Search

Protein chemical cross-linking and mass spectrometry enable the analysis of protein-protein interactions and protein topologies; however, complicated cross-linked peptide spectra require specialized algorithms to identify interacting sites. The Kojak cross-linking software application is a new, efficient approach to identify cross-linked peptides, enabling large-scale analysis of protein-protein interactions by chemical cross-linking techniques. The algorithm integrates spectral processing and scoring schemes adopted from traditional database search algorithms and can identify cross-linked peptides using many different chemical cross-linkers with or without heavy isotope labels. Kojak was used to analyze both novel and existing data sets and was compared to existing cross-linking algorithms. The algorithm provided increased cross-link identifications over existing algorithms and, equally importantly, the results in a fraction of computational time. The Kojak algorithm is open-source, cross-platform, and freely available. This software provides both existing and new cross-linking researchers alike an effective way to derive additional cross-link identifications from new or existing data sets. For new users, it provides a simple analytical resource resulting in more cross-link identifications than other methods.

We have extended the functionality of the Central Proteomics Facilities Pipeline (CPFP) to allow use of remote cloud and high performance computing (HPC) resources for shotgun proteomics data processing. CPFP has been modified to include modular local and remote scheduling for data processing jobs. The pipeline can now be run on a single PC or server, a local cluster, a remote HPC cluster, and/or the Amazon Web Services (AWS) cloud. We provide public images that allow easy deployment of CPFP in its entirety in the AWS cloud. This significantly reduces the effort necessary to use the software, and allows proteomics laboratories to pay for compute time ad hoc, rather than obtaining and maintaining expensive local server clusters. Alternatively the Amazon cloud can be used to increase the throughput of a local installation of CPFP as necessary. We demonstrate that cloud CPFP allows users to process data at higher speed than local installations but with similar cost and lower staff requirements. In addition to the computational improvements, the web interface to CPFP is simplified, and other functionalities are enhanced. The software is under active development at two leading institutions and continues to be released under an open-source license at http://cpfp.sourceforge.net.

Isobaric tagging techniques such as iTRAQ and TMT are widely used in quantitative proteomics and especially useful for samples that demand in vitro labeling. Due to diversity in choices of MS acquisition approaches, identification algorithms, and relative abundance deduction strategies, researchers are faced with a plethora of possibilities when it comes to data analysis. However, the lack of generic and flexible software tool often makes it cumbersome for researchers to perform the analysis entirely as desired. In this paper, we present MilQuant, mzXML-based isobaric labeling quantitator, a pipeline of freely available programs that supports native acquisition files produced by all mass spectrometer types and collection approaches currently used in isobaric tagging based MS data collection. Moreover, aside from effective normalization and abundance ratio deduction algorithms, MilQuant exports various intermediate results along each step of the pipeline, making it easy for researchers to customize the analysis. The functionality of MilQuant was demonstrated by four distinct datasets from different laboratories. The compatibility and extendibility of MilQuant makes it a generic and flexible tool that can serve as a full solution to data analysis of isobaric tagging-based quantitation., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Mass spectrometry-based proteomics is typically performed using high performance liquid chromatography (HPLC) to introduce peptides into the instrument via electrospray ionization. A variety of configurations exist with varying degrees of precision and cost, but the ultimate goal is the reproducible delivery of peptides in well-separated elution peaks. It is well-known that the quality of chromatography can have a dramatic effect on sample identification as well as run-to-run reproducibility, which is especially important for quantitative analyses. Despite the importance of the HPLC system for both shotgun and targeted proteomics, there are few tools available to monitor HPLC performance. In this paper, we describe a new open-source software application, named ChromEval, to allow rapid assessment of HPLC performance, as well as to provide other metrics of mass spectrometer performance, including mass accuracy calibration. ChromEval permits the user to visually monitor the elution of a set of standard peptides in quality control runs interspersed among a regular workflow. To perform these tasks, ChromEval searches mzXML files using Tandem and presents the peptide results in a graphical user interface (GUI) that allows fast assessment of chromatography by visualization of superimposed elution peaks. This tool facilitates the identification and troubleshooting of chromatography problems such as retention time shifts and variance in sample loading due to autosampler error. It also provides crude but consistent metrics of instrument performance including mass accuracy calibration and number of peptides identified from the standard mixture. ChromEval generates easily interpretable data quickly and thereby enables go/no-go decision making during intensive instrument operation.

Spectral library searching is a new approach in proteomic data analysis that promises to address some of the shortcomings of sequence database searching, currently the dominant method for inferring peptide identifications from tandem mass spectra. In spectral searching, a spectral library is first meticulously compiled from a large collection of previously observed and identified peptide MS/MS spectra. The unknown spectrum can then be identified by comparing it to all the candidates in the spectral library for the best match. It offers the benefits of tremendous speed gain and increase in sensitivity and selectivity, compared to sequence searching. This article provides a concise roadmap for the proteomics researchers to start using spectral library searching in their data analysis workflow.