Ulcerative colitis is a chronic inflammatory disease of the colon that carries a significant disease burden in children. Therefore, new therapeutic approaches are being explored to help children living with this disease. Fecal microbiota transplantation (FMT) has been successful in some children with ulcerative colitis. However, the mechanism of its therapeutic effect in this patient population is not well understood. To characterize changes in gut microbial and metabolomic profiles after FMT, we performed 16S rRNA gene sequencing, shotgun metagenomic sequencing, virome analysis and untargeted metabolomics by gas chromatography-time of flight-mass spectrometry on stool samples collected before and after FMT from four children with ulcerative colitis who responded to this treatment. Alpha diversity of the gut microbiota increased after intervention, with species richness rising from 251 (S.D. 125) to 358 (S.D. 27). In responders, the mean relative abundance of bacteria in the class Clostridia shifted toward donor levels, increasing from 33% (S.D. 11%) to 54% (S.D. 16%). Patient metabolomic and viromic profiles exhibited a similar but less pronounced shift toward donor profiles after FMT. The fecal concentrations of several metabolites were altered after FMT, correlating with clinical improvement. Larger studies using a similar multi-omics approach may suggest novel strategies for the treatment of pediatric ulcerative colitis.

We develop a metagenomic data analysis pipeline, MicroPro, that takes into account all reads from known and unknown microbial organisms and associates viruses with complex diseases. We utilize MicroPro to analyze four metagenomic datasets relating to colorectal cancer, type 2 diabetes, and liver cirrhosis and show that including reads from unknown organisms significantly increases the prediction accuracy of the disease status for three of the four datasets. We identify new microbial organisms associated with these diseases and show viruses play important prediction roles in colorectal cancer and liver cirrhosis, but not in type 2 diabetes. MicroPro is freely available at https://github.com/zifanzhu/MicroPro . Electronic supplementary material The online version of this article (10.1186/s13059-019-1773-5) contains supplementary material, which is available to authorized users.

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We propose a deep learning–based knockoffs inference framework, DeepLINK, that guarantees the false discovery rate (FDR) control in high-dimensional settings. DeepLINK is applicable to a broad class of covariate distributions described by the possibly nonlinear latent factor models. It consists of two major parts: an autoencoder network for the knockoff variable construction and a multilayer perceptron network for feature selection with the FDR control. The empirical performance of DeepLINK is investigated through extensive simulation studies, where it is shown to achieve FDR control in feature selection with both high selection power and high prediction accuracy. We also apply DeepLINK to three real data applications to demonstrate its practical utility.