Background: The correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking. Results: We compare methods developed for single-cell and bulk RNA-seq, and specifically for microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, power, and correct identification of differentially abundant genera. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing. Conclusions: The multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

Background Butyrate has shown anti‐inflammatory and regenerative properties, providing symptomatic relief when orally supplemented in patients suffering from various colonic diseases. We investigated the effect of a colonic‐delivery formulation of butyrate on the fecal microbiota of patients with inflammatory bowel diseases (IBDs). Methods In this double‐blind, placebo‐controlled, pilot study, 49 IBD patients (n = 19 Crohn's disease, CD and n = 30 ulcerative colitis, UC) were randomized to oral administration of microencapsulated‐sodium‐butyrate (BLM) or placebo for 2 months, in addition to conventional therapy. Eighteen healthy volunteers (HVs) were recruited to provide a healthy microbiota model of the local people. Fecal microbiota from stool samples was assessed by 16S sequencing. Clinical disease activity and quality of life (QoL) were evaluated before and after treatment. Key Results At baseline, HVs showed a different microbiota composition compared with IBD patients. Sodium‐butyrate altered the gut microbiota of IBD patients by increasing bacteria able to produce SCFA in UC patients (Lachnospiraceae spp.) and the butyrogenic colonic bacteria in CD patients (Butyricicoccus). In UC patients, QoL was positively affected by treatment. Conclusions and Inferences Sodium‐butyrate supplementation increases the growth of bacteria able to produce SCFA with potentially anti‐inflammatory action. The clinical impact of this finding requires further investigation.

The comparison of several statistical methods currently used for detection of differentially expressed genes was attempted both by a simulation approach and by the analysis of data sets of human expressed sequence tags, obtained from UniGene. In the simulated mixed case, mimicking a situation close to reality, the general chi(2) test was unexpectedly the most efficient in multiple tag sampling experiments, especially when dealing with variations affecting weakly expressed genes. On the other hand, Audic and Claverie's method proved the most efficient for detecting differences in gene expression when dealing with pairwise comparisons. By applying the above methods on UniGene-based data sets concerning two human kidney tumours compared with normal kidney tissue, three novel genes overexpressed in these tumours were identified. Software and additional information on statistical methodologies, simulation approach and data are available at http://telethon.bio.unipd.it/bioinfo/IDEG6/.

The variability of results in microarray technology is in part due to the fact that independent scans of a single hybridised microarray give spot images that are not quite the same. To solve this problem and turn it to our advantage, we introduced the approach of multiple scanning and of image integration of microarrays. To this end, we have developed specific software that creates a virtual image that statistically summarises a series of consecutive scans of a microarray. We provide evidence that the use of multiple imaging (i) enhances the detection of differentially expressed genes; (ii) increases the image homogeneity; and (iii) reveals false-positive results such as differentially expressed genes that are detected by a single scan but not confirmed by successive scanning replicates. The increase in the final number of differentially expressed genes detected in a microarray experiment with this approach is remarkable; 50% more for microarrays hybridised with targets labelled by reverse transcriptase, and 200% more for microarrays developed with the tyramide signal amplification (TSA) technique. The results have been confirmed by semi-quantitative RT-PCR tests.

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