scholarly journals Gaussian graphical modeling for spectrometric data analysis

2022 ◽  
pp. 107416
Author(s):  
Laura Codazzi ◽  
Alessandro Colombi ◽  
Matteo Gianella ◽  
Raffaele Argiento ◽  
Lucia Paci ◽  
...  
Keyword(s):  
Data Analysis ◽  
Graphical Modeling ◽  
Spectrometric Data ◽  
Gaussian Graphical Modeling

Application of Factor Analysis in Imaging Mass Spectrometric Data Analysis

2014 ◽  
Vol 42 (8) ◽  
pp. 1099-1103 ◽  
Author(s):  
Yi CHEN ◽  
Fei TANG ◽  
Tie-Gang LI ◽  
Jiu-Ming HE ◽  
Zeper ABLIZ ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Data Analysis ◽  
Mass Spectrometric ◽  
Mass Spectrometric Data ◽  
Spectrometric Data

scholarly journals New Chromone, Isocoumarin, and Indole Alkaloid Derivatives from three Sponge-derived Fungal Strains

2016 ◽  
Vol 11 (9) ◽  
pp. 1934578X1601100 ◽  
Author(s):  
Ling-Hong Meng ◽  
Hui-Qin Chen ◽  
Imke Form ◽  
Belma Konuklugil ◽  
Peter Proksch ◽  
...  
Keyword(s):  
Data Analysis ◽  
Indole Alkaloid ◽  
Antimicrobial Activities ◽  
Fungal Strain ◽  
Mass Spectrometric ◽  
Mass Spectrometric Data ◽  
Spectrometric Data ◽  
Chromone Derivatives ◽  
Engyodontium Album ◽  
Ircinia Oros

Two new chromone derivatives, 2-hydroxymethyl-3-methyl-7-methoxychromone (1) and 2-hydroxymethyl-3- tert-butyl-7-methoxychromone (2), together with a related known compound, 2,3-dimethyl-7-methoxychromone (3), were isolated from Rhinocladiella sp. (IO2), a fungus obtained from the sponge Ircinia oros. Furthermore, a new isocoumarin derivative, 3-(3-chloro-2-hydroxypropyl)-8-hydroxy-6-methoxy-isochromen-1-one (4) and a known analogue 3-[( R)-3,3-dichloro-2-hydroxypropyl]-8-hydroxy-6-methoxy-1 H-isochromen-1-one (dichlorodiaportin, 5), were identified from sponge-derived fungal strain Clonostachys sp. (AP4.1), while a new indole alkaloid 1-(4-hydroxybenzoyl)indole-3-carbaldehyde (6) was obtained from the sponge-derived fungus Engyodontium album (IVB1b). The structures of these compounds were established by NMR spectroscopic and mass spectrometric data analysis, as well as by comparison with literature reports. Compounds 4 and 6 were examined for cytotoxic and antimicrobial activities, respectively. None of them showed potent activity.

scholarly journals Robust sparse Gaussian graphical modeling

2017 ◽  
Vol 161 ◽  
pp. 172-190 ◽  
Author(s):  
Kei Hirose ◽  
Hironori Fujisawa ◽  
Jun Sese
Keyword(s):  
Graphical Modeling ◽  
Gaussian Graphical Modeling

scholarly journals Robust Gaussian Graphical Modeling Vial1Penalization

Biometrics ◽  
2012 ◽  
Vol 68 (4) ◽  
pp. 1197-1206 ◽  
Author(s):  
Hokeun Sun ◽  
Hongzhe Li
Keyword(s):  
Graphical Modeling ◽  
Gaussian Graphical Modeling

scholarly journals Python in proteomics

2019 ◽  
Author(s):  
Hannes L Rost
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Open Source ◽  
Biological Sequence ◽  
Post Translational Modification ◽  
Development Environment ◽  
Spectrometric Data ◽  
Graphical Environment ◽  
High Flexibility ◽  
Prototype Software

Python is a versatile scripting language that is widely used in industry and academia. In bioinformatics, there are multiple packages supporting data analysis with Python that range from biological sequence analysis with Biopython to structural modeling and visualization with packages like PyMOL and PyRosetta, to numerical computation and advanced plotting with NumPy/SciPy. In the proteomics community, Python began to be widely used around 2012 when several mature Python packages were published including pymzML, Pyteomics and pyOpenMS. This has led to an ever-increasing interest in the Python programming language in the proteomics and mass spectrometry community. The number of publications referencing or using Python has risen eight fold since 2012 (compared with the same time period before 2012), with multiple open-source Python packages now supporting mass spectrometric data analysis and processing. Computing and data analysis in mass spectrometry is very diverse and in many cases must be tailored to a specific experiment. Often, multiple analysis steps have to be performed (identification, quantification, post-translational modification analysis, filtering, FDR analysis etc.) in an analysis pipeline, which requires high flexibility in the analysis. This is where Python truly shines, due to its flexibility, visualization capabilities and the ability to extend computation with a large number of powerful libraries. Python can be used to quickly prototype software, combine existing libraries into powerful analysis workflows while avoiding the trap of re- inventing the wheel for a new project. Here, we will describe data analysis with Python using the pyOpenMS package. An extended documentation and tutorial can also be found online at https://pyopenms.readthedocs.io. To allow the reader to follow all steps in the tutorial, we will also describe the installation process of the software. Our installation is based on Anaconda, an open- source Python distribution that includes the Spyder integrated development environment (IDE) that allows development with pyOpenMS in a graphical environment.

scholarly journals Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data

2011 ◽  
Vol 5 (1) ◽  
pp. 21 ◽  
Author(s):  
Jan Krumsiek ◽  
Karsten Suhre ◽  
Thomas Illig ◽  
Jerzy Adamski ◽  
Fabian J Theis
Keyword(s):  
High Throughput ◽  
Metabolomics Data ◽  
Graphical Modeling ◽  
Gaussian Graphical Modeling

scholarly journals Removing the hidden data dependency of DIA with predicted spectral libraries

2019 ◽  
Author(s):  
B. Van Puyvelde ◽  
S. Willems ◽  
R. Gabriels ◽  
S. Daled ◽  
L. De Clerck ◽  
...  
Keyword(s):  
Data Analysis ◽  
Clinical Proteomics ◽  
Spectrometric Data ◽  
Stochastic Sampling ◽  
Data Independent Acquisition ◽  
Use Of Data ◽  
Analysis Strategies ◽  
Stochastic Data ◽  
Hidden Data ◽  
Spectral Libraries

Data-Independent Acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data-dependent acquisition (DDA) libraries for deep peptide-centric detection. We here show that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter-laboratory comparison.Significance of the StudyData-independent acquisition (DIA) is quickly developing into the most comprehensive strategy to analyse a sample on a mass spectrometer. Correspondingly, a wave of data analysis strategies has followed suit, improving the yield from DIA experiments with each iteration. As a result, a worldwide wave of investments in DIA is already taking place in anticipation of clinical applications. Yet, there is considerable confusion about the most useful and efficient way to handle DIA data, given the plethora of possible approaches with little regard for compatibility and complementarity. In our manuscript, we outline the currently available peptide-centric DIA data analysis strategies in a unified graphic called the DIAmond DIAgram. This leads us to an innovative and easily adoptable approach based on predicted spectral information. Most importantly, our contribution removes what is arguably the biggest bottleneck in the field: the current need for Data Dependent Acquisition (DDA) prior to DIA analysis. Fractionation, stochastic data acquisition, processing and identification all introduce bias in the library. By generating libraries through data independent, i.e. deterministic acquisition, stochastic sampling in the DIA workflow is now fully omitted. This is a crucial step towards increased standardization. Additionally, our results demonstrate that a proteome-wide predicted spectral library can surrogate an exhaustive DDA Pan-Human library that was built based on 331 prior DDA runs.

scholarly journals Robust Gaussian graphical modeling

2006 ◽  
Vol 97 (7) ◽  
pp. 1525-1550 ◽  
Author(s):  
Masashi Miyamura ◽  
Yutaka Kano
Keyword(s):  
Graphical Modeling ◽  
Gaussian Graphical Modeling
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