Engineering topics

Shah, Anuj R

Publications and source records attributed to Shah, Anuj R.

6 records

DOE OSTI2013

MultiAlign: a multiple LC-MS analysis tool for targeted omics analysis

MultiAlign is a free software tool that aligns multiple liquid chromatography-mass spectrometry datasets to one another by clustering mass and LC elution features across datasets. Applicable to both label-free proteomics and metabolomics comparative analyses, the software can be operated in several modes. Clustered features can be matched to a reference database to identify analytes, used to generate abundance profiles, linked to tandem mass spectra based on parent precursor masses, and culled for targeted liquid chromatography-tandem mass spectrometric analysis. MultiAlign is also capable of tandem mass spectral clustering to describe proteome structure and find similarity in subsequent sample runs.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2011

Spectral archives: extending spectral libraries to analyze both identified and unidentified spectra.

Tandem mass spectrometry (MS/MS) experiments yield multiple, nearly identical spectra of the same peptide in various laboratories, but proteomics researchers typically do not leverage the unidentified spectra produced in other labs to decode spectra they generate. We propose a spectral archives approach that clusters MS/MS datasets, representing similar spectra by a single consensus spectrum. Spectral archives extend spectral libraries by analyzing both identified and unidentified spectra in the same way and maintaining information about peptide spectra that are common across species and conditions. Thus archives offer both traditional library spectrum similarity-based search capabilities along with new ways to analyze the data. By developing a clustering tool, MS-Cluster, we generated a spectral archive from ~1.18 billion spectra that greatly exceeds the size of existing spectral repositories. We advocate that publicly available data should be organized into spectral archives rather than be analyzed as disparate datasets, as is mostly the case today.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2010

An architecture for real time data acquisition and online signal processing for high throughput tandem mass spectrometry

Independent, greedy collection of data events using simple heuristics results in massive over-sampling of the prominent data features in large-scale studies over what should be achievable through “intelligent,” online acquisition of such data. As a result, data generated are more aptly described as a collection of a large number of small experiments rather than a true large-scale experiment. Nevertheless, achieving “intelligent,” online control requires tight interplay between state-of-the-art, data-intensive computing infrastructure developments and analytical algorithms. In this paper, we propose a Software Architecture for Mass spectrometry-based Proteomics coupled with Liquid chromatography Experiments (SAMPLE) to develop an “intelligent” online control and analysis system to significantly enhance the information content from each sensor (in this case, a mass spectrometer). Using online analysis of data events as they are collected and decision theory to optimize the collection of events during an experiment, we aim to maximize the information content generated during an experiment by the use of pre-existing knowledge to optimize the dynamic collection of events.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2010

Using support vector machine for improving protein-protein interaction prediction utilizing domain interactions

Understanding protein interactions is essential to gain insights into the biological processes at the whole cell level. The high-throughput experimental techniques for determining protein-protein interactions (PPI) are error prone and expensive with low overlap amongst them. Although several computational methods have been proposed for predicting protein interactions there is definite room for improvement. Here we present DomainSVM, a predictive method for PPI that uses computationally inferred domain-domain interaction values in a Support Vector Machine framework to predict protein interactions. DomainSVM method utilizes evidence of multiple interacting domains to predict a protein interaction. It outperforms existing methods of PPI prediction by achieving very high explanation ratios, precision, specificity, sensitivity and F-measure values in a 10 fold cross-validation study conducted on the positive and negative PPIs in yeast. A Functional comparison study using GO annotations on the positive and the negative test sets is presented in addition to discussing novel PPI predictions in Salmonella Typhimurium.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2010

An efficient data format for mass spectrometry based proteomics

The diverse range of mass spectrometry (MS) instrumentation along with corresponding proprietary and non-proprietary data formats has generated a proteomics community driven call for a standardized format to facilitate management, processing, storing, visualization, and exchange of both experimental and processed data. To date, significant efforts have been extended towards standardizing XML-based formats for mass spectrometry data representation, despite the recognized inefficiencies associated with storing large numeric datasets in XML. The proteomics community has periodically entertained alternate strategies for data exchange, e.g., using a common application programming interface or a database-derived format. However these efforts have yet to garner significant attention, mostly because they haven’t illustrated significant performance benefits over existing standards, but also due to issues such as extensibility to multi-dimensional separation systems, robustness of operation, and incomplete or mismatched vocabulary. Here, we describe a format based on standard database principles that offers multiple benefits over existing formats in terms of storage size, ease of processing, data retrieval times and extensibility to accommodate multi-dimensional separation systems.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2010

Applications in Data-Intensive Computing

This book chapter, to be published in Advances in Computers, Volume 78, in 2010 describes applications of data intensive computing (DIC). This is an invited chapter resulting from a previous publication on DIC. This work summarizes efforts coming out of the PNNL's Data Intensive Computing Initiative. Advances in technology have empowered individuals with the ability to generate digital content with mouse clicks and voice commands. Digital pictures, emails, text messages, home videos, audio, and webpages are common examples of digital content that are generated on a regular basis. Data intensive computing facilitates human understanding of complex problems. Data-intensive applications provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements through the development of new classes of software, algorithms, and hardware.

99 GENERAL AND MISCELLANEOUS↗