Investigation and application of signal suppression analysis for FTMS-based quantitative proteomics

dc.contributor.advisorZhang, Jianqiu
dc.contributor.authorMa, Xuepo
dc.contributor.committeeMemberHuang, Yufei
dc.contributor.committeeMemberJin, Yufang
dc.date.accessioned2024-02-12T14:52:12Z
dc.date.available2024-02-12T14:52:12Z
dc.date.issued2013
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractDue to high sensitivity and accuracy, Fourier Transform Mass Spectrum (FTMS) plays an important role in proteomics including protein identification and quantification. However, analysis of peptide profiles from a Liquid Chromatography Fourier Transform Mass Spectrometry (LC/FTMS) measurement reveals a non-linear distortion in intensity. Investigation of the measured <super>13</super>C/<super>12</super>C ratios comparing with theoretical ones shows that the non-linearity can be attributed to low intensity signal suppression of low abundance peptide peaks. Before any analysis of proteomic data, the suppression issue need first be studied. We developed an iterative algorithm that corrects the intensity distortions for peptides with relatively high abundance. This algorithm can be applied in a wide range of applications using FTMS. We also analyzed the distortion characteristics of the instrument for peptides with lower abundance, which should be considered when interpreting quantification results of FTMS. Taking the suppression in FTMS measurement into consideration, we developed a quantitative proteomic approach to predict Kaposi's sarcoma associated herpes virus (KSHV) mircroRNA (miR) targets based on <super>18</super>O labeling. We developed a method which integrates several improved <super>18</super>O/<super>16</super>O data processing algorithms developed in house and identified down regulated proteins as potential targets in KSHV miR transfected Human embryonic kidney 293T cells. We applied classical statistical tests such as t-test and several other tests devised by ourselves for picking differentially expressed proteins (DEPs). Combining the DEP prediction, PAR_CLIP and genetics method, we finally predicted three miR targets all of which are further confirmed by western-blotting.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent100 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/4314
dc.languageen
dc.subjectbiomarker
dc.subjectComputational biology
dc.subjectmass spectrometry
dc.subjectmicroRNA target
dc.subjectquantification
dc.subjectQuantitative proteomics
dc.subject.classificationElectrical engineering
dc.subject.classificationBioinformatics
dc.titleInvestigation and application of signal suppression analysis for FTMS-based quantitative proteomics
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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