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The LSST DESC data challenge 1: generation and analysis of synthetic images for next-generation surveys (2020)

Sánchez, J ; Walter, C W ; Awan, H ; [et al.]

Oxford University Press

In: Monthly Notices of the Royal Astronomical Society. 2020; 497(1): 210-228. Published 2020 Jul 13. doi: 10.1093/mnras/staa1957.

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Publication Date: 2020-07-13

Description: Data Challenge 1 (DC1) is the first synthetic data set produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST data set. DC1 is comprised of r-band observations of 40 deg2 to 10 yr LSST depth. We present each stage of the simulation and analysis process: (a) generation, by synthesizing sources from cosmological N-body simulations in individual sensor-visit images with different observing conditions; (b) reduction using a development version of the LSST Science Pipelines; and (c) matching to the input cosmological catalogue for validation and testing. We verify that testable LSST requirements pass within the fidelity of DC1. We establish a selection procedure that produces a sufficiently clean extragalactic sample for clustering analyses and we discuss residual sample contamination, including contributions from inefficiency in star–galaxy separation and imperfect deblending. We compute the galaxy power spectrum on the simulated field and conclude that: (i) survey properties have an impact of 50 per cent of the statistical uncertainty for the scales and models used in DC1; (ii) a selection to eliminate artefacts in the catalogues is necessary to avoid biases in the measured clustering; and (iii) the presence of bright objects has a significant impact (2σ–6σ) in the estimated power spectra at small scales (ℓ 〉 1200), highlighting the impact of blending in studies at small angular scales in LSST.

Print ISSN: 0035-8711

Electronic ISSN: 1365-2966

Topics: Physics

Published by Oxford University Press on behalf of The Royal Astronomical Society.

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PhosphoChain: a novel algorithm to predict kinase and phosphatase networks from high-throughput expression data (2013)

Chen, W.-M., Danziger, S. A., Chiang, J.-H., Aitchison, J. D.

Oxford University Press

In: Bioinformatics

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Publication Date: 2013-09-20

Description: Motivation: Protein phosphorylation is critical for regulating cellular activities by controlling protein activities, localization and turnover, and by transmitting information within cells through signaling networks. However, predictions of protein phosphorylation and signaling networks remain a significant challenge, lagging behind predictions of transcriptional regulatory networks into which they often feed. Results: We developed PhosphoChain to predict kinases, phosphatases and chains of phosphorylation events in signaling networks by combining mRNA expression levels of regulators and targets with a motif detection algorithm and optional prior information. PhosphoChain correctly reconstructed ~78% of the yeast mitogen-activated protein kinase pathway from publicly available data. When tested on yeast phosphoproteomic data from large-scale mass spectrometry experiments, PhosphoChain correctly identified ~27% more phosphorylation sites than existing motif detection tools (NetPhosYeast and GPS2.0), and predictions of kinase–phosphatase interactions overlapped with ~59% of known interactions present in yeast databases. PhosphoChain provides a valuable framework for predicting condition-specific phosphorylation events from high-throughput data. Availability: PhosphoChain is implemented in Java and available at http://virgo.csie.ncku.edu.tw/PhosphoChain/ or http://aitchisonlab.com/PhosphoChain Contact: john.aitchison@systemsbiology.org or jchiang@mail.ncku.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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NCS: incorporating positioning data to quantify nucleosome stability in yeast (2014)

Chiang, J.-H., Lin, C.-H.

Oxford University Press

In: Bioinformatics

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Publication Date: 2014-03-19

Description: Motivation: With the spreading technique of mass sequencing, nucleosome positions and scores for their intensity have become available through several previous studies in yeast, but relatively few studies have specifically aimed to determine the score of nucleosome stability. Based on mass sequencing data, we proposed a nucleosome center score (NCS) for quantifying nucleosome stability by measuring shifts of the nucleosome center, and then mapping NCS scores to nucleosome positions in Brogaard et al. ’s study. Results: We demonstrated the efficiency of NCS by known preference of A/T-based tracts for nucleosome formation, and showed that central nucleosomal DNA is more sensitive to A/T-based tracts than outer regions, which corresponds to the central histone tetramer-dominated region. We also found significant flanking preference around nucleosomal DNA for A/T-based dinucleotides, suggesting that neighboring sequences could affect nucleosome stability. Finally, the difference between results of NCS and Brogaard et al. ’s scores was addressed and discussed. Contacts: jchiang@mail.ncku.edu.tw Supplementary Information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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Lichen-forming fungus Caloplaca flavoruscens inhibits transcription factors and chromatin remodeling system in fungi (2016)

Kwon, Y., Cha, J., Chiang, J., Tran, G., Nislow, C., Hur, J.-S., Kwak, Y.-S.

Oxford University Press

In: FEMS Microbiology Letters

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Publication Date: 2016-05-20

Description: Lichen-forming fungi and extracts derived from them have been used as alternative medicine sources for millennia and recently there has been a renewed interest in their known bioactive properties for anticancer agents, cosmetics and antibiotics. Although lichen-forming fungus-derived compounds are biologically and commercially valuable, few studies have been performed to determine their modes of action. This study used chemical-genetic and chemogenomic high-throughput analyses to gain insight into the modes of action of Caloplaca flavoruscens extracts. High-throughput screening of 575 lichen extracts was performed and 39 extracts were identified which inhibited yeast growth. A C. flavoruscens extract was selected as a promising antifungal and was subjected to genome-wide haploinsufficiency profiling and homozygous profiling assays. These screens revealed that yeast deletion strains lacking Rsc8, Pro1 and Toa2 were sensitive to three concentrations (IC 25.5 , IC 25 and IC 50 , respectively) of C. flavoruscens extract. Gene-enrichment analysis of the data showed that C. flavoruscens extracts appear to perturb transcription and chromatin remodeling.

Keywords: Biotechnology & Synthetic Biology

Print ISSN: 0378-1097

Electronic ISSN: 1574-6968

Topics: Biology

Published by Oxford University Press

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Detection of significant cm to sub-mm band radio and {gamma}-ray correlated variability in Fermi bright blazars (2014)

Fuhrmann, L., Larsson, S., Chiang, J., Angelakis, E., Zensus, J. A., Nestoras, I., Krichbaum, T. P., Ungerechts, H., Sievers, A., Pavlidou, V., Readhead, A. C. S., Max-Moerbeck, W., Pearson, T. J.

Oxford University Press

In: Monthly Notices of the Royal Astronomical Society

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Publication Date: 2014-05-14

Description: The exact location of the -ray emitting region in blazars is still controversial. In order to attack this problem we present first results of a cross-correlation analysis between radio (11 cm to 0.8 mm wavelength, F-GAMMA programme) and -ray (0.1–300 GeV) ~3.5 yr light curves of 54 Fermi -bright blazars. We perform a source stacking analysis and estimate significances and chance correlations using mixed source correlations. Our results reveal: (i) the first highly significant multiband radio and -ray correlations (radio lagging rays) when averaging over the whole sample, (ii) average time delays (source frame: 76 ± 23 to 7 ± 9 d), systematically decreasing from cm to mm/sub-mm bands with a frequency dependence r, () –1 , in good agreement with jet opacity dominated by synchrotron self-absorption, (iii) a bulk -ray production region typically located within/upstream of the 3 mm core region ( 3mm, = 12 ± 8 d), (iv) mean distances between the region of -ray peak emission and the radio ‘ = 1 photosphere’ decreasing from 9.8 ± 3.0 pc (11 cm) to 0.9 ± 1.1 pc (2 mm) and 1.4 ± 0.8 pc (0.8 mm), (v) 3 mm/-ray correlations in nine individual sources at a significance level where one is expected by chance (probability: 4 10 –6 ), (vi) opacity and ‘time lag core shift’ estimates for quasar 3C 454.3 providing a lower limit for the distance of the bulk -ray production region from the supermassive black hole (SMBH) of ~0.8–1.6 pc, i.e. at the outer edge of the broad-line region (BLR) or beyond. A 3 mm = 1 surface at ~2–3 pc from the jet base (i.e. well outside the ‘canonical BLR’) finally suggests that BLR material extends to several parsec distances from the SMBH.

Print ISSN: 0035-8711

Electronic ISSN: 1365-2966

Topics: Physics

Published by Oxford University Press

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AutoBind: automatic extraction of protein-ligand-binding affinity data from biological literature (2012)

Chang, D. T.-H., Ke, C.-H., Lin, J.-H., Chiang, J.-H.

Oxford University Press

In: Bioinformatics

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Publication Date: 2012-08-08

Description: Motivation: Determination of the binding affinity of a protein–ligand complex is important to quantitatively specify whether a particular small molecule will bind to the target protein. Besides, collection of comprehensive datasets for protein–ligand complexes and their corresponding binding affinities is crucial in developing accurate scoring functions for the prediction of the binding affinities of previously unknown protein–ligand complexes. In the past decades, several databases of protein–ligand-binding affinities have been created via visual extraction from literature. However, such approaches are time-consuming and most of these databases are updated only a few times per year. Hence, there is an immediate demand for an automatic extraction method with high precision for binding affinity collection. Result: We have created a new database of protein–ligand-binding affinity data, AutoBind, based on automatic information retrieval. We first compiled a collection of 1586 articles where the binding affinities have been marked manually. Based on this annotated collection, we designed four sentence patterns that are used to scan full-text articles as well as a scoring function to rank the sentences that match our patterns. The proposed sentence patterns can effectively identify the binding affinities in full-text articles. Our assessment shows that AutoBind achieved 84.22% precision and 79.07% recall on the testing corpus. Currently, 13 616 protein–ligand complexes and the corresponding binding affinities have been deposited in AutoBind from 17 221 articles. Availability: AutoBind is automatically updated on a monthly basis, and it is freely available at http://autobind.csie.ncku.edu.tw/ and http://autobind.mc.ntu.edu.tw/ . All of the deposited binding affinities have been refined and approved manually before being released. Contact: jchiang@mail.ncku.edu.tw Supplementary Information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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Molecular mechanisms of system responses to novel stimuli are predictable from public data (2014)

Danziger, S. A., Ratushny, A. V., Smith, J. J., Saleem, R. A., Wan, Y., Arens, C. E., Armstrong, A. M., Sitko, K., Chen, W.-M., Chiang, J.-H., Reiss, D. J., Baliga, N. S., Aitchison, J. D.

Oxford University Press

In: Nucleic Acids Research

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Publication Date: 2014-02-11

Description: Systems scale models provide the foundation for an effective iterative cycle between hypothesis generation, experiment and model refinement. Such models also enable predictions facilitating the understanding of biological complexity and the control of biological systems. Here, we demonstrate the reconstruction of a globally predictive gene regulatory model from public data: a model that can drive rational experiment design and reveal new regulatory mechanisms underlying responses to novel environments. Specifically, using ~1500 publically available genome-wide transcriptome data sets from Saccharomyces cerevisiae , we have reconstructed an environment and gene regulatory influence network that accurately predicts regulatory mechanisms and gene expression changes on exposure of cells to completely novel environments. Focusing on transcriptional networks that induce peroxisomes biogenesis, the model-guided experiments allow us to expand a core regulatory network to include novel transcriptional influences and linkage across signaling and transcription. Thus, the approach and model provides a multi-scalar picture of gene dynamics and are powerful resources for exploiting extant data to rationally guide experimentation. The techniques outlined here are generally applicable to any biological system, which is especially important when experimental systems are challenging and samples are difficult and expensive to obtain—a common problem in laboratory animal and human studies.

Print ISSN: 0305-1048

Electronic ISSN: 1362-4962

Topics: Biology

Published by Oxford University Press

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