msVolcano: A flexible web application for visualizing quantitative proteomics data

Robust co-immunoprecipitation with mass spectrometry for Caenorhabditis elegans using solid-phase enhanced sample preparation

Studying protein interactions in vivo can reveal key molecular mechanisms of biological processes. Co-immunoprecipitation with mass spectrometry detects protein-protein interactions with high throughput. The nematode Caenorhabditis elegans is a powerful genetic model organism for in vivo studies. Yet its rigid and complex tissues require optimization for biochemistry applications to ensure reproducibility. The authors optimized co-immunoprecipitation with mass spectrometry by combining a native co-immunoprecipitation procedure with single-pot, solid-phase enhanced sample preparation. The authors' results for the highly conserved chromatin regulator FACT subunits HMG-3 and HMG-4 demonstrated that single-pot, solid-phase enhanced sample preparation-integrated co-immunoprecipitation with mass spectrometry procedures for C. elegans samples are highly robust. Moreover, in an accompanying study about the chromodomain factor MRG-1 (MRG15 in humans), the authors demonstrated remarkably high reproducibility for ten replicate experiments.

CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry-based protein-protein interaction/association data

The elucidation of a protein’s interaction/association network is important for defining its biological function. Mass spectrometry–based proteomic approaches have emerged as powerful tools for identifying protein–protein interactions (PPIs) and protein–protein associations (PPAs). However, interactome/association experiments are difficult to interpret, considering the complexity and abundance of data that are generated. Although tools have been developed to identify protein interactions/associations quantitatively, there is still a pressing need for easy-to-use tools that allow users to contextualize their results. To address this, we developed CANVS, a computational pipeline that cleans, analyzes, and visualizes mass spectrometry–based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard with simple requirements, allowing users to interface easily with the pipeline, analyze complex experimental data, and create PPI/A networks. The application integrates systems biology databases such as BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data.

AdaReg: data adaptive robust estimation in linear regression with application in GTEx gene expressions

The Genotype-Tissue Expression (GTEx) project provides a valuable resource of large-scale gene expressions across multiple tissue types. Under various technical noise and unknown or unmeasured factors, how to robustly estimate the major tissue effect becomes challenging. Moreover, different genes exhibit heterogeneous expressions across different tissue types. Therefore, we need a robust method which adapts to the heterogeneities of gene expressions to improve the estimation for the tissue effect. We followed the approach of the robust estimation based on γ-density-power-weight in the works of Fujisawa, H. and Eguchi, S. (2008). Robust parameter estimation with a small bias against heavy contamination. J. Multivariate Anal. 99: 2053-2081 and Windham, M.P. (1995). Robustifying model fitting. J. Roy. Stat. Soc. B: 599-609, where γ is the exponent of density weight which controls the balance between bias and variance. As far as we know, our work is the first to propose a procedure to tune the parameter γ to balance the bias-variance trade-off under the mixture models. We constructed a robust likelihood criterion based on weighted densities in the mixture model of Gaussian population distribution mixed with unknown outlier distribution, and developed a data-adaptive γ-selection procedure embedded into the robust estimation. We provided a heuristic analysis on the selection criterion and found that our practical selection trend under various γ's in average performance has similar capability to capture minimizer γ as the inestimable mean squared error (MSE) trend from our simulation studies under a series of settings. Our data-adaptive robustifying procedure in the linear regression problem (AdaReg) showed a significant advantage in both simulation studies and real data application in estimating tissue effect of heart samples from the GTEx project, compared to the fixed γ procedure and other robust methods. At the end, the paper discussed some limitations on this method and future work.

OmicsVolcano: software for intuitive visualization and interactive exploration of high-throughput biological data

Advances in omics technologies have generated exponentially larger volumes of biological data; however, their analyses and interpretation are limited to computationally proficient scientists. We created OmicsVolcano, an interactive open-source software tool to enable visualization and exploration of high-throughput biological data, while highlighting features of interest using a volcano plot interface. In contrast to existing tools, our software and user-interface design allow it to be used without requiring any programming skills to generate high-quality and presentation-ready images.

VolcaNoseR is a web app for creating, exploring, labeling and sharing volcano plots

Comparative genome- and proteome-wide screens yield large amounts of data. To efficiently present such datasets and to simplify the identification of hits, the results are often presented in a type of scatterplot known as a volcano plot, which shows a measure of effect size versus a measure of significance. The data points with the largest effect size and a statistical significance beyond a user-defined threshold are considered as hits. Such hits are usually annotated in the plot by a label with their name. Volcano plots can represent ten thousands of data points, of which typically only a handful is annotated. The information of data that is not annotated is hardly or not accessible. To simplify access to the data and enable its re-use, we have developed an open source and online web tool with R/Shiny. The web app is named VolcaNoseR and it can be used to create, explore, label and share volcano plots (https://huygens.science.uva.nl/VolcaNoseR). When the data is stored in an online data repository, the web app can retrieve that data together with user-defined settings to generate a customized, interactive volcano plot. Users can interact with the data, adjust the plot and share their modified plot together with the underlying data. Therefore, VolcaNoseR increases the transparency and re-use of large comparative genome- and proteome-wide datasets.

SETD1A Methyltransferase Is Physically and Functionally Linked to the DNA Damage Repair Protein RAD18

SETD1A is a SET domain-containing methyltransferase involved in epigenetic regulation of transcription. It is the main catalytic component of a multiprotein complex that methylates lysine 4 of histone H3, a histone mark associated with gene activation. In humans, six related protein complexes with partly nonredundant cellular functions share several protein subunits but are distinguished by unique catalytic SET-domain proteins. We surveyed physical interactions of the SETD1A-complex using endogenous immunoprecipitation followed by label-free quantitative proteomics on three subunits: SETD1A, RBBP5, and ASH2L. Surprisingly, SETD1A, but not RBBP5 or ASH2L, was found to interact with the DNA damage repair protein RAD18. Reciprocal RAD18 immunoprecipitation experiments confirmed the interaction with SETD1A, whereas size exclusion and protein network analysis suggested an interaction independent of the main SETD1A complex. We found evidence of SETD1A and RAD18 influence on mutual gene expression levels. Further, knockdown of the genes individually showed a DNA damage repair phenotype, whereas simultaneous knockdown resulted in an epistatic effect. This adds to a growing body of work linking epigenetic enzymes to processes involved in genome stability.

Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR

ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. Then, it is classical to use these peptide-level pieces of information to derive the identity and quantity of the sample proteins before proceeding with refined statistical processing at protein-level, so as to bring out proteins which abundance is significantly different between different groups of samples. To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provides a detailed protocol on how to perform these eight processing steps with ProStaR.

FACT Sets a Barrier for Cell Fate Reprogramming in Caenorhabditis elegans and Human Cells

The chromatin regulator FACT (facilitates chromatin transcription) is essential for ensuring stable gene expression by promoting transcription. In a genetic screen using Caenorhabditis elegans, we identified that FACT maintains cell identities and acts as a barrier for transcription factor-mediated cell fate reprogramming. Strikingly, FACT's role as a barrier to cell fate conversion is conserved in humans as we show that FACT depletion enhances reprogramming of fibroblasts. Such activity is unexpected because FACT is known as a positive regulator of gene expression, and previously described reprogramming barriers typically repress gene expression. While FACT depletion in human fibroblasts results in decreased expression of many genes, a number of FACT-occupied genes, including reprogramming-promoting factors, show increased expression upon FACT depletion, suggesting a repressive function of FACT. Our findings identify FACT as a cellular reprogramming barrier in C. elegans and humans, revealing an evolutionarily conserved mechanism for cell fate protection.