MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis

Genomic meta-analysis to combine relevant and homogeneous studies has been widely applied, but the quality control (QC) and objective inclusion/exclusion criteria have been largely overlooked. Currently, the inclusion/exclusion criteria mostly depend on ad-hoc expert opinion or naïve threshold by sample size or platform. There are pressing needs to develop a systematic QC methodology as the decision of study inclusion greatly impacts the final meta-analysis outcome. In this article, we propose six quantitative quality control measures, covering internal homogeneity of coexpression structure among studies, external consistency of coexpression pattern with pathway database, and accuracy and consistency of differentially expressed gene detection or enriched pathway identification. Each quality control index is defined as the minus log transformed P values from formal hypothesis testing. Principal component analysis biplots and a standardized mean rank are applied to assist visualization and decision. We applied the proposed method to 4 large-scale examples, combining 7 brain cancer, 9 prostate cancer, 8 idiopathic pulmonary fibrosis and 17 major depressive disorder studies, respectively. The identified problematic studies were further scrutinized for potential technical or biological causes of their lower quality to determine their exclusion from meta-analysis. The application and simulation results concluded a systematic quality assessment framework for genomic meta-analysis

Critical Assessment of Metagenome Interpretation:A benchmark of metagenomics software

International audienceIn metagenome analysis, computational methods for assembly, taxonomic profilingand binning are key components facilitating downstream biological datainterpretation. However, a lack of consensus about benchmarking datasets andevaluation metrics complicates proper performance assessment. The CriticalAssessment of Metagenome Interpretation (CAMI) challenge has engaged the globaldeveloper community to benchmark their programs on datasets of unprecedentedcomplexity and realism. Benchmark metagenomes were generated from newlysequenced ~700 microorganisms and ~600 novel viruses and plasmids, includinggenomes with varying degrees of relatedness to each other and to publicly availableones and representing common experimental setups. Across all datasets, assemblyand genome binning programs performed well for species represented by individualgenomes, while performance was substantially affected by the presence of relatedstrains. Taxonomic profiling and binning programs were proficient at high taxonomicranks, with a notable performance decrease below the family level. Parametersettings substantially impacted performances, underscoring the importance ofprogram reproducibility. While highlighting current challenges in computationalmetagenomics, the CAMI results provide a roadmap for software selection to answerspecific research questions

Profiling Early Lung Immune Responses in the Mouse Model of Tuberculosis

Tuberculosis (TB) is caused by the intracellular bacteria Mycobacterium tuberculosis, and kills more than 1.5 million people every year worldwide. Immunity to TB is associated with the accumulation of IFNγ-producing T helper cell type 1 (Th1) in the lungs, activation of M.tuberculosis-infected macrophages and control of bacterial growth. However, very little is known regarding the early immune responses that mediate accumulation of activated Th1 cells in the M.tuberculosis-infected lungs. To define the induction of early immune mediators in the M.tuberculosis-infected lung, we performed mRNA profiling studies and characterized immune cells in M.tuberculosis-infected lungs at early stages of infection in the mouse model. Our data show that induction of mRNAs involved in the recognition of pathogens, expression of inflammatory cytokines, activation of APCs and generation of Th1 responses occurs between day 15 and day 21 post infection. The induction of these mRNAs coincides with cellular accumulation of Th1 cells and activation of myeloid cells in M.tuberculosis-infected lungs. Strikingly, we show the induction of mRNAs associated with Gr1+ cells, namely neutrophils and inflammatory monocytes, takes place on day 12 and coincides with cellular accumulation of Gr1+ cells in M.tuberculosis-infected lungs. Interestingly, in vivo depletion of Gr1+ neutrophils between days 10–15 results in decreased accumulation of Th1 cells on day 21 in M.tuberculosis-infected lungs without impacting overall protective outcomes. These data suggest that the recruitment of Gr1+ neutrophils is an early event that leads to production of chemokines that regulate the accumulation of Th1 cells in the M.tuberculosis-infected lungs

MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities.

Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Because of the complex nature of these communities, existing metagenome binning methods often miss a large number of microbial species. In addition, most of the tools are not scalable to large datasets. Here we introduce automated software called MetaBAT that integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency for accurate metagenome binning. MetaBAT outperforms alternative methods in accuracy and computational efficiency on both synthetic and real metagenome datasets. It automatically forms hundreds of high quality genome bins on a very large assembly consisting millions of contigs in a matter of hours on a single node. MetaBAT is open source software and available at https://bitbucket.org/berkeleylab/metabat

MetaBAT: Metagenome Binning based on Abundance and Tetranucleotide frequency

Grouping large fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. On synthetic datasets MetaBAT on average achieves 98percent precision and 90percent recall at the strain level with 281 near complete unique genomes. Applying MetaBAT to a human gut microbiome data set we recovered 176 genome bins with 92percent precision and 80percent recall. Further analyses suggest MetaBAT is able to recover genome fragments missed in reference genomes up to 19percent, while 53 genome bins are novel. In summary, we believe MetaBAT is a powerful tool to facilitate comprehensive understanding of complex microbial communities