Multiplierz: An Extensible API Based Desktop Environment for Proteomics Data Analysis

BACKGROUND. Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge. RESULTS. We describe multiplierz, a flexible and open-source desktop environment for comprehensive proteomics data analysis. We use this framework to expose a prototype version of our recently proposed common API (mzAPI) designed for direct access to proprietary mass spectrometry files. In addition to routine data analytic tasks, multiplierz supports generation of information rich, portable spreadsheet-based reports. Moreover, multiplierz is designed around a "zero infrastructure" philosophy, meaning that it can be deployed by end users with little or no system administration support. Finally, access to multiplierz functionality is provided via high-level Python scripts, resulting in a fully extensible data analytic environment for rapid development of custom algorithms and deployment of high-throughput data pipelines. CONCLUSION. Collectively, mzAPI and multiplierz facilitate a wide range of data analysis tasks, spanning technology development to biological annotation, for mass spectrometry-based proteomics research.Dana-Farber Cancer Institute; National Human Genome Research Institute (P50HG004233); National Science Foundation Integrative Graduate Education and Research Traineeship grant (DGE-0654108

multiplierz: An Extensible API Based Desktop Environment for Proteomics Data Analysis

Background: Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge. Results: We describe multiplierz, a flexible and open-source desktop environment for comprehensive proteomics data analysis. We use this framework to expose a prototype version of our recently proposed common API (mzAPI) designed for direct access to proprietary mass spectrometry files. In addition to routine data analytic tasks, multiplierz supports generation of information rich, portable spreadsheet-based reports. Moreover, multiplierz is designed around a "zero infrastructure" philosophy, meaning that it can be deployed by end users with little or no system administration support. Finally, access to multiplierz functionality is provided via high-level Python scripts, resulting in a fully extensible data analytic environment for rapid development of custom algorithms and deployment of high-throughput data pipelines. Conclusion: Collectively, mzAPI and multiplierz facilitate a wide range of data analysis tasks, spanning technology development to biological annotation, for mass spectrometry-based proteomics research

Quantitative Assessment of Whole-Body Tumor Burden in Adult Patients with Neurofibromatosis

Purpose Patients with neurofibromatosis 1 (NF1), NF2, and schwannomatosis are at risk for multiple nerve sheath tumors and premature mortality. Traditional magnetic resonance imaging (MRI) has limited ability to assess disease burden accurately. The aim of this study was to establish an international cohort of patients with quantified whole-body internal tumor burden and to correlate tumor burden with clinical features of disease. Methods We determined the number, volume, and distribution of internal nerve sheath tumors in patients using whole-body MRI (WBMRI) and three-dimensional computerized volumetry. We quantified the distribution of tumor volume across body regions and used unsupervised cluster analysis to group patients based on tumor distribution. We correlated the presence and volume of internal tumors with disease-related and demographic factors. Results WBMRI identified 1286 tumors in 145/247 patients (59%). Schwannomatosis patients had the highest prevalence of tumors (P = 0.03), but NF1 patients had the highest median tumor volume (P = 0.02). Tumor volume was unevenly distributed across body regions with overrepresentation of the head/neck and pelvis. Risk factors for internal nerve sheath tumors included decreasing numbers of café-au-lait macules in NF1 patients (P = 0.003) and history of skeletal abnormalities in NF2 patients (P = 0.09). Risk factors for higher tumor volume included female gender (P = 0.05) and increasing subcutaneous neurofibromas (P = 0.03) in NF1 patients, absence of cutaneous schwannomas in NF2 patients (P = 0.06), and increasing age in schwannomatosis patients (p = 0.10). Conclusion WBMRI provides a comprehensive phenotype of neurofibromatosis patients, identifies distinct anatomic subgroups, and provides the basis for investigating molecular biomarkers that correlate with unique disease manifestations

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis

Quantitative Assessment of Whole-Body Tumor Burden in Adult Patients with Neurofibromatosis

Patients with neurofibromatosis 1 (NF1), NF2, and schwannomatosis are at risk for multiple nerve sheath tumors and premature mortality. Traditional magnetic resonance imaging (MRI) has limited ability to assess disease burden accurately. The aim of this study was to establish an international cohort of patients with quantified whole-body internal tumor burden and to correlate tumor burden with clinical features of disease.We determined the number, volume, and distribution of internal nerve sheath tumors in patients using whole-body MRI (WBMRI) and three-dimensional computerized volumetry. We quantified the distribution of tumor volume across body regions and used unsupervised cluster analysis to group patients based on tumor distribution. We correlated the presence and volume of internal tumors with disease-related and demographic factors.WBMRI identified 1286 tumors in 145/247 patients (59%). Schwannomatosis patients had the highest prevalence of tumors (P = 0.03), but NF1 patients had the highest median tumor volume (P = 0.02). Tumor volume was unevenly distributed across body regions with overrepresentation of the head/neck and pelvis. Risk factors for internal nerve sheath tumors included decreasing numbers of café-au-lait macules in NF1 patients (P = 0.003) and history of skeletal abnormalities in NF2 patients (P = 0.09). Risk factors for higher tumor volume included female gender (P = 0.05) and increasing subcutaneous neurofibromas (P = 0.03) in NF1 patients, absence of cutaneous schwannomas in NF2 patients (P = 0.06), and increasing age in schwannomatosis patients (p = 0.10).WBMRI provides a comprehensive phenotype of neurofibromatosis patients, identifies distinct anatomic subgroups, and provides the basis for investigating molecular biomarkers that correlate with unique disease manifestations

Interpreting Cancer Genomes Using Systematic Host Perturbations by Tumour Virus Proteins

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations associated with cancer predisposition and large numbers of somatic genomic alterations. However, it remains challenging to distinguish between background, or “passenger” and causal, or “driver” cancer mutations in these datasets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. To test the hypothesis that genomic variations and tumour viruses may cause cancer via related mechanisms, we systematically examined host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways that go awry in cancer, such as Notch signalling and apoptosis. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches result in increased specificity for cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate prioritization of cancer-causing driver genes so as to advance understanding of the genetic basis of human cancer

Immunopathological signatures in multisystem inflammatory syndrome in children and pediatric COVID-19

: Pediatric Coronavirus Disease 2019 (pCOVID-19) is rarely severe; however, a minority of children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) might develop multisystem inflammatory syndrome in children (MIS-C), with substantial morbidity. In this longitudinal multi-institutional study, we applied multi-omics (analysis of soluble biomarkers, proteomics, single-cell gene expression and immune repertoire analysis) to profile children with COVID-19 (n = 110) and MIS-C (n = 76), along with pediatric healthy controls (pHCs; n = 76). pCOVID-19 was characterized by robust type I interferon (IFN) responses, whereas prominent type II IFN-dependent and NF-κB-dependent signatures, matrisome activation and increased levels of circulating spike protein were detected in MIS-C, with no correlation with SARS-CoV-2 PCR status around the time of admission. Transient expansion of TRBV11-2 T cell clonotypes in MIS-C was associated with signatures of inflammation and T cell activation. The association of MIS-C with the combination of HLA A*02, B*35 and C*04 alleles suggests genetic susceptibility. MIS-C B cells showed higher mutation load than pCOVID-19 and pHC. These results identify distinct immunopathological signatures in pCOVID-19 and MIS-C that might help better define the pathophysiology of these disorders and guide therapy

Arborscapes: A Swarm-based Multi-agent Ecological Disturbance Model

This paper presents an agent-based, object-oriented ecological model of forest dynamics designed to examine the role of disturbance on diversity. Arborscapes is based on Swarm, an agent-based software platform that offers advantages for ecological modeling, including a suite of standardized libraries of objects, schedules, and probes, and architectural features such as inheritance, message passing, encapsulation, and hierarchical structure. Object-oriented models are more transparent, portable and more easily modified than process oriented models, and therefore promise to facilitate collaboration on computational experiments. The initial application of Arborscapes was the analysis of disturbance dynamics, but the model was designed to be modified for a variety of applications in the simulation of vegetation community dynamics