1
|
Abukhalaf M, Proksch C, Thieme D, Ziegler J, Hoehenwarter W. Changing turn-over rates regulate abundance of tryptophan, GS biosynthesis, IAA transport and photosynthesis proteins in Arabidopsis growth defense transitions. BMC Biol 2023; 21:249. [PMID: 37940940 PMCID: PMC10634109 DOI: 10.1186/s12915-023-01739-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/16/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Shifts in dynamic equilibria of the abundance of cellular molecules in plant-pathogen interactions need further exploration. We induced PTI in optimally growing Arabidopsis thaliana seedlings for 16 h, returning them to growth conditions for another 16 h. METHODS Turn-over and abundance of 99 flg22 responding proteins were measured chronologically using a stable heavy nitrogen isotope partial labeling strategy and targeted liquid chromatography coupled to mass spectrometry (PRM LC-MS). These experiments were complemented by measurements of mRNA and phytohormone levels. RESULTS Changes in synthesis and degradation rate constants (Ks and Kd) regulated tryptophane and glucosinolate, IAA transport, and photosynthesis-associated protein (PAP) homeostasis in growth/PTI transitions independently of mRNA levels. Ks values increased after elicitation while protein and mRNA levels became uncorrelated. mRNA returned to pre-elicitation levels, yet protein abundance remained at PTI levels even 16 h after media exchange, indicating protein levels were robust and unresponsive to transition back to growth. The abundance of 23 PAPs including FERREDOXIN-NADP( +)-OXIDOREDUCTASE (FNR1) decreased 16 h after PAMP exposure, their depletion was nearly abolished in the myc234 mutant. FNR1 Kd increased as mRNA levels decreased early in PTI, its Ks decreased in prolonged PTI. FNR1 Kd was lower in myc234, mRNA levels decreased as in wild type. CONCLUSIONS Protein Kd and Ks values change in response to flg22 exposure and constitute an additional layer of protein abundance regulation in growth defense transitions next to changes in mRNA levels. Our results suggest photosystem remodeling in PTI to direct electron flow away from the photosynthetic carbon reaction towards ROS production as an active defense mechanism controlled post-transcriptionally and by MYC2 and homologs. Target proteins accumulated later and PAP and auxin/IAA depletion was repressed in myc234 indicating a positive effect of the transcription factors in the establishment of PTI.
Collapse
Affiliation(s)
- Mohammad Abukhalaf
- Present address: Institute for Experimental Medicine, Christian-Albrechts University Kiel, Niemannsweg 11, 24105, Kiel, Germany
- Department Biochemistry of Plant Interactions, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06122, Halle (Saale), Germany
| | - Carsten Proksch
- Department Biochemistry of Plant Interactions, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06122, Halle (Saale), Germany
| | - Domenika Thieme
- Department Biochemistry of Plant Interactions, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06122, Halle (Saale), Germany
| | - Jörg Ziegler
- Department Molecular Signal Processing, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06122, Halle (Saale), Germany
| | - Wolfgang Hoehenwarter
- Department Biochemistry of Plant Interactions, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06122, Halle (Saale), Germany.
| |
Collapse
|
2
|
Arend M, Zimmer D, Xu R, Sommer F, Mühlhaus T, Nikoloski Z. Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale. Nat Commun 2023; 14:4781. [PMID: 37553325 PMCID: PMC10409818 DOI: 10.1038/s41467-023-40498-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 08/01/2023] [Indexed: 08/10/2023] Open
Abstract
Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.
Collapse
Affiliation(s)
- Marius Arend
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
- Bioinformatics and Mathematical Modeling Department, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria
| | - David Zimmer
- Computational Systems Biology, TU Kaiserslautern, 67663, Kaiserslautern, Germany
| | - Rudan Xu
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Frederik Sommer
- Molecular Biotechnology & Systems Biology, TU Kaiserslautern, 67663, Kaiserslautern, Germany
| | - Timo Mühlhaus
- Computational Systems Biology, TU Kaiserslautern, 67663, Kaiserslautern, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany.
- Bioinformatics and Mathematical Modeling Department, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria.
| |
Collapse
|
3
|
Son J, Na S, Paek E. DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning. Anal Chem 2023; 95:11193-11200. [PMID: 37459568 PMCID: PMC10401496 DOI: 10.1021/acs.analchem.3c00460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 07/05/2023] [Indexed: 08/02/2023]
Abstract
Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the peptide itself, such as its amino acid sequences or physicochemical properties, despite the fact that peptides detected by MS are dependent on many factors, including protein sample preparation, digestion, separation, ionization, and precursor selection during MS experiments. DbyDeep (Detectability by Deep learning) is an innovative end-to-end LSTM network model for peptide detectability prediction that incorporates sequence contexts of peptides and their cleavage sites (by protease). Utilizing the cleavage site contexts could improve the performance of prediction, and DbyDeep outperformed existing methods in predicting peptides recognizable from multiple MS/MS data sets with diverse species and MS instruments. We argue for the necessity of a learning model that encompasses several contexts associated with peptide detection, as opposed to depending just on peptide sequences. There is a Python implementation of DbyDeep at https://github.com/BISCodeRepo/DbyDeep.
Collapse
Affiliation(s)
- Juho Son
- Department
of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Seungjin Na
- Department
of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
- Institute
for Artificial Intelligence Research, Hanyang
University, Seoul 04763, Republic
of Korea
| | - Eunok Paek
- Department
of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
- Institute
for Artificial Intelligence Research, Hanyang
University, Seoul 04763, Republic
of Korea
| |
Collapse
|
4
|
Abdul-Khalek N, Wimmer R, Overgaard MT, Gregersen Echers S. Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach. Comput Struct Biotechnol J 2023; 21:3715-3727. [PMID: 37560124 PMCID: PMC10407266 DOI: 10.1016/j.csbj.2023.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 08/11/2023] Open
Abstract
Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.
Collapse
Affiliation(s)
- Naim Abdul-Khalek
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | - Reinhard Wimmer
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | | | | |
Collapse
|
5
|
Wang F, Dischinger K, Westrich LD, Meindl I, Egidi F, Trösch R, Sommer F, Johnson X, Schroda M, Nickelsen J, Willmund F, Vallon O, Bohne AV. One-helix protein 2 is not required for the synthesis of photosystem II subunit D1 in Chlamydomonas. PLANT PHYSIOLOGY 2023; 191:1612-1633. [PMID: 36649171 PMCID: PMC10022639 DOI: 10.1093/plphys/kiad015] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
In land plants and cyanobacteria, co-translational association of chlorophyll (Chl) to the nascent D1 polypeptide, a reaction center protein of photosystem II (PSII), requires a Chl binding complex consisting of a short-chain dehydrogenase (high chlorophyll fluorescence 244 [HCF244]/uncharacterized protein 39 [Ycf39]) and one-helix proteins (OHP1 and OHP2 in chloroplasts) of the light-harvesting antenna complex superfamily. Here, we show that an ohp2 mutant of the green alga Chlamydomonas (Chlamydomonas reinhardtii) fails to accumulate core PSII subunits, in particular D1 (encoded by the psbA mRNA). Extragenic suppressors arose at high frequency, suggesting the existence of another route for Chl association to PSII. The ohp2 mutant was complemented by the Arabidopsis (Arabidopsis thaliana) ortholog. In contrast to land plants, where psbA translation is prevented in the absence of OHP2, ribosome profiling experiments showed that the Chlamydomonas mutant translates the psbA transcript over its full length. Pulse labeling suggested that D1 is degraded during or immediately after translation. The translation of other PSII subunits was affected by assembly-controlled translational regulation. Proteomics showed that HCF244, a translation factor which associates with and is stabilized by OHP2 in land plants, still partly accumulates in the Chlamydomonas ohp2 mutant, explaining the persistence of psbA translation. Several Chl biosynthesis enzymes overaccumulate in the mutant membranes. Partial inactivation of a D1-degrading protease restored a low level of PSII activity in an ohp2 background, but not photoautotrophy. Taken together, our data suggest that OHP2 is not required for psbA translation in Chlamydomonas, but is necessary for D1 stabilization.
Collapse
Affiliation(s)
- Fei Wang
- Molecular Plant Sciences, LMU Munich, Planegg-Martinsried 82152, Germany
- UMR 7141, Centre National de la Recherche Scientifique/Sorbonne Université, Institut de Biologie Physico-Chimique, Paris 75005, France
- College of Life Sciences, Northwest University, Xi'an 710069, China
| | | | - Lisa Désirée Westrich
- Molecular Genetics of Eukaryotes, University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Irene Meindl
- Molecular Plant Sciences, LMU Munich, Planegg-Martinsried 82152, Germany
| | - Felix Egidi
- Molecular Plant Sciences, LMU Munich, Planegg-Martinsried 82152, Germany
| | - Raphael Trösch
- Molecular Genetics of Eukaryotes, University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Frederik Sommer
- Molecular Biotechnology and Systems Biology, University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Xenie Johnson
- UMR 7141, Centre National de la Recherche Scientifique/Sorbonne Université, Institut de Biologie Physico-Chimique, Paris 75005, France
| | - Michael Schroda
- Molecular Biotechnology and Systems Biology, University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Joerg Nickelsen
- Molecular Plant Sciences, LMU Munich, Planegg-Martinsried 82152, Germany
| | - Felix Willmund
- Molecular Genetics of Eukaryotes, University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Olivier Vallon
- UMR 7141, Centre National de la Recherche Scientifique/Sorbonne Université, Institut de Biologie Physico-Chimique, Paris 75005, France
| | | |
Collapse
|
6
|
Neely BA, Dorfer V, Martens L, Bludau I, Bouwmeester R, Degroeve S, Deutsch EW, Gessulat S, Käll L, Palczynski P, Payne SH, Rehfeldt TG, Schmidt T, Schwämmle V, Uszkoreit J, Vizcaíno JA, Wilhelm M, Palmblad M. Toward an Integrated Machine Learning Model of a Proteomics Experiment. J Proteome Res 2023; 22:681-696. [PMID: 36744821 PMCID: PMC9990124 DOI: 10.1021/acs.jproteome.2c00711] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.
Collapse
Affiliation(s)
- Benjamin A Neely
- National Institute of Standards and Technology, Charleston, South Carolina 29412, United States
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Isabell Bludau
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | | | - Lukas Käll
- Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Solna, Sweden
| | - Pawel Palczynski
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, Utah 84602, United States
| | - Tobias Greisager Rehfeldt
- Institute for Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | | | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Julian Uszkoreit
- Medical Proteome Analysis, Center for Protein Diagnostics (ProDi), Ruhr University Bochum, 44801 Bochum, Germany.,Medizinisches Proteom-Center, Medical Faculty, Ruhr University Bochum, 44801 Bochum, Germany
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), 85354 Freising, Germany
| | - Magnus Palmblad
- Leiden University Medical Center, Postbus 9600, 2300 RC Leiden, The Netherlands
| |
Collapse
|
7
|
Riley RM, Spencer Miko SE, Morin RD, Morin GB, Negri GL. PeptideRanger: An R Package to Optimize Synthetic Peptide Selection for Mass Spectrometry Applications. J Proteome Res 2023; 22:526-531. [PMID: 36701129 DOI: 10.1021/acs.jproteome.2c00538] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Targeted and semitargeted mass spectrometry-based approaches are reliable methods to consistently detect and quantify low abundance proteins including proteins of clinical significance. Despite their potential, the development of targeted and semitargeted assays is time-consuming and often requires the purchase of costly libraries of synthetic peptides. To improve the efficiency of this rate-limiting step, we developed PeptideRanger, a tool to identify peptides from protein of interest with physiochemical properties that make them more likely to be suitable for mass spectrometry analysis. PeptideRanger is a flexible, extensively annotated, and intuitive R package that uses a random forest model trained on a diverse data set of thousands of MS experiments spanning a variety of sample types profiled with different chromatography setups and instruments. To support a variety of applications and to leverage rapidly growing public MS databases, PeptideRanger can readily be retrained with experiment-specific data sets and customized to prioritize and filter peptides based on selected properties.
Collapse
Affiliation(s)
- Ryan M Riley
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver V5Z 1L3, Canada
| | | | - Ryan D Morin
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver V5Z 1L3, Canada.,Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby V5A 1S6, Canada
| | - Gregg B Morin
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver V5Z 1L3, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver V6T 1Z4, Canada
| | - Gian Luca Negri
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver V5Z 1L3, Canada
| |
Collapse
|
8
|
PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability. Int J Mol Sci 2022; 23:ijms232012385. [PMID: 36293242 PMCID: PMC9604182 DOI: 10.3390/ijms232012385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 12/03/2022] Open
Abstract
Peptide detectability is defined as the probability of identifying a peptide from a mixture of standard samples, which is a key step in protein identification and analysis. Exploring effective methods for predicting peptide detectability is helpful for disease treatment and clinical research. However, most existing computational methods for predicting peptide detectability rely on a single information. With the increasing complexity of feature representation, it is necessary to explore the influence of multivariate information on peptide detectability. Thus, we propose an ensemble deep learning method, PD-BertEDL. Bidirectional encoder representations from transformers (BERT) is introduced to capture the context information of peptides. Context information, sequence information, and physicochemical information of peptides were combined to construct the multivariate feature space of peptides. We use different deep learning methods to capture the high-quality features of different categories of peptides information and use the average fusion strategy to integrate three model prediction results to solve the heterogeneity problem and to enhance the robustness and adaptability of the model. The experimental results show that PD-BertEDL is superior to the existing prediction methods, which can effectively predict peptide detectability and provide strong support for protein identification and quantitative analysis, as well as disease treatment.
Collapse
|
9
|
Surrogate peptide selection and internal standardization for accurate quantification of endogenous proteins. Bioanalysis 2022; 14:949-961. [PMID: 36017716 DOI: 10.4155/bio-2022-0071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Relative quantification techniques have dominated the field of proteomics. However, biomarker discovery, mathematical model development and studies on transporter-mediated drug disposition still need absolute quantification of proteins. The quality of data of trace-level protein quantification is solely dependent on the specific selection of surrogate peptides. Selection of surrogate peptides has a major impact on the accuracy of the method. In this article, the advanced approaches for selection of surrogate peptides, which can provide absolute quantification of the proteins are discussed. In addition, internal standardization, which accounts for variations in the quantitation process to achieve absolute protein quantification is discussed.
Collapse
|
10
|
Medina-Ortiz D, Contreras S, Amado-Hinojosa J, Torres-Almonacid J, Asenjo JA, Navarrete M, Olivera-Nappa Á. Generalized Property-Based Encoders and Digital Signal Processing Facilitate Predictive Tasks in Protein Engineering. Front Mol Biosci 2022; 9:898627. [PMID: 35911960 PMCID: PMC9329607 DOI: 10.3389/fmolb.2022.898627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Computational methods in protein engineering often require encoding amino acid sequences, i.e., converting them into numeric arrays. Physicochemical properties are a typical choice to define encoders, where we replace each amino acid by its value for a given property. However, what property (or group thereof) is best for a given predictive task remains an open problem. In this work, we generalize property-based encoding strategies to maximize the performance of predictive models in protein engineering. First, combining text mining and unsupervised learning, we partitioned the AAIndex database into eight semantically-consistent groups of properties. We then applied a non-linear PCA within each group to define a single encoder to represent it. Then, in several case studies, we assess the performance of predictive models for protein and peptide function, folding, and biological activity, trained using the proposed encoders and classical methods (One Hot Encoder and TAPE embeddings). Models trained on datasets encoded with our encoders and converted to signals through the Fast Fourier Transform (FFT) increased their precision and reduced their overfitting substantially, outperforming classical approaches in most cases. Finally, we propose a preliminary methodology to create de novo sequences with desired properties. All these results offer simple ways to increase the performance of general and complex predictive tasks in protein engineering without increasing their complexity.
Collapse
Affiliation(s)
- David Medina-Ortiz
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas, Chile
| | - Sebastian Contreras
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- *Correspondence: Sebastian Contreras, ; Álvaro Olivera-Nappa,
| | - Juan Amado-Hinojosa
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile
| | - Jorge Torres-Almonacid
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas, Chile
| | - Juan A. Asenjo
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile
| | | | - Álvaro Olivera-Nappa
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile
- *Correspondence: Sebastian Contreras, ; Álvaro Olivera-Nappa,
| |
Collapse
|
11
|
Yang Y, Lin L, Qiao L. Deep learning approaches for data-independent acquisition proteomics. Expert Rev Proteomics 2021; 18:1031-1043. [PMID: 34918987 DOI: 10.1080/14789450.2021.2020654] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
INTRODUCTION Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field. AREAS COVERED This review discusses and provides an overview of the deep learning methods that are used for DIA data analysis, including spectral library prediction, feature scoring, and statistical control in peptide-centric analysis, as well as de novo peptide sequencing. Literature searches were performed for articles, including preprints, up to December 2021 from PubMed, Scopus, and Web of Science databases. EXPERT OPINION While spectral library prediction has broken through the limitation on proteome coverage of experimental libraries, the statistical burden due to the large query space is the remaining challenge of utilizing proteome-wide predicted libraries. Analysis of post-translational modifications is another promising direction of deep learning-based DIA methods.
Collapse
Affiliation(s)
- Yi Yang
- Department of Chemistry, Shanghai Stomatological Hospital, and Minhang Hospital, Fudan University, Shanghai China
| | - Ling Lin
- Department of Chemistry, Shanghai Stomatological Hospital, and Minhang Hospital, Fudan University, Shanghai China
| | - Liang Qiao
- Department of Chemistry, Shanghai Stomatological Hospital, and Minhang Hospital, Fudan University, Shanghai China
| |
Collapse
|
12
|
Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module. Int J Mol Sci 2021; 22:ijms222112080. [PMID: 34769509 PMCID: PMC8584443 DOI: 10.3390/ijms222112080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 10/30/2021] [Accepted: 11/02/2021] [Indexed: 11/17/2022] Open
Abstract
According to proteomics technology, as impacted by the complexity of sampling in the experimental process, several problems remain with the reproducibility of mass spectrometry experiments, and the peptide identification and quantitative results continue to be random. Predicting the detectability exhibited by peptides can optimize the mentioned results to be more accurate, so such a prediction is of high research significance. This study builds a novel method to predict the detectability of peptides by complying with the capsule network (CapsNet) and the convolutional block attention module (CBAM). First, the residue conical coordinate (RCC), the amino acid composition (AAC), the dipeptide composition (DPC), and the sequence embedding code (SEC) are extracted as the peptide chain features. Subsequently, these features are divided into the biological feature and sequence feature, and separately inputted into the neural network of CapsNet. Moreover, the attention module CBAM is added to the network to assign weights to channels and spaces, as an attempt to enhance the feature learning and improve the network training effect. To verify the effectiveness of the proposed method, it is compared with some other popular methods. As revealed from the experimentally achieved results, the proposed method outperforms those methods in most performance assessments.
Collapse
|
13
|
Krantz M, Zimmer D, Adler SO, Kitashova A, Klipp E, Mühlhaus T, Nägele T. Data Management and Modeling in Plant Biology. FRONTIERS IN PLANT SCIENCE 2021; 12:717958. [PMID: 34539712 PMCID: PMC8446634 DOI: 10.3389/fpls.2021.717958] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/29/2021] [Indexed: 05/25/2023]
Abstract
The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.
Collapse
Affiliation(s)
- Maria Krantz
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - David Zimmer
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Stephan O. Adler
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Anastasia Kitashova
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
| | - Edda Klipp
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Timo Mühlhaus
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Thomas Nägele
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
| |
Collapse
|
14
|
Odenkirk MT, Reif DM, Baker ES. Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments. Anal Chem 2021; 93:7763-7773. [PMID: 34029068 PMCID: PMC8465926 DOI: 10.1021/acs.analchem.0c04850] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.
Collapse
Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - David M Reif
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
| |
Collapse
|
15
|
Cheng H, Rao B, Liu L, Cui L, Xiao G, Su R, Wei L. PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only. Anal Chem 2021; 93:6481-6490. [DOI: 10.1021/acs.analchem.1c00354] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Hao Cheng
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Bing Rao
- School of Mechanical Electronic & Information Engineering, China University of Mining &Technology, Beijing 221008, China
| | - Lei Liu
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Lizhen Cui
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Guobao Xiao
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou 350000, China
| | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin 300384, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou 350000, China
| |
Collapse
|
16
|
Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
|
17
|
Buccitelli C, Selbach M. mRNAs, proteins and the emerging principles of gene expression control. Nat Rev Genet 2020; 21:630-644. [PMID: 32709985 DOI: 10.1038/s41576-020-0258-4] [Citation(s) in RCA: 465] [Impact Index Per Article: 116.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2020] [Indexed: 12/15/2022]
Abstract
Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA-protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.
Collapse
Affiliation(s)
| | - Matthias Selbach
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany. .,Charité - Universitätsmedizin Berlin, Berlin, Germany.
| |
Collapse
|
18
|
Hammel A, Sommer F, Zimmer D, Stitt M, Mühlhaus T, Schroda M. Overexpression of Sedoheptulose-1,7-Bisphosphatase Enhances Photosynthesis in Chlamydomonas reinhardtii and Has No Effect on the Abundance of Other Calvin-Benson Cycle Enzymes. FRONTIERS IN PLANT SCIENCE 2020; 11:868. [PMID: 32655601 PMCID: PMC7324757 DOI: 10.3389/fpls.2020.00868] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 05/27/2020] [Indexed: 05/18/2023]
Abstract
The productivity of plants and microalgae needs to be increased to feed the growing world population and to promote the development of a low-carbon economy. This goal can be achieved by improving photosynthesis via genetic engineering. In this study, we have employed the Modular Cloning strategy to overexpress the Calvin-Benson cycle (CBC) enzyme sedoheptulose-1,7-bisphosphatase (SBP1) up to threefold in the unicellular green alga Chlamydomonas reinhardtii. The protein derived from the nuclear transgene represented ∼0.3% of total cell protein. Photosynthetic rate and growth were significantly increased in SBP1-overexpressing lines under high-light and elevated CO2 conditions. Absolute quantification of the abundance of all other CBC enzymes by the QconCAT approach revealed no consistent differences between SBP1-overexpressing lines and the recipient strain. This analysis also revealed that the 11 CBC enzymes represent 11.9% of total cell protein in Chlamydomonas. Here, the range of concentrations of CBC enzymes turned out to be much larger than estimated earlier, with a 128-fold difference between the most abundant CBC protein (rbcL) and the least abundant (triose phosphate isomerase). Accordingly, the concentrations of the CBC intermediates are often but not always higher than the binding site concentrations of the enzymes for which they act as substrates. The enzymes with highest substrate to binding site ratios might represent good candidates for overexpression in subsequent engineering steps.
Collapse
Affiliation(s)
- Alexander Hammel
- Molecular Biotechnology & Systems Biology, TU Kaiserslautern, Kaiserslautern, Germany
| | - Frederik Sommer
- Molecular Biotechnology & Systems Biology, TU Kaiserslautern, Kaiserslautern, Germany
| | - David Zimmer
- Computational Systems Biology, TU Kaiserslautern, Kaiserslautern, Germany
| | - Mark Stitt
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Timo Mühlhaus
- Computational Systems Biology, TU Kaiserslautern, Kaiserslautern, Germany
| | - Michael Schroda
- Molecular Biotechnology & Systems Biology, TU Kaiserslautern, Kaiserslautern, Germany
| |
Collapse
|
19
|
Trentmann O, Mühlhaus T, Zimmer D, Sommer F, Schroda M, Haferkamp I, Keller I, Pommerrenig B, Neuhaus HE. Identification of Chloroplast Envelope Proteins with Critical Importance for Cold Acclimation. PLANT PHYSIOLOGY 2020; 182:1239-1255. [PMID: 31932409 PMCID: PMC7054872 DOI: 10.1104/pp.19.00947] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 12/20/2019] [Indexed: 05/04/2023]
Abstract
The ability of plants to withstand cold temperatures relies on their photosynthetic activity. Thus, the chloroplast is of utmost importance for cold acclimation and acquisition of freezing tolerance. During cold acclimation, the properties of the chloroplast change markedly. To provide the most comprehensive view of the protein repertoire of the chloroplast envelope, we analyzed this membrane system in Arabidopsis (Arabidopsis thaliana) using mass spectrometry-based proteomics. Profiling chloroplast envelope membranes was achieved by a cross comparison of protein intensities across the plastid and the enriched membrane fraction under both normal and cold conditions. We used multivariable logistic regression to model the probabilities for the classification of an envelope localization. In total, we identified 38 envelope membrane intrinsic or associated proteins exhibiting altered abundance after cold acclimation. These proteins comprise several solute carriers, such as the ATP/ADP antiporter nucleotide transporter2 (NTT2; substantially increased abundance) or the maltose exporter MEX1 (substantially decreased abundance). Remarkably, analysis of the frost recovery of ntt loss-of-function and mex1 overexpressor mutants confirmed that the comparative proteome is well suited to identify key factors involved in cold acclimation and acquisition of freezing tolerance. Moreover, for proteins with known physiological function, we propose scenarios explaining their possible roles in cold acclimation. Furthermore, spatial proteomics introduces an additional layer of complexity and enables the identification of proteins differentially localized at the envelope membrane under the changing environmental regime.
Collapse
Affiliation(s)
- Oliver Trentmann
- Technische Universität Kaiserslautern, Department of Biology, Plant Physiology, 67653 Kaiserslautern, Germany
| | - Timo Mühlhaus
- Technische Universität Kaiserslautern, Department of Biology, Computational Systems Biology, 67653 Kaiserslautern, Germany
| | - David Zimmer
- Technische Universität Kaiserslautern, Department of Biology, Computational Systems Biology, 67653 Kaiserslautern, Germany
| | - Frederik Sommer
- Technische Universität Kaiserslautern, Department of Biology, Molecular Biotechnology and Systems Biology, 67653 Kaiserslautern, Germany
| | - Michael Schroda
- Technische Universität Kaiserslautern, Department of Biology, Molecular Biotechnology and Systems Biology, 67653 Kaiserslautern, Germany
| | - Ilka Haferkamp
- Technische Universität Kaiserslautern, Department of Biology, Plant Physiology, 67653 Kaiserslautern, Germany
| | - Isabel Keller
- Technische Universität Kaiserslautern, Department of Biology, Plant Physiology, 67653 Kaiserslautern, Germany
| | - Benjamin Pommerrenig
- Technische Universität Kaiserslautern, Department of Biology, Plant Physiology, 67653 Kaiserslautern, Germany
| | - Horst Ekkehard Neuhaus
- Technische Universität Kaiserslautern, Department of Biology, Plant Physiology, 67653 Kaiserslautern, Germany
| |
Collapse
|
20
|
Li T, Chen L, Gan M. Quality control of imbalanced mass spectra from isotopic labeling experiments. BMC Bioinformatics 2019; 20:549. [PMID: 31694522 PMCID: PMC6833298 DOI: 10.1186/s12859-019-3170-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 10/22/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mass spectra are usually acquired from the Liquid Chromatography-Mass Spectrometry (LC-MS) analysis for isotope labeled proteomics experiments. In such experiments, the mass profiles of labeled (heavy) and unlabeled (light) peptide pairs are represented by isotope clusters (2D or 3D) that provide valuable information about the studied biological samples in different conditions. The core task of quality control in quantitative LC-MS experiment is to filter out low-quality peptides with questionable profiles. The commonly used methods for this problem are the classification approaches. However, the data imbalance problems in previous control methods are often ignored or mishandled. In this study, we introduced a quality control framework based on the extreme gradient boosting machine (XGBoost), and carefully addressed the imbalanced data problem in this framework. RESULTS In the XGBoost based framework, we suggest the application of the Synthetic minority over-sampling technique (SMOTE) to re-balance data and use the balanced data to train the boosted trees as the classifier. Then the classifier is applied to other data for the peptide quality assessment. Experimental results show that our proposed framework increases the reliability of peptide heavy-light ratio estimation significantly. CONCLUSIONS Our results indicate that this framework is a powerful method for the peptide quality assessment. For the feature extraction part, the extracted ion chromatogram (XIC) based features contribute to the peptide quality assessment. To solve the imbalanced data problem, SMOTE brings a much better classification performance. Finally, the XGBoost is capable for the peptide quality control. Overall, our proposed framework provides reliable results for the further proteomics studies.
Collapse
Affiliation(s)
- Tianjun Li
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Long Chen
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China.
| | - Min Gan
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian, China
| |
Collapse
|
21
|
Serrano G, Guruceaga E, Segura V. DeepMSPeptide: peptide detectability prediction using deep learning. Bioinformatics 2019; 36:1279-1280. [DOI: 10.1093/bioinformatics/btz708] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 09/02/2019] [Accepted: 09/11/2019] [Indexed: 01/16/2023] Open
Abstract
Abstract
Summary
The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the results and the presence of degenerated peptides. However, considering in the analysis only those peptides that could be detected by mass spectrometry, also called proteotypic peptides, increases the accuracy of the results. Several approaches have been applied to predict peptide detectability based on the physicochemical properties of the peptides. In this manuscript, we present DeepMSPeptide, a bioinformatic tool that uses a deep learning method to predict proteotypic peptides exclusively based on the peptide amino acid sequences.
Availability and implementation
DeepMSPeptide is available at https://github.com/vsegurar/DeepMSPeptide.
Supplementary information
Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Guillermo Serrano
- Bioinformatics Platform, Center for Applied Medical Research, University of Navarra, Pamplona
| | - Elizabeth Guruceaga
- Bioinformatics Platform, Center for Applied Medical Research, University of Navarra, Pamplona
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Victor Segura
- Bioinformatics Platform, Center for Applied Medical Research, University of Navarra, Pamplona
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| |
Collapse
|