metabolomics studies characterise biological samples and identify metabolites . to investigate the small molecule complement of organisms , liquid chromatography coupled to tandem mass spectrometry ( lc - ms ) is a routine technique commonly used .
lc - ms is applied in profiling , fingerprinting , or untargeted mode in a variety of areas including environmental , plant , and biomedical research .
modern lc - ms systems can detect more mass traces than ever before thanks to high mass accuracy and resolution producing complex data for every sample .
the data typically consists of mass - to - charge ( m / z ) , time , and intensity triplets that describe for every detected ion mass the strength of the ion beam and the time it is detected by the spectrometer .
processing and interpreting lc - ms data is extraordinarily difficult because of the high dynamic range , chemical diversity , and metabolite numbers typically found in metabolome samples .
the partially convoluted , densely populated signal landscape contains systematic and random noise amongst true signals of varying intensity and shape . additionally , formation of ion clusters , adducts , or fragmentation implies that many of the extracted peaks or features can belong to the same compound . proprietary and free libraries addressing the data processing problem have been developed .
these tools can be grouped into three categories : command - line , stand - alone graphical user interface ( gui ) , and web - based tools , each offering unique advantages , which are reviewed and discussed elsewhere . however , due to the nature of metabolomics data , processing requires complex data analysis workflows , bundling different programs , traversing parameter space , pulling in additional information from databases , and performing statistical multivariate analysis .
workflow platforms such as the konstanz information miner ( knime ) offer the potential for an all - in - one solution .
knime is a workflow platform that supports a wide range of functionality and has an active bioinformatics community .
openms , another library for lc / ms data management and analyses has recently been added to its bioinformatics suite .
it has the advantage of ease - of - use and rapid development of complex pipelines while maintaining flexibility due to modularity .
here we present masscascade and its plug - in masscascade - knime , a library and node - suite for stepwise lc - ms metabolomics data processing .
we give an overview of the architecture of the library and plug - in and discuss the advantages of a unified workflow environment .
the core library masscascade and the plug - in masscascade - knime , from here on referred to as  plug - in , are written in java version 1.7 .
the open - source projects are released under gpl version 3 and are available on the project hosting website bitbucket , including documentation : https://bitbucket.org/sbeisken/masscascadeknime/wiki/home the stand - alone core library contains a collection of data processing algorithms and a visualisation framework , which are wrapped in the plug - in .
the plug - in provides its own workflow data model that utilises the core library for data processing and visualisation , hence enabling workflow - based node - wise execution and inspection of data processing tasks .
these are defined as follows where s ( scan ) is a set of m / z - intensity pairs s at scan time t ; f ( feature or ion trace ) is a triplet containing a time vector t , an intensity vector i , and a characteristic retention time tr for a given m / z ; fs ( feature set ) is a set of features f at an averaged characteristic retention time tr , i.e. features that are believed  by a chosen criterion such as the inner product between their spectral vectors  to belong together .
each method takes a set of parameters including one or many ms instances , applies the method s function on the instance , and returns a new ms instance .
this way a snapshot of the data can be serialised to disk after any processing step if required . that is essential for workflow environments , where intermediate results need to be accessible .
the library supports the hupo psi mzml 1.1.0 specification , superseding the older mzxml and mzdata formats , and thermo scientific s raw file format .
input data should be centroided with one of the many available file converters such as proteowizard or by using the implemented wavelet - based centroider . on read - in
, files are individually converted in memory to an internal representation and  optionally  serialized to disk .
smaller files can be quickly processed in a server environment in this way given enough memory .
the library implements methods for noise and signal filtering , as well as methods for feature extraction and deconvolution .
methods for signal identification include database links to pubchem or massbank , and functions for isotope , adduct , and ion annotation . for a detailed list of implemented algorithms ,
the library works stand - alone enabling its use in compute intensive tasks . for local applications , a plug - in was developed to simplify its use .
the knime plug - in encapsulates the methods provided in the masscascade library and exposes each method to the user as an individual node .
the ms instances exchanged between nodes are represented as data cell types that comply with the platform s tabular data model , hereafter referred to as ms cells .
nodes can be configured through their configuration dialogs , which also validate any set method parameters .
after successful execution , the resulting ms cells are accessible via the node s out - port .
nodes can only be configured for out - ports with ms cells of the same type as required by the in - port .
the workflow reads lc - ms samples and carries out several processing steps , represented by nodes .
the window in the foreground shows ion chromatograms of features from the same compound spectrum .
nodes for data visualisation are built on top of the core library s visualisation framework .
every ms cell can be inspected after every execution step , allowing results to be scrutinized and parameters to be adjusted .
further information about node usage and implementation of additional nodes can be found online in the documentation of the project .
these are defined as follows where s ( scan ) is a set of m / z - intensity pairs s at scan time t ; f ( feature or ion trace ) is a triplet containing a time vector t , an intensity vector i , and a characteristic retention time tr for a given m / z ; fs ( feature set ) is a set of features f at an averaged characteristic retention time tr , i.e. features that are believed  by a chosen criterion such as the inner product between their spectral vectors  to belong together .
each method takes a set of parameters including one or many ms instances , applies the method s function on the instance , and returns a new ms instance .
this way a snapshot of the data can be serialised to disk after any processing step if required . that is essential for workflow environments , where intermediate results need to be accessible .
the library supports the hupo psi mzml 1.1.0 specification , superseding the older mzxml and mzdata formats , and thermo scientific s raw file format .
input data should be centroided with one of the many available file converters such as proteowizard or by using the implemented wavelet - based centroider .
on read - in , files are individually converted in memory to an internal representation and  optionally  serialized to disk .
smaller files can be quickly processed in a server environment in this way given enough memory .
the library implements methods for noise and signal filtering , as well as methods for feature extraction and deconvolution .
methods for signal identification include database links to pubchem or massbank , and functions for isotope , adduct , and ion annotation . for a detailed list of implemented algorithms ,
the library works stand - alone enabling its use in compute intensive tasks . for local applications , a plug - in was developed to simplify its use .
the knime plug - in encapsulates the methods provided in the masscascade library and exposes each method to the user as an individual node .
the ms instances exchanged between nodes are represented as data cell types that comply with the platform s tabular data model , hereafter referred to as ms cells .
nodes can be configured through their configuration dialogs , which also validate any set method parameters .
after successful execution , the resulting ms cells are accessible via the node s out - port .
nodes can only be configured for out - ports with ms cells of the same type as required by the in - port .
the workflow reads lc - ms samples and carries out several processing steps , represented by nodes .
the window in the foreground shows ion chromatograms of features from the same compound spectrum .
nodes for data visualisation are built on top of the core library s visualisation framework .
every ms cell can be inspected after every execution step , allowing results to be scrutinized and parameters to be adjusted .
further information about node usage and implementation of additional nodes can be found online in the documentation of the project .
using the masscascade - knime plug - in , we successfully pre - processed and identified reference samples from 58 compounds  30 in positive , 16 in negative  and 12 in both ion modes ( see section one and two in the supplementary material ) .
metabolite standards for the development and validation of masscascade  , as part of the data set mtbls38 in the metabolights database .
they , in turn , form part of a metabolomics study on the ripening behaviour of solanum lycopersicum ( mtbls36 ) , where masscascade was used for data processing , analysis , and metabolite identification . in this study , a feature matrix of a total of 219 samples was generated after noise reduction , baseline correction , alignment , and feature annotation to single out significantly different metabolites between the genotypes investigated .
in addition , masscascade enabled rapid fingerprinting of the sample groups to remove outliers , e.g. from erroneous sample preparation , and branching of the processing workflow to explore the data ( submission pending ) . in this application note , we focus on the subset of metabolite standards to highlight features of masscascade in a straight - forward application : identification of reference standards .
initially , the workflow was built and tested on a single sample to quickly explore the parameter space for every node and find the best set of parameters .
spreadsheets including possible adducts and the reference library were read in for positive and negative ion mode .
once the workflow was set up , all samples were run through the pipeline in batch mode where different ion modes are handled automatically .
h ) , and , if present , isotopes and fragments in ms1 were manually extracted from the result list . to verify identities ,
the isolated signal and retention time information were compared to a previously established , manually - curated , in - house database . see  metabolite_assignment in the supplementary material for the list of identified metabolites .
we found that masscascade is useful to automatically narrow down the list of putative signals in the ms samples and identify the main peak plus related peaks in the feature sets .
the correct metabolites were identified in all but two samples . in those two samples ,
the peak shape of the main peak was very narrow ( 3 scans ) and was filtered out .
the peaks were recovered by isolating the two samples in the workflow and running a part of it again with less stringent scan width settings .
in contrast to openms , masscascade provides a higher level interface more accessible to experimentalists .
the availability of two ( or more ) libraries for lc / ms data processing in the same workflow environment gives users a convenient option to harness the advantages of each library , custom tailored to their processing challenges . in comparison
, these synergetic benefits can already be seen in the availability and use of multiple cheminformatics toolkits in knime . building a workflow with the masscascade plug - in
this form of visual programming reduces the possibility of errors and is particularly user - friendly .
the implemented methods can be re - executed and visualised quickly with different parameters , thus , enabling data exploration , supported by generic knime nodes and specialized nodes from other providers : nodes to read comma separated ( csv ) or excel ( xls ) spreadsheets can be used to read lists of contaminants for exclusion .
nodes for row - wise filtering and other table utilities help to organise the data set .
the presence of cheminformatics plug - ins in combination with masscascade opens up additional synergies , e.g. by using managed libraries of small molecules libraries as input for m / z identification .
another important feature of workflow environments in general is that all workflows , their configuration and even the data , can be exchanged between collaborators , which is extremely important for the expansion and use of metabolomics studies .
we presented masscascade , an open - source library for processing lc - ms metabolomics data , and its plug - in masscascade - knime .
the java library can be used stand - alone or in combination with the plug - in .
it comprises basic algorithms for frequent tasks in lc - ms data processing . through the plug - in
, users can build complex workflows with other knime nodes for chem- or bioinformatics or with generic data analysis and visualisation tools , that go beyond the actual masscascade functionality . the plug - in offers a modular , step - by - step solution for building complex worfklows with the ability to inspect the output of each method in between nodes .
its ease - of - use and the ready availability of additional nodes with complimentary methods for further data analysis are its key features .
visual workflows also help in the standardisation of data processing and analysis , much needed in the field of metabolomics , where the diversity of instruments and variables is challenging .
future work involves optimisation of the processing methods in all categories and application of the library and plug - in to complex biological studies as demonstration of the synergistic effects of performing lc - ms analysis in an open workflow environment .
as a service to our authors and readers , this journal provides supporting information supplied by the authors .
such materials are peer reviewed and may be re - organized for online delivery , but are not copy - edited or typeset . technical support issues arising from supporting information ( other than missing files ) should be addressed to the authors
 liquid chromatography coupled to mass spectrometry ( lc - ms ) is commonly applied to investigate the small molecule complement of organisms . 
 several software tools are typically joined in custom pipelines to semi - automatically process and analyse the resulting data . 
 general workflow environments like the konstanz information miner ( knime ) offer the potential of an all - in - one solution to process lc - ms data by allowing easy integration of different tools and scripts . 
 we describe masscascade and its workflow plug - in for processing lc - ms data . the java library integrates frequently used algorithms in a modular fashion , thus enabling it to serve as back - end for graphical front - ends . 
 the functions available in masscascade have been encapsulated in a plug - in for the workflow environment knime , allowing combined use with e.g. statistical workflow nodes from other providers and making the tool intuitive to use without knowledge of programming . 
 the design of the software guarantees a high level of modularity where processing functions can be quickly replaced or concatenated . 
 masscascade is an open - source library for lc - ms data processing in metabolomics . 
 it embraces the concept of visual programming through its knime plug - in , simplifying the process of building complex workflows . 
 the library was validated using open data . 
1 Introduction
2 Description
2.1 Core Library
2.2 KNIME Plug-In
3 Results and Discussion
4 Conclusions
Supporting Information