基因序列数据信息量的估计

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-07-25 DOI:10.1093/jrsssc/qlad062

Steinar Thorvaldsen, O. Hössjer

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引用次数: 0

摘要

分析遗传信息的一个突出问题是缺乏这样做的数学框架。本文提供了一些新的统计方法来模拟和分析蛋白质、蛋白质家族及其序列的信息含量。我们讨论了如何理解遗传信息的定性方面，如何估计遗传信息的定量方面，并实现了一个统计模型，其中定性遗传函数与其自信息的概率度量联合表示。采用拒绝抽样的方法对Cath和Pfam数据库中蛋白质家族的功能信息进行了估计。科学工作可能把这些信息的组成部分作为分子生物学的一个基本方面。

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Estimating the information content of genetic sequence data

A prominent problem in analysing genetic information has been a lack of mathematical frameworks for doing so. This article offers some new statistical methods to model and analyse information content in proteins, protein families, and their sequences. We discuss how to understand the qualitative aspects of genetic information, how to estimate the quantitative aspects of it, and implement a statistical model where the qualitative genetic function is represented jointly with its probabilistic metric of self-information. The functional information of protein families in the Cath and Pfam databases are estimated using a method inspired by rejection sampling. Scientific work may place these components of information as one of the fundamental aspects of molecular biology.

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来源期刊

Journal of the Royal Statistical Society Series C-Applied Statistics 数学-统计学与概率论

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.