TY  - JOUR
A1  - Ryngajllo, Malgorzata
A1  - Childs, Liam H.
A1  - Lohse, Marc
A1  - Giorgi, Federico M.
A1  - Lude, Anja
A1  - Selbig, Joachim
A1  - Usadel, Björn
T1  - SLocX  predicting subcellular localization of Arabidopsis proteins leveraging gene expression data
JF  - Frontiers in plant science
N2  - Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mito-chondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.
KW  - subcellular localization
KW  - support vector machine
KW  - prediction
KW  - gene expression
Y1  - 2011
U6  - https://doi.org/10.3389/fpls.2011.00043
SN  - 1664-462X
VL  - 2
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  - 
TY  - JOUR
A1  - Webber, Heidi
A1  - Lischeid, Gunnar
A1  - Sommer, Michael
A1  - Finger, Robert
A1  - Nendel, Claas
A1  - Gaiser, Thomas
A1  - Ewert, Frank
T1  - No perfect storm for crop yield failure in Germany
JF  - Environmental research letters
N2  - Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.
KW  - crop yield failure
KW  - extreme events
KW  - support vector machine
KW  - process-based crop model
KW  - Germany
Y1  - 2020
U6  - https://doi.org/10.1088/1748-9326/aba2a4
SN  - 1748-9326
VL  - 15
IS  - 10
PB  - IOP Publ. Ltd.
CY  - Bristol
ER  -