scholarly journals BlackSheep: A Bioconductor and Bioconda package for differential extreme value analysis

2019 ◽  
Author(s):  
Lili Blumenberg ◽  
Emily Kawaler ◽  
MacIntosh Cornwell ◽  
Shaleigh Smith ◽  
Kelly Ruggles ◽  
...  
Keyword(s):  
Differential Expression Analysis ◽  
Shotgun Proteomics ◽  
Small Sample ◽  
Extreme Value Analysis ◽  
Differential Analysis ◽  
Value Analysis ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Parametric Description

AbstractUnbiased assays such as shotgun proteomics and RNA-seq provide high-resolution molecular characterization of tumors. These assays measure molecules with highly varied distributions, making interpretation and hypothesis testing challenging. Samples with the most extreme measurements for a molecule can reveal the most interesting biological insights, yet are often excluded from analysis. Furthermore, rare disease subtypes are, by definition, underrepresented in cancer cohorts. To provide a strategy for identifying molecules aberrantly enriched in small sample cohorts, we present BlackSheep--a package for non-parametric description and differential analysis of genome-wide data, available at https://github.com/ruggleslab/blackSheep. BlackSheep is a complementary tool to other differential expression analysis methods that may be underpowered when analyzing small subgroups in a larger cohort.

scholarly journals Technical Note: The Normal Quantile Transformation and its application in a flood forecasting system

2011 ◽  
Vol 8 (5) ◽  
pp. 9275-9297 ◽  
Author(s):  
K. Bogner ◽  
F. Pappenberger ◽  
H. L. Cloke
Keyword(s):  
Gaussian Model ◽  
Flood Forecasting ◽  
Technical Note ◽  
Small Sample ◽  
Extreme Value Analysis ◽  
Practical Implementation ◽  
Value Analysis ◽  
Regression Methods ◽  
Forecasting System ◽  
Small Sample Sizes

Abstract. The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Density Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as Normal-Score Transform. In this paper some possible problems caused by small sample sizes for the applicability in flood forecasting systems will be discussed and illustrated by examples. For the practical implementation commands and examples from the freely available and widely used statistical computing language R (R Development Core Team, 2011) will be given (represented in Courier font) and possible solutions are suggested by combining extreme value analysis and non-parametric regression methods.

scholarly journals Fast Detection of Differential Chromatin Domains with SCIDDO

2018 ◽  
Author(s):  
Peter Ebert ◽  
Marcel H. Schulz
Keyword(s):  
Histone Modifications ◽  
Chromatin Immunoprecipitation ◽  
Regulatory Elements ◽  
Differential Analysis ◽  
Chromatin Domains ◽  
Chromatin Dynamics ◽  
Fast Detection ◽  
Genome Wide ◽  
Chromatin Immunoprecipitation Sequencing

AbstractThe generation of genome-wide maps of histone modifications using chromatin immunoprecipitation sequencing (ChIP-seq) is a common approach to dissect the complexity of the epigenome. However, interpretation and differential analysis of histone ChIP-seq datasets remains challenging due to the genomic co-occurrence of several marks and their difference in genomic spread. Here we present SCIDDO, a fast statistical method for the detection of differential chromatin domains (DCDs) from chromatin state maps. DCD detection simplifies relevant tasks such as the characterization of chromatin changes in differentially expressed genes or the examination of chromatin dynamics at regulatory elements. SCIDDO is available at github.com/ptrebert/sciddo

scholarly journals Hedgehog signaling controls segmentation dynamics and diversity via msx1 in a spider embryo

2020 ◽  
Vol 6 (37) ◽  
pp. eaba7261
Author(s):  
Yasuko Akiyama-Oda ◽  
Hiroki Oda
Keyword(s):  
Hedgehog Signaling ◽  
The Body ◽  
Body Region ◽  
Altered States ◽  
Genome Wide ◽  
Body Patterning ◽  
Genome Wide Data ◽  
Hh Signaling ◽  
Anterior Posterior

Hedgehog (Hh) signaling plays fundamental roles in animal body patterning. Understanding its mechanistic complexity requires simple tractable systems that can be used for these studies. In the early spider embryo, Hh signaling mediates the formation of overall anterior-posterior polarity, yet it remains unclear what mechanisms link the initial Hh signaling activity with body axis segmentation, in which distinct periodic stripe-forming dynamics occur depending on the body region. We performed genome-wide searches for genes that transcriptionally respond to altered states of Hh signaling. Characterization of genes negatively regulated by Hh signaling suggested that msx1, encoding a conserved transcription factor, functions as a key segmentation gene. Knockdown of msx1 prevented all dynamic processes causing spatial repetition of stripes, including temporally repetitive oscillations and bi-splitting, and temporally nonrepetitive tri-splitting. Thus, Hh signaling controls segmentation dynamics and diversity via msx1. These genome-wide data from an invertebrate illuminate novel mechanistic features of Hh-based patterning.

scholarly journals Characterization of Aircraft Icing Environments with Supercooled Large Drops for Application to Commercial Aircraft Certification

2012 ◽  
Vol 51 (2) ◽  
pp. 265-284 ◽  
Author(s):  
Stewart G. Cober ◽  
George A. Isaac
Keyword(s):  
Liquid Water Content ◽  
Extreme Value Analysis ◽  
Ice Crystal ◽  
Aircraft Icing ◽  
Commercial Aircraft ◽  
Value Analysis ◽  
Research Aircraft ◽  
The Difference ◽  
Drop Size Distributions

AbstractObservations of aircraft icing environments that included supercooled large drops (SLD) greater than 100 μm in diameter have been analyzed. The observations were collected by instrumented research aircraft from 134 flights during six field programs in three different geographic regions of North America. The research aircraft were specifically instrumented to accurately measure the microphysics characteristics of SLD conditions. In total 2444 SLD icing environments were observed at 3-km resolution. Each observation had an average liquid water content (LWC) > 0.005 g m−3, drops > 100 μm in diameter, ice crystal concentrations <1 L−1, and an average static temperature ≤0°C. SLD conditions were observed approximately 5% of the in-flight time. The SLD observations were segregated into four subsets, which included conditions with maximum drop sizes <500 μm and >500 μm in diameter, each with median drop volume diameters <40 μm and >40 μm. For each SLD subset, the observations were used to develop envelopes of maximum LWC values as a function of horizontal extent and temperature. In addition, characteristic drop size distributions were developed for each SLD subset. The maximum LWC values physically represent either the 99% or 99.9% LWC values, as determined from an extreme value analysis of the data. The analysis is sufficient for simulation of SLD environments with either numerical icing accretion models or wind-tunnel icing simulations. The SLD envelopes are similar in structure and supplemental to existing aircraft icing envelopes, the difference being that the existing envelopes did not explicitly incorporate SLD conditions.

scholarly journals Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

2020 ◽  
Author(s):  
Xiaoyu Tan ◽  
Su Li ◽  
Liyong Hu ◽  
Chunlei Zhang
Keyword(s):  
Drought Stress ◽  
Brassica Napus ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Abiotic Factor ◽  
Brassica Napus L ◽  
Network Nodes ◽  
Genome Wide ◽  
Non Coding Rnas

Abstract Background: Drought stress is a major abiotic factor that affects rapeseed (Brassica napus L.) productivity. Though previous studies indicated that long non-coding RNAs (lncRNAs) play a key role in response to drought stress, a scheme for genome-wide identification and characterization of lncRNAs’ response to drought stress is still lacking, especially in the case of B. napus. In order to further understand the molecular mechanism of the response of B. napus to drought stress, we compared changes in the transcriptome between Q2 (a drought-tolerant genotype) and Qinyou8 (a drought-sensitive genotype) in response to drought stress and rehydration treatment at the seedling stage. Results: A total of 5,546 down-regulated and 6,997 up-regulated mRNAs were detected in Q2 compared with 7,824 and 10,251 in Qinyou8, respectively; 369 down-regulated and 108 up-regulated lncRNAs were detected in Q2 compared with 449 and 257 in Qinyou8, respectively. LncRNA- mRNA interaction network analysis indicated that the co-expression network of Q2 was composed of 145 network nodes and 5,175 connections, while the co-expression network of Qinyou8 was composed of 305 network nodes and 22,327 connections. We further identified 34 TFs corresponding to 126 differentially expressed lncRNAs in Q2, and 45 TFs corresponding to 359 differentially expressed lncRNAs in Qinyou8. Differential expression analysis of lncRNAs indicated that up- and down-regulated mRNAs co-expressed with lncRNAs participated in different metabolic pathways and were involved in different regulatory mechanisms in the two genotypes. Notably, some lncRNAs were co-expressed with BnaC07g44670D, which are associated with plant hormone signal transduction. Additionally, some mRNAs which were co-located with XLOC_052298, XLOC_094954 and XLOC_012868 were mainly categorized as signal transport and defense/stress response. Conclusions: The results of this study increased our understanding of expression characterization of rapeseed lncRNAs in response to drought stress and re-watering, which would be useful to provide a reference for the further study of the function and action mechanisms of lncRNAs under drought stress and re-watering.

scholarly journals Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

2020 ◽  
Author(s):  
Xiaoyu Tan ◽  
Su Li ◽  
Liyong Hu ◽  
Chunlei Zhang
Keyword(s):  
Drought Stress ◽  
Brassica Napus ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Abiotic Factor ◽  
Brassica Napus L ◽  
Network Nodes ◽  
Genome Wide ◽  
Non Coding Rnas

Abstract Background: Drought stress is a major abiotic factor that affects rapeseed ( Brassica napus L.) productivity. Though previous studies indicated that long non-coding RNAs (lncRNAs) play a key role in response to drought stress, a scheme for genome-wide identification and characterization of lncRNAs’ response to drought stress is still lacking, especially in the case of B . napus . In order to further understand the molecular mechanism of the response of B . napus to drought stress, we compared changes in the transcriptome between Q2 (a drought-tolerant genotype) and Qinyou8 (a drought-sensitive genotype) in response to drought stress and rehydration treatment at the seedling stage. Results: A total of 5,546 down-regulated and 6,997 up-regulated mRNAs were detected in Q2 compared with 7,824 and 10,251 in Qinyou8, respectively; 369 down-regulated and 108 up-regulated lncRNAs were detected in Q2 compared with 449 and 257 in Qinyou8, respectively. LncRNA- mRNA interaction network analysis indicated that the co-expression network of Q2 was composed of 145 network nodes and 5,175 connections, while the co-expression network of Qinyou8 was composed of 305 network nodes and 22,327 connections. We further identified 34 TFs corresponding to 126 differentially expressed lncRNAs in Q2, and 45 TFs corresponding to 359 differentially expressed lncRNAs in Qinyou8. Differential expression analysis of lncRNAs indicated that up- and down-regulated mRNAs co-expressed with lncRNAs participated in different metabolic pathways and were involved in different regulatory mechanisms in the two genotypes . Notably, some lncRNAs were co-expressed with BnaC07g44670D, which are associated with plant hormone signal transduction. Additionally, some mRNAs which were co-located with XLOC_052298, XLOC_094954 and XLOC_012868 were mainly categorized as signal transport and defense/stress response. Conclusions: The results of this study increased our understanding of expression characterization of rapeseed lncRNAs in response to drought stress and re-watering, which would be useful to provide a reference for the further study of the function and action mechanisms of lncRNAs under drought stress and re-watering.

scholarly journals Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress

2019 ◽  
Author(s):  
Xiaoyu Tan ◽  
Su Li ◽  
Liyong Hu ◽  
Chunlei Zhang
Keyword(s):  
Drought Stress ◽  
Brassica Napus ◽  
Differential Expression Analysis ◽  
Abiotic Factor ◽  
Brassica Napus L ◽  
Network Nodes ◽  
Drought Tolerant ◽  
Genome Wide ◽  
Non Coding Rnas

Abstract Background: Drought stress is a major abiotic factor that affects rapeseed ( Brassica napus L.) productivity. Though previous studies indicated that long non-coding RNAs (lncRNAs) play a key role in response to drought stress, a scheme for genome-wide identification and characterization of lncRNAs’ response to drought stress is still lacking, especially in the case of B . napus . In order to further understand the molecular mechanism of the response of B . napus to drought stress, we compared changes in the transcriptome between Q2 (a drought-tolerant genotype) and Qinyou8 (a drought-sensitive genotype) in response to drought stress and rehydration treatment at the seedling stage. Results: A total of 5,546 down-regulated and 6,997 up-regulated mRNAs were detected in Q2 compared with 7,824 and 10,251 in Qinyou8, respectively; 369 down-regulated and 108 up-regulated lncRNAs were detected in Q2 compared with 449 and 257 in Qinyou8, respectively. We further identified 34 TFs corresponding to 126 differently expressed lncRNAs in Q2, and 45 TFs corresponding to 359 differently expressed lncRNAs in Qinyou8. Differential expression analysis of lncRNAs indicated that up- and down-regulated mRNAs co-expressed with lncRNAs participated in different metabolic pathways and were involved in different regulatory mechanisms in the two genotypes . Notably, some lncRNAs were co-expressed and co-located with BnaC07g44670D, which are associated with plant hormone signal transduction. Additionally, some mRNAs which were co-located with LNC_002535 (XLOC_052298), LNC_004924 (XLOC_094954) and LNC_000539 (XLOC_012868) were mainly categorized as signal transport and defense/stress response. Finally, co-expression network analysis indicated that the co-expression network of Q2 was composed of 145 network nodes and 5,175 connections, while the co-expression network of Qinyou8 was composed of 305 network nodes and 22,327 connections. Conclusions: The differentially expressed mRNAs and lncRNAs may play important roles in response to drought stress and rehydration treatments and could provide basic information for new ways to improve the drought resistance of Rapeseed Brassica napus .

Spatial Features of Extreme Waves in Gulf of Mexico

2020 ◽  
Author(s):  
Ryota Wada ◽  
Philip Jonathan ◽  
Takuji Waseda
Keyword(s):  
Gulf Of Mexico ◽  
Sample Size ◽  
Small Sample Size ◽  
Extreme Value ◽  
Small Sample ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Wave Hindcast ◽  
Wave Data ◽  
Effective Manner

Abstract Extreme value analysis of significant wave height using data from a single location often incurs large uncertainty due to small sample size. Including wave data from nearby locations increases sample size at the risk of introducing dependency between extreme events and hence violating modelling assumptions. In this work, we consider extreme value analysis of spatial wave data from the 109-year GOMOS wave hindcast for the Gulf of Mexico, seeking to incorporate the effects of spatial dependence in a simple but effective manner. We demonstrate that, for estimation of return values at a given location, incorporation of data from a circular disk region with radius of approximately 5° (long.-lat.), centred at the location of interest, provides an appropriate basis for extreme value analysis using the STM-E approach of Wada et al. (2018).

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