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\begin{document}

\title{TopDownApp: An open and modular platform for analysis and visualisation
of top-down proteomics data}



\author[1]{Mathias Walzer}%
\author[2]{Kyowon Jeong}%
\author[3]{David L. Tabb}%
\author[1]{Juan Antonio Vizcaino}%
\affil[1]{EMBL-EBI}%
\affil[2]{University of Tubingen Faculty of Science}%
\affil[3]{Institut Pasteur}%


\vspace{-1em}



  
  \date{June 08, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Although Top-down (TD) proteomics techniques, aimed at the analysis of
intact proteins and proteoforms, are becoming increasingly popular,
efforts are needed at different levels to generalise its adoption. In
this context, there are numerous improvements that are possible in the
area of open science including the FAIR (Findability, Accessibility,
Interoperability and Reusability) data principles. These include e.g.
increased data sharing practices and availability of tailored open data
standards. Additionally, the field would benefit from the development of
open analysis workflows that can enable e.g. data reuse of public
datasets, something that is increasingly common in other proteomics
fields. We present an open and modular platform for the analysis and
visualisation of TD proteomics data called TopDownApp. It can be used as
a flexible analysis platform, through the use of a common workflow
engine, common data formats for input/output, and software
containerisation. It can also serve as a tool for visual inspection
through its simple setup. As a key point, it can also be used as a
development platform for new tools through the use of Python, a modular
design, software containerisation and common data formats. TopDownApp is
open source and freely available at:
https://github.com/mwalzer/TopDownApp.%
\end{abstract}%



\sloppy


\textbf{Technical Brief}

TopDownApp: An open and modular platform for analysis and visualisation
of top-down proteomics data

Mathias Walzer\textsuperscript{1,*}, Kyowon Jeong\textsuperscript{2},
David L. Tabb\textsuperscript{3}, Juan Antonio
Vizca\selectlanguage{ngerman}íno\textsuperscript{1,*}

\textsuperscript{1}European Molecular Biology Laboratory, EMBL-European
Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD,
United Kingdom

\textsuperscript{2}Applied Bioinformatics, Computer Science Department,
University of Tübingen, Tübingen, Germany, 72076.

\textsuperscript{3}Institut Pasteur, Université Paris Cité, CNRS UAR
2024, Mass Spectrometry for Biology Unit, 75015 Paris, France.

\textsuperscript{*}Corresponding authors: Mathias Walzer and Juan
Antonio Vizcaíno

\textbf{Keywords:} top-down proteomics; open analysis pipeline;
visualisation; open science

\textbf{Abstract}

Although Top-down (TD) proteomics techniques, aimed at the analysis of
intact proteins and proteoforms, are becoming increasingly popular,
efforts are needed at different levels to generalise its adoption. In
this context, there are numerous improvements that are possible in the
area of open science including the FAIR (Findability, Accessibility,
Interoperability and Reusability) data principles. These include e.g.
increased data sharing practices and availability of tailored open data
standards. Additionally, the field would benefit from the development of
open analysis workflows that can enable e.g. data reuse of public
datasets, something that is increasingly common in other proteomics
fields.

We present an open and modular platform for the analysis and
visualisation of TD proteomics data called TopDownApp. It can be used as
a flexible analysis platform, through the use of a common workflow
engine, common data formats for input/output, and software
containerisation. It can also serve as a tool for visual inspection
through its simple setup. As a key point, it can also be used as a
development platform for new tools through the use of Python, a modular
design, software containerisation and common data formats. TopDownApp is
open source and freely available at:
\emph{\url{https://github.com/mwalzer/TopDownApp}}.

\textbf{Main Text}

Proteomics techniques are increasingly tackling the identification and
quantification of intact proteoforms, via the so-called top-down (TD)
proteomics mass spectrometry (MS)-based approaches, in addition to the
more widely used `bottom-up' proteomics analysis. Proteoforms are the
distinct molecular \emph{species} of an expressed and translated gene
including the precise combination of sequence variants, alternative
splicing events, post-translational proteolysis (e.g. signal peptide
cleavage) and post-translational modifications (PTMs) {[}1{]}.

The increasing popularity of open science practices in proteomics has
resulted in the dramatic growth of publicly available MS-based
proteomics datasets. The PRIDE database
(\emph{https://www.ebi.ac.uk/pride/}) {[}2{]} is the largest proteomics
data repository worldwide and is one of the founding members of the
ProteomeXchange consortium {[}3{]}. As of May 2023, PRIDE stores
approximately 34,000 datasets. ProteomeXchange resources adopt the FAIR
data principles (Findability, Accessibility, Interoperability and
Reusability) {[}4{]}. One of the main benefits of making data publicly
available is to enable data reuse and the reproducibility of the
analysis, facilitating an independent assessment of the results
described in the corresponding publications. Additionally, as is already
happening in other proteomics fields, new knowledge and tools are being
generated from data reuse activities, for instance by applying machine
learning techniques {[}5{]}. Furthermore, in the context of data
interoperability, it is important to highlight that ProteomeXchange
resources implement the main open data standards developed by the
Proteomics Standards Initiative {[}6{]}, such as mzML (for MS data)
{[}7{]} and mzTab (for peptide/protein identification and
quantification) {[}8,9{]}.

However, in the context of open science practices, TD proteomics lags
behind others, more widely implemented, proteomics fields. For instance,
the total number of submitted TD proteomics datasets to PRIDE is still
small (approximately 231 datasets, of which are still 50 private, as of
May 2023), although the trend is changing slowly. In our view, this is
due to two reasons. On one hand, the number of TD proteomics
practitioners is still relatively low when compared with bottom-up
approaches. On the other hand and more importantly, TD datasets are not
being made available at the same pace as in other proteomics fields.
Furthermore, to increase data reuse of TD proteomics data, it is
important to develop open software for enabling computational data
(re-)analysis and visualisation. Indeed, advances in topics such as data
integration and the reproducibility of the computational analyses,
including scaling-up analysis capabilities for increasingly large
experiments, are key to match the developments in other proteomics
fields, and in the life sciences as a whole {[}10{]}.

We here introduce an open, modular and flexible platform called
TopDownApp for the analysis and visualisation of TD proteomics data,
which can be applied for instance to public datasets. Since the TD data
analysis methodology is still quite dynamic, a flexible and modular
approach to data analysis was necessary. This is why TopDownApp was
implemented using automated and modular analysis workflows -- i.e. a
flexible succession of tools, inputs/outputs connected through open data
standard formats (as interoperability layer) and a modular choice of
tools (through software containerisation) through Nextflow {[}11{]}. In
the context of TD data analysis, currently, the most common and
generally successive tasks are: MS raw file access, deconvolution and
proteoform identification.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/TopDownAppOverview/TopDownAppOverview}
\caption{{\textbf{Figure 1.} Overview of the use (left panel) and the processes
(right panels) for TopDownApp. Local input selected through the browser
(of \emph{.RAW} files and a \emph{.fasta} sequence database) is
processed via Nextflow and dedicated tool software containers. The
workflow is then started through the browser, and after processing the
results can be inspected in the browser. The Nextflow workflows can also
be used independently to process large batches of data.%
}}
\end{center}
\end{figure}

Additionally, workflows that already support the deconvolution
visualisation, can be used through a user interface (UI), as shown in
Fig.1. The workflow results are reported in multiple formats, the tool
native output, and of note, the mzML and mzTab format adapted for TD
data. Table 1 includes the tools and the corresponding versions that are
part of TopDownApp at the time of writing.

\par\null\selectlanguage{english}
\begin{table}[H]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCC}
Tool Name & Tool Use & Tool Version \\
https://sciwheel.com/work/citation?ids=8113952\&pre=\&suf=\&sa=0 & Conversion from Thermo Raw files to mzML & v1.4 \\
https://sciwheel.com/work/citation?ids=9929122\&pre=\&suf=\&sa=0 & Deconvolution & v3-pre.51bcc6e \\
https://sciwheel.com/work/citation?ids=12172363\&pre=\&suf=\&sa=0 & Deconvolution \& Identification & v1.5 \\
https://sciwheel.com/work/citation?ids=3552253\&pre=\&suf=\&sa=0 & Workflow management system & v22.1 \\
https://sciwheel.com/work/citation?ids=14763476\&pre=\&suf=\&sa=0 & Graphical User Interface & v0.14 \\
https://sciwheel.com/work/citation?ids=8519389\&pre=\&suf=\&sa=0 & Computational access to mzML & v4.5 \\
\end{tabulary}
\caption{{Tools in use with the current version (May 2023) of TopDownApp. See the
code repository for details about the most recent versions (if
applicable).%
}}
\end{table}Tool Name Tool Use Tool Version ThermoRawFileParser {[}12{]} Conversion
from Thermo Raw files to mzML v1.4.2 FLASHDeconv {[}13{]} Deconvolution
v3-pre.51bcc6e TopPIC-suite {[}14{]} Deconvolution \& Identification
v1.5.3 Nextflow {[}11{]} Workflow management system v22.10.4 Panel
(Python) {[}15{]} Graphical User Interface v0.14.4 Pyteomics {[}16{]}
Computational access to mzML v4.5.6 Table 1: Tools in use with the
current version (May 2023) of TopDownApp. See the code repository for
details about the most recent versions (if applicable). Table 1: Tools
in use with the current version (May 2023) of TopDownApp. See the code
repository for details about the most recent versions (if applicable).
Table 1: Tools in use with the current version (May 2023) of TopDownApp.
See the code repository for details about the most recent versions (if
applicable).

TopDownApp is open source and freely available under an Apache 2.0
licence to allow for free use and software development. The source code
can be found at\emph{https://github.com/mwalzer/TopDownApp}. The
application is designed to work on local machines as well as in remote
settings. Yet, the framework can be used for development of scaled-up
and/or automated use cases, since the different parts are functional on
their own or in combination. For example, the workflow can be used to
automate the analysis, in particular on research compute
infrastructures, which frequently support the use of Singularity
containers {[}17{]}. All tools used in the workflow have to be
containerised and can be specified via a customisable configuration
file. Note however that the workflow needs to reflect potential tool
command and parameter changes first, and that data hand-over
compatibility needs to be ensured (preferably via the use of mzML and
mzTab).

In addition to the containerisation of the tools, the processes of
running the workflow and the data visualisation are containerised as
well. As a result, the minimal setup for a user is the container system
(Singularity/Docker) and a combined container (see Supplementary
Material). The application can be started via a single command and then
used through a web browser (see Supplementary Material and the code
repository at\emph{https://github.com/mwalzer/TopDownApp}for more
details). Here, a local MS raw file and a protein sequence database (in
fasta format) can be selected as input, and the protein modification
parameters can also be set. Currently, the user interface allows to
select a number of standard protein modifications and combinations
thereof. From there, the analysis workflow can be started, and the
results can be inspected once the data analysis process is finished. The
successfully deconvolved spectra can be selected for visualisation
against their original form from a table (see Supplementary Figure S2).
Within each spectrum, each peak can be selected for deconvolution
visualisation. Identified spectra are listed in a table, from which one
can also select the corresponding spectra to be visualised. New
visualisations can be developed using a Python notebook and added to the
user interface (see Supplementary Material). The identification results
are also available to download in the form of a development version of
the mzTab standard format for TD proteomics data (see Supplementary
Material).

The mzTab format was chosen because it has proven to be effective for
representing identification data in other quickly developing
specialisations of MS technologies, including metabolomics and
lipidomics {[}8{]}. Furthermore, the format adaptations necessary for TD
data are minor, and we expect a straightforward process to establish a
community agreed extension of mzTab. Likewise, the representation of TD
peak data in mzML is already supported by the data standard. However for
complete compatibility, the deconvolution data needs to be represented
and formalised in a format extension, for which we developed a
functional proposal here. We used a TD spectrum reporting convention
that attaches deconvolution information such as charge and isotope
target as the userParam section of the spectrum schema representation
and an implicit\emph{m/z} array value reinterpretation as the mass. This
is compatible with the current release of the widely used Python library
for mzML consumption (and other PSI data standards), Pyteomics {[}16{]}
and many other mzML capable software libraries. For now, only
FLASHDeconv supports the deconvolution specific userParam section in
mzML output, and thus visualisation of deconvolution is exclusive to
FLASHDeconv. For both mzML and mzTab, input from the community will be
needed for both to become accepted data standards for representing TD
data. For details on the format specification extensions, see the
Supplementary Material.

We demonstrate the utility of TopDownApp with two datasets, the first to
showcase examples of visualisation, and the second to provide a
re-assessment of a previously published human dataset. The first is
an\emph{E. coli} lysate, measured using a Thermo Scientific Orbitrap
Eclipse (MassIVE dataset accession MSV000087484) {[}18{]}. There, we
used an \emph{E. coli} (strain: K12 MG1655 i) proteome database from
UniProtKB/SwissProt (canonical; release 2023\_01), and selected one
modification (Oxidation {[}unimod:35{]}). The workflow configuration was
FLASHDeconv for deconvolution and TopPIC for identification. We used the
UI to visualise the precursor deconvolutions. Figure 2 shows how
TopDownApp can be used to examine the signal quality of the precursors
corresponding to the identified proteoforms, which is crucial
information for the quality control of identification {[}19{]} {[}20{]}.
Figure 2 (left and right panels) show visualisation examples of high and
low quality precursor signals, respectively. For each precursor mass,
TopDownApp shows all its differently charged isotope packets in the
input raw spectrum (blue colour coded peaks) as well as noisy peaks
around them (red peaks). Users can easily distinguish signal and noise
components by colour and do not need to search for separate
\emph{m/z}value regions to observe peaks with different charges. In this
way, TopDownApp organises the precursor information from different
places in the spectrum for easy appraisal.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/TopDownAppCompPanel/TopDownAppCompPanel}
\caption{{\textbf{Figure 2.} Example of a successful and unsatisfactory
deconvolution process from the analysis of the E.coli dataset.
\textbf{Left Panel:} Deconvolution of scan 8557, monoisotopic peak mass
8564.733, exemplifying a successful deconvolution with blue peaks for
target peaks, pink peaks declared noise, and dashed curve showing a good
approximation of the isotope traces. \textbf{Right Panel:} deconvolution
of a poor quality signal in scan 8722, monoisotopic peak mass 13101.864.
\textbf{~}
{\label{111156}}%
}}
\end{center}
\end{figure}

A second benchmarking dataset included erythrocyte data from the human
Blood Proteoform Atlas: concretely, datasets\emph{PXD026123}(DS-01: 35
LC-MS/MS runs) and\emph{PXD026159}(DS-53: 6 LC-MS/MS runs). We applied a
minimal modification setting (Oxidation {[}unimod:35{]}, Methylation
{[}unimod:34{]}) and used a human sequence database from
UniProtKB/SwissProt (canonical; release 2023\_01), applying a
spectrum-level and proteoform-level FDR (False Discovery Rate) cutoff of
1\%.

Table 2 shows the analysis results using different tools for
identification, split by identified proteins (accessions), number of
truncated sequences for PrSM with or without PTMs, number of
proteoforms, and PrSMs. The samples of PXD026123 were subject to an
enrichment strategy, explaining the overall greater number of
identifications as opposed to PXD026159. Switching from TopMG
identification software to TopPIC also noticeably affects the number of
observed identifications. This can be explained considering the
different goals of the identification analysis for the two. TopMG is
designed ``for identifying highly modified proteoforms'', whereas TopPIC
is built for the characterisation of proteoforms at the proteome level
{[}21{]}. This is further corroborated by comparing the overlap of
accessions (75 of 209(TopPIC)/101(TopMG) in PXD026123 and 7 of 55/24 in
PXD026159) and overlap of proteoform sequences (126 of max. 4826 unique
PrSM sequences and 50 of max. 3085 unique PrSM sequences) for both
methods (for details see Supplementary Figure S1).

\par\null\selectlanguage{english}
\begin{table}[H]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCCCCCC}
Data Set & Deconvolution & Search Engine & Protein Accessions & Truncations & Proteoforms & PrSMs \\
PXD026123 & FLASHDeconv & TopMG & 102 & 228 & 303 & 1477 \\
 & FLASHDeconv & TopPIC & 209 & 731 & 1799 & 7476 \\
PXD026159 & FLASHDeconv & TopMG & 24 & 94 & 108 & 472 \\
 & FLASHDeconv & TopPIC & 55 & 303 & 801 & 4408 \\
\end{tabulary}
\caption{{Table 2. Results from the batch application of the default workflow
setting (FLASHDeconv + TopPIC) in TopdownApp plus an additional workflow
setting using~ (FLASHDeconv + TopMG) for~ datasets PXD026123 and
PXD0261259.
{\label{883001}}%
}}
\end{table}Data Set Deconvolution Search Engine Protein Accessions Truncations
Proteoforms PrSMs PXD026123 FLASHDeconv TopMG 102 228 303 1477
FLASHDeconv TopPIC 209 731 1799 7476 PXD026159 FLASHDeconv TopMG 24 94
108 472 FLASHDeconv TopPIC 55 303 801 4408 Table 2. Results from the
batch application of the default workflow setting (FLASHDeconv + TopPIC)
in TopdownApp plus an additional workflow setting using (FLASHDeconv +
TopMG) for datasets PXD026123 and PXD0261259. Table 2. Results from the
batch application of the default workflow setting (FLASHDeconv + TopPIC)
in TopdownApp plus an additional workflow setting using (FLASHDeconv +
TopMG) for datasets PXD026123 and PXD0261259. Table 2. Results from the
batch application of the default workflow setting (FLASHDeconv + TopPIC)
in TopdownApp plus an additional workflow setting using (FLASHDeconv +
TopMG) for datasets PXD026123 and PXD0261259. Table 2. Results from the
batch application of the default workflow setting (FLASHDeconv + TopPIC)
in TopdownApp plus an additional workflow setting using (FLASHDeconv +
TopMG) for datasets PXD026123 and PXD0261259. Table 2. Results from the
batch application of the default workflow setting (FLASHDeconv + TopPIC)
in TopdownApp plus an additional workflow setting using (FLASHDeconv +
TopMG) for datasets PXD026123 and PXD0261259. Table 2. Results from the
batch application of the default workflow setting (FLASHDeconv + TopPIC)
in TopdownApp plus an additional workflow setting using (FLASHDeconv +
TopMG) for datasets PXD026123 and PXD0261259. Table 2. Results from the
batch application of the default workflow setting (FLASHDeconv + TopPIC)
in TopdownApp plus an additional workflow setting using (FLASHDeconv +
TopMG) for datasets PXD026123 and PXD0261259.

All reanalysed datasets have been made available via PRIDE datasets
including input, output, and intermediate output of the Nextflow
workflow, as datasets PXD042651 and PXD042650, respectively.

Our TopDownApp is free and open to use, and can be used on a local
computer or institutional server-setup, installation as simple as
downloading and running the containerised app. However, unlike other
computing environments for TD data processing like TDPortal or TopPIC
Gateway {[}22,23{]}, the workflows themselves are not reconfigurable via
GUI. Instead, high-throughput analysis can be conducted on a huge
variety of compute infrastructures through application of the underlying
nextflows. Changing the sequence or the type of tools in the workflow
needs to be coded in nextflow. As a beneficial side effect of this,
adding new tools into the workflow can be simply achieved by the user
with a few lines of configuration change for any software if it can be
called from the command line and has compatible in-/output. Another
distinction is the direct integration of results visualisation as main
component of the UI of TopDownApp.

In the future, the TopDownApp could be enhanced with additional
deconvolution and identification software modules, such as MSPathFinderT
{[}24{]}, to further increase the software options for data analysis.
Moreover, the inclusion of a dedicated label-free quantification module,
such as FLASHQuant, would be ideal to enable quantitative analysis. To
ensure robust and reproducible results, adopting additional control
strategies at different levels including proteins, protein isoforms, and
proteoforms, would be highly beneficial. Currently, parameter
configuration through the browser is not fully implemented due to the
wide variety of supported parameters. Adoption of other PSI standard
data formats, such as the ProForma 2.0 notation {[}25{]} for proteoform
presentation, would improve data interoperability. Additionally, we
expect that the availability of an open data analysis workflow will
enable the reuse and reanalysis of TD proteomics datasets in the public
domain. This would open many possibilities such as the integration of
the results in popular bioinformatics resources such as PRIDE, UniProtKB
{[}26{]} and the Human Proteoform Atlas {[}27{]}, making TD and
proteoform data more FAIR. Furthermore, in our view, the availability of
open source analysis platforms will be essential to the success of the
envisioned Human Proteoform Project {[}28{]}.

We expect that the availability of TopDownApp as an open and shared
development platform will aid the reproducibility of TD data analysis,
providing a basis for new, automated, and easy to share TD data analysis
workflows. Further, we envision an improved accessibility to
deconvolution and identification data. As we demonstrated one method of
visualisation of TD identification results, we hope this to serve as a
basis for more analysis results to be made (visually) accessible to
current practitioners. In the medium-long term we expect that this and
analogous efforts can improve the integration of proteoform data in
bioinformatics resources so that all biomedical researchers can benefit
from the adoption of open science practices in the TD proteomics field.

\textbf{Acknowledgements}

J.A.V, K. J., D.L.T. and M.W. have been funded by the EU Horizon 2020
grant `EPIC-XS' {[}grant number 823839{]}. D.L.T. was also supported by
the EU Horizon 2020 research and innovation programme under {[}grant
number 829157{]}. J.A.V. was also funded by EMBL-core funding. The
authors would also like to thank Deepti J. Kundu for her help with
providing the statistics about public TD proteomics datasets in PRIDE
and the whole PRIDE team for their ongoing support to keep data FAIR.

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