Retention time alignment algorithms for LC/MS data must consider non-linear shifts

Podwojski, Katharina; Fritsch, Arno; Chamrad, Daniel C.; Paul, Wolfgang J.; Sitek, Barbara; Stühler, Kai; Mutzel, Petra; Stephan, Christian; Meyer, Helmut E.; Urfer, Wolfgang; Ickstadt, Katja; Rahnenführer, Jörg

doi:10.1093/bioinformatics/btp052

Cited by 85 publications

(71 citation statements)

References 16 publications

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“…Retention time information for peptide identifications was extracted from SQT files corresponding to any peptide observed with a q-value Յ 0.01 across sample sets. Sample sets were aligned using an iterative strategy fitting a straight line through peptide retention time features (25) across sample sets until a Pearson score of R 2 Ͼ 0.99 was achieved. XIC areas for peptide MS1 spectra were integrated at mass tolerances of 20 ppm using retention time windows of Ϯ5.0 min surrounding initial peptide identification events.…”

Section: Preparation Of a Dilution Series Of Bovine Peptide Standard Inmentioning

confidence: 99%

Maximizing Peptide Identification Events in Proteomic Workflows Using Data-Dependent Acquisition (DDA)

Bateman

Goulding

Shulman

et al. 2014

Molecular & Cellular Proteomics

107

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Current analytical strategies for collecting proteomic data using data-dependent acquisition (DDA) are limited by the low analytical reproducibility of the method. Proteomic discovery efforts that exploit the benefits of DDA, such as providing peptide sequence information, but that enable improved analytical reproducibility, represent an ideal scenario for maximizing measureable peptide identifications in "shotgun"-type proteomic studies. Therefore, we propose an analytical workflow combining DDA with retention time aligned extracted ion chromatogram (XIC) areas obtained from high mass accuracy MS1 data acquired in parallel. We applied this workflow to the analyses of sample matrixes prepared from mouse blood plasma and brain tissues and observed increases in peptide detection of up to 30.5% due to the comparison of peptide MS1 XIC areas following retention time alignment of co-identified peptides. Furthermore, we show that the approach is quantitative using peptide standards diluted into a complex matrix. These data revealed that peptide MS1 XIC areas provide linear response of over three orders of magnitude down to low femtomole (fmol) levels. These findings argue that augmenting "shotgun" proteomic workflows with retention time alignment of peptide identifications and comparative analyses of corresponding peptide MS1 XIC areas improve the analytical performance of global proteomic discovery methods using DDA. Molecular & Cellular Proteomics 13: 10.1074/ mcp.M112.026500, 329-338, 2014.Label-free methods in mass spectrometry-based proteomics, such as those used in common "shotgun" proteomic studies, provide peptide sequence information as well as relative measurements of peptide abundance (1-3). A common data acquisition strategy is based on data-dependent acquisition (DDA) 1 where the most abundant precursor ions are selected for tandem mass spectrometry (MS/MS) analysis (1-2). DDA attempts to minimize redundant peptide precursor selection and maximize the depth of proteome coverage (2). However, the analytical reproducibility of peptide identifications obtained using DDA-based methods result in Ͻ75% overlap between technical replicates (3-4). Comparisons of peptide identifications between replicate analyses have shown that the rate of new peptide identifications increases sharply following two replicate sample injections and gradually tapers off after approximately five replicate injections (4). This phenomenon is due, in part, to the semirandom sampling of peptides in a DDA experiment (5).Alternate label-free methods focused on measuring the abundance of intact peptide ions, such as the accurate mass and time tag (AMT) approach (6 -8, 42), are aimed at differential analyses of extracted ion chromatogram (XIC) areas integrated from high mass accuracy peptide precursor mass spectra (MS1 spectra) exhibiting discrete chromatographic elution times. This method is particularly powerful for the analysis of post-translationally modified (PTM) peptides as pairing the low abundance of PTM candidates with the variabl...

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Section: Preparation Of a Dilution Series Of Bovine Peptide Standard Inmentioning

confidence: 99%

Maximizing Peptide Identification Events in Proteomic Workflows Using Data-Dependent Acquisition (DDA)

Bateman

Goulding

Shulman

et al. 2014

Molecular & Cellular Proteomics

107

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“…This could introduce an important amount of missed matches into the analysis using files containing very different samples as seen in (5). Furthermore, the default settings for the run OpenMS alignment module applies a linear retention time correction model, which may fail to correctly compensate for the retention time differences (36).…”

Section: Resultsmentioning

confidence: 99%

An Adaptive Alignment Algorithm for Quality-controlled Label-free LC-MS

Sandin

Ali

Hansson

et al. 2013

Molecular & Cellular Proteomics

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Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software. Molecular & Cellular Proteomics 12: 10.1074/mcp.O112.021907, 1407-1420, 2013. Shotgun liquid chromatography (LC)1 -MS/MS is widely used for quantitative proteomics research. There are several LC-MS/MS approaches that rely on chemical labels as internal standards, and additionally numerous label-free workflows exist. Of these options, label-free quantification has lately emerged as a viable approach because of its lack of limitations concerning the number and types of samples under investigation (1-3). Despite the advantages, there are a number of data analysis challenges (4) associated with the label-free methodology that may unnecessarily increase technical variation. This has led to an uncertainty toward the usage of the label-free workflows, to much extent because of difficulties in assessing how well the data analysis works for the experiment at hand. There is a large amount of software available for label-free data processing and recent studies have shown that the results of data analysis vary considerably with the choice of software (5-7).Two major types of label-free quantification exist; spectral count and precursor-based. The former is based on the number of times a peptide has been subjected to fragmentation using MS/MS. The counts for the peptide content of a protein are subsequently recalculated into a protein count, which is related to the protein abundance (3). Although straightforward, this quantification strategy has proven less accurate than the precursor-based method as the number of spectra necessary to quantify more subtle expression changes increases exponentially (8). As quantification is coupled to identification, the dependence on the semirandom MS/MS sampling process leads to variability in protein abundance (9). The quantification coverage is further limited as the commonly employed dynamic exclusion for increasing the number of identifications introduces saturation effects into the analysis (8). In contrast, label-free quantification using precursor intensities shows great potential for large-scale proteomic analyses (1-3, 10 -13).Data from a label-free sample consists of LC-MS files that can be visualized individually as t...

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“…This alignment algorithm effectively divides a chromatogram into hundreds of small time windows, within which all [17]. The divide-and-conquer algorithm shown here applies to retention time shift profiles of any shapes.…”

Section: Methodsmentioning

confidence: 99%

Retention Time Alignment of LC/MS Data by a Divide-and-Conquer Algorithm

Zhang

2012

J. Am. Soc. Mass Spectrom.

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Liquid chromatography-mass spectrometry (LC/MS) has become the method of choice for characterizing complex mixtures. These analyses often involve quantitative comparison of components in multiple samples. To achieve automated sample comparison, the components of interest must be detected and identified, and their retention times aligned and peak areas calculated. This article describes a simple pairwise iterative retention time alignment algorithm, based on the divide-and-conquer approach, for alignment of ion features detected in LC/MS experiments. In this iterative algorithm, ion features in the sample run are first aligned with features in the reference run by applying a single constant shift of retention time. The sample chromatogram is then divided into two shorter chromatograms, which are aligned to the reference chromatogram the same way. Each shorter chromatogram is further divided into even shorter chromatograms. This process continues until each chromatogram is sufficiently narrow so that ion features within it have a similar retention time shift. In six pairwise LC/MS alignment examples containing a total of 6507 confirmed true corresponding feature pairs with retention time shifts up to five peak widths, the algorithm successfully aligned these features with an error rate of 0.2%. The alignment algorithm is demonstrated to be fast, robust, fully automatic, and superior to other algorithms. After alignment and gap-filling of detected ion features, their abundances can be tabulated for direct comparison between samples.

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Retention time alignment algorithms for LC/MS data must consider non-linear shifts

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 85 publications

References 16 publications

Maximizing Peptide Identification Events in Proteomic Workflows Using Data-Dependent Acquisition (DDA)

Maximizing Peptide Identification Events in Proteomic Workflows Using Data-Dependent Acquisition (DDA)

An Adaptive Alignment Algorithm for Quality-controlled Label-free LC-MS

Retention Time Alignment of LC/MS Data by a Divide-and-Conquer Algorithm

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