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Browsing by Author "Li, Ziwei"
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Item Evaluation of top-down mass spectral identification with homologous protein sequences(Biomed Central, 2018-12-28) Li, Ziwei; He, Bo; Kou, Qiang; Wang, Zhe; Wu, Si; Liu, Yunlong; Feng, Weixing; Liu, Xiaowen; Medical and Molecular Genetics, School of MedicineBACKGROUND: Top-down mass spectrometry has unique advantages in identifying proteoforms with multiple post-translational modifications and/or unknown alterations. Most software tools in this area search top-down mass spectra against a protein sequence database for proteoform identification. When the species studied in a mass spectrometry experiment lacks its proteome sequence database, a homologous protein sequence database can be used for proteoform identification. The accuracy of homologous protein sequences affects the sensitivity of proteoform identification and the accuracy of mass shift localization. RESULTS: We tested TopPIC, a commonly used software tool for top-down mass spectral identification, on a top-down mass spectrometry data set of Escherichia coli K12 MG1655, and evaluated its performance using an Escherichia coli K12 MG1655 proteome database and a homologous protein database. The number of identified spectra with the homologous database was about half of that with the Escherichia coli K12 MG1655 database. We also tested TopPIC on a top-down mass spectrometry data set of human MCF-7 cells and obtained similar results. CONCLUSIONS: Experimental results demonstrated that TopPIC is capable of identifying many proteoform spectrum matches and localizing unknown alterations using homologous protein sequences containing no more than 2 mutations.Item Improving alignment accuracy on homopolymer regions for semiconductor-based sequencing technologies(BioMed Central, 2016-08-22) Feng, Weixing; Zhao, Sen; Xue, Dingkai; Song, Fengfei; Li, Ziwei; Chao, Duojiao; He, Bo; Hao, Yangyang; Wang, Yadong; Liu, Yunlong; Department of Medical and Molecular Genetics, IU School of MedicineBACKGROUND: Ion Torrent and Ion Proton are semiconductor-based sequencing technologies that feature rapid sequencing speed and low upfront and operating costs, thanks to the avoidance of modified nucleotides and optical measurements. Despite of these advantages, however, Ion semiconductor sequencing technologies suffer much reduced sequencing accuracy at the genomic loci with homopolymer repeats of the same nucleotide. Such limitation significantly reduces its efficiency for the biological applications aiming at accurately identifying various genetic variants. RESULTS: In this study, we propose a Bayesian inference-based method that takes the advantage of the signal distributions of the electrical voltages that are measured for all the homopolymers of a fixed length. By cross-referencing the length of homopolymers in the reference genome and the voltage signal distribution derived from the experiment, the proposed integrated model significantly improves the alignment accuracy around the homopolymer regions. CONCLUSIONS: Besides improving alignment accuracy on homopolymer regions for semiconductor-based sequencing technologies with the proposed model, similar strategies can also be used on other high-throughput sequencing technologies that share similar limitations.