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JKU/UAS International PhD Program

Recent developments of high resolution mass spectrometers carry great potential for research in biological and health issues. Still, in commonly used approaches mass spectra are identified by database search, i.e., searching for a match in a sequence database of known proteins. This approach lacks the possibility of identifying unknown proteins or unconsidered biologically relevant modifications, especially for poorly studied organisms.

The aim of this project is to develop algorithms and bioinformatic concepts incorporating de novo identification, blind modification search, and genomics data information for peptide and protein identification in proteomics analyses, exploiting all potential benefits of newly developed machines. A new approach for de novo identification, i.e., for explaining mass spectra in the absence of sequence database information, shall be developed to detect unknown peptides and shall be combined with adequate and fast blind modification search. Alternative splice sites and amino acid substitutions that occur, e.g., due to single nucleotide variants, may also hamper spectrum identification as those peptide compositions are often missing in used protein databases. Discovery of those changes is of high value especially for the emerging field of personalized medicine, e.g., in drug efficiency studies. Thus, information from next generation sequencing data of RNA sequences shall also be included in the spectrum identification approaches to detect sample specific sequence variances.


Researchers

FH-Prof. PD DI Dr. Stephan Dreiseitl

DI(FH) Viktoria Dorfer MSc

Sebastian Dorl MSc

Prof.(FH) DI Dr. Stephan Winkler

Duration

2015 - present

Research Areas

Proteomics

Data Mining

Research Institutions

Research Center Hagenberg

University of Applied Sciences, Upper Austria, Hagenberg Campus

Research focus

Software technology and application

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Finished Projects

JKU/UAS International PhD Program

Recent developments of high resolution mass spectrometers carry great potential for research in biological and health issues. Still, in commonly used approaches mass spectra are identified by database search, i.e., searching for a match in a sequence database of known proteins. This approach lacks the possibility of identifying unknown proteins or unconsidered biologically relevant modifications, especially for poorly studied organisms.

The aim of this project is to develop algorithms and bioinformatic concepts incorporating de novo identification, blind modification search, and genomics data information for peptide and protein identification in proteomics analyses, exploiting all potential benefits of newly developed machines. A new approach for de novo identification, i.e., for explaining mass spectra in the absence of sequence database information, shall be developed to detect unknown peptides and shall be combined with adequate and fast blind modification search. Alternative splice sites and amino acid substitutions that occur, e.g., due to single nucleotide variants, may also hamper spectrum identification as those peptide compositions are often missing in used protein databases. Discovery of those changes is of high value especially for the emerging field of personalized medicine, e.g., in drug efficiency studies. Thus, information from next generation sequencing data of RNA sequences shall also be included in the spectrum identification approaches to detect sample specific sequence variances.


Researchers

FH-Prof. PD DI Dr. Stephan Dreiseitl

DI(FH) Viktoria Dorfer MSc

Sebastian Dorl MSc

Prof.(FH) DI Dr. Stephan Winkler

Duration

2015 - present

Research Areas

Proteomics

Data Mining

Research Institutions

Research Center Hagenberg

University of Applied Sciences, Upper Austria, Hagenberg Campus

Research focus

Software technology and application

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