Motivation: Direct analysis of microbial communities in the environment and human body has become more convenient and reliable owing to the advancements of high-throughput sequencing techniques for 16S rRNA gene profiling. Inferring the correlation relationship among members of microbial communities is of fundamental importance for genomic survey study. Traditional Pearson correlation analysis treating the observed data as absolute abundances of the microbes may lead to spurious results because the data only represent relative abundances. Special care and appropriate methods are required prior to correlation analysis for these compositional data. Results: In this article, we first discuss the correlation definition of latent variables for compositional data. We then propose a novel method called CCLasso based on least squares with $ℓ1$ penalty to infer the correlation network for latent variables of compositional data from metagenomic data. An effective alternating direction algorithm from augmented Lagrangian method is used to solve the optimization problem. The simulation results show that CCLasso outperforms existing methods, e.g. SparCC, in edge recovery for compositional data. It also compares well with SparCC in estimating correlation network of microbe species from the Human Microbiome Project. Availability and implementation: CCLasso is open source and freely available from https://github.com/huayingfang/CCLasso under GNU LGPL v3. Contact:dengmh@pku.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Understanding of the RNA editing process has been broadened considerably by the next generation sequencing technology; however, several issues regarding this regulatory step remain unresolved – the strategies to accurately delineate the editome, the mechanism by which its profile is maintained, and its evolutionary and functional relevance. Here we report an accurate and quantitative profile of the RNA editome for rhesus macaque, a close relative of human. By combining genome and transcriptome sequencing of multiple tissues from the same animal, we identified 31,250 editing sites, of which 99.8% are A-to-G transitions. We verified 96.6% of editing sites in coding regions and 97.5% of randomly selected sites in non-coding regions, as well as the corresponding levels of editing by multiple independent means, demonstrating the feasibility of our experimental paradigm. Several lines of evidence supported the notion that the adenosine deamination is associated with the macaque editome – A-to-G editing sites were flanked by sequences with the attributes of ADAR substrates, and both the sequence context and the expression profile of ADARs are relevant factors in determining the quantitative variance of RNA editing across different sites and tissue types. In support of the functional relevance of some of these editing sites, substitution valley of decreased divergence was detected around the editing site, suggesting the evolutionary constraint in maintaining some of these editing substrates with their double-stranded structure. These findings thus complement the “continuous probing” model that postulates tinkering-based origination of a small proportion of functional editing sites. In conclusion, the macaque editome reported here highlights RNA editing as a widespread functional regulation in primate evolution, and provides an informative framework for further understanding RNA editing in human. RNA editing is a co-transcriptional process that introduces differences between RNA and its corresponding DNA sequence. Currently, the next generation sequencing have allowed study of the editome in a comprehensive and efficient manner. However, fundamental issues involving accurate mapping of the editome as well as its regulation and functional outcome remain unresolved. To further unveil the underlying mechanisms from the evolutionary perspective, we report here the editome profile in rhesus macaque, one of our closest evolutionary relatives. We identified a list of 31,250 RNA-editing sites and deciphered an accurate and informative editome across multiple tissues and animals. We found that the adenosine deamination is associated with the macaque editome, in that both the sequence context and the expression profile of ADARs are relevant factors in determining the quantitative variance of RNA editing across different sites and tissue types. Importantly, some of these RNA-editing events represent functional regulation, rather than neutral signals, as suggested by substitution valley of decreased divergence detected around the editing sites, an indication of selective constraint in maintaining some of these editing substrates with their double-stranded structure. The macaque editome thus provides an informative evolutionary context for an in-depth understanding of RNA editing regulation.

Summary: Marine Group II archaea are widely distributed in global oceans and dominate the total archaeal community within the upper euphotic zone of temperate waters. However, factors controlling the distribution of MGII are poorly delineated and the physiology and ecological functions of these still‐uncultured organisms remain elusive. In this study, we investigated the planktonic MGII associated with particles and in free‐living forms in the Pearl River Estuary (PRE) over a 10‐month period. We detected high abundance of particle‐associated MGII in PRE (up to ∼10 ⁸ 16S rRNA gene copies/l), which was around 10‐fold higher than the free‐living MGII in the same region, and an order of magnitude higher than previously reported in other marine environments. 10‰ salinity appeared to be a threshold value for these MGII because MGII abundance decreased sharply below it. Above 10‰ salinity, the abundance of MGII on the particles was positively correlated with phototrophs and MGII in the surface water was negatively correlated with irradiance. However, the abundances of those free‐living MGII showed positive correlations with salinity and temperature, suggesting the different physiological characteristics between particle‐attached and free‐living MGIIs. A nearly completely assembled metagenome, MGIIa_P, was recovered using metagenome binning methods. Compared with the other two MGII genomes from surface ocean, MGIIa_P contained higher proportions of glycoside hydrolases, indicating the ability of MGIIa_P to hydrolyse glycosidic bonds in complex sugars in PRE. MGIIa_P is the first assembled MGII metagenome containing a catalase gene, which might be involved in scavenging reactive oxygen species generated by the abundant phototrophs in the eutrophic PRE. Our study presented the widespread and high abundance of MGII in the water columns of PRE, and characterized the determinant abiotic factors affecting their distribution. Their association with heterotrophs, preference for particles and resourceful metabolic traits indicate MGII might play a significant role in metabolising organic matters in the PRE and other temperate estuarine systems.

The increasing quality and the reducing cost of high-throughput sequencing technologies for 16S rRNA gene profiling enable researchers to directly analyze microbe communities in natural environments. The direct interactions among microbial species of a given ecological system can help us understand the principles of community assembly and maintenance under various conditions. Compositionality and dimensionality of microbiome data are two main challenges for inferring the direct interaction network of microbes. In this article, we use the logistic normal distribution to model the background mechanism of microbiome data, which can appropriately deal with the compositional nature of the data. The direct interaction relationships are then modeled via the conditional dependence network under this logistic normal assumption. We then propose a novel penalized maximum likelihood method called gCoda to estimate the sparse structure of inverse covariance for latent normal variables to address the high dimensionality of the microbiome data. An effective Majorization-Minimization algorithm is proposed to solve the optimization problem in gCoda. Simulation studies show that gCoda outperforms existing methods (e.g., SPIEC-EASI) in edge recovery of inverse covariance for compositional data under a variety of scenarios. gCoda also performs better than SPIEC-EASI for inferring direct microbial interactions of mouse skin microbiome data.