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Pregled bibliografske jedinice broj: 762195

A novel computational approach applicable to human microbiome studies – urinary tract microbiome example


Diminić, Janko; Cindrić, Mario; Perica, Kristina; Ceprnja, Marina; Melvan, Ena; Hranueli, Daslav; Žučko, Jurica
A novel computational approach applicable to human microbiome studies – urinary tract microbiome example // The 2nd Microbiome R and D and Business Collaboration Forum : Europe : abstracts
London, Ujedinjeno Kraljevstvo, 2015. str. 1-1 (poster, nije recenziran, sažetak, stručni)


Naslov
A novel computational approach applicable to human microbiome studies – urinary tract microbiome example

Autori
Diminić, Janko ; Cindrić, Mario ; Perica, Kristina ; Ceprnja, Marina ; Melvan, Ena ; Hranueli, Daslav ; Žučko, Jurica

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, stručni

Izvornik
The 2nd Microbiome R and D and Business Collaboration Forum : Europe : abstracts / - , 2015, 1-1

Skup
Microbiome R and D and Business Collaboration Forum : Europe (2 ; 2015)

Mjesto i datum
London, Ujedinjeno Kraljevstvo, 7-8.05.2015

Vrsta sudjelovanja
Poster

Vrsta recenzije
Nije recenziran

Ključne riječi
Mass fingerprint ; mass spectrometry sequencing ; protein identification

Sažetak
Peptide mass fingerprinting is a term which describes technique which utilizes ESI or MALDI MS followed by tandem mass spectrometry sequencing. This technique has become a cornerstone for protein identification. Today, applications using peptide mass fingerprinting in biomedical analyses are a major driving force behind its rapid development. However, efficient and accurate analyses of generally big protein tandem mass spectrometry data sets require robust software. In terms of final goal, which is data interpretation, the role of software and underlying algorithms is at least equally important as the technique itself, a fact which is often neglected. High-throughput mass spectrometry instruments can readily generate hundreds of thousands of spectra. This fact combined with the ever growing size of genomic databases imposes tremendous demands for potential successful software solutions. In fact, it is the process of comparing large-scale mass spectrometry data with large databases that remains the toughest bottleneck in proteomics. Here we present a completely novel approach based on natural language processing which is not just another improvement of existing approaches, but represents a paradigm shift. It doesn't rely on peak intensity for database peptide matching and it uses newly developed concept of microbial proteome fingerprints for strain/species identification. Since this new algorithm doesn't rely on sequence alignment but instead utilizes a concept of singular proteome fingerprints rather than sets of unrelated peptides, it proposes an elegant solution for this most troubling step in proteome analyses. Abandoning BLAST and other alignment based methods, results in far superior processing speed, accuracy and sensitivity. The above mentioned algorithm can be used to analyse not only proteomes but also metaproteomes coming from mixed microbe communities as in the case presented – human urine samples taken from a hospital. The method itself is completely generic, not developed with any specific platform in mind, which makes it highly versatile, able to turn any existing device into highly efficient metaproteome analyser without significant costs related to purchase of new equipment.

Izvorni jezik
Engleski



POVEZANOST RADA


Ustanove
Institut "Ruđer Bošković", Zagreb