A two-stage statistical procedure for feature selection and comparison in functional analysis of metagenomes.

MOTIVATION: With the advance of new sequencing technologies producing massive short reads data, metagenomics is rapidly growing, especially in the fields of environmental biology and medical science. The metagenomic data are not only high dimensional with large number of features and limited number of samples but also complex with a large number of zeros and skewed distribution. Efficient computational and statistical tools are needed to deal with these unique characteristics of metagenomic sequencing data. In metagenomic studies, one main objective is to assess whether and how multiple microbial communities differ under various environmental conditions. RESULTS: We propose a two-stage statistical procedure for selecting informative features and identifying differentially abundant features between two or more groups of microbial communities. In the functional analysis of metagenomes, the features may refer to the pathways, subsystems, functional roles and so on. In the first stage of the proposed procedure, the informative features are selected using elastic net as reducing the dimension of metagenomic data. In the second stage, the differentially abundant features are detected using generalized linear models with a negative binomial distribution. Compared with other available methods, the proposed approach demonstrates better performance for most of the comprehensive simulation studies. The new method is also applied to two real metagenomic datasets related to human health. Our findings are consistent with those in previous reports. AVAILABILITY: R code and two example datasets are available at http://cals.arizona.edu/∼anling/software.htm. SUPPLEMENTARY INFORMATION: Supplementary file is available at Bioinformatics online.

Accurate genome relative abundance estimation for closely related species in a metagenomic sample.

BACKGROUND: Metagenomics has a great potential to discover previously unattainable information about microbial communities. An important prerequisite for such discoveries is to accurately estimate the composition of microbial communities. Most of prevalent homology-based approaches utilize solely the results of an alignment tool such as BLAST, limiting their estimation accuracy to high ranks of the taxonomy tree. RESULTS: We developed a new homology-based approach called Taxonomic Analysis by Elimination and Correction (TAEC), which utilizes the similarity in the genomic sequence in addition to the result of an alignment tool. The proposed method is comprehensively tested on various simulated benchmark datasets of diverse complexity of microbial structure. Compared with other available methods designed for estimating taxonomic composition at a relatively low taxonomic rank, TAEC demonstrates greater accuracy in quantification of genomes in a given microbial sample. We also applied TAEC on two real metagenomic datasets, oral cavity dataset and Crohn's disease dataset. Our results, while agreeing with previous findings at higher ranks of the taxonomy tree, provide accurate estimation of taxonomic compositions at the species/strain level, narrowing down which species/strains need more attention in the study of oral cavity and the Crohn's disease. CONCLUSIONS: By taking account of the similarity in the genomic sequence TAEC outperforms other available tools in estimating taxonomic composition at a very low rank, especially when closely related species/strains exist in a metagenomic sample.

Class I lysine deacetylases facilitate glucocorticoid-induced transcription.

Nuclear receptors use lysine acetyltransferases and lysine deacetylases (KDACs) in regulating transcription through histone acetylation. Lysine acetyltransferases interact with steroid receptors upon binding of an agonist and are recruited to target genes. KDACs have been shown to interact with steroid receptors upon binding to an antagonist. We have shown previously that KDAC inhibitors (KDACis) potently repress the mouse mammary tumor virus promoter through transcriptional mechanisms and impair the ability of the glucocorticoid receptor (GR) to activate it, suggesting that KDACs can play a positive role in GR transactivation. In the current study, we extended this analysis to the entire GR transcriptome and found that the KDACi valproic acid impairs the ability of agonist-bound GR to activate about 50% of its target genes. This inhibition is largely due to impaired transcription rather than defective GR processing and was also observed using a structurally distinct KDACi. Depletion of KDAC1 expression mimicked the effects of KDACi in over half of the genes found to be impaired in GR transactivation. Simultaneous depletion of KDACs 1 and 2 caused full or partial impairment of several more GR target genes. Altogether we found that Class I KDAC activity facilitates GR-mediated activation at a sizable fraction of GR-activated target genes and that KDAC1 alone or in coordination with KDAC2 is required for efficient GR transactivation at many of these target genes. Finally, our work demonstrates that KDACi exposure has a significant impact on GR signaling and thus has ramifications for the clinical use of these drugs.