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Browsing by Author "Vyatkina, Kira"
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Item De Novo Sequencing of Peptides from High-Resolution Bottom-Up Tandem Mass Spectra using Top-Down Intended Methods(Wiley, 2017-12) Vyatkina, Kira; Dekker, Lennard J. M.; Wu, Si; VanDuijn, Martijn M.; Liu, Xiaowen; Tolić, Nikola; Luider, Theo M.; Paša-Tolić, Ljiljana; BioHealth Informatics, School of Informatics and ComputingDespite high-resolution mass spectrometers are becoming accessible for more and more laboratories, tandem (MS/MS) mass spectra are still often collected at a low resolution. And even if acquired at a high resolution, software tools used for their processing do not tend to benefit from that in full, and an ability to specify a relative mass tolerance in this case often remains the only feature the respective algorithms take advantage of. We argue that a more efficient way to analyze high-resolution MS/MS spectra should be with methods more explicitly accounting for the precision level, and sustain this claim through demonstrating that a de novo sequencing framework originally developed for (high-resolution) top-down MS/MS data is perfectly suitable for processing high-resolution bottom-up datasets, even though a top-down like deconvolution performed as the first step will leave in many spectra at most a few peaks.Item Top-down analysis of protein samples by de novo sequencing techniques(Oxford, 2016-09) Vyatkina, Kira; Wu, Si; Dekker, Lennard J. M.; VanDuijn, Martijn M.; Liu, Xiaowen; Tolić, Nikola; Luider, Theo M.; Paša-Tolić, Ljiljana; Pevzner, Pavel A.; Department of Biohealth Informatics, School of Informatics and ComputingMotivation: Recent technological advances have made high-resolution mass spectrometers affordable to many laboratories, thus boosting rapid development of top-down mass spectrometry, and implying a need in efficient methods for analyzing this kind of data. Results: We describe a method for analysis of protein samples from top-down tandem mass spectrometry data, which capitalizes on de novo sequencing of fragments of the proteins present in the sample. Our algorithm takes as input a set of de novo amino acid strings derived from the given mass spectra using the recently proposed Twister approach, and combines them into aggregated strings endowed with offsets. The former typically constitute accurate sequence fragments of sufficiently well-represented proteins from the sample being analyzed, while the latter indicate their location in the protein sequence, and also bear information on post-translational modifications and fragmentation patterns. Availability and Implementation: Freely available on the web at http://bioinf.spbau.ru/en/twister.