Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

State-of-the-art computational methods to predict protein-protein interactions with high accuracy and coverage

Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.

Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Network Reconstruction and Significant Pathway Extraction Using Phosphoproteomic Data from Cancer Cells

Protein phosphorylation acts as an efficient switch controlling deregulated key signaling pathway in cancer. Computational biology aims to address the complexity of reconstructed networks but overrepresents well-known proteins and lacks information on less-studied proteins. A bioinformatic tool to reconstruct and select relatively small networks that connect signaling proteins to their targets in specific contexts is developed. It enables to propose and validate new signaling axes of the Syk kinase. To validate the potency of the tool, it is applied to two phosphoproteomic studies on oncogenic mutants of the well-known phosphatidyl-inositol 3-kinase (PIK3CA) and the unfamiliar Src-related tyrosine kinase lacking C-terminal regulatory tyrosine and N-terminal myristoylation sites (SRMS) kinase. By combining network reconstruction and signal propagation, comprehensive signaling networks from large-scale experimental data are built and multiple molecular paths from these kinases to their targets are extracted. Specific paths from two distinct PIK3CA mutants are retrieved, and their differential impact on the HER3 receptor kinase is explained. In addition, to address the missing connectivities of the SRMS kinase to its targets in interaction pathway databases, phospho-tyrosine and phospho-serine/threonine proteomic data are integrated. The resulting SRMS-signaling network comprises casein kinase 2, thereby validating its currently suggested role downstream of SRMS. The computational pipeline is publicly available, and contains a user-friendly graphical interface (http://doi.org/10.5281/zenodo.3333687).