For most of us bioinformaticians, this includes Python, R, Perl, and bash command line utilities (like sed, awk, cut, sort, etc). There are also people who code in Java, Ruby, C++, and Matlab.
So the bottom line? Whichever language lets you get the work done most easily is the right one for you. Answering this question should include a careful survey of the libraries and other code that you can pull from, as well as information on your own preferences and experience. If you're doing microarray analysis, it's hard to beat the R/bioconductor libraries, but that's absolutely the wrong language for someone wrangling most types of large sequencing data sets.
无论好坏，SAS 都是生物制药领域事实上的编程环境。如果您要在生物信息学领域为辉瑞公司、默克公司和拜耳公司工作，您最好拥有 SAS 技能。 SAS 程序员的需求量很大。
Best or not, SAS is the de facto programming enviroment in biopharmas. If you were to work for the Pfizers, Mercks and Bayers of the world in bioinformatics, you had better have SAS skills. SAS programmers are in great demand.
What's the "best" language is both subjective and potentially different from task to task, but for bioinformatic work, I personally use R, Perl, Delphi and C (quite frequently a combination of several of these).
我主要研究 HMM 和蛋白质序列。我一开始用 C 语言编写，但后来改用了 Python，我对此很满意。我发现快速原型化更容易，并且代码更容易维护。
I work mainly with HMMs and protein sequences. I started out writing in C, but have since switched to Python, which I'm happy with. I find it's easier to prototype something quickly and results in easier to maintain code.
Here's a freely available academic paper written on the subject that evaluates the different languages, and in different situations: http://www.biomedcentral.com/1471-2105/9/82
They grouped 6 commonly used languages into 3 different levels.
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您可能会对 BioStar 的这个帖子感兴趣：
对于我们大多数生物信息学家来说，这包括 Python、R、Perl 和 bash 命令行实用程序（如 sed、awk） 、剪切、排序等）。还有一些人使用 Java、Ruby、C++ 和 Matlab 进行编码。
那么底线是什么呢？无论哪种语言可以让您最轻松地完成工作，都适合您。回答这个问题应该包括仔细调查您可以从中获取的库和其他代码，以及有关您自己的偏好和经验的信息。如果您正在进行微阵列分析，则很难击败 R/bioconductor 库，但对于处理大多数类型的大型测序数据集的人来说，这绝对是错误的语言。
You'll likely be interested in this thread over at BioStar:
For most of us bioinformaticians, this includes Python, R, Perl, and bash command line utilities (like sed, awk, cut, sort, etc). There are also people who code in Java, Ruby, C++, and Matlab.
So the bottom line? Whichever language lets you get the work done most easily is the right one for you. Answering this question should include a careful survey of the libraries and other code that you can pull from, as well as information on your own preferences and experience. If you're doing microarray analysis, it's hard to beat the R/bioconductor libraries, but that's absolutely the wrong language for someone wrangling most types of large sequencing data sets.
生物信息学没有一种正确的语言。
重要的BLAST 测序工具是用 C++ 编写的
用于比对蛋白质结构的 MATT 工具是编写的用于
一般来说，我看到很多 C 和 C++ 用于性能关键型代码，而很多脚本语言则用于其他方面。
There's no one right language for bioinformatics.
The important BLAST sequencing tool is written in C++
The MATT tool for aligning protein structures is written in C
Some of my colleagues in computational biology use Ruby.
In general, I see a lot of C and C++ for performance-critical code and a lot of scripting languages otherwise.
Python + scipy 很不错（而且免费）。
http://www.vetta.org/2008 /05/scipy-the-embarrassing-way-to-code/
http://www.google.com/search?hl=en&source =hp&q=python+bioinformatics&aq=0&aqi=g9g-m1&aql=&oq=python+bio&gs_rfai=CeE1nPpMNTN2IJZ-yMZX6pcIKAAAAqgQFT9DLSgo
您甚至不需要真正学习新语法放弃 Matlab 转而使用 SciPy。
Python + scipy are decent (and FREE).
http://www.vetta.org/2008/05/scipy-the-embarrassing-way-to-code/
http://www.google.com/search?hl=en&source=hp&q=python+bioinformatics&aq=0&aqi=g9g-m1&aql=&oq=python+bio&gs_rfai=CeE1nPpMNTN2IJZ-yMZX6pcIKAAAAqgQFT9DLSgo
You do not even need to learn new syntax really when when dropping Matlab for SciPy.
无论好坏，SAS 都是生物制药领域事实上的编程环境。如果您要在生物信息学领域为辉瑞公司、默克公司和拜耳公司工作，您最好拥有 SAS 技能。 SAS 程序员的需求量很大。
Best or not, SAS is the de facto programming enviroment in biopharmas. If you were to work for the Pfizers, Mercks and Bayers of the world in bioinformatics, you had better have SAS skills. SAS programmers are in great demand.
什么是“最好”的语言既是主观的，而且可能因任务而异，但对于生物信息工作，我个人使用 R、Perl、Delphi 和 C（经常是其中几种的组合）。
What's the "best" language is both subjective and potentially different from task to task, but for bioinformatic work, I personally use R, Perl, Delphi and C (quite frequently a combination of several of these).
我主要研究 HMM 和蛋白质序列。我一开始用 C 语言编写，但后来改用了 Python，我对此很满意。我发现快速原型化更容易，并且代码更容易维护。
I work mainly with HMMs and protein sequences. I started out writing in C, but have since switched to Python, which I'm happy with. I find it's easier to prototype something quickly and results in easier to maintain code.
这是一篇关于该主题的免费学术论文，评估了不同语言和不同情况下的情况： http://www.biomedcentral.com/1471-2105/9/82
他们将 6 种常用语言分为 3 个不同的级别。
一些一般性结论：
这是另一篇很好的免费学术文章，讨论了建立生物信息学技能的方法：http://dx.plos.org/10.1371/journal.pcbi.1000589
Here's a freely available academic paper written on the subject that evaluates the different languages, and in different situations: http://www.biomedcentral.com/1471-2105/9/82
They grouped 6 commonly used languages into 3 different levels.
Some general conclusions:
Here's another good free academic article discussing ways to build bioinformatics skills: http://dx.plos.org/10.1371/journal.pcbi.1000589