Computational Omics and Systems Biology Group

Community standards for open cell migration data

A. N. Gonzalez-Beltran, P. Masuzzo, C. Ampe, G. Bakker, S. Besson, R. H. Eibl, P. Friedl, M. Gunzer, M. Kittisopikul, S. E. Le Dévédec, S. Leo, J. Moore, Y. Paran, J. Prilusky, P. Rocca-Serra, P. Roudot, M. Schuster, G. Sergeant, S. Strömblad, J. R. Swedlow, M. van Erp, M. Van Troys, A. Zaritsky, S. Sansone, and L. Martens
GIGASCIENCE, 2020

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.

DeepLC can predict retention times for peptides that carry as-yet unseen modifications

R. Bouwmeester, R. Gabriels, N. Hulstaert, L. Martens, and S. Degroeve
bioRxiv, 2020

The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex LC-MS identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open modification searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We here therefore present DeepLC, a novel deep learning peptide retention time predictor utilizing a new peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides, and, more importantly, accurately predicts retention times for modifications not seen during training. DeepLC is available under the permissive Apache 2.0 open source license and comes with a user-friendly graphical user interface, as well as a Python package on PyPI, Bioconda, and BioContainers for effortless workflow integration.

Generalized calibration across liquid chromatography setups for generic prediction of small-molecule retention times

R. Bouwmeester, L. Martens, and S. Degroeve
ANALYTICAL CHEMISTRY, 2020

Accurate prediction of liquid chromatographic retention times from small-molecule structures is useful for reducing experimental measurements and for improved identification in targeted and untargeted MS. However, different experimental setups (e.g., differences in columns, gradients, solvents, or stationary phase) have given rise to a multitude of prediction models that only predict accurate retention times for a specific experimental setup. In practice this typically results in the fitting of a new predictive model for each specific type of setup, which is not only inefficient but also requires substantial prior data to be accumulated on each such setup. Here we introduce the concept of generalized calibration, which is capable of the straightforward mapping of retention time models between different experimental setups. This concept builds on the database-controlled calibration approach implemented in PredRet and fits calibration curves on predicted retention times instead of only on observed retention times. We show that this approach results in substantially higher accuracy of elution-peak prediction than is achieved by setup-specific models.

Connecting MetaProteomeAnalyzer and PeptideShaker to Unipept for seamless end-to-end metaproteomics data analysis

T. Van Den Bossche, P. Verschaffelt, K. Schallert, H. Barsnes, P. Dawyndt, D. Benndorf, B. Y. Renard, B. Mesuere, L. Martens, and T. Muth
JOURNAL OF PROTEOME RESEARCH, 2020

Although metaproteomics, the study of the collective proteome of microbial communities, has become increasingly powerful and popular over the past few years, the field has lagged behind on the availability of user-friendly, end-to-end pipelines for data analysis. We therefore describe the connection from two commonly used metaproteomics data processing tools in the field, MetaProteomeAnalyzer and PeptideShaker, to Unipept for downstream analysis. Through these connections, direct end-to-end pipelines are built from database searching to taxonomic and functional annotation.

Unipept CLI 2.0 : adding support for visualisations and functional annotations

P. Verschaffelt, P. Van Thienen, T. Van Den Bossche, F. Van der Jeugt, C. De Tender, L. Martens, P. Dawyndt, and B. Mesuere
BIOINFORMATICS, 2020

Unipept is an ecosystem of tools developed for fast metaproteomics data-analysis consisting of a web application, a set of web services (application programming interface, API) and a command-line interface (CLI). After the successful introduction of version 4 of the Unipept web application, we here introduce version 2.0 of the API and CLI. Next to the existing taxonomic analysis, version 2.0 of the API and CLI provides access to Unipept’s powerful functional analysis for metaproteomics samples. The functional analysis pipeline supports retrieval of Enzyme Commission numbers, Gene Ontology terms and InterPro entries for the individual peptides in a metaproteomics sample. This paves the way for other applications and developers to integrate these new information sources into their data processing pipelines, which greatly increases insight into the functions performed by the organisms in a specific environment. Both the API and CLI have also been expanded with the ability to render interactive visualizations from a list of taxon ids. These visualizations are automatically made available on a dedicated website and can easily be shared by users.

The CEP5 peptide promotes abiotic stress tolerance, as revealed by quantitative proteomics, and attenuates the AUX/IAA equilibrium in Arabidopsis

S. Smith, S. Zhu, L. Joos, I. Roberts, N. Nikonorova, L. D. Vu, E. Stes, H. Cho, A. Larrieu, W. Xuan, B. Goodall, B. Van De Cotte, J. M. Waite, A. Rigal, S. R. Harborough, G. Persiau, S. Vanneste, G. K. Kirschner, E. Vandermarliere, L. Martens, Y. Stahl, D. Audenaert, J. Friml, G. Felix, R. Simon, M. Bennett, A. Bishopp, G. De Jaeger, K. Ljung, S. Kepinski, S. Robert, J. Nemhauser, I. Hwang, K. Gevaert, T. Beeckman, and I. De Smet
MOLECULAR & CELLULAR PROTEOMICS, 2020

Peptides derived from non-functional precursors play important roles in various developmental processes, but also in (a)biotic stress signaling. Our (phospho)proteome-wide analyses of C-terminally encoded peptide 5 (CEP5)-mediated changes revealed an impact on abiotic stress-related processes. Drought has a dramatic impact on plant growth, development and reproduction, and the plant hormone auxin plays a role in drought responses. Our genetic, physiological, biochemical and pharmacological results demonstrated that CEP5-mediated signaling is relevant for osmotic and drought stress tolerance in Arabidopsis, and that CEP5 specifically counteracts auxin effects. Specifically, we found that CEP5 signaling stabilizes AUX/IAA transcriptional repressors, suggesting the existence of a novel peptide-dependent control mechanism that tunes auxin signaling. These observations align with the recently described role of AUX/IAAs in stress tolerance and provide a novel role for CEP5 in osmotic and drought stress tolerance.

The age of data-driven proteomics : how machine learning enables novel workflows

R. Bouwmeester, R. Gabriels, T. Van Den Bossche, L. Martens, and S. Degroeve
PROTEOMICS, 2020

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges.

Removing the hidden data dependency of DIA with predicted spectral libraries

B. Van Puyvelde, S. Willems, R. Gabriels, S. Daled, L. De Clerck, S. Vande Casteele, A. Staes, F. Impens, D. Deforce, L. Martens, S. Degroeve, and M. Dhaenens
PROTEOMICS, 2020

Data‐independent acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data‐dependent acquisition (DDA) libraries for deep peptide‐centric detection. Here, it is shown that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter‐laboratory comparison.

ThermoRawFileParser : modular, scalable and cross-platform RAW file conversion

N. Hulstaert, J. Shofstahl, T. Sachsenberg, M. Walzer, H. Barsnes, L. Martens, and Y. Perez-Riverol
JOURNAL OF PROTEOME RESEARCH, 2020

The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the number of samples analyzed per experiment as well as by the growing amount of data obtained in each analytical run. In order to process these large amounts of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures. Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we present ThermoRawFileParser, an open-source, cross-platform tool that converts Thermo RAW files into open file formats such as MGF and the HUPO-PSI standard file format mzML. To ensure the broadest possible availability and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda package and BioContainers container around ThermoRawFileParser. In addition, we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools. Finally, we performed a benchmark of ThermoRawFileParser and msconvert to verify that the converted mzML files contain reliable quantitative results.

Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions

A. S. Ferreira Diamantino Coelho e Silva, R. Bouwmeester, L. Martens, and S. Degroeve
BIOINFORMATICS, 2020