HistoSnap: A Novel Software Tool to Extract m/z-Specific Images from Large MSHC Datasets.

We describe an informatics tool for comfortable browsing through highly complex, multi-gigabyte mass spectrometry histochemistry (MSHC) datasets, via clever ion-specific image extraction.The package is developed particularly for the untargeted localization/discovery of biomolecules such as endogenous (neuro)secretory peptides on histological sections of biobanked formaldehyde-fixed paraffin-embedded (FFPE) samples straight from tissue banks.Atmospheric pressure-MALDI-Orbitrap MSHC data of sections through human pituitary adenomas in which two well-known human neuropeptides are detected are used as an example to demonstrate the key features of the novel software, named HistoSnap.

MS1-Based Data Analysis Approaches for FFPE Tissue Imaging of Endogenous Peptide Ions by Mass Spectrometry Histochemistry (MSHC).

Ambiguous reports in the literature exist regarding the use and usefulness of formalin-fixed paraffin-embedded (FFPE) tissues in mass spectrometry imaging (MSI). Especially for the study of endogenous (non-tryptic) peptides, several studies have concluded that MSI on archived FFPE tissue bank samples is virtually impossible. We here illustrate that by employing a variant of MSI, called mass spectrometry histochemistry (MSHC), biomolecular tissue localization data are obtained that unequivocally comprise endogenous peptides. We here discuss different informatics steps in a data analysis workflow to help filter peptide-related features out of large and complex datasets generated by atmospheric pressure matrix-assisted laser desorption/ionization high-resolution (Orbitrap mass analyzer) MSHC. These include, in addition to accurate mass measurements, Kendrick mass defect filtering and isotopic distribution scrutiny.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows.

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

LNCipedia 5: towards a reference set of human long non-coding RNAs.

While long non-coding RNA (lncRNA) research in the past has primarily focused on the discovery of novel genes, today it has shifted towards functional annotation of this large class of genes. With thousands of lncRNA studies published every year, the current challenge lies in keeping track of which lncRNAs are functionally described. This is further complicated by the fact that lncRNA nomenclature is not straightforward and lncRNA annotation is scattered across different resources with their own quality metrics and definition of a lncRNA. To overcome this issue, large scale curation and annotation is needed. Here, we present the fifth release of the human lncRNA database LNCipedia (https://lncipedia.org). The most notable improvements include manual literature curation of 2482 lncRNA articles and the use of official gene symbols when available. In addition, an improved filtering pipeline results in a higher quality reference lncRNA gene set.

SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification.

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in well-annotated mammalian species. The advances in sequencing technology have created a need for studies and tools that can characterize these novel variants. Here, we present SQANTI, an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse transcriptome using Pacific Biosciences (PacBio) long reads and illustrate how the tool is effective in characterizing and describing the composition of the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, resulting more frequently in novel ORFs than novel UTRs, and are enriched in both general metabolic and neural-specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read-based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases, we find that alternative isoforms are elusive to proteogenomics detection. SQANTI allows the user to maximize the analytical outcome of long-read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes.

Unbiased Protein Association Study on the Public Human Proteome Reveals Biological Connections between Co-Occurring Protein Pairs.

Mass-spectrometry-based, high-throughput proteomics experiments produce large amounts of data. While typically acquired to answer specific biological questions, these data can also be reused in orthogonal ways to reveal new biological knowledge. We here present a novel method for such orthogonal data reuse of public proteomics data. Our method elucidates biological relationships between proteins based on the co-occurrence of these proteins across human experiments in the PRIDE database. The majority of the significantly co-occurring protein pairs that were detected by our method have been successfully mapped to existing biological knowledge. The validity of our novel method is substantiated by the extremely few pairs that can be mapped to existing knowledge based on random associations between the same set of proteins. Moreover, using literature searches and the STRING database, we were able to derive meaningful biological associations for unannotated protein pairs that were detected using our method, further illustrating that as-yet unknown associations present highly interesting targets for follow-up analysis.

Noncoding after All: Biases in Proteomics Data Do Not Explain Observed Absence of lncRNA Translation Products.

Over the past decade, long noncoding RNAs (lncRNAs) have emerged as novel functional entities of the eukaryotic genome. However, the scientific community remains divided over the amount of true noncoding transcripts among the large number of unannotated transcripts identified by recent large scale and deep RNA-sequencing efforts. Here, we systematically exclude possible technical reasons underlying the absence of lncRNA-encoded proteins in mass spectrometry data sets, strongly suggesting that the large majority of lncRNAs is indeed not translated.

Database Search Engines: Paradigms, Challenges and Solutions.

The first step in identifying proteins from mass spectrometry based shotgun proteomics data is to infer peptides from tandem mass spectra, a task generally achieved using database search engines. In this chapter, the basic principles of database search engines are introduced with a focus on open source software, and the use of database search engines is demonstrated using the freely available SearchGUI interface. This chapter also discusses how to tackle general issues related to sequence database searching and shows how to minimize their impact.

Pladipus Enables Universal Distributed Computing in Proteomics Bioinformatics.

The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity; however, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here we propose a free and open-source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. Pladipus is straightforward to install and operate thanks to its user-friendly graphical interface, allowing complex bioinformatics tasks to be run easily on a network instead of a single computer. As a result, any researcher can benefit from the increased computational efficiency provided by distributed computing, hence empowering them to tackle more complex bioinformatics challenges. Notably, it enables any research group to perform large-scale reprocessing of publicly available proteomics data, thus supporting the scientific community in mining these data for novel discoveries.

Exploring the potential of public proteomics data.

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

An update on LNCipedia: a database for annotated human lncRNA sequences.

The human genome is pervasively transcribed, producing thousands of non-coding RNA transcripts. The majority of these transcripts are long non-coding RNAs (lncRNAs) and novel lncRNA genes are being identified at rapid pace. To streamline these efforts, we created LNCipedia, an online repository of lncRNA transcripts and annotation. Here, we present LNCipedia 3.0 (http://www.lncipedia.org), the latest version of the publicly available human lncRNA database. Compared to the previous version of LNCipedia, the database grew over five times in size, gaining over 90,000 new lncRNA transcripts. Assessment of the protein-coding potential of LNCipedia entries is improved with state-of-the art methods that include large-scale reprocessing of publicly available proteomics data. As a result, a high-confidence set of lncRNA transcripts with low coding potential is defined and made available for download. In addition, a tool to assess lncRNA gene conservation between human, mouse and zebrafish has been implemented.

Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory.

Modern day proteomics generates ever more complex data, causing the requirements on the storage and processing of such data to outgrow the capacity of most desktop computers. To cope with the increased computational demands, distributed architectures have gained substantial popularity in the recent years. In this review, we provide an overview of the current techniques for distributed computing, along with examples of how the techniques are currently being employed in the field of proteomics. We thus underline the benefits of distributed computing in proteomics, while also pointing out the potential issues and pitfalls involved.

Exercise-induced immunosuppression: roles of reactive oxygen species and 5'-AMP-activated protein kinase dephosphorylation within immune cells.

We previously proposed 5'-AMP-activated protein kinase (AMPK) dephosphorylation within immune cells as an intracellular mechanism linking exercise and immunosuppression. In this study, AMPK phosphorylation underwent transient (<1 h) decreases (53.8+/-7.2% basal) immediately after exercise (45 min of cycling at 70% VO2max) in a cohort of 16 adult male participants. Similar effects were seen with running. However, because exercise-induced inactivation of AMPK was previously shown to occur in an AMP-independent manner, the means by which AMPK is inactivated in this context is not yet clear. To investigate the hypothesis that exercise-induced inactivation of AMPK is mediated via signaling mechanisms distinct from changes in cellular AMP-to-ATP ratios, reactive oxygen species (ROS) and intracellular Ca2+ signaling were investigated in mononuclear cells before and after exercise and in cultured monocytic MM6 cells. In in vitro studies, treatment with an antioxidant (ascorbic acid, 4 h, 50 microM) decreased MM6 cell intracellular ROS levels (88.0+/-5.2% basal) and induced dephosphorylation of AMPK (44.7+/-17.6% basal). By analogy, the fact that exercise decreased mononuclear cell ROS content (32.8+/-16.6% basal), possibly due to downregulation (43.4+/-8.0% basal) of mRNA for NOX2, the catalytic subunit of the cytoplasmic ROS-generating enzyme NADPH oxidase, may provide an explanation for the AMPK-dephosphorylating effect of exercise. In contrast, exercise-induced Ca2+ signaling events did not seem to be coupled to changes in AMPK activity. Thus we propose that the exercise-induced decreases in both intracellular ROS and AMPK phosphorylation seen in this study constitute evidence supporting a role for ROS in controlling AMPK, and hence immune function, in the context of exercise-induced immunosuppression.