Extramural Update September 2003

Background: Comprehensive description of the behavior of cellular components in a quantitative manner is essential for systematic understanding of biological events. Recent LC-MS/MS (tandem mass spectrometry coupled with liquid chromatography) technology, in combination with the SILAC (Stable Isotope Labeling by Amino acids in Cell culture) method, has enabled us to make relative quantitation at the proteome level. The recent report by Blagoev et al. (Nat. Biotechnol., 22, 1139–1145, 2004) indicated that this method was also applicable for the time-course analysis of cellular signaling events. Relative quatitation can easily be performed by calculating the ratio of peak intensities corresponding to differentially labeled peptides in the MS spectrum. As currently available software requires some GUI applications and is time-consuming, it is not suitable for processing largescale proteome data. Results: To resolve this difficulty, we developed an algorithm that automatically detects the peaks in each spectrum. Using this algorithm, we developed a software tool named AYUMS that automatically identifies the peaks corresponding to differentially labeled peptides, compares these peaks, calculates each of the peak ratios in mixed samples, and integrates them into one data sheet. This software has enabled us to dramatically save time for generation of the final report. Conclusion: AYUMS is a useful software tool for comprehensive quantitation of the proteome data generated by LC-MS/MS analysis. This software was developed using Java and runs on Linux, Windows, and Mac OS X. Please contact ayums@ims.u-tokyo.ac. jp if you are interested in the application. The project web page is http://www.csml. org/ayums/. Published: 18 January 2007 BMC Bioinformatics 2007, 8:15 doi:10.1186/1471-2105-8-15 Received: 16 August 2006 Accepted: 18 January 2007 This article is available from: http://www.biomedcentral. com/1471-2105/8/15 © 2007 Saito et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Page 1 of 10 (page number not for citation purposes) BMC Bioinformatics 2007, 8:15 http://www.biomedcentral. com/1471-2105/8/15 Background The LC-MS/MS system is one of the most frequently used instruments for shotgun protein identification [1-6]. Protein identification by LC-MS/MS analysis consists mainly of the following five steps: (i) The samples are prepared from protein mixtures by peptide fragmentation with a protease, e.g., trypsin. (ii) In the LC column, the digested peptides are separated according to their hydrophobicity and/or polarity (iii) In the survey scan (MS-1) mode, the peptides eluted from the LC system are continuously introduced into the mass spectrometer by electrospray ionization (ESI). (iv) The detector in the MS-1 mode separates peptides according to the mass/charge ratio (m/z) and selects the peaks with high intensity. (v) In the MS/ MS (MS-2) mode, the selected peptides are separated from other components and randomly fragmented by physical impact. The detector integrates the intensity of each fragment, leading to the generation of MS/MS spectra. Recent development of quantitative proteomics technology has made it possible to perform quantitative analysis of large-scale proteome data generated using the LC-MS/ MS system. SILAC (Stable Isotope Labeling by Amino acids in Cell culture) is one of the most effective methods for comparative analysis of the expression status of proteins among samples [7-10], including time-course analysis [11]. The SILAC method has undergone some modifications. One of the well-modified SILAC methods is as follows: (i) Target cells are incubated in three types of media, namely, media containing (1) natural arginine, (2) arginine containing stable isotope of 13C, or (3) arginine with two types of stable isotopes, 13C and 15N. (ii) The samples prepared from differentially labeled cells are mixed in equal proportions and introduced into the LC-MS/MS system. (iii) The peak derived from the same amino acid sequence is shifted in proportion to the difference of the number of neutrons between the samples. Relative quantitation can be performed by comparing the peak intensities of differentially labeled peptides [11]. The above method is widely used for describing various biological events [10-12]. For example, Blagoev et al. reported the global quantitative dynamics of phosphotyrosine-based signaling events by measuring the fold activation of related proteins at different time points [11]. Several types of software, e.g., SEQUEST [13], MOWSE [14], Mascot [15], ProteinProspector [16], and ProFound [17], have been developed for protein identification based on MS or MS/MS data. These software tools deduce a corresponding protein/peptide sequence from the measured data and generate a report with additional information, e.g. reliability score, gene ID, and modification if any. For quantitation, MZmine version 0.60 was developed for differential analyses of the LC/MS profile data [18]. Although this software uses a GUI interface with a powerful batch-processing function, its application is restricted to the analyses of LC/MS data. For further analyses using LC-MS/MS in combination with the SILAC method, MSQuant [19] has been developed. MSQuant has a GUI interface and runs on Windows OS. However, this software is not in stable operation and requires a huge memory (e.g., 2 GB) to run. In the present study, we have developed a completely automatic console-based software tool that is highly customized for LC-MS/MS proteome data obtained by the SILAC method. Here we report a new algorithm for peak detection, details of the data analysis pipeline, and a new platform-independent open source software, AYUMS, developed using this algorithm. Furthermore, we compare the results obtained by manual operation with those obtained using this software and discuss the respective performances. Implementation AYUMS consists of a series of steps for processing LC-MS/ MS data. The scope of this software is focused on data processing for extracting quantitative information from the raw data. Therefore, other tools should be used for the statistical analyses based on the information produced by AYUMS. This software is implemented as a stand-alone Java application and requires JRE 1.4. 2 or higher version. In contrast to MSQuant (which runs only on Windows), AYUMS is platform-independent, i.e., it runs on any of Windows, Unix, or Mac OS X. In addition, the generation of the final report is completely automatic. Software design Our aim was to develop a software tool that automatically executes the calculation of the peak ratios of differentially labeled peptides analyzed by LC-MS/MS. To achieve this, we adopted a console-based user interface (CUI). AYUMS requires two input files – an LC-MS/MS raw data file and a database search result file containing the information on the identified peptides/proteins. AYUMS generates an output report in a comma-separated value (CSV) format. The flow chart of AYUMS is shown in Figure 1 and the contents of the flow chart are described in the following sections. Input data style and conversion of the raw data file In the first stage (Stage 1 in Figure 1), AYUMS requires two files, namely, (i) a Mascot HTML file and (ii) a binary file in our original format (ayums format). For generating the Mascot HTML file, a peak list file is first prepared from the raw MS/MS data file using ProteinLynx (Micromass, UK). This peak list is searched against the protein database using Mascot (Matrix Science, UK) and the output of the database search is saved as an HTML file. The binary file is Page 2 of 10 (page number not for citation purposes) BMC Bioinformatics 2007, 8:15 http://www.biomedcentral. com/1471-2105/8/15 Page 3 of 10 (page number not for citation purposes) Flow chart of AYUMS igure 1 Flow chart of AYUMS. The procedure for AYUMS is illustrated in the flow chart. It consists of four stages: Stage 1, generation of an MS binary file, Stage 2, parsing of Mascot HTML, Stage 3, analysis of the spectrum data, Stage 4, generation of the analyzed reports. Stage 3 is subdivided into five steps, as described above. Is it the ayums format ? Start Convert to ASCII style using Databridge Convert to the ayums format Parse the Mascot HTML file (Stage 2) Obtain the SCAN and EXPERIMENT number at MS-2 from the Mascot HTML file Obtain retention time at LC and m/z value of the parent peptide (MS-1) from the ayums format of the MS-2 NO