MaxQuant and MSstats in Galaxy Enable Reproducible Cloud-Based Analysis of Quantitative Proteomics Experiments for Everyone.

Quantitative mass spectrometry-based proteomics has become a high-throughput technology for the identification and quantification of thousands of proteins in complex biological samples. Two frequently used tools, MaxQuant and MSstats, allow for the analysis of raw data and finding proteins with differential abundance between conditions of interest. To enable accessible and reproducible quantitative proteomics analyses in a cloud environment, we have integrated MaxQuant (including TMTpro 16/18plex), Proteomics Quality Control (PTXQC), MSstats, and MSstatsTMT into the open-source Galaxy framework. This enables the web-based analysis of label-free and isobaric labeling proteomics experiments via Galaxy's graphical user interface on public clouds. MaxQuant and MSstats in Galaxy can be applied in conjunction with thousands of existing Galaxy tools and integrated into standardized, sharable workflows. Galaxy tracks all metadata and intermediate results in analysis histories, which can be shared privately for collaborations or publicly, allowing full reproducibility and transparency of published analysis. To further increase accessibility, we provide detailed hands-on training materials. The integration of MaxQuant and MSstats into the Galaxy framework enables their usage in a reproducible way on accessible large computational infrastructures, hence realizing the foundation for high-throughput proteomics data science for everyone.

Combinatorial Omics Analysis Reveals Perturbed Lysosomal Homeostasis in Collagen VII-deficient Keratinocytes.

The extracellular matrix protein collagen VII is part of the microenvironment of stratified epithelia and critical in organismal homeostasis. Mutations in the encoding gene COL7A1 lead to the skin disorder dystrophic epidermolysis bullosa (DEB), are linked to skin fragility and progressive inflammation-driven fibrosis that facilitates aggressive skin cancer. So far, these changes have been linked to mesenchymal alterations, the epithelial consequences of collagen VII loss remaining under-addressed. As epithelial dysfunction is a principal initiator of fibrosis, we performed a comprehensive transcriptome and proteome profiling of primary human keratinocytes from DEB and control subjects to generate global and detailed images of dysregulated epidermal molecular pathways linked to loss of collagen VII. These revealed downregulation of interaction partners of collagen VII on mRNA and protein level, but also increased abundance of S100 pro-inflammatory proteins in primary DEB keratinocytes. Increased TGF-ß signaling because of loss of collagen VII was associated with enhanced activity of lysosomal proteases in both keratinocytes and skin of collagen VII-deficient individuals. Thus, loss of a single structural protein, collagen VII, has extra- and intracellular consequences, resulting in inflammatory processes that enable tissue destabilization and promote keratinocyte-driven, progressive fibrosis.

Genome sequence of the fungus Glarea lozoyensis: the first genome sequence of a species from the Helotiaceae family.

The anamorphic fungus Glarea lozoyensis mutant strain 74030 is an overproducer of pneumocandin B(0), which is chemically converted into Cancidas, a potent antibiotic against clinically important fungal pathogens. Pneumocandins are acylated, cyclic hexapeptides with unusual hydroxylated amino acids. With the Glarea lozoyensis genome, the first species from the large polyphyletic family Helotiaceae has been sequenced.

Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework.

BACKGROUND: Data-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging owing to the high complexity and large data and sample size, which require specialized software and vast computing infrastructures. Most available open-source DIA software necessitates basic programming skills and covers only a fraction of a complete DIA data analysis. In consequence, DIA data analysis often requires usage of multiple software tools and compatibility thereof, severely limiting the usability and reproducibility. FINDINGS: To overcome this hurdle, we have integrated a suite of open-source DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef, and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their use on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate the usability of an all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided to further increase access for the proteomics community. CONCLUSION: The integration of an open-source DIA analysis suite in the web-based and user-friendly Galaxy framework in combination with extensive training material empowers a broad community of researches to perform reproducible and transparent DIA data analysis.

ChiRA: an integrated framework for chimeric read analysis from RNA-RNA interactome and RNA structurome data.

BACKGROUND: With the advances in next-generation sequencing technologies, it is possible to determine RNA-RNA interaction and RNA structure predictions on a genome-wide level. The reads from these experiments usually are chimeric, with each arm generated from one of the interaction partners. Owing to short read lengths, often these sequenced arms ambiguously map to multiple locations. Thus, inferring the origin of these can be quite complicated. Here we present ChiRA, a generic framework for sensitive annotation of these chimeric reads, which in turn can be used to predict the sequenced hybrids. RESULTS: Grouping reference loci on the basis of aligned common reads and quantification improved the handling of the multi-mapped reads in contrast to common strategies such as the selection of the longest hit or a random choice among all hits. On benchmark data ChiRA improved the number of correct alignments to the reference up to 3-fold. It is shown that the genes that belong to the common read loci share the same protein families or similar pathways. In published data, ChiRA could detect 3 times more new interactions compared to existing approaches. In addition, ChiRAViz can be used to visualize and filter large chimeric datasets intuitively. CONCLUSION: ChiRA tool suite provides a complete analysis and visualization framework along with ready-to-use Galaxy workflows and tutorials for RNA-RNA interactome and structurome datasets. Common read loci built by ChiRA can rescue multi-mapped reads on paralogous genes without requiring any information on gene relations. We showed that ChiRA is sensitive in detecting new RNA-RNA interactions from published RNA-RNA interactome datasets.

Accessible and reproducible mass spectrometry imaging data analysis in Galaxy.

BACKGROUND: Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. FINDINGS: We have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Furthermore, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research. CONCLUSION: The Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access, together with high levels of reproducibility and transparency.

MicroRNA Profiling in Aqueous Humor of Individual Human Eyes by Next-Generation Sequencing.

PURPOSE: Extracellular microRNAs (miRNAs) in aqueous humor were suggested to have a role in transcellular signaling and may serve as disease biomarkers. The authors adopted next-generation sequencing (NGS) techniques to further characterize the miRNA profile in single samples of 60 to 80 µL human aqueous humor. METHODS: Samples were obtained at the outset of cataract surgery in nine independent, otherwise healthy eyes. Four samples were used to extract RNA and generate sequencing libraries, followed by an adapter-driven amplification step, electrophoretic size selection, sequencing, and data analysis. Five samples were used for quantitative PCR (qPCR) validation of NGS results. Published NGS data on circulating miRNAs in blood were analyzed in comparison. RESULTS: One hundred fifty-eight miRNAs were consistently detected by NGS in all four samples; an additional 59 miRNAs were present in at least three samples. The aqueous humor miRNA profile shows some overlap with published NGS-derived inventories of circulating miRNAs in blood plasma with high prevalence of human miR-451a, -21, and -16. In contrast to blood, miR-184, -4448, -30a, -29a, -29c, -19a, -30d, -205, -24, -22, and -3074 were detected among the 20 most prevalent miRNAs in aqueous humor. Relative expression patterns of miR-451a, -202, and -144 suggested by NGS were confirmed by qPCR. CONCLUSIONS: Our data illustrate the feasibility of miRNA analysis by NGS in small individual aqueous humor samples. Intraocular cells as well as blood plasma contribute to the extracellular aqueous humor miRNome. The data suggest possible roles of miRNA in intraocular cell adhesion and signaling by TGF-ß and Wnt, which are important in intraocular pressure regulation and glaucoma.

Characterization and phylogenetic analysis of the mitochondrial genome of Glarea lozoyensis indicates high diversity within the order Helotiales.

BACKGROUND: Glarea lozoyensis is a filamentous fungus used for the industrial production of non-ribosomal peptide pneumocandin B0. In the scope of a whole genome sequencing the complete mitochondrial genome of the fungus has been assembled and annotated. It is the first one of the large polyphyletic Helotiaceae family. A phylogenetic analysis was performed based on conserved proteins of the oxidative phosphorylation system in mitochondrial genomes. RESULTS: The total size of the mitochondrial genome is 45,038 bp. It contains the expected 14 genes coding for proteins related to oxidative phosphorylation,two rRNA genes, six hypothetical proteins, three intronic genes of which two are homing endonucleases and a ribosomal protein rps3. Additionally there is a set of 33 tRNA genes. All genes are located on the same strand. Phylogenetic analyses based on concatenated mitochondrial protein sequences confirmed that G. lozoyensis belongs to the order of Helotiales and that it is most closely related to Phialocephala subalpina. However, a comparison with the three other mitochondrial genomes known from Helotialean species revealed remarkable differences in size, gene content and sequence. Moreover, it was found that the gene order found in P. subalpina and Sclerotinia sclerotiorum is not conserved in G. lozoyensis. CONCLUSION: The arrangement of genes and other differences found between the mitochondrial genome of G. lozoyensis and those of other Helotiales indicates a broad genetic diversity within this large order. Further mitochondrial genomes are required in order to determine whether there is a continuous transition between the different forms of mitochondrial genomes or G. lozoyensis belongs to a distinct subgroup within Helotiales.