MSPminer: abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data.

MOTIVATION: Analysis toolkits for shotgun metagenomic data achieve strain-level characterization of complex microbial communities by capturing intra-species gene content variation. Yet, these tools are hampered by the extent of reference genomes that are far from covering all microbial variability, as many species are still not sequenced or have only few strains available. Binning co-abundant genes obtained from de novo assembly is a powerful reference-free technique to discover and reconstitute gene repertoire of microbial species. While current methods accurately identify species core parts, they miss many accessory genes or split them into small gene groups that remain unassociated to core clusters. RESULTS: We introduce MSPminer, a computationally efficient software tool that reconstitutes Metagenomic Species Pan-genomes (MSPs) by binning co-abundant genes across metagenomic samples. MSPminer relies on a new robust measure of proportionality coupled with an empirical classifier to group and distinguish not only species core genes but accessory genes also. Applied to a large scale metagenomic dataset, MSPminer successfully delineates in a few hours the gene repertoires of 1661 microbial species with similar specificity and higher sensitivity than existing tools. The taxonomic annotation of MSPs reveals microorganisms hitherto unknown and brings coherence in the nomenclature of the species of the human gut microbiota. The provided MSPs can be readily used for taxonomic profiling and biomarkers discovery in human gut metagenomic samples. In addition, MSPminer can be applied on gene count tables from other ecosystems to perform similar analyses. AVAILABILITY AND IMPLEMENTATION: The binary is freely available for non-commercial users at www.enterome.com/downloads. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Impact of laparoscopic Roux-en-Y gastric bypass and sleeve gastrectomy on gut microbiota: a metagenomic comparative analysis.

BACKGROUND: Bariatric surgery is an effective therapeutic procedure for morbidly obese patients. The 2 most common interventions are sleeve gastrectomy (SG) and laparoscopic Roux-en-Y gastric bypass (LRYGB). OBJECTIVES: The aim of this study was to compare microbiome long-term microbiome after SG and LRYGB surgery in obese patients. SETTING: University Hospital, France; University Hospital, United States; and University Hospital, Switzerland. METHODS: Eighty-nine and 108 patients who underwent SG and LRYGB, respectively, were recruited. Stools were collected before and 6 months after surgery. Microbial DNA was analyzed with shotgun metagenomic sequencing (SOLiD 5500 xl Wildfire). MSPminer, a novel innovative tool to characterize new in silico biological entities, was used to identify 715 Metagenomic Species Pan-genome. One hundred forty-eight functional modules were analyzed using GOmixer and KEGG database. RESULTS: Both interventions resulted in a similar increase of Shannon's diversity index and gene richness of gut microbiota, in parallel with weight loss, but the changes of microbial composition were different. LRYGB led to higher relative abundance of aero-tolerant bacteria, such as Escherichia coli and buccal species, such as Streptococcus and Veillonella spp. In contrast, anaerobes, such as Clostridium, were more abundant after SG, suggesting better conservation of anaerobic conditions in the gut. Enrichment of Akkermansia muciniphila was also observed after both surgeries. Function-level changes included higher potential for bacterial use of supplements, such as vitamin B12, B1, and iron upon LRYGB. CONCLUSION: Microbiota changes after bariatric surgery depend on the nature of the intervention. LRYGB induces greater taxonomic and functional changes in gut microbiota than SG. Possible long-term health consequences of these alterations remain to be established.

Game and balance multicast architecture algorithms for sensor grid.

We propose a scheme to attain shorter multicast delay and higher efficiency in the data transfer of sensor grid. Our scheme, in one cluster, seeks the central node, calculates the space and the data weight vectors. Then we try to find a new vector composed by linear combination of the two old ones. We use the equal correlation coefficient between the new and old vectors to find the point of game and balance of the space and data factorsbuild a binary simple equation, seek linear parameters, and generate a least weight path tree. We handled the issue from a quantitative way instead of a qualitative way. Based on this idea, we considered the scheme from both the space and data factor, then we built the mathematic model, set up game and balance relationship and finally resolved the linear indexes, according to which we improved the transmission efficiency of sensor grid. Extended simulation results indicate that our scheme attains less average multicast delay and number of links used compared with other well-known existing schemes.