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Geena 2, improved automated analysis of MALDI/TOF mass spectra

Overview of attention for article published in BMC Bioinformatics, March 2016
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Average Attention Score compared to outputs of the same age and source

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4 tweeters

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Title
Geena 2, improved automated analysis of MALDI/TOF mass spectra
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0911-2
Pubmed ID
Authors

Paolo Romano, Aldo Profumo, Mattia Rocco, Rosa Mangerini, Fabio Ferri, Angelo Facchiano

Abstract

Mass spectrometry (MS) is producing high volumes of data supporting oncological sciences, especially for translational research. Most of related elaborations can be carried out by combining existing tools at different levels, but little is currently available for the automation of the fundamental steps. For the analysis of MALDI/TOF spectra, a number of pre-processing steps are required, including joining of isotopic abundances for a given molecular species, normalization of signals against an internal standard, background noise removal, averaging multiple spectra from the same sample, and aligning spectra from different samples. In this paper, we present Geena 2, a public software tool for the automated execution of these pre-processing steps for MALDI/TOF spectra. Geena 2 has been developed in a Linux-Apache-MySQL-PHP web development environment, with scripts in PHP and Perl. Input and output are managed as simple formats that can be consumed by any database system and spreadsheet software. Input data may also be stored in a MySQL database. Processing methods are based on original heuristic algorithms which are introduced in the paper. Three simple and intuitive web interfaces are available: the Standard Search Interface, which allows a complete control over all parameters, the Bright Search Interface, which leaves to the user the possibility to tune parameters for alignment of spectra, and the Quick Search Interface, which limits the number of parameters to a minimum by using default values for the majority of parameters. Geena 2 has been utilized, in conjunction with a statistical analysis tool, in three published experimental works: a proteomic study on the effects of long-term cryopreservation on the low molecular weight fraction of serum proteome, and two retrospective serum proteomic studies, one on the risk of developing breat cancer in patients affected by gross cystic disease of the breast (GCDB) and the other for the identification of a predictor of breast cancer mortality following breast cancer surgery, whose results were validated by ELISA, a completely alternative method. Geena 2 is a public tool for the automated pre-processing of MS data originated by MALDI/TOF instruments, with a simple and intuitive web interface. It is now under active development for the inclusion of further filtering options and for the adoption of standard formats for MS spectra.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 33%
Student > Master 4 17%
Student > Bachelor 4 17%
Lecturer 2 8%
Researcher 2 8%
Other 2 8%
Unknown 2 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 17%
Computer Science 3 13%
Agricultural and Biological Sciences 2 8%
Psychology 2 8%
Chemistry 2 8%
Other 9 38%
Unknown 2 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 October 2016.
All research outputs
#3,578,769
of 8,566,293 outputs
Outputs from BMC Bioinformatics
#1,881
of 3,712 outputs
Outputs of similar age
#109,077
of 289,698 outputs
Outputs of similar age from BMC Bioinformatics
#83
of 134 outputs
Altmetric has tracked 8,566,293 research outputs across all sources so far. This one has received more attention than most of these and is in the 57th percentile.
So far Altmetric has tracked 3,712 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 289,698 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.