Browsing by Author "Department of Biohealth Informatics, IU School of Informatics and Computing"

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    Computational Analysis of Drought Stress-Associated miRNAs and miRNA Co-Regulation Network in Physcomitrella patens.
    (Elsevier, 2011-04) Wan, Ping; Wu, Jun; Zhou, Yuan; Xiao, Junshu; Feng, Jie; Zhao, Weizhong; Xiang, Shen; Jiang, Guanglong; Chen, Jake Yue; Department of Biohealth Informatics, IU School of Informatics and Computing
    miRNAs are non-coding small RNAs that involve diverse biological processes. Until now, little is known about their roles in plant drought resistance. Physcomitrella patens is highly tolerant to drought; however, it is not clear about the basic biology of the traits that contribute P. patens this important character. In this work, we discovered 16 drought stress-associated miRNA (DsAmR) families in P. patens through computational analysis. Due to the possible discrepancy of expression periods and tissue distributions between potential DsAmRs and their targeting genes, and the existence of false positive results in computational identification, the prediction results should be examined with further experimental validation. We also constructed an miRNA co-regulation network, and identified two network hubs, miR902a-5p and miR414, which may play important roles in regulating drought-resistance traits. We distributed our results through an online database named ppt-miRBase, which can be accessed at http://bioinfor.cnu.edu.cn/ppt_miRBase/index.php. Our methods in finding DsAmR and miRNA co-regulation network showed a new direction for identifying miRNA functions.
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    Identification of Glycopeptides with Multiple Hydroxylysine O-Glycosylation Sites by Tandem Mass Spectrometry
    (ACS, 2015-11) Zhang, Yanlin; Yu, Chuan-Yih; Song, Ehwang; Li, Shuai Cheng; Mechref, Yehia; Tang, Haixu; Liu, Xiaowen; Department of Biohealth Informatics, IU School of Informatics and Computing
    Glycosylation is one of the most common post-translational modifications in proteins, existing in ∼50% of mammalian proteins. Several research groups have demonstrated that mass spectrometry is an efficient technique for glycopeptide identification; however, this problem is still challenging because of the enormous diversity of glycan structures and the microheterogeneity of glycans. In addition, a glycopeptide may contain multiple glycosylation sites, making the problem complex. Current software tools often fail to identify glycopeptides with multiple glycosylation sites, and hence we present GlycoMID, a graph-based spectral alignment algorithm that can identify glycopeptides with multiple hydroxylysine O-glycosylation sites by tandem mass spectra. GlycoMID was tested on mass spectrometry data sets of the bovine collagen α-(II) chain protein, and experimental results showed that it identified more glycopeptide-spectrum matches than other existing tools, including many glycopeptides with two glycosylation sites.
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    A method for identifying discriminative isoform-specific peptides for clinical proteomics application
    (BioMed Central, 2016-08-22) Zhang, Fan; Chen, Jake Yue; Department of Biohealth Informatics, IU School of Informatics and Computing
    BACKGROUND: Clinical proteomics application aims at solving a specific clinical problem within the context of a clinical study. It has been growing rapidly in the field of biomarker discovery, especially in the area of cancer diagnostics. Until recently, protein isoform has not been viewed as a new class of early diagnostic biomarkers for clinical proteomics. A protein isoform is one of different forms of the same protein. Different forms of a protein may be produced from single-nucleotide polymorphisms (SNPs), alternative splicing, or post-translational modifications (PTMs). Previous studies have shown that protein isoforms play critical roles in tumorigenesis, disease diagnosis, and prognosis. Identifying and characterizing protein isoforms are essential to the study of molecular mechanisms and early detection of complex diseases such as breast cancer. However, there are limitations with traditional methods such as EST sequencing, Microarray profiling (exon array, Exon-exon junction array), mRNA next-generation sequencing used for protein isoform determination: 1) not in the protein level, 2) no connectivity about connection of nonadjacent exons, 3) no SNPs and PTMs, and 4) low reproducibility. Moreover, there exist the computational challenges of clinical proteomics studies: 1) low sensitivity of instruments, 2) high data noise, and 3) high variability and low repeatability, although recent advances in clinical proteomics technology, LC-MS/MS proteomics, have been used to identify candidate molecular biomarkers in diverse range of samples, including cells, tissues, serum/plasma, and other types of body fluids. RESULTS: Therefore, in the paper, we presented a peptidomics method for identifying cancer-related and isoform-specific peptide for clinical proteomics application from LC-MS/MS. First, we built a Peptidomic Database of Human Protein Isoforms, then created a peptidomics approach to perform large-scale screen of breast cancer-associated alternative splicing isoform markers in clinical proteomics, and lastly performed four kinds of validations: biological validation (explainable index), exon array, statistical validation of independent samples, and extensive pathway analysis. CONCLUSIONS: Our results showed that alternative splicing isoform makers can act as independent markers of breast cancer and that the method for identifying cancer-specific protein isoform biomarkers from clinical proteomics application is an effective one for increasing the number of identified alternative splicing isoform markers in clinical proteomics.