OGRE: Overlap Graph-based metagenomic Read clustEring

The microbes that live in an environment can be identified from the genomic material that is present, also referred to as the metagenome. Using Next Generation Sequencing techniques this genomic material can be obtained from the environment, resulting in a large set of sequencing reads. A proper assembly of these reads into contigs or even full genomes allows one to identify the microbial species and strains that live in the environment. Assembling a metagenome is a challenging task and can benefit from clustering the reads into species-specific bins prior to assembly. In this paper we propose OGRE, an Overlap-Graph based Read clustEring procedure for metagenomic read data. OGRE is the only method that can successfully cluster reads in species-specific bins for large metagenomic datasets without running into computation time- or memory issues

Leveraging the power of place in citizen science for effective conservation decision making

Many citizen science projects are place-based - built on in-person participation and motivated by local conservation. When done thoughtfully, this approach to citizen science can transform humans and their environment. Despite such possibilities, many projects struggle to meet decision-maker needs, generate useful data to inform decisions, and improve social-ecological resilience. Here, we define leveraging the ‘power of place’ in citizen science, and posit that doing this improves conservation decision making, increases participation, and improves community resilience. First, we explore ‘place’ and identify five place dimensions: social-ecological, narrative and name-based, knowledge-based, emotional and affective, and performative. We then thematically analyze 134 case studies drawn from CitSci.org (n = 39), The Stewardship Network New England (TSN-NE; n = 39), and Earthwatch (n = 56) regarding: (1) use of place dimensions in materials (as one indication of leveraging the power of place), (2) intent for use of data in decision-making, and (3) evidence of such use. We find that 89% of projects intend for data to be used, 46% demonstrate no evidence of use, and 54% provide some evidence of use. Moreover, projects used in decision making leverage more (t = − 4.8, df = 117; p \u3c 0.001) place dimensions (= 3.0; s = 1.4) than those not used in decision making (= 1.8; s = 1.2). Further, a Principal Components Analysis identifies three related components (aesthetic, narrative and name-based, and social-ecological). Given these findings, we present a framework for leveraging place in citizen science projects and platforms, and recommend approaches to better impart intended outcomes. We discuss place in citizen science related to relevance, participation, resilience, and scalability and conclude that effective decision making as a means towards more resilient and sustainable communities can be strengthened by leveraging the power of place in citizen science

OGRE: Overlap Graph-based metagenomic Read clustEring

MOTIVATION: The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in large volumes of sequencing reads. A promising approach to reduce the size of metagenomic datasets is by clustering reads into groups based on their overlaps. Clustering reads are valuable to facilitate downstream analyses, including computationally intensive strain-aware assembly. As current read clustering approaches cannot handle the large datasets arising from high-throughput metagenome sequencing, a novel read clustering approach is needed. In this article, we propose OGRE, an Overlap Graph-based Read clustEring procedure for high-throughput sequencing data, with a focus on shotgun metagenomes. RESULTS: We show that for small datasets OGRE outperforms other read binners in terms of the number of species included in a cluster, also referred to as cluster purity, and the fraction of all reads that is placed in one of the clusters. Furthermore, OGRE is able to process metagenomic datasets that are too large for other read binners into clusters with high cluster purity. CONCLUSION: OGRE is the only method that can successfully cluster reads in species-specific clusters for large metagenomic datasets without running into computation time- or memory issues. AVAILABILITY AND IMPLEMENTATION: Code is made available on Github (https://github.com/Marleen1/OGRE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

A search for astrophysical point sources of 100 TeV gamma rays by the UMC collaboration

The CASA‐MIA experiment is a very large extensive air shower detector with good angular resolution. This instrument has been used to search the entire visible sky for astrophysical point sources of 100 TeV gamma rays. Approximately 90% of the isotropic charged cosmic ray background is rejected by measuring the muon content of the showers. Stringent limits are placed on the possible flux of 100 TeV sources across a large part of the Northern sky.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87711/2/1203_1.pd

Evidence for a third, Ir -associated histocompatibility region in the H-2 complex of the mouse

Skin grafts transplanted from B10.HTT donors onto (A.TL × B10)F 1 recipients are rapidly rejected despite the fact that the B10.HTT and A.TL strains should be carrying the same H-2 chromosomes and that both the donor and the recipient contain the B10 genome. The rejection is accompanied by a production of cytotoxic antibodies against antigens controlled by the Ir region of the H-2 complex. These unexpected findings are interpreted as evidence for a third histocompatibility locus in the H-2 complex, H-2I , located in the Ir region close to H-2K . The B10.HTT and A.TL strains are postulated to differ at this hypothetical locus, and the difference between the two strains is explained as resulting from a crossing over between the H-2 t1 and H-2 s chromosomes in the early history of the B10.HTT strain. The H-2 genotypes of the B10.HTT and A.TL strains are assumed to be H-2K s Ir s / k Ss k H-2D d and H-2K s Ir k Ss k H-2D d , respectively. Thus, the H-2 chromosomes of the two strains differ only in a portion of the Ir region, including the H-2I locus. The B10.HTT( H-2 tt ) and B10.S(7R)( H-2 th ) strains differ in a relatively minor histocompatibility locus, possibly residing in the Tla region outside of the H-2 complex.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46727/1/251_2005_Article_BF01564045.pd