Assessing specialized metabolite diversity of Alnus species by a digitized LC–MS/MS data analysis workflow

Abstract Phenotypic data analysis is a key component in crop breeding to extract meaningful insights from data in making better breeding decisions. Each year the rainfed rice breeding (RRB) program at IRRI conducts trials in the national agricultural research and extension systems (NARES) network-partner sites across South Asia, Southeast Asia and Africa. Analyzing the data from the network trials and sharing the results with the partners in the best possible format is a daunting task. It is crucial to demystify data analysis to the NARES partners for making better breeding decisions. Here, we provide an overview of how RRB program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly , LaTeX and HTML to develop a unique data analysis workflow and redesigned it to a reproducible document for better interpretation, visualization and seamlessly sharing with partners. The generated report is the state-of-the-art implementation of analysis workflow and outputs either in text, tables or graphics in a unified way as one document. The analysis is highly reproducible and can be regenerated based at any time. The plots are built with enhanced dynamic and interactive visualizations to aid in better understanding and extract information with ease. Tables are highly interactive and manageable rendering liberty to be exported within the document in numerous formats. The source code and demo data set for download and use is available at https://github.com/whussain2/Analysis-pipeline . Conclusively, the analysis workflow and document we presented is not limited to IRRI’s RRB program but is applicable to any organization or institute with full-fledged breeding programs.

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Small Angle Scattering Data Analysis Assisted by Machine Learning Methods

MRS Advances ◽

10.1557/adv.2020.130 ◽

2020 ◽

Vol 5 (29-30) ◽

pp. 1577-1584

Author(s):

Changwoo Do ◽

Wei-Ren Chen ◽

Sangkeun Lee

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Small Angle ◽

Confusion Matrix ◽

Small Angle Scattering ◽

Scattering Data ◽

Search Spaces ◽

Angle Scattering ◽

Analysis Workflow ◽

The Right

ABSTRACTSmall angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.

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DATA ANALYSIS WORKFLOW FOR UXO CLASSIFICATION

10.4133/sageep.27-164 ◽

2014 ◽

Author(s):

Dean Keiswetter ◽

Tom Furuya

Keyword(s):

Data Analysis ◽

Analysis Workflow

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A plugin-based approach to data analysis for the AMS experiment on the ISS

EPJ Web of Conferences ◽

10.1051/epjconf/201921405038 ◽

2019 ◽

Vol 214 ◽

pp. 05038

Author(s):

Valerio Formato

Keyword(s):

Data Analysis ◽

Execution Time ◽

Experiment Data ◽

Meaningful Information ◽

Multiple Users ◽

Data Formats ◽

Typical Data ◽

Analysis Workflow ◽

Data Result

In many HEP experiments a typical data analysis workflow requires each user to read the experiment data in order to extract meaningful information and produce relevant plots for the considered analysis. Multiple users accessing the same data result in a redundant access to the data itself, which could be factorized effectively improving the CPU efficiency of the analysis jobs and relieving stress from the storage infrastructure. To address this issue we present a modular and lightweight solution where the users code is embedded in different "analysis plugins" which are then collected and loaded at runtime for execution, where the data is read only once and shared between all the different plugins. This solution was developed for one of the data analysis groups within the AMS collaboration but is easily extendable to all kinds of analyses and workloads that need I/O access on AMS data or custom data formats and can even adapted with little effort to another HEP experiment data. This framework could then be easily embedded into a "analysis train" and we will discuss a possible implementation and different ways to optimise CPU efficiency and execution time.

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Integrating a generalized data analysis workflow with the Single-probe mass spectrometry experiment for single cell metabolomics

Analytica Chimica Acta ◽

10.1016/j.aca.2019.03.006 ◽

2019 ◽

Vol 1064 ◽

pp. 71-79 ◽

Cited By ~ 3

Author(s):

Renmeng Liu ◽

Genwei Zhang ◽

Mei Sun ◽

Xiaoliang Pan ◽

Zhibo Yang

Keyword(s):

Mass Spectrometry ◽

Data Analysis ◽

Single Cell ◽

Mass Spectrometry Experiment ◽

Single Probe ◽

Analysis Workflow ◽

Cell Metabolomics

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Proteomic Data Analysis Workflow for Discovery of Candidate Biomarker Peaks Predictive of Clinical Outcome for Patients with Acute Myeloid Leukemia†

Journal of Proteome Research ◽

10.1021/pr070482e ◽

2008 ◽

Vol 7 (6) ◽

pp. 2332-2341 ◽

Cited By ~ 14

Author(s):

Jenny Forshed ◽

Maria Pernemalm ◽

Chuen Seng Tan ◽

Marita Lindberg ◽

Lena Kanter ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Data Analysis ◽

Clinical Outcome ◽

Myeloid Leukemia ◽

Proteomic Data ◽

Analysis Workflow ◽

Acute Myeloid

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An automated proteomic data analysis workflow for mass spectrometry

BMC Bioinformatics ◽

10.1186/1471-2105-10-s11-s17 ◽

2009 ◽

Vol 10 (Suppl 11) ◽

pp. S17 ◽

Cited By ~ 14

Author(s):

Ken Pendarvis ◽

Ranjit Kumar ◽

Shane C Burgess ◽

Bindu Nanduri

Keyword(s):

Mass Spectrometry ◽

Data Analysis ◽

Proteomic Data ◽

Analysis Workflow

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Semi-Automated Glycoproteomic Data Analysis of LC-MS Data Using GlycopeptideGraphMS in Process Development of Monoclonal Antibody Biologics

Frontiers in Chemistry ◽

10.3389/fchem.2021.661406 ◽

2021 ◽

Vol 9 ◽

Author(s):

Kuin Tian Pang ◽

Shi Jie Tay ◽

Corrine Wan ◽

Ian Walsh ◽

Matthew S. F. Choo ◽

...

Keyword(s):

Data Analysis ◽

Method Development ◽

Process Development ◽

Glycosylation Pattern ◽

Mass Spectrometers ◽

Unit Operations ◽

Therapeutic Outcomes ◽

Analysis Workflow ◽

Fluorescent Tagging ◽

The Right

The glycosylation of antibody-based proteins is vital in translating the right therapeutic outcomes of the patient. Despite this, significant infrastructure is required to analyse biologic glycosylation in various unit operations from biologic development, process development to QA/QC in bio-manufacturing. Simplified mass spectrometers offer ease of operation as well as the portability of method development across various operations. Furthermore, data analysis would need to have a degree of automation to relay information back to the manufacturing line. We set out to investigate the applicability of using a semiautomated data analysis workflow to investigate glycosylation in different biologic development test cases. The workflow involves data acquisition using a BioAccord LC-MS system with a data-analytical tool called GlycopeptideGraphMS along with Progenesis QI to semi-automate glycoproteomic characterisation and quantitation with a LC-MS1 dataset of a glycopeptides and peptides. Data analysis which involved identifying glycopeptides and their quantitative glycosylation was performed in 30 min with minimal user intervention. To demonstrate the effectiveness of the antibody and biologic glycopeptide assignment in various scenarios akin to biologic development activities, we demonstrate the effectiveness in the filtering of IgG1 and IgG2 subclasses from human serum IgG as well as innovator drugs trastuzumab and adalimumab and glycoforms by virtue of their glycosylation pattern. We demonstrate a high correlation between conventional released glycan analysis with fluorescent tagging and glycopeptide assignment derived from GraphMS. GraphMS workflow was then used to monitor the glycoform of our in-house trastuzumab biosimilar produced in fed-batch cultures. The demonstrated utility of GraphMS to semi-automate quantitation and qualitative identification of glycopeptides proves to be an easy data analysis method that can complement emerging multi-attribute monitoring (MAM) analytical toolsets in bioprocess environments.

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A framework for inserting visually-supported inferences into geographical analysis workflow: application to road safety research

10.31219/osf.io/mfja8 ◽

2022 ◽

Author(s):

Roger Beecham ◽

Robin Lovelace

Keyword(s):

Data Analysis ◽

Medical Research ◽

Road Safety ◽

Categorical Variables ◽

Complex Data ◽

Geographical Analysis ◽

Policy Interventions ◽

Safety Research ◽

Analysis Workflow ◽

Road Crash

Road safety research is a data-rich field with large social impacts. Like in medical research, the ambition is to build knowledge around risk factors that can save lives. Unlike medical research, road safety research generates empirical findings from messy observational datasets. Records of road crashes contain numerous intersecting categorical variables, dominating patterns that are complicated by confounding and, when conditioning on data to make inferences net of this, observed effects that are subject to uncertainty due to diminishing sample sizes. We demonstrate how visual data analysis approaches can inject rigour into exploratory analysis of such datasets. A framework is presented whereby graphics are used to expose, model and evaluate spatial patterns in observational data, as well as protect against false discovery. The framework is supported through an applied data analysis of national crash patterns recorded in STATS19, the main source of road crash information in Great Britain. Our framework moves beyond typical depictions of exploratory data analysis and helps navigate complex data analysis decision spaces typical in modern geographical analysis settings, generating data-driven outputs that support effective policy interventions and public debate.

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