Dark蛋白质组数据库:疾病研究。

Q2 Biochemistry, Genetics and Molecular Biology

High-Throughput Pub Date : 2020-06-30 DOI:10.3390/ht9030015

Nelson Perdigão, Pedro M C Pina, Cátia Rocha, João Manuel R S Tavares, Agostinho Rosa

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引用次数: 1

摘要

有一种误解认为蛋白质的内在紊乱等同于黑暗。本研究的目的是在Swiss-Prot和Dark蛋白质组数据库的范围内，建立紊乱和黑暗之间的关系。三个不同的预测因子被用来计算Swiss-Prot蛋白的紊乱。对所使用的预测因子和可视化范式所得到的结果进行分析，得出了与之前相同的结论:混乱与黑暗基本无关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Dark Proteome Database: Studies on Disorder.

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Dark Proteome Database: Studies on Disorder.

There is a misconception that intrinsic disorder in proteins is equivalent to darkness. The present study aims to establish, in the scope of the Swiss-Prot and Dark Proteome databases, the relationship between disorder and darkness. Three distinct predictors were used to calculate the disorder of Swiss-Prot proteins. The analysis of the results obtained with the used predictors and visualization paradigms resulted in the same conclusion that was reached before: disorder is mostly unrelated to darkness.

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来源期刊

High-Throughput Biochemistry, Genetics and Molecular Biology-Biotechnology

CiteScore

3.60

自引率

0.00%

发文量

审稿时长

9 weeks

期刊介绍： High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: -Microarrays -DNA Sequencing -RNA Sequencing -Protein Identification and Quantification -Cell-based Approaches -Omics Technologies -Imaging -Bioinformatics -Computational Biology/Chemistry -Statistics -Integrative Omics -Drug Discovery and Development -Microfluidics -Lab-on-a-chip -Data Mining -Databases -Multiplex Assays