A Joint Announcement on Genome Sequence Standards Genome project standards in a new era of sequencing P. S. G. Chain 1,2,3,4,22,* , D. V. Grafham 5,* , R. S. Fulton 6 , M. G. FitzGerald 7 , J. Hostetler 8 , D. Muzny 9 , J. C. Detter 1,10 , J. Ali 11 , B.Birren 7 , D. C. Bruce 1, 10 , C. Buhay 9 , J. R. Cole 3,4 , Y. Ding 9 , S. Dugan 9 , D. Field 12 , G. M. Garrity 3,4 , R. Gibbs 9 , T. Graves 6 , C. S. Han 1, 10 , S. H. Harrison 3 , S. Highlander 9 , P. Hugenholtz 1 , H. M. Khouri 13 , C. D. Kodira 7,23 , E. Kolker 14,15 , N. C. Kyrpides 1 , D. Lang 1,2 , A. Lapidus 1 , S. A. Malfatti 1,2 , V. Markowitz 16 , T. Metha 7 , K. E. Nelson 8 , J. Parkhill 5 , S. Pitluck 1 , X. Qin 9 , T. D. Read 17 , J. Schmutz 18 , S. Sozhamannan 19 , R. Strausberg 8 , G. Sutton 8 , N. R. Thomson 5 , J. M. Tiedje 3,4 , G. Weinstock 6 , A. Wollam 6 , and the entire GSC 20 and HMP Jumpstart 21 consortia. U.S. Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, California 94550, USA Microbiology & Molecular Genetics, Michigan State University, East Lansing, Michigan 48824, USA Center for Microbial Ecology, Michigan State University, East Lansing, Michigan 48824, USA The Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom The Genome Center, Washington University School of Medicine, St Louis, Missouri 63108, USA The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02141, USA J. Craig Venter Institute, Rockville, Maryland 20850, USA Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada Natural Environmental Research Council Centre for Ecology and Hydrology, Oxford, Oxfordshire OX1 3SR, UK National Center for Biotechnology Information, National Library of Medicine, Rockville, Maryland 20850, USA Seattle Children’s Hospital and Research Institute, Seattle, Washington 98101, USA Biomedical & Health Informatics Division, MEBI, University of Washington School of Medicine, Seattle, Washington 98195, USA Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA Emory GRA Genomics Core, Emory University School of Medicine, Atlanta, Georgia 30322, USA HudsonAlpha Genome Sequencing Center, HudsonAlpha Institute, Huntsville, Alabama 35806, USA Biological Defense Research Directorate, Naval Medical Research Center, Silver Spring, Maryland 20910, USA Genomic Standards Consortium Human Microbiome Project Jumpstart Consortium Current address: Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA Current address: 454 Life Sciences, Branford, Connecticut 06405, USA *Address correspondence to Patrick Chain (pchain@lanl.gov) and Darren Grafham (dg1@sanger.ac.uk)

Genome Project Standards in a New Era of Sequencing

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms Changes in the microbiome can impact the health of the environmental niche in which they reside In order to learn more about these communities, different approaches based on data from multiple omics have been pursued Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes

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Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis.

分析了基于处理器硬件虚拟化技术实现的KVM子系统的架构。针对KVM跟踪独立事件信息的局限性,提出一种新的KVM事件跟踪机制(kvmtrace)来达到性能调节的目的,并使用relayfs接口进行了设计与实现。同时探讨了Linux kernel Markers实现机制及其在kvmtrace的实际应用。

Kernel-based virtual machine事件跟踪机制的设计与实现

The innate immune system is the first line of defense in response to nonself and danger signals from microbial invasion or tissue injury. It is increasingly recognized that each organ uses unique sets of cells and molecules that orchestrate regional innate immunity. The cells that execute the task of innate immunity are many and consist of not only “professional” immune cells but also nonimmune cells, such as renal epithelial cells. Despite a high level of sophistication, deregulated innate immunity is common and contributes to a wide range of renal diseases, such as sepsis-induced kidney injury, GN, and allograft dysfunction. This review discusses how the innate immune system recognizes and responds to nonself and danger signals. In particular, the roles of renal epithelial cells that make them an integral part of the innate immune apparatus of the kidney are highlighted.

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How the Innate Immune System Senses Trouble and Causes Trouble

The composition of the gut microbiome in industrialized populations differs from those living traditional lifestyles. However, it has been difficult to separate the contributions of human genetic and geographic factors from lifestyle. Whether shifts away from the foraging lifestyle that characterize much of humanity’s past influence the gut microbiome, and to what degree, remains unclear. Here, we characterize the stool bacterial composition of four Himalayan populations to investigate how the gut community changes in response to shifts in traditional human lifestyles. These groups led seminomadic hunting–gathering lifestyles until transitioning to varying levels of agricultural dependence upon farming. The Tharu began farming 250–300 years ago, the Raute and Raji transitioned 30–40 years ago, and the Chepang retain many aspects of a foraging lifestyle. We assess the contributions of dietary and environmental factors on their gut-associated microbes and find that differences in the lifestyles of Himalayan foragers and farmers are strongly correlated with microbial community variation. Furthermore, the gut microbiomes of all four traditional Himalayan populations are distinct from that of the Americans, indicating that industrialization may further exacerbate differences in the gut community. The Chepang foragers harbor an elevated abundance of taxa associated with foragers around the world. Conversely, the gut microbiomes of the populations that have transitioned to farming are more similar to those of Americans, with agricultural dependence and several associated lifestyle and environmental factors correlating with the extent of microbiome divergence from the foraging population. The gut microbiomes of Raute and Raji reveal an intermediate state between the Chepang and Tharu, indicating that divergence from a stereotypical foraging microbiome can occur within a single generation. Our results also show that environmental factors such as drinking water source and solid cooking fuel are significantly associated with the gut microbiome. Despite the pronounced differences in gut bacterial composition across populations, we found little differences in alpha diversity across lifestyles. These findings in genetically similar populations living in the same geographical region establish the key role of lifestyle in determining human gut microbiome composition and point to the next challenging steps of determining how large-scale gut microbiome reconfiguration impacts human biology.

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Gut microbiome transition across a lifestyle gradient in Himalaya.

Background
It is well understood that distinct communities of bacteria are present at different sites of the body, and that changes in the structure of these communities have strong implications for human health. Yet, challenges remain in understanding the complex interconnections between the bacterial taxa within these microbial communities and how they change during the progression of diseases. Many recent studies attempt to analyze the human microbiome using traditional ecological measures and cataloging differences in bacterial community membership. In this paper, we show how to push metagenomic analyses beyond mundane questions related to the bacterial taxonomic profiles that differentiate one sample from another.

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Microbial "social networks"

Flash caching has emerged as a promising solution to the scalability problems of storage systems by using fast flash memory devices as the cache for slower primary storage. But its adoption faces serious obstacles due to the limited capacity and endurance of flash devices. This paper presents CacheDedup, a solution that addresses these limitations using in-line deduplication. First, it proposes a novel architecture that integrates the caching of data and deduplication metadata (source addresses and fingerprints of the data) and efficiently manages these two components. Second, it proposes duplication-aware cache replacement algorithms (D-LRU, DARC) to optimize both cache performance and endurance. The paper presents a rigorous analysis of the algorithms to prove that they do not waste valuable cache space and that they are efficient in time and space usage. The paper also includes an experimental evaluation using real-world traces, which confirms that CacheDedup substantially improves I/O performance (up to 20% reduction in miss ratio and 51% in latency) and flash endurance (up to 89% reduction in writes sent to the cache device) compared to traditional cache management. It also shows that the proposed architecture and algorithms can be extended to support the combination of compression and deduplication for flash caching and improve its performance and endurance.

/pdf/cachededup-in-line-deduplication-for-flash-caching-29z6eodvh3.pdf

Juan Riveros

Papers

Microbial "social networks"

CacheDedup: in-line deduplication for flash caching

Microbiome Analysis: State of the Art and Future Trends