How To Download Camera Results From Xcms Online
Curr Opin Chem Biol. Author manuscript; available in PMC 2017 Feb ane.
Published in last edited class as:
PMCID: PMC4831061
NIHMSID: NIHMS740427
A Roadmap for the XCMS Family of Software Solutions in Metabolomics
Nathaniel G. Mahieu
1 Department of Chemistry, Washington Academy in St. Louis, St. Louis, MO 63130
2 Department of Medicine, Washington University Schoolhouse of Medicine, St. Louis, MO 63110
Jessica Lloyd Genenbacher
1 Section of Chemistry, Washington University in St. Louis, St. Louis, MO 63130
2 Department of Medicine, Washington University Schoolhouse of Medicine, St. Louis, MO 63110
Gary J. Patti
1 Section of Chemistry, Washington Academy in St. Louis, St. Louis, MO 63130
two Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110
Abstract
Global profiling of metabolites in biological samples past liquid chromatography/mass spectrometry results in datasets too large to evaluate manually. Fortunately, a variety of software programs are now available to automate the data analysis. Selection of the appropriate processing solution is dependent upon experimental pattern. Most metabolomic studies a decade agone had a relatively simple experimental blueprint in which the intensities of compounds were compared between simply 2 sample groups. More recently, nonetheless, increasingly sophisticated applications have been pursued. Examples include comparison compound intensities between multiple sample groups and unbiasedly tracking the fate of specific isotopic labels. The latter types of applications take necessitated the development of new software programs, which have introduced additional functionalities that facilitate data analysis. The objective of this review is to provide an overview of the freely available bioinformatic solutions that are either based upon or are compatible with the algorithms in XCMS, which we broadly refer to here as the "XCMS family" of software. These include CAMERA, credentialing, Warpgroup, metaXCMS, XthirteenCMS, and XCMS Online. Together, these informatic technologies can accommodate most cut-edge metabolomic applications and offer some advantages when compared to the original XCMS program.
Introduction
Information from liquid chromatography/mass spectrometry (LC/MS)-based untargeted metabolomic experiments are highly circuitous. Therefore, bioinformatic software is typically required for processing of the results. At this fourth dimension, there are many reliable software solutions available.[1-9] It is not the purpose of this review to comprehensively detail each, nor is it our intent to provide any type of comparative evaluation. Rather, we will exclusively focus on a pick of freely available software solutions which are interoperable with the XCMS programme. Some of these software solutions bear variants of the XCMS name, while others practice not. Nosotros broadly refer to the class as a whole as the "XCMS family".
Defining the Needs: A General Bioinformatic Workflow
Historically, the bioinformatic workflow for processing untargeted metabolomic data has involved three general steps: feature detection, correspondence determination, and context-dependent analysis of the resulting measured values (Figure i and Figure 2).[10,11] Each is briefly outlined below.
The bioinformatic workflow for processing untargeted metabolomic data with XCMS. The workflow has three general steps: ane. Feature detection, 2. Correspondence determination, and iii. Additional context-dependent assay. These steps are numbered in blood-red on the schematic. Later on acquisition of LC/MS profiling information, feature detection is performed on the raw information to generate a peaks table (pace i). Next, retention time drift is corrected (step 2a). The OBI-warp algorithm implemented within XCMS operates on the raw data to determine retentiveness time drift. This produces a retention time correction map which, together with the peaks table, is used to institute correspondence and generate a groups table (step 2b). The peaks tabular array and the groups table are the input for a variety of further analyses. The 3rd footstep is dependent upon experimental objectives. In the standard XCMS assay, footstep iii is statistical assay. The other programs listed utilize the peaks table and groups table to achieve different aims such as adduct and antiquity annotation, multiple-factor assay, and isotopic label tracking.
Schematic of the centWave and OBI-warp algorithms as implemented within XCMS. A. The beginning stride in centWave is to discover sequent scans in which peaks are detected within a specific mass error (top). These are referred to as regions of interest (ROIs). 2 such ROIs are displayed hither and boxed in red. Adjacent, extracted ion chromatograms are created for each ROI (bottom). Extracted ion chromatograms that display a summit shape are then added to the peaks table, as illustrated by the light-green checkmark and arrow. B. OBI-warp aligns a query sample to a reference sample. Here nosotros illustrate a representative case in which two features are shifted in the query sample compared to the reference sample. Application of the correction curve to the query (bottom) brings the samples into alignment.
1. The kickoff and perchance well-nigh of import footstep is feature detection (likewise known as summit detection or peak picking). The purpose of this step is to extract signals in the dataset that ascend from real compounds, while attempting to exclude signals resulting from various noise sources.[12] Extracted signals with a unique mass-to-accuse ratio and retention fourth dimension are recorded as features. (Effigy 2A)
2. The second step in the workflow is establishing correspondence between the features detected from different sample runs. Correspondence refers to establishing which features from different belittling runs "correspond" to the aforementioned analyte. Establishing correspondence is arguably the almost challenging step in the processing of untargeted metabolomic data.[11] Although the same analyte may be detected in multiple experimental runs, the measured mass-to-charge ratio and retention time of the analyte can vary in each run due to factors such as temperature fluctuation and column deposition (Effigy 3A).
Illustrating the correspondence problem. A. Extracted ion chromatograms of citrate from three samples bear witness that its retention time and its measured mass-to-accuse values vary between three samples run dorsum to back. B. Uncorrected retention time drift of all features detected in sample 2 as compared to sample 1 (top). Uncorrected drift remaining subsequently OBI-warp correction. (bottom). Note that though correction reduced the overall migrate, there is no global correction which volition perfectly align all peaks due to multiple, compound-specific drifts occurring at a single retention time.
In practice, the majority of investigators performing LC/MS-based metabolomics currently affirm correspondence past adjustment the time domains of each run with fourth dimension-warping techniques. (Figure 2B) The objective is to correct for migrate factors so that features can be grouped between samples by direct matching of retention time. Although the alignment approach for establishing correspondence has enabled many laboratories to successfully clarify untargeted metabolomic data, many drift factors are chemical compound specific and therefore global-alignment techniques only reduce the total drift but do not eliminate it (Effigy 3B). Accordingly, there remains a dandy need for robust correspondence determination algorithms and this is an active area of research involvement.[11]
three. The last footstep of the workflow is context dependent. Analyses diverge, depending on experimental goals. In the simple cases when the objective is to compare sample classes, this step amounts to performing statistical analysis on the intensities of detected features. For more than advanced objectives such equally isotope tracing or tandem mass spectral analysis, additional algorithms are required.
Introducing XCMS
In 2006, the XCMS software was published as one of the first programs to provide a consummate solution to the bioinformatic workflow outlined in a higher place for processing untargeted metabolomic information.[13] The "Ten" in the XCMS acronym is used to denote that the software can be practical to any form of chromatography. To date, yet, XCMS has been mostly used to process LC/MS-based metabolomic data. The original XCMS software used the matchedFilter algorithm to accomplish characteristic detection, the retcor.peakgroups algorithm to perform alignment (an application of LOESS regression to well-behaved peak groups), and the group.density algorithm to group aligned features across samples on the ground of m/z bins. In recent years, a new algorithm for feature detection called centWave and a new algorithm for alignment called OBI-warp have been implemented within XCMS (Figure 2).[xiv,15] Information technology is worth noting that while these algorithms have led to meliorate overall XCMS functioning, there is even so great opportunity for comeback. Information technology is exciting to consider, for instance, that there are hundreds of published algorithms for acme detection and correspondence conclusion which have not yet been implemented inside XCMS for comparative evaluation.[11,16]
Applying the centWave, OBI-warp, and group.density algorithms within XCMS results in what are known as the peaks tabular array and the groups table (Effigy ane). In the standard application of XCMS, the peaks tabular array and the groups table are then used to create a diffreport. The diffreport provides statistics on feature groups that have altered intensities between sample groups.[17] When the original XCMS software was published in 2006, generating such a diffreport in the programming language R was considered cutting edge. From the diffreport, investigators can count the number of features detected from a sample to crudely compare metabolomic workflows.[eighteen,xix] More importantly, researchers can use the adamant p-values and fold changes to observe features with statistically meaning changes in intensity betwixt two sample groups. Even so, the XCMS diffreport likewise has some serious limitations. It does not provide metabolite identifications, which generally require matching tandem mass spectra from the inquiry sample to the tandem mass spectra of authentic standards.[20] Additionally, the diffreport does non provide a reliable approximation of metabolites detected due to adducts, isotopes, fragments, and artifacts.[21,22] Indeed, depending on experimental conditions, more 50% of the features on a diffreport can be a result of fragments and artifacts.[23] As the field of metabolomics has evolved over the final decade, there has been a major push to amend comment the XCMS diffreport. Multiple bioinformatic strategies which are interoperable with the XCMS program accept at present emerged that enable identification of adducts, isotopes, artifacts, and in some cases fifty-fifty structures.[24] A option of these resources is detailed in the sections that follow.
Also note that the XCMS diffreport was designed for evaluating features with contradistinct intensities between only two sample classes. Nevertheless, in that location are a growing number of applications with more sophisticated experimental designs involving multifactorial assay and stable isotope labeling. These types of applications require that step iii of the bioinformatic workflow shown in Figure 1 diverge from that of the standard XCMS program. Thus, new software solutions have been developed that operate on the peaks table and the groups tabular array with unique algorithms. Examples will exist highlighted below.
A Clarification on Terminology
As multiple programs have emerged with variants of the XCMS proper name, it may be confusing for new investigators to distinguish which software is appropriate to employ for specific applications. Every bit an example, XCMS2 was the first programme to be related in proper noun to the original XCMS software.[25] Sometimes the plan'south name is written as XCMS2, which may suggest that information technology implements a new generation of algorithms for the core functionalities of XCMS. All the same, XCMS2 merely differs from XCMS in its power to process tandem mass spectral data. Nosotros will not discuss XCMS2 further in this review. Processing of tandem mass spectral data will be covered in our discussion of XCMS Online.
Below, we highlight software programs which are interoperable with XCMS and provide key solutions to some common challenges in untargeted metabolomics. Near of these programs apply the XCMS peaks table and/or groups table as their inputs. Therefore, collectively, we refer to them as the XCMS family unit of software.
CAMERA: Annotating Isotopologues, Adducts, Clusters, and Fragments
When a metabolite is analyzed past electrospray ionization-mass spectrometry (ESI-MS), it is usually detected as more a unmarried ion species in the same mass spectrum due to the presence of isotopologues, adducts, clusters, and in-source fragments.[26] Considering these ion species accept different mass-to-charge values, XCMS reports each every bit a unique feature.[27] This increases the complication of the XCMS diffreport and complicates statistical analysis as well as chemical compound identification.
Given that adducts, clusters, and fragments are generally formed at the source in ESI-MS, they share the same memory time every bit the parent compound. Similarly, isotopes commonly exercise not influence retentiveness.[28] Thus, a strategy widely employed to group these types of related features is evaluation of chromatographic height shape similarity.[29] The approach has been used past several software programs, just here we describe CAMERA because it was designed for postprocessing of the XCMS output.[28] Similar XCMS, CAMERA is freely available from the Bioconductor repository.
In addition to group related features, CAMERA also attempts to annotate ion species past applying a rule tabular array. The rule table works for identifying isotopes, frequent adducts such every bit sodium and chloride, and common neutral losses or cluster-ions. Users likewise have the choice to combine LC/MS data from positive and negative modes to improve the reliability of ion annotations.
Credentialing: Annotating Artifacts
In a conventional LC/MS-based metabolomic experiment, the XCMS diffreport includes a big number of "artifactual" features. These features significantly complicate interpretation of the data because they are not directly associated with the sample only rather ascend from contaminants introduced during analysis or from chemic noise, bioinformatic noise, etc.[21] Unfortunately, data in the XCMS diffreport is insufficient to discriminate artifactual features from biological features. Artifacts are particularly problematic when attempting to translate metabolomic data at the comprehensive level. When evaluating different analytical methods to compare metabolome coverage, for case, it has been demonstrated that higher characteristic numbers do not necessarily correlate with more detected metabolites.[21] In part, this is because artifacts are highly variable and change as a part of extraction procedure, separation technology, mobile phase, instrumentation, and mass spectrometer settings.
Currently, approaches to place artifacts in metabolomic data rely upon stable isotopes.[21,22] While these strategies have proven effective, we should signal out that their application is limited to samples which can be cultured with labels (clinical specimens remain a challenge). One approach for removing artifacts, known equally credentialing, was designed to be interoperable with the XCMS software.[21] In the credentialing scheme, artifactual features are distinguished by growing cells on heavy isotopic carbon and mixing them with natural-abundance samples at defined ratios. Notably, only features of cellular origin will accept appropriate isotopic partners at the appropriate ratios. Thus, without structurally identifying every feature, artifacts can be filtered from the dataset computationally by using the credentialing software algorithms. With this platform, the number of "credentialed features" tin be used (instead of total features) as a more reliable metric to criterion analytical performance.
Warpgroup: Improving Quantitation with Consensus Integration Bounds
The standard XCMS workflow employs the centWave and grouping.density algorithms to find peaks in each sample independently. In this scheme, the data used to group peaks is merely the average chiliad/z and retention fourth dimension from all samples analyzed. Further, as each sample's raw information are treated in isolation, differences in integration regions between samples contribute to increased variance in the processed dataset. Warpgroup is an XCMS compatible package that addresses these limitations with consensus integration spring analysis.[thirty] Warpgroup applies dynamic time warping and graph analysis to improve the precision of metabolomic data processing. Warpgroup improvements include: correspondence decision that leverages the local extracted ion chromatogram topography; detection and group of height subregions; selection of similar integration bounds for each group; intelligent missing value filling; and reporting of several parameters which allow the filtering of bioinformatic noise.
The benefits of Warpgroup are due to the retrospective combination of several independent rounds of summit detection. For an E. coli dataset, as an example, application of Warpgroup resulted in an increase in the number of unique detected analytes by 26% and halved the hateful coefficient of variation of all analytes (compared to the XCMS algorithms solitary).[thirty] Warpgroup is implemented in a full general manner and is applicative to all fourth dimension series data, including metabolomic data from other software packages.
metaXCMS: Finding Shared Alterations Amid Multiple Sample Classes
The original XCMS algorithms were designed to compare the intensities of features from merely two sample groups. The challenge of applying simple pairwise comparisons is that knocking out a single protein tin can lead to hundreds or thousands of changes in characteristic intensities due to the interconnectivity of related pathways.[31] For example, knocking out a protein may decrease the product of that protein. However, decreased levels of the protein's production may then itself lead to a cascade of other context-dependent metabolic alterations. Determining which metabolites are altered straight every bit a issue of knocking out a protein from those that are contradistinct indirectly is challenging. Thus, information technology has become increasingly common in metabolomics to look for dysregulation shared amid multiple sample groups as a strategy for information reduction. metaXCMS enables such multiple-factor comparisons by operating on XCMS diffreports.[32,33]
The power of assessing shared metabolic differences among multiple sample groups is peradventure best demonstrated by example. When control C. elegans worms were compared to long-lived C. elegans worms in which the germ line had been removed past glp-1 mutation, ~44% of the total detected features (13639) were altered with a p-value <0.05 and a fold modify >2.[34] From these data alone, features directly associated with increased life span could not exist distinguished from those features that were altered from glp-i mutation simply that did non affect life span. Because germ line induced extensions in life bridge are dependent upon the FOXO transcription factor DAF-16, double mutant daf-16;glp-ane worms are short lived. Thus, a comparison of long-lived glp-1 worms to both wildtype worms and brusk-lived daf-16;glp-1 worms with metaXCMS revealed shared features that were uniquely altered in glp-1 induced longevity. Past performing similar analyses of other long-lived worms with metaXCMS, the number of detected features directly associated with longevity was ultimately reduced to six.[34]
TenxiiiCMS: Unbiased Mapping of Isotopic Fates
Although the intensities of thousands of features are measured past LC/MS-based untargeted metabolomics, these information provide simply a static snapshot of cellular metabolism and exercise not generally capture the complex dynamics of biochemical pathways.[35] To quantitate metabolic fluxes and to determine the contribution of specific nutrients to metabolite/macromolecular synthesis, investigators typically use isotope-labeled tracers.[36] A number of robust approaches, such as metabolic flux analysis, are well established for these types of studies.[1] Most of the approaches use mass spectrometry or NMR to measure isotopic labeling in a targeted set of compounds.
In contempo years, in that location has been a growing interest to integrate untargeted metabolomic technologies with stable isotopic tracers. Ane potential advantage of such an experimental blueprint is the unbiased and comprehensive tracking of metabolite fates.[37,38] By following the metabolism of a labeled chemical compound fed to a biological system comprehensively as a part of time by using LC/MS-based metabolomic approaches, new metabolite transformations may exist discovered. Additionally, by comparing labeling patterns betwixt different phenotypes using global metabolomic technologies, it is possible to place relative changes in flux distributions.[39]
The original XCMS software was not designed to back up experiments involving isotopic labels. Although analysis of isotopic labels can exist accomplished by using XCMS together with CAMERA, the X13CMS software was recently developed specifically to support experimental designs based on stable isotopes.[28,40] To utilize XthirteenCMS, LC/MS data acquired from samples with and without isotopic characterization are first processed past XCMS. The XCMS results are and then forwarded to XxiiiCMS, which identifies isotopologue groups corresponding to isotopically labeled compounds. Grouping of isotopologues is performed without any a priori cognition except input of isotopic label(south) used, instrument mass accuracy, and chromatographic migrate tolerance. The labeling pattern of each compound determined to be isotopically enriched can be quantitatively compared from multiple sample groups by using the getIsoDiffReport algorithm implemented within X13CMS.
XCMS Online: Metabolomics on the Cloud
The bioinformatic resources discussed up to this indicate are distributed equally R packages and operated through a control-line interface or customized scripts. One major advantage of this format is flexibility. Researchers can alter the XCMS algorithms to suit their ain specific needs. The modular nature of the original XCMS software has made information technology interoperable with new generations of programs for untargeted metabolomics and enabled multiple enquiry laboratories to better upon the original XCMS algorithms.[14,15,41,42]
A limitation of distributing XCMS as an R bundle is that many users do non have the programming expertise to use a control-line interface. This can be particularly problematic for clinical and biological laboratories. In response to this event, an intuitive graphical interface was developed to process untargeted metabolomic data which implements many of the algorithms described in this review including those in XCMS, Camera, metaXCMS, too every bit others. The platform, chosen XCMS Online, is deject based.[43] Investigators upload untargeted metabolomic data by simply dragging and dropping their files into the program. Parameters are then selected and processing occurs on the deject. Researches receive an eastward-postal service notifying them when processing is complete. Results tin then exist viewed online, or downloaded for later use. An advantage unique to XCMS Online is that data are directly searched confronting the METLIN metabolite database.[44] When users upload both MS and MS/MS data, the matching tin be performed on the basis of accurate mass and fragmentation patterns.[24] Thus, within XCMS Online, features on the diffreport tin exist annotated as possible isotopes, adducts, or structures.
Concluding Remarks
In that location are many reliable bioinformatic solutions for processing untargeted metabolomic data. The XCMS software is one platform-agnostic solution which is widely used. The success of XCMS is related to it being open source and highly modular. This has enabled multiple laboratories to contribute to its development with algorithms such as centWave and OBI-warp. There are a multitude of additional algorithms available that are relevant to the processing of untargeted metabolomic information, and it is recommended that their potential to improve XCMS functioning be evaluated in the future. Given that XCMS is open source and modular, information technology is as well interoperable with new generations of metabolomic software implemented within R and aimed at achieving advanced functionalities (eastward.g., better notation of features, multifactorial assay, unbiased tracking of isotopic labels, etc.). Consequently, the cadre algorithms within XCMS have become an important piece of many bioinformatic pipelines. Hopefully the roadmap for these pipelines that nosotros take provided here will be useful in helping researchers chose a software platform well-nigh uniform with their experimental objectives.
Acknowledgements
GJP received financial support for this piece of work from the National Institutes of Health Grants R01 ES022181 and L30 AG0 038036, every bit well as the Alfred P. Sloan Foundation, the Camille & Henry Dreyfus Foundation, and the Pew Scholars Program in the Biomedical Sciences.
Footnotes
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4831061/
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