Mass Spectrometry-Based Vitreous Proteomics: Validated Methods and Analysis Pipeline.

Retinal diseases like diabetic retinopathy and age-related macular degeneration affect millions of individuals worldwide and often lead to vision loss. Vitreous fluid abuts the retina, is accessible for sampling, and contains many proteins related to retinal disease. Therefore, analysis of vitreous is an important tool for studying retinal disease. Because it is rich in proteins and extracellular vesicles, mass spectrometry-based proteomics is an excellent method for vitreous analysis. Here, we discuss important variables to consider when performing vitreous proteomics via mass spectrometry.

Functional Annotation Routines Used by ABRF Bioinformatics Core Facilities - Observations, Comparisons, and Considerations.

The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.

APOBEC3A drives deaminase domain-independent chromosomal instability to promote pancreatic cancer metastasis.

Despite efforts in understanding its underlying mechanisms, the etiology of chromosomal instability (CIN) remains unclear for many tumor types. Here, we identify CIN initiation as a previously undescribed function for APOBEC3A (A3A), a cytidine deaminase upregulated across cancer types. Using genetic mouse models of pancreatic ductal adenocarcinoma (PDA) and genomics analyses in human tumor cells we show that A3A-induced CIN leads to aggressive tumors characterized by enhanced early dissemination and metastasis in a STING-dependent manner and independently of the canonical deaminase functions of A3A. We show that A3A upregulation recapitulates numerous copy number alterations commonly observed in patients with PDA, including co-deletions in DNA repair pathway genes, which in turn render these tumors susceptible to poly (ADP-ribose) polymerase inhibition. Overall, our results demonstrate that A3A plays an unexpected role in PDA as a specific driver of CIN, with significant effects on disease progression and treatment.

Bioinformatics Core Survey Highlights the Challenges Facing Data Analysis Facilities.

Over the last decade, the cost of -omics data creation has decreased 10-fold, whereas the need for analytical support for those data has increased exponentially. Consequently, bioinformaticians face a second wave of challenges: novel applications of existing approaches (e.g., single-cell RNA sequencing), integration of -omics data sets of differing size and scale (e.g., spatial transcriptomics), as well as novel computational and statistical methods, all of which require more sophisticated pipelines and data management. Nonetheless, bioinformatics cores are often asked to operate under primarily a cost-recovery model, with limited institutional support. Seeing the need to assess bioinformatics core operations, the Association of Biomolecular Resource Facilities Genomics Bioinformatics Research Group conducted a survey to answer questions about staffing, services, financial models, and challenges to better understand the challenges bioinformatics core facilities are currently faced with and will need to address going forward. Of the respondent groups, we chose to focus on the survey data from smaller cores, which made up the majority. Although all cores indicated similar challenges in terms of changing technologies and analysis needs, small cores tended to have the added challenge of funding their operations largely through cost-recovery models with heavy administrative burdens.

Bioinformatics Analysis of Whole Exome Sequencing Data.

This chapter contains a step-by-step protocol for identifying somatic SNPs and small Indels from next-generation sequencing data of tumor samples and matching normal samples. The workflow presented here is largely based on the Broad Institute's "Best Practices" guidelines and makes use of their Genome Analysis Toolkit (GATK) platform. Variants are annotated with population allele frequencies and curated resources such as GnomAD and ClinVar and curated effect predictions from dbNSFP using VCFtools, SnpEff, and SnpSift.

VIGOR: Interactive Visual Exploration of Graph Query Results.

Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR's ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors.

A chromosome-centric human proteome project (C-HPP) to characterize the sets of proteins encoded in chromosome 17.

We report progress assembling the parts list for chromosome 17 and illustrate the various processes that we have developed to integrate available data from diverse genomic and proteomic knowledge bases. As primary resources, we have used GPMDB, neXtProt, PeptideAtlas, Human Protein Atlas (HPA), and GeneCards. All sites share the common resource of Ensembl for the genome modeling information. We have defined the chromosome 17 parts list with the following information: 1169 protein-coding genes, the numbers of proteins confidently identified by various experimental approaches as documented in GPMDB, neXtProt, PeptideAtlas, and HPA, examples of typical data sets obtained by RNASeq and proteomic studies of epithelial derived tumor cell lines (disease proteome) and a normal proteome (peripheral mononuclear cells), reported evidence of post-translational modifications, and examples of alternative splice variants (ASVs). We have constructed a list of the 59 "missing" proteins as well as 201 proteins that have inconclusive mass spectrometric (MS) identifications. In this report we have defined a process to establish a baseline for the incorporation of new evidence on protein identification and characterization as well as related information from transcriptome analyses. This initial list of "missing" proteins that will guide the selection of appropriate samples for discovery studies as well as antibody reagents. Also we have illustrated the significant diversity of protein variants (including post-translational modifications, PTMs) using regions on chromosome 17 that contain important oncogenes. We emphasize the need for mandated deposition of proteomics data in public databases, the further development of improved PTM, ASV, and single nucleotide variant (SNV) databases, and the construction of Web sites that can integrate and regularly update such information. In addition, we describe the distribution of both clustered and scattered sets of protein families on the chromosome. Since chromosome 17 is rich in cancer-associated genes, we have focused the clustering of cancer-associated genes in such genomic regions and have used the ERBB2 amplicon as an example of the value of a proteogenomic approach in which one integrates transcriptomic with proteomic information and captures evidence of coexpression through coordinated regulation.

Mechanisms of thermal adaptation revealed from the genomes of the Antarctic Archaea Methanogenium frigidum and Methanococcoides burtonii.

We generated draft genome sequences for two cold-adapted Archaea, Methanogenium frigidum and Methanococcoides burtonii, to identify genotypic characteristics that distinguish them from Archaea with a higher optimal growth temperature (OGT). Comparative genomics revealed trends in amino acid and tRNA composition, and structural features of proteins. Proteins from the cold-adapted Archaea are characterized by a higher content of noncharged polar amino acids, particularly Gln and Thr and a lower content of hydrophobic amino acids, particularly Leu. Sequence data from nine methanogen genomes (OGT 15 degrees -98 degrees C) were used to generate 1111 modeled protein structures. Analysis of the models from the cold-adapted Archaea showed a strong tendency in the solvent-accessible area for more Gln, Thr, and hydrophobic residues and fewer charged residues. A cold shock domain (CSD) protein (CspA homolog) was identified in M. frigidum, two hypothetical proteins with CSD-folds in M. burtonii, and a unique winged helix DNA-binding domain protein in M. burtonii. This suggests that these types of nucleic acid binding proteins have a critical role in cold-adapted Archaea. Structural analysis of tRNA sequences from the Archaea indicated that GC content is the major factor influencing tRNA stability in hyperthermophiles, but not in the psychrophiles, mesophiles or moderate thermophiles. Below an OGT of 60 degrees C, the GC content in tRNA was largely unchanged, indicating that any requirement for flexibility of tRNA in psychrophiles is mediated by other means. This is the first time that comparisons have been performed with genome data from Archaea spanning the growth temperature extremes from psychrophiles to hyperthermophiles.