H Busra Cagirici1, Hikmet Budak2, Taner Z Sen3. 1. US Department of Agriculture - Agricultural Research Service, Crop Improvement Genetics Research Unit, Western Regional Research Center, 800 Buchanan St, Albany, CA, 94710, USA. 2. Montana BioAgriculture Inc., Missoula, MT, USA. 3. US Department of Agriculture - Agricultural Research Service, Crop Improvement Genetics Research Unit, Western Regional Research Center, 800 Buchanan St, Albany, CA, 94710, USA. taner.sen@usda.gov.
Abstract
BACKGROUND: G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies. RESULTS: G4Boost predicted the quadruplex folding state with an accuracy greater then 93% and an F1-score of 0.96, and the folding energy with an RMSE of 4.28 and R2 of 0.95 only by the means of sequence intrinsic feature. G4Boost was successfully applied and validated to predict the stability of experimentally-determined G4 structures, including for plants and humans. CONCLUSION: G4Boost outperformed the three machine-learning based prediction tools, DeepG4, Quadron, and G4RNA Screener, in terms of both accuracy and F1-score, and can be highly useful for G4 prediction to understand gene regulation across species including plants and humans.
BACKGROUND: G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies. RESULTS: G4Boost predicted the quadruplex folding state with an accuracy greater then 93% and an F1-score of 0.96, and the folding energy with an RMSE of 4.28 and R2 of 0.95 only by the means of sequence intrinsic feature. G4Boost was successfully applied and validated to predict the stability of experimentally-determined G4 structures, including for plants and humans. CONCLUSION: G4Boost outperformed the three machine-learning based prediction tools, DeepG4, Quadron, and G4RNA Screener, in terms of both accuracy and F1-score, and can be highly useful for G4 prediction to understand gene regulation across species including plants and humans.
Authors: Ronny Lorenz; Stephan H Bernhart; Christian Höner Zu Siederdissen; Hakim Tafer; Christoph Flamm; Peter F Stadler; Ivo L Hofacker Journal: Algorithms Mol Biol Date: 2011-11-24 Impact factor: 1.405
Authors: Andrew L Wolfe; Kamini Singh; Yi Zhong; Philipp Drewe; Vinagolu K Rajasekhar; Viraj R Sanghvi; Konstantinos J Mavrakis; Man Jiang; Justine E Roderick; Joni Van der Meulen; Jonathan H Schatz; Christina M Rodrigo; Chunying Zhao; Pieter Rondou; Elisa de Stanchina; Julie Teruya-Feldstein; Michelle A Kelliher; Frank Speleman; John A Porco; Jerry Pelletier; Gunnar Rätsch; Hans-Guido Wendel Journal: Nature Date: 2014-07-27 Impact factor: 49.962