Effective Bioinformatics Programming - Part 3 No comments yet

All Things Unix

Bioinformatics started with Unix. At the Human Genome Center, for a long time, I had the one and only PC. (We got a request from our users for a PC-based client for the Search Launcher). Everything else was Solaris (Unix) and Mac, which was followed by Linux.

Unix supports a number of nifty commands like grep, strings, df, du, ls, etc. These commands are run inside the shell, or command line interpreter, for the operating system (Unix). There have been a number of these shells in the history of Unix development.

The bash shell http://en.wikipedia.org/wiki/Bash is the default shell for the Linux environment. This shell provides several unique capabilities over other shells. For instance, bash supports a history buffer of system commands. With the history buffer, the “up” arrow will return the previous command. The history command lets you view a history of past commands. The bang operator (!) lets you rerun a previous command from the history buffer. (Which saves a lot of typing!)

bash enables a user to redirect program output. The pipeline feature allows the user to connect a series of commands. With the pipeline (“|”) operator, a chain of commands can be linked together where the output of one command is the input to the next command an so forth.

A shell script (http://en.wikipedia.org/wiki/Shell_script) is script written for the shell or command line interpreter. Shell scripts enable batch processing. Together with the cron command, these scripts can be set to run automatically at times when system usage is minimum.

For general information about bash, go to the Bash Reference Manual at http://www.gnu.org/software/bash/manual/bashref.html.

A whole wealth of bash shell script examples is available at - http://tldp.org/LDP/abs/html/.

Unix on Other Platforms

Cygwin (http://www.cygwin.com/) is a Linux-like environment for windows. The basic download installs a minimum environment, but you can add additional packages at any time. Go to http://cygwin.com/packages/ for a list of Cygwin packages available for download.

Apple’s OS X is based on Unix. Other than the MACH kernel, the OS is BSD-derived. Their Java package is usually not the latest as Apple has to port Java due to differences such as the graphics portion.

All Things Software – Documenting and Archiving

I’ve run into all sorts of approaches to program code documentation in my career. A lead engineer demanded that every line of assembler code be documented. A senior programmer insisted that code should be self-documenting.

By that, she used variable names such as save_the_file_to_the_home_directory, and so on. Debugging these programs was a real pain. The first thing you had to do was set up aliases for all the unwieldy names.

The FORTRAN programmers cried when variable names longer than 6 characters were allowed in version 77 of VAX FORTRAN.. Personally, I thought it was great. The same with IMPLICIT NONE.

In the ancient times, FORTRAN integers variables had to start with i thru n. Real variables could use the other letters. The IMPLICIT NONE directive told the compiler to shut that off.

All FORTRAN variables had to be in capital letters. But you could stuff strings into integer variables which I found extremely useful. All FORTRAN statements had to begin with a number. This number usually started at 10 and went up in increments of 10.

At one time Microsoft used Hungarian notation (http://en.wikipedia.org/wiki/Hungarian_notation) for variables in most of their documentation. In this method, the name of the variable indicated it’s use. For example, lAccountNumber was a long integer.

The IDEs (Eclipse, NetBeans, and others) will automatically create the header comment with a list of variables. The user just adds the proper definitions. (If you’re using Java, the auto comment is JavaDoc compatible, etc.)

Otherwise, Java supports the JavaDoc tool, Python has PyDoc, and Ruby has RDoc.

Personally, I feel that software programs should be read like a book, with documentation providing the footnotes, such as an overview of what the code in question does and a definition of the main variables for both input and output. Module/Object documentation should also note who uses the function and why. Keep variable names short but descriptive and make comments meaningful.

Keep code clean, but don’t go overboard. I worked with one programmer who stated, “My code is so clean you could eat off it.” I found that a little too obnoxious, not to mention overly optimistic as a number of bugs popped out as time went by.

Archiving Code

Version Control Systems (VCS) have evolved as source code projects became larger and more complex.

RCS (Revision Control System) meant that the days of the keeping the Emacs numbered files (e.g. foo.~1~) as backups were over. RCS used the diff concept (just kept a list of the changes make to a file as a backup strategy).

I found this unsuited for what I had to do – revert to an old version in a matter of seconds.

CVS was much, much better. CVS was replaced by Subversion. But they’re centralized repository structure can create problems. You basically check out what you want to work on from a library and check it back in when you’re done. This can be a slow process depending on network usage or central server available.

The current favorite is Git. Git was created by Linus Torvalds (of Linux fame). Git is a free, open source distributed version control system. (http://git-scm.com/).

Everyone on the project has a copy of all project files complete with revision histories and tracking capabilities. Permissions allow exchanges between users and merging to a central location is fast.

The IDE’s (Eclipse and NetBeans) will have CVS and Subversion plug ins already configured for accessing those repositories. NetBeans also supports Mercurical. Plug ins for the other versioning software modules are available on the web. The Eclipse plug in for Git is available at http://git.wiki.kernel.org/index.php/EclipsePlugin.

System Backup

Always have a plan B. My plan A had IT backup my systems on a weekly to monthly basis based on usage. A natural disaster completely decimated my systems. No problem, I thought, I have system backup. Imagine how I felt when I heard that IT had not archived a single on of my systems in over three years! Well, I had a plan B. I had a mirror of the most important stuff on an old machine and other media. We were back up almost immediately.

The early Tandem NonStop systems (now known as HP Integrity NonStop) automatically mirrored your system in real-time, so down time was not a problem.

Real-time backup is expensive and unless you’re a bank or airline, it’s not necessary.

Snapshot Backup on Linux with rsync

If you’re running Linux, Mac, Solaris, or any Unix-based system, you can use rsync for generating automatic rotating “snapshot” style back-ups. These systems generally have rsync already installed. If not, the source is available at – http://rsync.samba.org/.

This website - http://www.mikerubel.org/computers/rsync_snapshots/ will tell you everything you need to know to implement rsync based backups, complete with sample scripts.

Properly configured, the method can also protect against hard disk failure, root compromises, or even back up a network of heterogeneous desktops automatically.

Acknowledgment – Thanks, Bill!

I want to thank Bill Eaton for his assistance with these blog entries on Effective Bioinformatics Programming. He filled in a lot of the technical details, performed product analysis, and gave me direction in writing these blog entries.

To Be Continued - Part 4

Part 4 will cover relational database management systems (RDBMS), HPC (high performance computing) - parallel processing, FPGC, clusters, grids, and other topics.

Effective Bioinformatics Programming - Part 1 No comments yet

The PLOS Computational Biology website recently published “A Quick Guide for Developing Effective Bioinformatics Programming Skills” by Joel T. Dudley and Atul J. Butte (http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000589).

This article is a good that survey covers all the latest topics and mentions all the currently-popular buzzwords circulating above, around, and through the computing ionosphere. It’s a good article, but I can envision readers’ eyes glazing over about page 3. It’s a lot of computer-speak in a little space.

I’ll add in a few things they skipped or merely skimmed over to give a better overview of what’s out there and how it pertains to bioinformatics.

They state that a biologist should put together a Technology Toolbox. They continue, “The most fundamental and versatile tools in your technology toolbox are programming languages.”

Programming Concepts

Programming languages are important, but I think that Programming Concepts are way, way more important. A good grasp of programming concepts will enable you to understand any programming language.

To get a good handle on programming concepts, I recommend at book. This book, Structure and Implementation of Computer Programs from MIT Press (http://mitpress.mit.edu/sicp/),is the basis for an intro to computer science at MIT. It’s called the Wizard Book or the Purple Book.

I got the 1984 version of the book which used the LISP language. The current 1996 version is based on LISP/Scheme. Scheme is basically a cleaned-up LISP, in case you’re interested.

Best of all course (and the down loadable book) are freely available from MIT through the MIT OpenCourseWare website – http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-001Spring-2005/CourseHome/index.htm.

There’s a blog entry - http://onlamp.com/pub/wlg/8397 - that goes into further explanation about the course and the book..

And just because you can program, it doesn’t mean you know (or even need to know) all the concepts. For instance, my partner for a engineering education extension course was an electrical engineer who was programming microprocessors. When the instructor mentioned the term “scope” in reference to some topic, he turned to me and asked, “What’s scope?”

According to MIT’s purple book –” In a procedure definition, the bound variables declared as the formal parameters of the procedure have the body of the procedure as their scope.”

You don’t need to know about scope to program in assembler, because everything you need is right there. (In case you’re wondering, I consider assembler programmers to be among the programming elites.)

Programming Languages

The article mentions Perl, Python, and Ruby as the “preferred and most prudent choices” in which to seek mastery for bioinformatics.

These languages are selected because “they simplify the programming process by obviating the need to manage many lower level details of program execution (e.g. memory management), affording the programmer the ability to focus foremost on application logic…”

Let me add the following. There are differences in programming languages. By that, I mean compiled vs scripted. Languages such as C, C++, and Fortran are compiled. Program instructions written in these languages are parsed and translated into object code, or a language specific to the computer architecture the code is to run on. Compiled code has a definite speed advantage, but if the code is the main or any supporting module is changed, the entire project must be recompiled. Since the program is compiled into the machine code of a specific computer architecture, portability of the code is limited.

Perl, Python, and Ruby are examples of scripted or interpreted languages. These languages are translated into byte code which is optimized and compressed, but is not machine code. This byte code is then interpreted by a virtual machine (or byte code interpreter) usually written in C.

An interpreted program runs more slowly than a compiled program. Every line of an interpreted program must be analyzed as it is read. But the code isn’t particularly tied to one machine architecture making portability easier (provided the byte code interpreter is present). Since code is only interpreted at run time, extensions and modifications to the code base is easier, making these languages great for beginning programmers or rapid prototyping.

But, let’s get back to the memory management. This, and processing speed will be a huge deal in next gen data analysis and management.

Perl automatic memory management has a problem with circularity, as Perl (and Ruby and Python) count references.

If object 1 points to object 2 and object 2 points back to 1 , but nothing else in the program points to either object 1 or object 2 (this is a weak reference), these objects don’t get destroyed. They remain in memory. If these objects get created again and again, it’s called a memory leak.

I also have to ask – What about C/C++ , Fortran, and even Turbo Pascal? The NCBI Toolkit is written in C/C++. If you work with foreign scientists, you will probably see a lot Fortran.

Debugging

You can’t mention programming with mentioning debugging. I consider the act of debugging code an art form any serious programmer should doggedly pursue.

Here’s a link to a ebook, The Art of Debugging – http://www.circlemud.org/cdp/hacker/. It’s mainly Unix-based, C-centric and a little dated. But good stuff never goes out of style.

Chapter 4, Debugging: Theory explains various debugging techniques. Chapter 5 – Profiling talks about profiling your code, or determining where your program is spending most of its time.

He also mentions core dumps. A core is what happens when your C/C++/Fortran program crashes in Unix/Linux. You can examine this core to determine where your program went wrong. (It gives you a place to start.)

The Linux Foundation Developer Network has an on-line tutorial – Zen and the Art of Debugging C/C++ in Linux with GDB – http://ldn.linuxfoundation.org/article/zen-and-art-debugging-cc-linux-with-gdb. They write a C program (incorporating a bug), create a make file, compile, and then use gdb to find the problem. You are also introduced to several Unix/Linux commands in the process.

You can debug Perl by invoking it with the -d switch. Perl usually crashes at the line number that caused the problem and some explanation of what went wrong.

The -d option also turns on parser debugging output for Python.

Object Dumps

One of the most useful utilities in Unix/Linux is od (object dump). You can examine files in octal (default), hex, or ASCII characters

od is very handy for examining data structures, finding hidden characters, and reverse engineering.

If you think you’re code is right, the problem may be in what you are trying to read. Use od to get a good look at the input data.

That’s it for Part 1. Part 2 will cover Open Source, project management, archiving source code and other topics.