bioconductor package. Supplementary information Supplementary data are available at Bioinformatics online.

Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.