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本文引用的文献

1
The secreted protein discovery initiative (SPDI), a large-scale effort to identify novel human secreted and transmembrane proteins: a bioinformatics assessment.分泌蛋白发现计划(SPDI):一项旨在鉴定新型人类分泌蛋白和跨膜蛋白的大规模研究——生物信息学评估
Genome Res. 2003 Oct;13(10):2265-70. doi: 10.1101/gr.1293003. Epub 2003 Sep 15.
2
The mouse secretome: functional classification of the proteins secreted into the extracellular environment.小鼠分泌组:分泌到细胞外环境中的蛋白质的功能分类。
Genome Res. 2003 Jun;13(6B):1350-9. doi: 10.1101/gr.983703.
3
An analysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms.使用基于计算机的预测算法对白色念珠菌基因组数据库中的可溶性分泌蛋白进行分析。
Yeast. 2003 May;20(7):595-610. doi: 10.1002/yea.988.
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Ensembl 2002: accommodating comparative genomics.Ensembl 2002:适应比较基因组学。
Nucleic Acids Res. 2003 Jan 1;31(1):38-42. doi: 10.1093/nar/gkg083.
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NCBI Reference Sequence project: update and current status.美国国立生物技术信息中心参考序列项目:更新与现状
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Prediction of protein signal sequences.蛋白质信号序列的预测
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Sequence conserved for subcellular localization.亚细胞定位的序列保守区。
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The Bioperl toolkit: Perl modules for the life sciences.生物Perl工具包:用于生命科学的Perl模块。
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Engineered viruses to select genes encoding secreted and membrane-bound proteins in mammalian cells.工程病毒用于在哺乳动物细胞中筛选编码分泌蛋白和膜结合蛋白的基因。
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Porcine gene discovery by normalized cDNA-library sequencing and EST cluster assembly.通过标准化cDNA文库测序和EST聚类组装进行猪基因发现。
Mamm Genome. 2002 Aug;13(8):475-8. doi: 10.1007/s00335-001-2072-4.

识别人类、河豚和猪的分泌蛋白组。

Identifying secretomes in people, pufferfish and pigs.

作者信息

Klee Eric W, Carlson Daniel F, Fahrenkrug Scott C, Ekker Stephen C, Ellis Lynda B M

机构信息

Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA.

出版信息

Nucleic Acids Res. 2004 Feb 27;32(4):1414-21. doi: 10.1093/nar/gkh286. Print 2004.

DOI:10.1093/nar/gkh286
PMID:14990746
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC390277/
Abstract

The proteins processed by the secretory pathway (secretome) are critical players in the development of multi-cellular eukaryotic organisms but have yet to be comprehensively studied at the genomic level. In this study, we use the Target P algorithm to predict human (13-20% of proteins found in individual datasets) and Fugu (14%) secretomes based on analysis of their nearly complete proteomes. We combine internal processing with prediction software to automate secreted protein identification and overcome one of the major challenges associated with EST data: identification of the minority of clones that encode N-terminally-complete proteins. We discuss the use of these methods to predict secreted proteins in EST-based consensus sequence sets, and we validate these predictions using an assay for cell-free cotranslational translocation. Analysis of TIGR Porcine Gene Index 4.0 as a test dataset resulted in the identification of 352 N-terminally-complete, putative secreted proteins. In functional agreement with our predictions, 34 of 40 (85%) of these cDNAs were verified to be cotranslationally translocated in an in vitro translation system. The methods developed here are specifically designed to accept partial open reading frames and improve secreted protein predictions in eukaryotic transcriptomes, and are valuable for the analysis and annotation of eukaryotic EST databases.

摘要

通过分泌途径加工的蛋白质(分泌组)是多细胞真核生物发育中的关键参与者,但尚未在基因组水平上进行全面研究。在本研究中,我们基于对人类(在各个数据集中发现的蛋白质的13 - 20%)和河豚(14%)几乎完整蛋白质组的分析,使用Target P算法预测其分泌组。我们将内部处理与预测软件相结合,以自动识别分泌蛋白,并克服与EST数据相关的主要挑战之一:识别编码N端完整蛋白质的少数克隆。我们讨论了使用这些方法预测基于EST的共有序列集中的分泌蛋白,并使用无细胞共翻译转运测定法验证了这些预测。将TIGR猪基因索引4.0作为测试数据集进行分析,结果鉴定出352个N端完整的推定分泌蛋白。与我们的预测在功能上一致,这些cDNA中有40个中的34个(85%)在体外翻译系统中被验证为共翻译转运。这里开发的方法专门设计用于接受部分开放阅读框,并改进真核转录组中分泌蛋白的预测,对于真核EST数据库的分析和注释很有价值。