1,635 research outputs found
Model-based clustering with data correction for removing artifacts in gene expression data
The NIH Library of Integrated Network-based Cellular Signatures (LINCS)
contains gene expression data from over a million experiments, using Luminex
Bead technology. Only 500 colors are used to measure the expression levels of
the 1,000 landmark genes measured, and the data for the resulting pairs of
genes are deconvolved. The raw data are sometimes inadequate for reliable
deconvolution leading to artifacts in the final processed data. These include
the expression levels of paired genes being flipped or given the same value,
and clusters of values that are not at the true expression level. We propose a
new method called model-based clustering with data correction (MCDC) that is
able to identify and correct these three kinds of artifacts simultaneously. We
show that MCDC improves the resulting gene expression data in terms of
agreement with external baselines, as well as improving results from subsequent
analysis.Comment: 28 page
A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data
Inferring gene regulatory networks is an important problem in systems
biology. However, these networks can be hard to infer from experimental data
because of the inherent variability in biological data as well as the large
number of genes involved. We propose a fast, simple method for inferring
regulatory relationships between genes from knockdown experiments in the NIH
LINCS dataset by calculating posterior probabilities, incorporating prior
information. We show that the method is able to find previously identified
edges from TRANSFAC and JASPAR and discuss the merits and limitations of this
approach
Kuiper Belt Occultation Predictions
Here we present observations of seven large Kuiper Belt objects. From these observations, we extract a point source catalog with ∼0.01″ precision, and astrometry of our target Kuiper Belt objects with 0.04–0.08″ precision within that catalog. We have developed a new technique to predict the future occurrence of stellar occultations by Kuiper Belt objects. The technique makes use of a maximum likelihood approach which determines the best-fit adjustment to cataloged orbital elements of an object. Using simulations of a theoretical object, we discuss the merits and weaknesses of this technique compared to the commonly adopted ephemeris offset approach. We demonstrate that both methods suffer from separate weaknesses, and thus together provide a fair assessment of the true uncertainty in a particular prediction. We present occultation predictions made by both methods for the seven tracked objects, with dates as late as 2015. Finally, we discuss observations of three separate close passages of Quaoar to field stars, which reveal the accuracy of the element adjustment approach, and which also demonstrate the necessity of considering the uncertainty in stellar position when assessing potential occultations
FAPRI 1998 World Agricultural Outlook
The Food and Agricultural Policy Research Institute (FAPRI) prepares in the late fall of every year a preliminary baseline based on agricultural production, consumption, and trade. This is followed by an extensive outside review, re-evaluation of projections, and completion of the final baseline in January. The baseline assumes continuation of current agricultural and trade policies by world governments and assumes normal weather in the future. Macroeconomic assumptions on world general economic activity are derived from both Project LINK and the WEFA Group projections
The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment
The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in
operation since July 2014. This paper describes the second data release from
this phase, and the fourteenth from SDSS overall (making this, Data Release
Fourteen or DR14). This release makes public data taken by SDSS-IV in its first
two years of operation (July 2014-2016). Like all previous SDSS releases, DR14
is cumulative, including the most recent reductions and calibrations of all
data taken by SDSS since the first phase began operations in 2000. New in DR14
is the first public release of data from the extended Baryon Oscillation
Spectroscopic Survey (eBOSS); the first data from the second phase of the
Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2),
including stellar parameter estimates from an innovative data driven machine
learning algorithm known as "The Cannon"; and almost twice as many data cubes
from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous
release (N = 2812 in total). This paper describes the location and format of
the publicly available data from SDSS-IV surveys. We provide references to the
important technical papers describing how these data have been taken (both
targeting and observation details) and processed for scientific use. The SDSS
website (www.sdss.org) has been updated for this release, and provides links to
data downloads, as well as tutorials and examples of data use. SDSS-IV is
planning to continue to collect astronomical data until 2020, and will be
followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14
happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov
2017 (this is the "post-print" and "post-proofs" version; minor corrections
only from v1, and most of errors found in proofs corrected
Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium
Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P < 5×10−8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (<5%). In the trans-ethnic fine mapping of 47 BMI loci and 27 WHRadjBMI loci that were locus-wide significant (P < 0.05 adjusted for effective number of variants per locus) from the African ancestry sex-combined and sex-stratified analyses, 26 BMI loci and 17 WHRadjBMI loci contained ≤ 20 variants in the credible sets that jointly account for 99% posterior probability of driving the associations. The lead variants in 13 of these loci had a high probability of being causal. As compared to our previous HapMap imputed GWAS for BMI and WHRadjBMI including up to 71,412 and 27,350 African ancestry individuals, respectively, our results suggest that 1000 Genomes imputation showed modest improvement in identifying GWAS loci including low frequency variants. Trans-ethnic meta-analyses further improved fine mapping of putative causal variants in loci shared between the African and European ancestry populations
A framework for human microbiome research
A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies
Structure, function and diversity of the healthy human microbiome
Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in
part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273
to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander;
U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.;
U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.;
R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.;
R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to
D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and
R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.;
R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was
supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves
and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang,
F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J.
V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.);
DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research;
U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and
R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and
D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research
Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF
DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US
Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL
Laboratory-Directed Research and Development grant 20100034DR and the US
Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research
Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career
Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe
J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by
the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial
Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of
Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis
of the HMPdata was performed using National Energy Research Scientific Computing
resources, the BluBioU Computational Resource at Rice University
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