Bioinformatics Challenges in Proteomics

Englbrecht, Claudia; Facius, Axel

doi:10.2174/138620705774962454

Cited by 14 publications

(11 citation statements)

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“…Another new and very interesting direction in the development of CIs is the definition of new measures to characterize 2-D electrophoresis protein maps [269,270]. Proteomics maps can be described with different matrices [271].…”

Section: Cis For Proteomic Mapsmentioning

confidence: 99%

“…Consequently, blood proteome mass spectra represent a potential tool for the early detection of diseases through the use of Bioinformatic techniques [270,276]. The data collected by the Human Proteome Organization's Plasma Proteome Pilot project phase was analyzed and functional annotation of the human plasma proteome was carried out [277,278].…”

Section: Graphs and Cis For Blood Proteome And/ Or Single Protein Masmentioning

confidence: 99%

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Proteomics, networks and connectivity indices

et al. 2008

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Describing the connectivity of chemical and/or biological systems using networks is a straight gate for the introduction of mathematical tools in proteomics. Networks, in some cases even very large ones, are simple objects that are composed at least by nodes and edges. The nodes represent the parts of the system and the edges geometric and/or functional relationships between parts. In proteomics, amino acids, proteins, electrophoresis spots, polypeptidic fragments, or more complex objects can play the role of nodes. All of these networks can be numerically described using the so-called Connectivity Indices (CIs). The transformation of graphs (a picture) into CIs (numbers) facilitates the manipulation of information and the search for structure-function relationships in Proteomics. In this work, we review and comment on the challenges and new trends in the definition and applications of CIs in Proteomics. Emphasis is placed on 1-D-CIs for DNA and protein sequences, 2-D-CIs for RNA secondary structures, 3-D-topographic indices (TPGIs) for protein function annotation without alignment, 2-D-CIs and 3-D-TPGIs for the study of drug-protein or drug-RNA quantitative structure-binding relationships, and pseudo 3-D-CIs for protein surface molecular recognition. We also focus on CIs to describe Protein Interaction Networks or RNA co-expression networks. 2-D-CIs for patient blood proteome 2-DE maps or mass spectra are also covered.

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Section: Cis For Proteomic Mapsmentioning

confidence: 99%

Section: Graphs and Cis For Blood Proteome And/ Or Single Protein Masmentioning

confidence: 99%

Proteomics, networks and connectivity indices

et al. 2008

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“…The raw output of an MS spectrum (A) has to be processed to correct noise, peak broadening due to the detection system, instrument distortion and saturation effects to generate discrete peptide mass fingerprints (B) (modified from Ref. 125).…”

Section: Technological Backgroundmentioning

confidence: 99%

The potential of proteomics and peptidomics for allergy and asthma research

Crameri¹

2005

Allergy

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The human genome project (1-3) initiated a dramatic change of the research strategy applied in life sciences from the punctual investigation of defined projects to global approaches. The 'omics' projects aim at studying the full characteristics of a given biological sample in a single set of experiments. Despite the impact of genomics in molecular biology (4) and medicine (5), the information encoded by genomes does not represent more than a rough ground plan for an organism. This information might restrict but does not describe the dynamics of the life processes occurring in cells and organisms. Healthy as well as disease status are governed by complex processes, which start from the activation of a (limited) number of genes located in the genome either on the same chromosome or in different chromosome segments. Gene activation, translation, transcription and maturation of mRNAs, as well as posttranslational modifications give rise to a multitude of mature proteins and peptides (6) with structures, which are not directly deducible from genome sequences. Although proteins and peptides are products of gene expression, there are much more different forms of functional proteins and peptides than genes encoding them. As a consequence of the tightly regulated steady state between synthesis, posttranslational modifications and degradation every cell, tissue and organ of an organism contains a complex pool of proteins, protein fragments and peptides adequately modified to fulfill their biological functions. This result in the equilibrium of different functional forms of a given protein in varying concentrations embedded into extremely complex interaction networks involving a multitude of other different proteins or peptides (Fig. 1). Because of the dynamic nature of gene expression, protein synthesis and posttranslational modifications, the identification and quantification of proteins alone is not sufficient to understand functional interactions. In this context it is important to realize that changes as small as the addition of a single phosphate, cleavage of a leader peptide, amidation or Progress in the field of proteomics, the branch of biology that studies the full set of proteins derived from a given genome, is moving fast. Two-dimensional gel electrophoresis (2DG) separation of complex protein mixtures and the subsequent analysis of isolated protein spots by mass spectrometry allow fast and accurate identification of proteins. The comparison of spots from different samples separated on customized 2D gels allows the detection of punctual differences in their mobility and facilitates tracing back differences in protein expression, presence of isoforms, splice variants and posttranslational modifications by mass spectrometry. In spite of significant analytical challenges owing to the high complexity of the proteome and the challenge deriving from the necessity to process huge amounts of raw data generated by mass spectrometric profiling, proteomics has evolved to an indispensable tool in life sciences. A restricte...

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“…Another interesting direction in this regard is the definition of new TIs measures to characterize 2DE protein maps [18,19]. Proteomic maps can be described with different matrices [20].…”

Section: Introductionmentioning

confidence: 99%

Study of Parasitic Infections, Cancer, and other Diseases with Mass-Spectrometry and Quantitative Proteome-Disease Relationships

Pérez-Montoto¹,

Ubeira²,

González-Dı́az

2009

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We can understand Mass-Spectra Quantitative Proteome-Disease Relationships (MS-QPDRs) as models useful to detect Disease Biomarkers or to predict Drug Toxicity effects based on Mass-Spectra outcomes from samples of human body tissues, parasites, or other organisms. MS-QPDR development and practical use is an emerging area combining Proteomics and Bioinformatics; which involves computational, molecular, and legal sciences. We detect, at least two tendencies on QPDR development. The first tendency (type 1) uses Statistical, Artificial Intelligence, Machine Learning and/or Non-Linear Signal processing to fish for single MS biomarker signals directly within MS data. A recent alternative (type 2) uses Graph Theory to construct Complex Network representations of MS data. Next, we can calculate graph parameters called Mass-Spectra Topological Indices (MS-TIs) useful to describe the graph. The last step is similar to the first tendency but it uses MS-TIs as inputs (instead of MS signals) to seek the MS-QPDR model. There are many examples of QPDR models based on scheme 1. However, there has been little effort to seek QPDR models with scheme 2. On the other hand, MS-QPDR models can be obtained from different body fluids; the case of Human Blood Proteome (BP) is one of the most interesting. The outcomes obtained by Mass Spectrometry (MS) analysis of Serum Protein Profile (SPP) of Blood Proteome (BP) are very useful for the early detection of diseases and drug induced toxicities. In the present work we review, discuss, and outline some perspectives on the use of QPDR models based on the two types of schemes. We also refer to the recent implementation of the internet portal called BioAims for QPDR analysis (http://miaja.tic.udc.es/Bio-AIMS/ ) for free use by the research community.

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Bioinformatics Challenges in Proteomics

Cited by 14 publications

References 50 publications

Proteomics, networks and connectivity indices

Proteomics, networks and connectivity indices

The potential of proteomics and peptidomics for allergy and asthma research

Study of Parasitic Infections, Cancer, and other Diseases with Mass-Spectrometry and Quantitative Proteome-Disease Relationships

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