A global metagenomic map of urban microbiomes and antimicrobial resistance.

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms.

Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques.

Introduction: As one of the most prevalent chronic diseases in the United States, diabetes, especially type 2 diabetes, affects the health of millions of people and puts an enormous financial burden on the US economy. We aimed to develop predictive models to identify risk factors for type 2 diabetes, which could help facilitate early diagnosis and intervention and also reduce medical costs.

Methods: We analyzed cross-sectional data on 138,146 participants, including 20,467 with type 2 diabetes, from the 2014 Behavioral Risk Factor Surveillance System.

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics.

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations. Our strategy toward accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs (a modified six-frame translation considering alternative start codons) in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome.

edgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology.

The edgeR package, an R-based tool within the Bioconductor project, offers a flexible statistical framework for detection of changes in abundance based on counts. In this chapter, we illustrate the use of edgeR on a human embryonic stem cell dataset, in particular for RNA-seq and ChIP-seq data. We focus on a step-by-step statistical analysis of differential expression, going from raw data to a list of putative differentially expressed genes and give examples of integrative analysis using the ChIP-seq data.

The adaptive imbalance in base excision-repair enzymes generates microsatellite instability in chronic inflammation.

Chronic infection and associated inflammation are key contributors to human carcinogenesis. Ulcerative colitis (UC) is an oxyradical overload disease and is characterized by free radical stress and colon cancer proneness. Here we examined tissues from noncancerous colons of ulcerative colitis patients to determine (a) the activity of two base excision-repair enzymes, AAG, the major 3-methyladenine DNA glycosylase, and APE1, the major apurinic site endonuclease; and (b) the prevalence of microsatellite instability (MSI).