E-value:

The E-value provides information about the likelihood that a given sequence match is purely by chance. The lower the E-value, the less likely the database match is a result of random chance and therefore the more significant the match is.

Empirical interpretation of the E-value is as follows:

If E-value < 1e-50 (or 1 X 10-50), there should be an extremely high confidence that the database match is a result of homologous relationships.

If E-value is between 0.01 and 1e-50, the match can be considered a result of homology.

If E-value is between 10 and 0.01, the match is considered not significant, but may hint at a tentative remote homology relationship. Additional evidence is needed to confirm the tentative relationship.

If E-value > 10, the sequences under consideration are either unralated or related by extremely distant realtionships that fall below the limit of detection with the current method.

Because the E-value is proportionally affected by the database size, an obvious problem is that as the database grows, the E-value for a given sequence match also increases.

Because the genuine evolutionary relationship beween the two sequence remains constant, the decrease in credibility of the sequence match as the database grows means that one may "lose" previously detected homologs as the database enlarges. Thus, an alternative to E-value calculations is needed.

The E-value is very important, the lower the better

bitscore:

A bitscore is another prominant statistical indicator used in addition to the E-value in a BLAST output. The bitscore measures sequence similarity independent of query sequence length and database size and is normalized based on the raw pairwise alignment score. The bitscore (S) is determined by the following formula: S = (λ * S - lnK) / ln2  where λ is the Gumble distribution constant, S is the raw alignment score, and K is a constant associated with the scoring matrix used. Clearly, the bitscore (S) is linearly related to the raw alignment score (S). Thus, the higher the bit score, the more highly significant the match is. The bit score provides a constant statistical indicator for  searching different databases of different size or for searching the same database at different times as the database enlarges.

identity:

Identity 35% means that 35% of AA in your sequence match to other sequences in database, There isn't something like "acceptable percentage". It always depends on what you are looking for:

If you have unkown protein sequence and you would like to know the homology sequences, information about identity (even 35%) is valuable.

If you have known protein and you need to confirm the sequence, the identity 35% is small and may suggest that something went wrong during your analysis.

E-value identity bitscore的更多相关文章

  1. ASP.NET Core 之 Identity 入门(一)

    前言 在 ASP.NET Core 中,仍然沿用了 ASP.NET里面的 Identity 组件库,负责对用户的身份进行认证,总体来说的话,没有MVC 5 里面那么复杂,因为在MVC 5里面引入了OW ...

  2. ASP.NET Core 之 Identity 入门(三)

    前言 在上一篇文章中,我们学习了 CookieAuthentication 中间件,本篇的话主要看一下 Identity 本身. 最早2005年 ASP.NET 2.0 的时候开始, Web 应用程序 ...

  3. ASP.NET Core 之 Identity 入门(二)

    前言 在 上篇文章 中讲了关于 Identity 需要了解的单词以及相对应的几个知识点,并且知道了Identity处在整个登入流程中的位置,本篇主要是在 .NET 整个认证系统中比较重要的一个环节,就 ...

  4. 从Membership 到 .NET4.5 之 ASP.NET Identity

    我们前面已经讨论过了如何在一个网站中集成最基本的Membership功能,然后深入学习了Membership的架构设计.正所谓从实践从来,到实践从去,在我们把Membership的结构吃透之后,我们要 ...

  5. TSQL Identity 用法全解

    Identity是标识值,在SQL Server中,有ID列,ID属性,ID值,ID列的值等术语. Identity属性是指在创建Table时,为列指定的Identity属性,其语法是:column_ ...

  6. MVC5 - ASP.NET Identity登录原理 - Claims-based认证和OWIN

    在Membership系列的最后一篇引入了ASP.NET Identity,看到大家对它还是挺感兴趣的,于是来一篇详解登录原理的文章.本文会涉及到Claims-based(基于声明)的认证,我们会详细 ...

  7. ASP.NET Identity入门系列教程(一) 初识Identity

    摘要 通过本文你将了解ASP.NET身份验证机制,表单认证的基本流程,ASP.NET Membership的一些弊端以及ASP.NET Identity的主要优势. 目录 身份验证(Authentic ...

  8. 列属性:RowGUIDCol、Identity 和 not for replication

    Table Column有两个特殊的属性RowGUIDCol 和 Identity,用于标记数据列: $ROWGUID 用于引用被属性 RowGUIDCol 标识的UniqueIdentifier 类 ...

  9. SQL Server 合并复制遇到identity range check报错的解决

        最近帮一个客户搭建跨洋的合并复制,由于数据库非常大,跨洋网络条件不稳定,因此只能通过备份初始化,在初始化完成后向海外订阅端插入数据时发现报出如下错误: Msg 548, Level 16, S ...

随机推荐

  1. Kubernetes增强型调度器Volcano算法分析【华为云技术分享】

    [摘要] Volcano 是基于 Kubernetes 的批处理系统,源自于华为云开源出来的.Volcano 方便 AI.大数据.基因.渲染等诸多行业通用计算框架接入,提供高性能任务调度引擎,高性能异 ...

  2. Java匹马行天下之JavaSE核心技术——反射机制

    Java反射机制 一.什么是反射? 在运行状态中,对于任意一个类,都能够获取到这个类的所有属性和方法,对于任意一个对象,都能够调用它的任意一个方法和属性(包括私有的方法和属性),这种动态获取的信息以及 ...

  3. Spring+Mybatis动态切换数据源

    功能需求是公司要做一个大的运营平台: 1.运营平台有自身的数据库,维护用户.角色.菜单.部分以及权限等基本功能. 2.运营平台还需要提供其他不同服务(服务A,服务B)的后台运营,服务A.服务B的数据库 ...

  4. How to call a stored procedure in EF Core 3.0 via FromSqlRaw(转载)

    问: I recently migrated from EF Core 2.2 to EF Core 3.0. Unfortunately, I haven't found a way to call ...

  5. 华为 鸿蒙系统(HarmonyOS)

    HarmonyOS Ⅰ. 鸿蒙系统简介 鸿蒙系统(HarmonyOS),是第一款基于微内核的全场景分布式OS,是华为自主研发的操作系统.2019年8月9日,鸿蒙系统在华为开发者大会<HDC.20 ...

  6. PIE SDK缓冲区分析算法

    1.算法功能简介 缓冲区分析是指有点.线.面实体为基础,自动建立其周围一定宽度范围内的缓冲区多边形图层,然后建立该图层与目标图层的叠加,进行分析而得到的所需的结果.他是用来解决邻近度问题的控件分析工具 ...

  7. 使用Python3导出MySQL查询数据

    整理个Python3导出MySQL查询数据d的脚本. Python依赖包: pymysql xlwt Python脚本: #!/usr/bin/env python # -*- coding: utf ...

  8. nginx日志设置

    环境:nginx1.16.1 (1)日志类型:access_log(访问日志) error_log(错误日志)rewrite_log 访问日志:通过访问日志我们可以得到用户的IP地址.浏览器的信息,请 ...

  9. AI-图像基础知识-01

        目前人工智能Artificial Intelligence主要分为两大分支: 计算机视常见:Computer Vision,简称CV   CV主要是研究如何让机器看懂世界的一种技术,通过各种光 ...

  10. AAC的RTP封装中的AU头分析

      解码器收到一个RTP的AAC流,发现RTP流里的音频里带有4个字节AU头,然后才是AAC的ADTS头.     这种情况之前已经出现过多次,每次我们都告知对方,不要往AAC前面加AU头,解码器不支 ...