Murasaki: A Fast, Parallelizable Algorithm to Find Anchors from Multiple Genomes

BACKGROUND: With the number of available genome sequences increasing rapidly, the magnitude of sequence data required for multiple-genome analyses is a challenging problem. When large-scale rearrangements break the collinearity of gene orders among genomes, genome comparison algorithms must first identify sets of short well-conserved sequences present in each genome, termed anchors. Previously, anchor identification among multiple genomes has been achieved using pairwise alignment tools like BLASTZ through progressive alignment tools like TBA, but the computational requirements for sequence comparisons of multiple genomes quickly becomes a limiting factor as the number and scale of genomes grows. METHODOLOGY/PRINCIPAL FINDINGS: Our algorithm, named Murasaki, makes it possible to identify anchors within multiple large sequences on the scale of several hundred megabases in few minutes using a single CPU. Two advanced features of Murasaki are (1) adaptive hash function generation, which enables efficient use of arbitrary mismatch patterns (spaced seeds) and therefore the comparison of multiple mammalian genomes in a practical amount of computation time, and (2) parallelizable execution that decreases the required wall-clock and CPU times. Murasaki can perform a sensitive anchoring of eight mammalian genomes (human, chimp, rhesus, orangutan, mouse, rat, dog, and cow) in 21 hours CPU time (42 minutes wall time). This is the first single-pass in-core anchoring of multiple mammalian genomes. We evaluated Murasaki by comparing it with the genome alignment programs BLASTZ and TBA. We show that Murasaki can anchor multiple genomes in near linear time, compared to the quadratic time requirements of BLASTZ and TBA, while improving overall accuracy. CONCLUSIONS/SIGNIFICANCE: Murasaki provides an open source platform to take advantage of long patterns, cluster computing, and novel hash algorithms to produce accurate anchors across multiple genomes with computational efficiency significantly greater than existing methods. Murasaki is available under GPL at http://murasaki.sourceforge.net

Occupational Exposure to Endocrine-Disrupting Chemicals and Birth Weight and Length of Gestation: A European Meta-Analysis

BACKGROUND: Women of reproductive age can be exposed to endocrine-disrupting chemicals (EDCs) at work and exposure to EDCs in pregnancy may affect fetal growth. OBJECTIVES: We assessed whether maternal occupational exposure to EDCs during pregnancy as classified by application of a job exposure matrix was associated with birth weight, term low birth weight (LBW), length of gestation, and preterm delivery. METHODS: Using individual participant data from 133,957 mother-child pairs in 13 European cohorts spanning births from 1994 to 2011, we linked maternal job titles with exposure to 10 EDC groups as assessed through a job exposure matrix. For each group, we combined the two levels of exposure categories (possible and probable) and compared birth outcomes with the unexposed group (exposure unlikely). We performed meta-analyses of cohort-specific estimates. RESULTS: Eleven percent of pregnant women were classified as exposed to EDCs at work during pregnancy based on job title. Classification of exposure to one or more EDC group was associated with an increased risk of term LBW (OR 1.25, 95%CI 1.04, 1.49), as were most specific EDC groups; this association was consistent across cohorts. Further, the risk increased with increasing number of EDC groups (OR 2.11 95%CI 1.10, 4.06 for exposure to 4 or more EDC groups). There were few associations (p < 0.05) with the other outcomes; women holding job titles classified as exposed to bisphenol A or brominated flame retardants were at higher risk for longer length of gestation. CONCLUSION: Results from our large population-based birth cohort design indicate that employment during pregnancy in occupations classified as possibly or probably exposed to EDCs was associated with an increased risk of term LBW

Satellite remote sensing data can be used to model marine microbial metabolite turnover

Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km2) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes’ predicted relative abundance was highly correlated (Pearson Correlation 0.72, P-value <10−6) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO2 were significantly correlated with observed surface CO2 fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ~3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology

Microbial diversity in waters, sediments and microbial mats evaluated using fatty acid-based methods

The review summarises recent advances towards a greater comprehensive assessment of microbial diversity in aquatic environments using the fatty acid methyl esters and phospholipid fatty acids approaches. These methods are commonly used in microbial ecology because they do not require the culturing of micro-organisms, are quantitative and reproducible and provide valuable information regarding the structure of entire microbial communities. Because some fatty acids are associated with taxonomic and functional groups of micro-organisms, they allow particular groups of micro-organisms to be distinguished. The integration of fatty acid-based methods with stable isotopes, RNA and DNA analyses enhances our knowledge of the role of micro-organisms in global nutrient cycles, functional activity and phylogenetic lineages within microbial communities. Additionally, the analysis of fatty acid profiles enables the shifts in the microbial diversity in pristine and contaminated environments to be monitored. The main objective of this review is to present the use of lipid-based approaches for the characterisation of microbial communities in water columns, sediments and biomats

Microbial sources of intact polar diacylglycerolipids in the Western North Atlantic Ocean

► Microibal sources of intact polar diacylglycerolipids were identified. ► Photoautotrophs are sole source of sulfoquinovosyldiacylglycerol. ► Eukaryotic phytoplankton are likely source of a betaine lipid. ► Heterotrophic bacteria are sole source of phosphatidylglycerol.Intact polar membrane lipids are essential components of microbial membranes and recent work has uncovered a diversity of them occurring in the ocean. While it has long been understood that lipid composition varies across microbial groups, the microbial origins of the intact polar lipids in the surface ocean remain to be fully explained. This study focused on identifying the microbial sources of intact polar diacylglycerolipids (IP-DAGs) in the surface waters of the western North Atlantic Ocean. We used three approaches to define these microbial sources: (i) 13C tracing to identify photoautotrophic and heterotrophic production of the major classes of IP-DAGs, (ii) cell sorting flow cytometry of Prochlorococcus, Synechococcus and heterotrophic bacteria to determine IP-DAG composition and (iii) regrowth incubations targeting IP-DAG production by heterotrophic bacteria. Stable isotope tracing indicated that sulfoquinovosyldiacylglycerol (SQDG) and diacylglyceryl-trimethyl-homoserine (DGTS) were produced predominantly by photoautotrophs, while phosphatidylglycerol (PG) production was dominated by heterotrophic bacteria. Of the cells sorted with flow cytometry, Prochlorococcus and Synechococcus were found to have abundant glycolipids, while heterotrophic bacteria were dominated by phospholipids. The regrowth incubations showed that the growth of heterotrophic bacteria correlated with an increase in the concentration of PG, phosphatidylethanolamine (PE) and monoglycosyldiacylglycerol (MGDG). The finding of MGDG in heterotrophic bacteria differs from previous work, which had asserted that the membranes of heterotrophic bacteria in this environment were composed entirely of phospholipids. Overall, our findings indicate that phytoplankton are the primary source of SQDG and DGTS, while heterotrophic bacteria are the dominant source of PG, making these three compounds promising biomarkers for the study of microbes in the surface ocean