prospects for proteomics Directed Genomic and Genetic Analyses in Disease Discoveries.

Sanjoy K Bhattacharya
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Abstract

Proteomic discoveries are usually made using database searches for identification of proteins in a given protein sample derived from cells or tissues. High throughput searches leave a number of peptides not analyzed for a variety of reasons, such as posttranslational modification or a mutation that results changes in the peptide that is not present in databases. Such mutations may be critically important in causing disease conditions. Accounts from ocular diseases are presented where the search provided results often from non-conventional databases (such as structural database instead of protein database) due to the presence of information about a mutant peptide. We contemplate that better algorithms and the ability to determine probabilities of different amino acids in the available sequence may permit combinatorial analysis with genomics which may help identify new disease associated mutations directly from the sequence of the captured peptides. In addition, the de novo analysis of spectra of the unidentified peptides may provide mutation or polymorphism information enabling additional insight about the disease association of a mutation or posttranslational modification.

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蛋白质组学指导的基因组和遗传分析在疾病发现中的前景。
蛋白质组学的发现通常是通过数据库搜索来鉴定来自细胞或组织的特定蛋白质样本中的蛋白质。高通量搜索由于各种原因导致许多肽未被分析,例如翻译后修饰或突变导致数据库中不存在的肽发生变化。这种突变可能在引起疾病条件方面至关重要。由于存在有关突变肽的信息,因此通常从非传统数据库(如结构数据库而不是蛋白质数据库)中搜索提供的结果来自眼部疾病。我们认为,更好的算法和确定可用序列中不同氨基酸概率的能力可能允许与基因组学进行组合分析,这可能有助于直接从捕获的肽序列中识别新的疾病相关突变。此外,对未识别肽谱的从头分析可以提供突变或多态性信息,从而进一步了解突变或翻译后修饰与疾病的关联。
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