Revealing the amino acid composition of proteins within an expanded genetic code.

The genetic code can be manipulated to reassign codons for the incorporation of non-standard amino acids (NSAA). Deletion of release factor 1 in Escherichia coli enhances translation of UAG (Stop) codons, yet may also extended protein synthesis at natural UAG terminated messenger RNAs. The fidelity of protein synthesis at reassigned UAG codons and the purity of the NSAA containing proteins produced require careful examination. Proteomics would be an ideal tool for these tasks, but conventional proteomic analyses cannot readily identify the extended proteins and accurately discover multiple amino acid (AA) insertions at a single UAG. To address these challenges, we created a new proteomic workflow that enabled the detection of UAG readthrough in native proteins in E. coli strains in which UAG was reassigned to encode phosphoserine. The method also enabled quantitation of NSAA and natural AA incorporation at UAG in a recombinant reporter protein. As a proof-of-principle, we measured the fidelity and purity of the phosphoserine orthogonal translation system (OTS) and used this information to improve its performance. Our results show a surprising diversity of natural AAs at reassigned stop codons. Our method can be used to improve OTSs and to quantify amino acid purity at reassigned codons in organisms with expanded genetic codes.

Development of a highly automated and multiplexed targeted proteome pipeline and assay for 112 rat brain synaptic proteins.

We present a comprehensive workflow for large scale (>1000 transitions/run) label-free LC-MRM proteome assays. Innovations include automated MRM transition selection, intelligent retention time scheduling that improves S/N by twofold, and automatic peak modeling. Improvements to data analysis include a novel Q/C metric, normalized group area ratio, MLR normalization, weighted regression analysis, and data dissemination through the Yale protein expression database. As a proof of principle we developed a robust 90 min LC-MRM assay for mouse/rat postsynaptic density fractions which resulted in the routine quantification of 337 peptides from 112 proteins based on 15 observations per protein. Parallel analyses with stable isotope dilution peptide standards (SIS), demonstrate very high correlation in retention time (1.0) and protein fold change (0.94) between the label-free and SIS analyses. Overall, our method achieved a technical CV of 11.4% with >97.5% of the 1697 transitions being quantified without user intervention, resulting in a highly efficient, robust, and single injection LC-MRM assay.

THE DESIGN AND USE OF A MINIMALLY-INVASIVE, EXPANDABLE RETRACTOR FOR DEEP-SEATED BRAIN LESIONS.

Access to deep-seated brain lesions (e.g., tumors, aneurysms, hematomas, and other malformations) is challenging due to the potential for retraction-induced injury. Traditionally, neurosurgeons use dissection and blade retractors to push apart tissue to visualize and operate on target lesions. These blades apply focal pressure onto the brain, resulting in ischemia, edema, and parenchymal trauma, leading to complications in up to 29% of cases. Tubular retractors were introduced to distribute forces radially and have led to improved safety and clinical outcomes. However, reports indicate that tubular retractors still led to complications in up to 9.1% of cases. Other concerns include significant pressure in the direction of insertion and the displacement of anatomic landmarks leading to inaccurate stereotaxis. We present a novel, minimally-invasive brain retractor that utilizes an expandable soft balloon to further reduce retraction-induced injury and increase stereotactic accuracy with a minimal port of entry. The device consists of a balloon catheter system, a clear sheath, and integration with neuronavigation stylets. This approach can reduce the rate of iatrogenic injury and improve clinical outcomes for brain lesion operations. Furthermore, we illustrate the efficacy of this device in use compared to those of conventional tubular and blade retractors in a pig cadaver.

A Clinical Decision Support System for Monitoring Post-Colonoscopy Patient Follow-Up and Scheduling.

This paper describes a natural language processing (NLP)-based clinical decision support (CDS) system that is geared towards colon cancer care coordinators as the end users. The system is implemented using a metadata- driven Structured Query Language (SQL) function (discriminant function). For our pilot study, we have developed a training corpus consisting of 2,085 pathology reports from the VA Connecticut Health Care System (VACHS). We categorized reports as "actionable"- requiring close follow up, or "non-actionable"- requiring standard or no follow up. We then used 600 distinct pathology reports from 6 different VA sites as our test corpus. Analysis of our test corpus shows that our NLP approach yields 98.5% accuracy in identifying cases that required close clinical follow up. By integrating this into our cancer care tracking system, our goal is to ensure that patients with worrisome pathology receive appropriate and timely follow-up and care.

YPED: an integrated bioinformatics suite and database for mass spectrometry-based proteomics research.

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database (YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a single laboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography-tandem mass spectrometry (LC-MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring (MRM)/selective reaction monitoring (SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.

Exploring the portability of informatics capabilities from a clinical application to a bioscience application.

This report describes XDesc (eXperiment Description), a pilot project that serves as a case study exploring the degree to which an informatics capability developed in a clinical application can be ported for use in the biosciences. In particular, XDesc uses the Entity-Attribute-Value database implementation (including a great deal of metadata-based functionality) developed in TrialDB, a clinical research database, for use in describing the samples used in microarray experiments stored in the Yale Microarray Database (YMD). XDesc was linked successfully to both TrialDB and YMD, and was used to describe the data in three different microarray research projects involving Drosophila. In the process, a number of new desirable capabilities were identified in the bioscience domain. These were implemented on a pilot basis in XDesc, and subsequently "folded back" into TrialDB itself, enhancing its capabilities for dealing with clinical data. This case study provides a concrete example of how informatics research and development in clinical and bioscience domains has the potential for synergy and for cross-fertilization.

Exploring issues of quality of service in a Next Generation Internet testbed: a case study using PathMaster.

This case study describes a project that explores issues of quality of service (QoS) relevant to the next-generation Internet (NGI), using the PathMaster application in a testbed environment. PathMaster is a prototype computer system that analyzes digitized cell images from cytology specimens and compares those images against an image database, returning a ranked set of "similar" cell images from the database. To perform NGI testbed evaluations, we used a cluster of nine parallel computation workstations configured as three subclusters using Cisco routers. This architecture provides a local "simulated Internet" in which we explored the following QoS strategies: (1) first-in-first-out queuing, (2) priority queuing, (3) weighted fair queuing, (4) weighted random early detection, and (5) traffic shaping. The study describes the results of using these strategies with a distributed version of the PathMaster system in the presence of different amounts of competing network traffic and discusses certain of the issues that arise. The goal of the study is to help introduce NGI QoS issues to the Medical Informatics community and to use the PathMaster NGI testbed to illustrate concretely certain of the QoS issues that arise.

X!!Tandem, an improved method for running X!tandem in parallel on collections of commodity computers.

The widespread use of mass spectrometry for protein identification has created a demand for computationally efficient methods of matching mass spectrometry data to protein databases. A search using X!Tandem, a popular and representative program, can require hours or days to complete, particularly when missed cleavages and post-translational modifications are considered. Existing techniques for accelerating X!Tandem by employing parallelism are unsatisfactory for a variety of reasons. The paper describes a parallelization of X!Tandem, called X!!Tandem, that shows excellent speedups on commodity hardware and produces the same results as the original program. Furthermore, the parallelization technique used is unusual and potentially useful for parallelizing other complex programs.

YPED: a web-accessible database system for protein expression analysis.

We have developed an integrated web-accessible software system called the Yale Protein Expression Database (YPED) to address the need for storage, retrieval, and integrated analysis of large amounts of data from high throughput proteomic technologies. YPED is an open source system which integrates gel analysis results with protein identifications from DIGE experiments. The system associates the DIGE gel spots and image, analyzed with DeCyder, with mass spectrometric protein identifications from selected gel spots. Following in gel trypsin digestion, proteins in spots of interest are analyzed using MALDI-TOF/TOF on an AB 4700 or, more recently, on an AB 4800 with protein identifications performed by Mascot in conjunction with the AB GPS Explorer system. In addition to DIGE, YPED currently handles protein identifications from MudPIT, iTRAQ, and ICAT experiments. Sample descriptions are compatible with the evolving MIAPE standards. Tandem MS/MS results from MudPIT, and ICAT analyses are validated with the Trans-Proteomic Pipeline and then stored in the database for viewing and linking to the identified proteins. Researchers can view, subset, and download their data through a secure Web interface that includes a table containing proteins identified, a sample summary, the sample description, and a clickable gel image for DIGE samples. Tools are available to facilitate sample comparison and the viewing of phosphoproteins. A summary report with PANTHER Classification System annotations is also available to aid in biological interpretation of the results. The source code is open-source and is available from http://yped.med.yale.edu/yped_dist.

Probabilistic enrichment of phosphopeptides by their mass defect.

The mass defect, that is, the difference between the nominal and actual monoisotopic masses, of a phosphorus in a phosphate group is greater than for most other atoms present in proteins. When the mass defects of tryptic peptides derived from the human proteome are plotted against their masses, phosphopeptides tend to fall off the regression line. By calculating the masses of all potential tryptic peptides from the human proteome, we show that regions of higher phosphorylation probability exist on such a plot. We developed a transformation function to estimate the mass defect of a peptide from its monoisotopic mass and empirically defined a simple formula for a user-selectable discriminant line that categorizes a peptide mass according to its probability of being phosphorylated. Our method performs similarly well on phosphopeptides derived from a database of experimentally validated phosphoproteins. The method is relatively insensitive to mass measurement error of up to 20 ppm. The approach can be used with a tandem mass spectrometer in real time to rapidly select and rank order the possible phosphopeptides from a mixture of unmodified peptides for subsequent phosphorylation site mapping and peptide sequence analysis.

YPED: a proteomics database for protein expression analysis.

We have developed the Yale Protein Expression Database (YPED) to address the storage, retrieval, and integrated analysis of proteomics data generated by Yale's Keck Protein Chemistry and Mass Spectrometry Facility. YPED is Web-accessible and currently handles sample requisition, result reporting and sample comparison for ICAT, DIGE and MUDPIT samples. Sample descriptions are compatible with the evolving MIAPE standards. Peptides and proteins identified using Sequest or Mascot are validated with the Trans-Proteomic Pipeline developed at the Institute of Systems Biology and data from the resulting XML file are stored in the database. Researchers can view, subset and download their data through a secure Web interface.

The integration of similar clinical research data collection instruments.

We devised an algorithm for integrating similar clinical research data collection instruments to create a common measurement instrument. We tested this algorithm using questions from several similar surveys. We encountered differing levels of granularity among questions and responses across surveys resulting in either the loss of granularity or data. This algorithm may make survey integration more systematic and efficient.