Evidence map›Paper›PMID 26961516›Full record

ArticleBMC bioinformatics2016

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

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

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Paolo RomanoIRCCS AOU San Martino IST, Genoa, Italy. paolo.romano@hsanmartino.it.
Aldo ProfumoIRCCS AOU San Martino IST, Genoa, Italy. aldo.profumo@hsanmartino.it.
Mattia RoccoIRCCS AOU San Martino IST, Genoa, Italy. mattia.rocco@hsanmartino.it.
Rosa MangeriniIRCCS AOU San Martino IST, Genoa, Italy. rosa.mangerini@hsanmartino.it.
Fabio FerriDipartimento di Scienza e Alta Tecnologia and To.Sca.Lab, Università dell'Insubria, Como, Italy. fabio.ferri@uninsubria.it.
Angelo FacchianoCNR - Istituto di Scienze dell'Alimentazione, Avellino, Italy. angelo.facchiano@isa.cnr.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMass 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.

resultsGeena 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.

conclusionsGeena 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.

Indexed as

SoftwareAlgorithmsAutomationBlood ProteinsBreast NeoplasmsDatabases, FactualFemaleHumansProteomeProteomicsRetrospective StudiesSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationBlood ProteinsProteome

Identifiers

PMID26961516
PMCPMC4896264

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.