Biology subjects

Perez-Riverol, Y.

Publications and source records attributed to Perez-Riverol, Y..

33 records · Page 2Linked to original sources

bioRxiv2020

Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison

Here we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can either be provided manually by the user (table format), or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other websites (https://github.com/kusterlab/universal_spectrum_explorer).

bioinformatics↗

bioRxiv2020

BioContainers Registry: searching for bioinformatics tools, packages and containers

1BioContainers is an open-source project that aims to create, store, and distribute bioinformatics software containers and packages. The BioContainers community has developed a set of guidelines to standardize the software containers including the metadata, versions, licenses, and/or software dependencies. BioContainers supports multiple packaging and containers technologies such as Conda, Docker, and Singularity. Here, we introduce the BioContainers Registry and Restful API to make containerized bioinformatics tools more findable, accessible, interoperable, and reusable (FAIR). BioContainers registry provides a fast and convenient way to find and retrieve bioinformatics tools packages and containers. By doing so, it will increase the use of bioinformatics packages and containers while promoting replicability and reproducibility in research.

bioinformatics↗

bioRxiv2020

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease

Congenital Heart Disease (CHD) affects approximately 7-9 children per 1000 live births. Numerous genetic studies have established a role for rare genomic variants at the copy number variation (CNV) and single nucleotide variant level. In particular, the role of de novo mutations (DNM) has been highlighted in syndromic and non-syndromic CHD. To identify novel haploinsufficient CHD disease genes we performed an integrative analysis of CNVs and DNMs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm (TAA). We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed mutation rate testing for DNMs identified in 2,489 parent-offspring trios. Our combined analysis revealed 21 genes which were significantly affected by rare genomic deletions and/or constrained non-synonymous de novo mutations in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in singletons and small cases series, or show new associations with CHD. In addition, a systems level analysis revealed shared contribution of CNV deletions and DNMs in CHD probands, affecting protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function. Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes.

genetics↗

bioRxiv2020

User-friendly, scalable tools and workflows for single-cell analysis

Single-cell RNA-Seq (scRNA-Seq) data analysis requires expertise in command-line tools, programming languages and scaling on compute infrastructure. As scRNA-Seq becomes widespread, computational pipelines need to be more accessible, simpler and scalable. We introduce an interactive analysis environment for scRNA-Seq, based on Galaxy, with ~70 functions from major single-cell analysis tools, which can be run on compute clusters, cloud providers or single machines, to bring compute to the data in scRNA-Seq.

bioinformatics↗

bioRxiv2020

The Omics Discovery REST interface

The Omics Discovery Index is an open source platform that can be used to access, discover and disseminate omics datasets. OmicsDI integrates proteomics, genomics, metabolomics, models and transcriptomics datasets. Using an efficient indexing system, OmicsDI integrates different biological entities including genes, transcripts, proteins, metabolites and the corresponding publications from PubMed. In addition, it implements a group of pipelines to estimate the impact of each dataset by tracing the number of citations, reanalysis and biological entities reported by each dataset. Here, we present the OmicsDI REST interface to enable programmatic access to any dataset in OmicsDI or all the datasets for a specific provider (database). Clients can perform queries on the API using different metadata information such as sample details (species, tissues, etc), instrumentation (mass spectrometer, sequencer), keywords and other provided annotations. In addition, we present two different libraries in R and Python to facilitate the development of tools that can programmatically interact with the OmicsDI REST interface.

bioinformatics↗

bioRxiv2019

Phoenix Enhancer: an online service/tool for proteomics data mining using clustered spectra

MotivationSpectrum clustering has been used to enhance proteomics data analysis: some originally unidentified spectra can potentially be identified and individual peptides can be evaluated to find potential mis-identifications by using clusters of identified spectra. The Phoenix Enhancer provides an infrastructure to analyze tandem mass spectra and the corresponding peptides in the context of previously identified public data. Based on PRIDE Cluster data and a newly developed pipeline, four functionalities are provided: i) evaluate the original peptide identifications in an individual dataset, to find low confidence peptide spectrum matches (PSMs) which could correspond to mis-identifications; ii) provide confidence scores for all originally identified PSMs, to help users evaluate their quality (complementary to getting a global false discovery rate); iii) identify potential new PSMs for originally unidentified spectra; and iv) provide a collection of browsing and visualization tools to analyze and export the results. In addition to the web based service, the code is open-source and easy to re-deploy on local computers using Docker containers. AvailabilityThe service of Phoenix Enhancer is available at http://enhancer.ncpsb.org. All source code is freely available in GitHub (https://github.com/phoenix-cluster/) and can be deployed in the Cloud and HPC architectures. Contactbaimz@cqupt.edu.cn Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2019

An integrated landscape of protein expression in human cancer

Using public proteomics datasets, mostly available through the PRIDE database, we assembled a proteomics resource for 191 cancer cell lines and 246 clinical tumour samples, across 13 cancer lineages. We found that baseline protein abundance in cell lines was generally representative of tumours. However, when considering differences in protein expression between tumour subtypes, as exemplified in the breast lineage, many of these changes were no longer recapitulated in the cell line models. Integration of proteomics and transcriptomics data suggested that the level of transcriptional control in cell lines changed significantly depending on their lineage. Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis study including cancer-related proteomics datasets. We anticipate this aggregated dataset will be of significant aid to future studies requiring a reference to baseline protein expression in cancer.

bioinformatics↗

bioRxiv2019

Proteomics Standards Initiative Extended FASTA Format (PEFF)

Mass spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs), in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI Extended FASTA Format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backwards compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff.

bioinformatics↗

bioRxiv2019

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

The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the number of samples analysed 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 presented ThermoRawFileParser, an open-source, crossplatform tool that converts Thermo RAW files into open file formats such as MGF and to 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 and BioContainers containers 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.

bioinformatics↗

bioRxiv2019

Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines.

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, the bioinformatics analysis is becoming an increasingly complex and convoluted process involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are targeted and design for single desktop application limiting the scalability and reproducibility of the data analysis. In this paper we overview the key steps of metabolomic and proteomics data processing including main tools and software use to perform the data analysis. We discuss the combination of software containers with workflows environments for large scale metabolomics and proteomics analysis. Finally, we introduced to the proteomics and metabolomics communities a new approach for reproducible and large-scale data analysis based on BioContainers and two of the most popular workflows environments: Galaxy and Nextflow.

bioinformatics↗

bioRxiv2018

Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud

Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator. AvailabilityAll code has been contributed to the Galaxy Project and is available (since Galaxy 17.05) at https://github.com/galaxyproject/ in the galaxy and galaxy-kubernetes repositories. https://public.phenomenal-h2020.eu/ is an example deployment. Suppl. InformationSupplementary Files are available online. Contactpmoreno@ebi.ac.uk, European Molecular Biology Laboratory, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, Tel: +44-1223-494267, Fax: +44-1223-484696.

bioinformatics↗

bioRxiv2018

Protein inference using PIA workflows and PSI standard file formats

Proteomics using LC-MS/MS has become one of the main methods to analyze the proteins in biological samples in high-throughput. But the existing mass spectrometry instruments are still limited with respect to resolution and measurable mass ranges, which is one of the main reasons why shotgun proteomics is the major approach. Here, proteins are digested, which leads to the identification and quantification of peptides instead. While often neglected, the important step of protein inference needs to be conducted to infer from the identified peptides to the actual proteins in the original sample.\n\nIn this work, we highlight some of the previously published and newly added features of the tool PIA - Protein Inference Algorithms, which helps the user with the protein inference of measured samples. We also highlight the importance of the usage of PSI standard file formats, as PIA is the only current software supporting all available standards used for spectrum identification and protein inference. Additionally, we briefly describe the benefits of working with workflow environments for proteomics analyses and show the new features of the PIA nodes for the KNIME Analytics Platform. Finally, we benchmark PIA against a recently published dataset for isoform detection.\n\nPIA is open source and available for download on GitHub (https://github.com/mpc-bioinformatics/pia) or directly via the community extensions inside the KNIME analytics platform.

bioinformatics↗

bioRxiv2018

Quantifying the impact of public omics data

The amount of omics data in the public domain is increasing every year [1, 2]. Public availability of datasets is growing in all disciplines, because it is considered to be a good scientific practice (e.g. to enable reproducibility), and/or it is mandated by funding agencies, scientific journals, etc. Science is now a data intensive discipline and therefore, new and innovative ways for data management, data sharing, and for discovering novel datasets are increasingly required [3, 4]. However, as data volumes grow, quantifying its impact becomes more and more important. In this context, the FAIR (Findable, Accessible, Interoperable, Reusable) principles have been developed to promote good scientific practises for scientific data and data resources [5]. In fact, recently, several resources [1, 2, 6] have been created to facilitate the Findability (F) and Accessibility (A) of biomedical datasets. These principles put a specific emphasis on enhancing the ability of both individuals and software to discover and re-use digital objects in an automated fashion throughout their entire life cycle [5]. While data resources typically assign an equal relevance to all datasets (e.g. as results of a query), the usage patterns of the data can vary enormously, similarly to other \"research products\" such as publications. How do we know which datasets are getting more attention? More generally, how can we quantify the scientific impact of datasets?

bioinformatics↗

bioRxiv2017

A protein standard that emulates homology for the characterization of protein inference algorithms

A natural way to benchmark the performance of an analytical experimental setup is to use samples of known content, and see to what degree one can correctly infer the content of such a sample from the data. For shotgun proteomics, one of the inherent problems of interpreting data is that the measured analytes are peptides and not the actual proteins themselves. As some proteins share proteolytic peptides, there might be more than one possible causative set of proteins resulting in a given set of peptides and there is a need for mechanisms that infer proteins from lists of detected peptides. A weakness of commercially available samples of known content is that they consist of proteins that are deliberately selected for producing tryptic peptides that are unique to a single protein. Unfortunately, such samples do not expose any complications in protein inference. For a realistic benchmark of protein inference procedures, there is, therefore, a need for samples of known content where the present proteins share peptides with known absent proteins. Here, we present such a standard, that is based on E. coli expressed human protein fragments. To illustrate the usage of this standard, we benchmark a set of different protein inference procedures on the data. We observe that inference procedures excluding shared peptides provide more accurate estimates of errors compared to methods that include information from shared peptides, while still giving a reasonable performance in terms of the number of identified proteins. We also demonstrate that using a sample of known protein content without proteins with shared tryptic peptides can give a false sense of accuracy for many protein inference methods.

bioinformatics↗

bioRxiv2017

Accurate And Fast Feature Selection Workflow For High-Dimensional Omics Data

We are moving into the age of Big Data in biomedical research and bioinformatics. This trend could be encapsulated in this simple formula: D = S x F, where the volume of data generated (D) increases in both dimensions: the number of samples (S) and the number of sample features (F). Frequently, a typical bioinformatics problem (e.g. classification) includes redundant and irrelevant features that can result, in the worst-case scenario, in false positive results. Then, Feature Selection (FS) constitutes an enormous challenge. Despite the number and diversity of algorithms available, the proper choice of an approach for facing a specific problem often falls in a grey zone. In this study, we select a subset of FS methods to develop an efficient workflow and an R package for bioinformatics machine learning problems. We cover relevant issues concerning FS, ranging from domains problems to algorithm solutions and computational tools. Finally, we use seven different proteomics and gene expression datasets to evaluate the workflow and guide the FS process.

bioinformatics↗