Python编程在生物信息学中的应用

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"Bioinformatics Programming Using Python.pdf" 是一本由 Mitchell L. Model 撰写的书籍，主要讨论如何使用 Python 语言进行生物信息学编程。该书由 O'Reilly Media 出版，适用于教育、商业或销售推广用途，并提供在线版本。在生物信息学领域，Python 是一个非常流行的语言选择，因其简洁的语法和强大的库支持而备受青睐。本书旨在教授读者如何利用 Python 来解决生物信息学中的问题，包括数据处理、序列比对、基因组分析、蛋白质结构预测等多个方面。书中可能涵盖的知识点可能包括： 1. **Python基础知识**：介绍 Python 的基本语法，如变量、数据类型（如字符串、列表、元组、字典）、控制流（条件语句、循环）以及函数定义。 2. **生物信息学数据格式**：讲解常见的生物信息学数据格式，如 FASTA、GenBank、BED 和 GFF 文件，以及如何用 Python 读取和解析这些格式的数据。 3. **序列比对**：探讨如何使用 Python 实现序列比对算法，如 Smith-Waterman 和 Needleman-Wunsch，以及比对工具的使用，例如 ClustalW 或 MUSCLE。 4. **基因组分析**：介绍基因组数据的处理，如基因预测、SNP 检测、重测序数据分析等，可能会涉及 BioPython 库的使用。 5. **蛋白质结构与功能预测**：探讨如何使用 Python 进行蛋白质结构预测，如二级结构预测、折叠识别，以及如何通过序列相似性搜索预测蛋白质功能。 6. **生物数据库交互**：讲解如何使用 Python 访问和检索 NCBI、UniProt 等公共生物信息学数据库，以及 EBI 的 Web Services。 7. **数据可视化**：介绍如何使用 Python 工具（如 Matplotlib 和 Seaborn）来可视化生物信息学数据，帮助理解分析结果。 8. **机器学习与数据挖掘**：可能涉及到使用 Python 的 Scikit-learn 库进行分类、回归和聚类分析，用于预测基因表达、蛋白质相互作用等生物问题。 9. **算法实现**：书中可能会讲解一些基础的生物信息学算法，如 BLAST、PHYLIP 等，鼓励读者自己实现这些算法以增进理解。 10. **软件工程实践**：介绍良好的编程习惯，如代码组织、版本控制（Git）和测试，以确保生物信息学程序的可靠性和可维护性。这本书是针对希望利用 Python 进行生物信息学研究或分析的初学者和专业人士，它提供了从基础到高级的全面教程，帮助读者构建必要的技能来处理生物学数据和解决实际问题。

examples are drawn from bioinformatics. You should find the book readable even

if you are just curious about programming and don’t plan to do any yourself.

Students

This book could serve as a textbook for a one-semester course in bioinformatics

programming or an equivalent independent study effort. If you are majoring in a

life science, the technical competence you can gain from this book will enable you

to make significant contributions to the projects in which you participate. If you

are majoring in computer science or software engineering but are intrigued by

bioinformatics, this book will give you an opportunity to apply your technical

education in that field. In any case, nothing in the book should be intimidating to

any student with a basic background either in one of the life sciences or in

computing.

Technical staff

You’re probably already doing some work managing and manipulating data in

support of life science research and development, and you may be accustomed to

writing small scripts and performing system maintenance tasks. Perhaps you’re

frustrated by the limits of your knowledge of computing techniques. Regardless,

you have developed an interest in the science and technology of bioinformatics.

You want to learn more about those fields and develop your skills in working with

biological data. Whatever your training and responsibilities, you should find this

book both approachable and helpful.

Programmers

Bioinformatics software differs from most other software in important, though

hard to pin down, ways. Python also differs from other programming languages in

ways that you will probably find intriguing. This book moves quickly into signifi-

cant technical material—it does not follow the pattern of a traditional kind of

“Programming in...” or “Learning...” or “Introduction to...” book. Though it

makes no attempt to provide a bioinformatics primer, the book includes sufficient

examples and explanations to intrigue programmers curious about the field and

its unusual software needs.

I would like to point out to computer scientists and experienced soft-

ware developers who may read this book that some very particular

choices were made for the purposes of presentation to its intended au-

dience. At the risk of sounding arrogant, I assure you that these are

backed by deep theoretical knowledge, extensive experience, and a full

awareness of alternatives. These choices were made with the intention

of simplifying technical vocabulary and presenting as clear and uniform

a view of Python programming as possible. They also were based on the

assumption that most people making use of what they learn in this book

will not move on to more advanced programming or large-scale software

development.

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Some things that will appear strange to anyone with significant programming experi-

ence are in reality true to a pure “Pythonic” approach. It is delightful to have the

opportunity to write in this vocabulary without the need to accommodate more tradi-

tional terminology.

The most significant example of this is that the word “variable” is never used in the

context of assignment statements or function calls. Python does not assign values to

variables in the way that traditional “values in a box” languages do. Instead, like some

of the languages that influenced its design, what Python does is assign names to values.

The assignment statement should be read from left to right as assigning a name to an

existing value. This is a very real distinction that goes beyond the ways languages such

as Java and C++ refer to objects through pointer-valued variables.

Another aspect of the book’s heavily Pythonic approach is its routine use of compre-

hensions. Approached by someone familiar with other languages, these can appear

quite mysterious. For someone learning Python as a first language, though, they can

be more natural and easier to use than the corresponding combinations of assignments,

tests, and loops or iterations.

Python

This section introduces the Python language and gives instructions for installing and

running Python on your machine.

Some Context

There are many kinds of programming languages, with different purposes, styles, in-

tended uses, etc. Professional programmers often spend large portions of their careers

working with a single language, or perhaps a few similar ones. As a result, they are often

unaware of the many ways and levels at which programming languages can differ. For

educational and professional development purposes, it can be extremely valuable for

programmers to encounter languages that are fundamentally different from the ones

with which they are familiar.

The effects of such an encounter are similar to learning a foreign human language from

a different culture or language family. Learning Portuguese when you know Spanish is

not much of a mental stretch. Learning Russian when you are a native English speaker

is. Similarly, learning Java is quite easy for experienced C++ programmers, but learning

Lisp, Smalltalk, ML, or Perl would be a completely different experience.

Broadly speaking, programming languages embody combinations of four paradigms.

Some were designed with the intention of staying within the bounds of just one, or

perhaps two. Others mix multiple paradigms, although in these cases one is usually

dominant. The paradigms are:

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Procedural

This is the traditional kind of programming language in which computation is

described as a series of steps to be executed by the computer, along with a few

mechanisms for branching, repetition, and subroutine calling. It dates back to the

earliest days of computing and is still a core aspect of most modern languages,

including those designed for other paradigms.

Declarative

Declarative programming is based on statements of facts and logical deduction

systems that derive further facts from those. The primary embodiment of the logic

programming paradigm is Prolog, a language used fairly widely in Artificial Intel-

ligence (AI) research and applications starting in the 1980s. As a purely logic-based

language, Prolog expresses computation as a series of predicate calculus assertions,

in effect creating a puzzle for the system to solve.

Functional

In a purely functional language, all computation is expressed as function calls. In

a truly pure language there aren’t even any variable assignments, just function

parameters. Lisp was the earliest functional programming language, dating back

to 1958. Its name is an acronym for “LISt Processing language,” a reference to the

kind of data structure on which it is based.

Lisp became the dominant language of AI in the 1960s and still plays a major role

in AI research and applications. The language has evolved substantially from its

early beginnings and spawned many implementations and dialects, although most

of these disappeared as hardware platforms and operating systems became more

standardized in the 1980s.

A huge standardization effort combining ideas from several major dialects and a

great many extensions, including a complete object-oriented (see below) compo-

nent, was undertaken in the late 1980s. This effort resulted in the now-dominant

CommonLisp.

Two important dialects with long histories and extensive current

use are Scheme and Emacs Lisp, the scripting language for the Emacs editor. Other

functional programming languages in current use are ML and Haskell.

Object-oriented

Object-oriented programming was invented in the late 1960s, developed in the

research community in the 1970s, and incorporated into languages that spread

widely into both academic and commercial environments in the 1980s (primarily

Smalltalk, Objective-C, and C++). In the 1990s this paradigm became a key part

of modern software development approaches. Smalltalk and Lisp continued to be

used, C++ became dominant, and Java was introduced. Mac OS X, though built

on a Unix-like kernel, uses Objective-C for upper layers of the system, especially

the user interface, as do applications built for Mac OS X. JavaScript, used primarily

to program web browser actions, is another object-oriented language. Once a

* See

http://www.lispworks.com/documentation/HyperSpec/Body/01_ab.htm.

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radical innovation, object-oriented programming is today very much a mainstream

paradigm.

Another dimension that distinguishes programming languages is their primary inten-

ded use. There have been languages focused on string matching, languages designed

for embedded devices, languages meant to be easy to learn, languages built for efficient

execution, languages designed for portability, languages that could be used interac-

tively, languages based largely on list data structures, and many other kinds.

Language designers, whether consciously or not, make choices in these and other

dimensions. Subsequent evolutions of their languages are subject to market forces,

intellectual trends, hardware developments, and so on. These influences may help a

language mature and reach a wider audience. They may also steer the language in

directions somewhat different from those originally intended.

The Python Language

Simply put, Python is a beautiful language. It is effective for everything from teaching

new programmers to advanced computer science study, from simple scripts to sophis-

ticated advanced applications. It has always had some purchase in bioinformatics, and

in recent years its popularity has been increasing rapidly. One goal of this book is to

help significantly expand Python’s use for bioinformatics programming.

Python features a syntax in which the ends of statements are marked only by the end

of a line, and statements that form part of a compound statement are indented relative

to the lines of code that introduce them. The semicolons or keywords that end state-

ments and the braces that group statements in other languages are entirely absent.

Programmers familiar with “standard syntax” languages often find Python’s unclut-

tered syntax deeply disconcerting. New programmers have no such problem, and for

them, this simple and readable syntax is far easier to deal with than the visually arcane

constructions using punctuation (with the attendant compilation errors that must be

confronted). Traditional programmers should reconsider Python’s syntax after per-

forming this experiment:

1. Open a file containing some well-formatted code.

2. Delete all semicolons, braces, and terminal keywords such as end, endif, etc.

3. Look at the result.

To the human eye, the simplified code is easier to read—and it looks an awful lot like

Python. It turns out that the semicolons, terminal keywords, and braces are primarily

for the benefit of the compiler. They are not really necessary for human writers and

readers of program code. Python frees the programmer from the drudgery of serving

as a compiler assistant.

Python is an interesting and powerful language with respect to computing paradigms.

Its skeleton is procedural, and it has been significantly influenced by functional

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programming, but it has evolved into a fundamentally object-oriented language. (There

is no declarative programming component—of the four paradigms, declarative pro-

gramming is the one least amenable to fitting together with another.) Few, if any, other

languages provide a blend like this as seamlessly and elegantly as does Python.

Installing Python

This book uses Python 3, the language’s first non-backward-compatible release. With

a few minor changes, noted where applicable, Python 2.x will work for most of the

book’s examples. There are a few notes about Python 2 in Chapters

1, 3, and 5; they

are there not just to help you if you find yourself using Python 2 for some work, but

also for when you read Python 2 code. The major exception is that print was a statement

in Python 2 but is now a function, allowing for more flexibility. Also, Python 3 reor-

ganized and renamed some of its library modules and their contents, so using Python

2.x with examples that demonstrate the use of certain modules would involve more

than a few minor changes.

Determing Which Version of Python Is Installed

Some

version of Python 2 is probably installed on your computer, unless you are using

Windows. Typing the following into a command-line window (using % as an example

of a command-line prompt) will tell you which version of Python is installed as the

program called python:

% python -V

The name of the executable for Python 3 may be python3 instead of just python. You

can type this:

% python3 -V

to see if that is the case.

If you are running Python in an integrated development environment—in particular

IDLE, which is part of the Python installation—type the following at the prompt

(>>>) of its interactive shell window to get information about its version:

>>> from sys import version

>>> version

If this shows a version earlier than 3, look for another version of the IDE on your

computer, or install one that uses Python 3. (The Python installation process installs

the GUI-based IDLE for whatever version of Python is being installed.)

The current release of Python can be downloaded from http://python.org/download/.

Installers are available for OS X and Windows. With most distributions of Linux, you

should be able to install Python through the usual package mechanisms. (Get help from

someone who knows how to do that if you don’t.) You can also download the source,

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