How Perl Saved the Human Genome Project
by Lincoln Stein
Reprinted courtesy of the Perl Journal,
http://www.tpj.com
Lincoln Stein's website is
http://stein.cshl.org
DATE: Early February, 1996
LOCATION: Cambridge, England, in the conference room of the largest DNA
sequencing center in Europe.
OCCASION: A high level meeting between the computer scientists of this
center and the largest DNA sequencing center in the United States.
THE PROBLEM: Although the two centers use almost identical laboratory
techniques, almost identical databases, and almost identical data
analysis tools, they still can't interchange data or meaningfully
compare results.
THE SOLUTION: Perl.
The human genome project was inaugurated at the beginning of the decade
as an ambitious international effort to determine the complete DNA
sequence of human beings and several experimental animals. The
justification for this undertaking is both scientific and medical. By
understanding the genetic makeup of an organism in excruciating detail,
it is hoped that we will be better able to understand how organisms
develop from single eggs into complex multicellular beings, how food is
metabolized and transformed into the constituents of the body, how the
nervous system assembles itself into a smoothly functioning ensemble.
From the medical point of view, the wealth of knowledge that will come
from knowing the complete DNA sequence will greatly accelerate the
process of finding the causes of (and potential cures for) human
diseases.
Six years after its birth, the genome project is ahead of schedule.
Detailed maps of the human and all the experimental animals have been
completed (mapping out the DNA using a series of landmarks is an
obligatory first step before determining the complete DNA sequence).
The sequence of the smallest model organism, yeast, is nearly
completed, and the sequence of the next smallest, a tiny soil-dwelling
worm, isn't far behind. Large scale sequencing efforts for human DNA
started at several centers a number of months ago and will be in full
swing within the year.
The scale of the human DNA sequencing project is enough to send your
average Unix system administrator running for cover. From the
information-handling point of view, DNA is a very long string
consisting of the four letters G, A, T and C (the letters are
abbreviations for the four chemical units that form the "rungs" of the
DNA double helix ladder). The goal of the project is to determine the
order of letters in the string. The size of the string is impressive
but not particularly mind-boggling: 3 x 10^9 letters long, or some 3
gigabytes of storage space if you use 1 byte to store each letter and
don't use any compression techniques.
Three gigabytes is substantial but certainly manageable by today's
standards. Unfortunately, this is just the storage space required to
store the finished data. The storage requirements for the experimental
data needed to determine this sequence is far more vast. The essential
problem is that DNA sequencing technology is currently limited to
reading stretches of at most 500 contiguous letters. In order to
determine sequences longer than that, the DNA must be sequenced as
small overlapping fragments called "reads" and the jigsaw puzzle
reassembled by algorithms that look for areas where the sequences
match. Because the DNA sequence is nonrandom (similar but
not-entirely-identical motifs appear many times throughout the genome),
and because DNA sequencing technology is noisy and error-prone, one
ends up having to sequence each region of DNA five to 10 times in order
to reliably assemble the reads into the true sequence. This increases
the amount of data to manage by an order of magnitude. On top of this
is all the associated information that goes along with laboratory work:
who performed the experiment, when it was performed, the section of the
genome was sequenced, the identity and version of the software used to
assemble the sequence, any comments someone wants to attach to the
experiment, and so forth. In addition, one generally wants to store the
raw output from the machine that performs the sequencing itself. Each
500 letters of sequence generates a data file that's 20-30 kilobytes
long!
That's not the whole of it. It's not enough just to determine the
sequence of the DNA. Within the sequence are functional areas scattered
among long stretches of nonfunctional areas. There are genes, control
regions, structural regions, and even a few viruses that got entangled
in human DNA long ago and persist as fossilized remnants. Because the
genes and control regions are responsible for health and disease, one
wants to identify and mark them as the DNA sequence is assembled. This
type of annotation generates yet more data that needs to be tracked and
stored.
Altogether, people estimate that some 1 to 10 terabytes of information
will need to be stored in order to see the human genome project to its
conclusion.
So what's Perl got to do with it? From the beginning researchers
realized that informatics would have to play a large role in the genome
project. An informatics core formed an integral part of every genome
center that was created. The mission of these cores was two-fold: to
provide computer support and databasing services for their affiliated
laboratories, and to develop data analysis and management software for
use by the genome community as a whole.
It's fair to say that the initial results of the informatics groups
efforts were mixed. Things were slightly better on the laboratory
management side of the coin. Some groups attempted to build large
monolithic systems on top of complex relational databases; they were
thwarted time and again by the highly dynamic nature of biological
research. By the time a system that could deal with the ins and outs of
a complex laboratory protocol had been designed, implemented and
debugged, the protocol had been superseded by new technology and the
software engineers had to go back to the drawing board.
Most groups, however, learned to build modular, loosely-coupled systems
whose parts could be swapped in and out without retooling the whole
system. In my group, for example, we discovered that many data analysis
tasks involve a sequence of semi-independent steps. Consider the steps
that one may want to perform on a bit of DNA that has just been
sequenced (Figure 1). First there's a basic quality check on the
sequence: is it long enough? Are the number of ambiguous letters below
the maximum limit? Then there's the "vector check". For technical
reasons, the human DNA must be passed through a bacterium before it can
be sequenced (this is the process of "cloning"). Not infrequently, the
human DNA gets lost somewhere in the process and the sequence that's
read consists entirely of the bacterial vector. The vector check
ensures that only human DNA gets into the database. Next there's a
check for repetitive sequences. Human DNA is full of repetitive
elements that make fit! ting the sequencing jigsaw puzzle together
challenging. The repetitive sequence check tries to match the new
sequence against a library of known repetitive elements. A penultimate
step is to attempt to match the new sequence against other sequences in
a large community database of DNA sequences. Often a match at this
point will provide a clue to the function of the new DNA sequence.
After performing all these checks, the sequence along with the
information that's been gathered about it along the way is loaded into
the local laboratory database.
The process of passing a DNA sequence through these independent
analytic steps looks kind of like a pipeline, and it didn't take us
long to realize that a Unix pipe could handle the job. We developed a
simple Perl-based data exchange format called "boulderio" that allowed
loosely coupled programs to add information to a pipe-based I/O stream.
Boulderio is based on tag/value pairs. A Perl module makes it easy for
programs to reach into the input stream, pull out only the tags they're
interested in, do something with them, and drop new tags into output
the stream. Any tags that the program isn't interested in are just
passed through to standard output so that other programs in the
pipeline can get to them.
Using this type of scheme, the process of analyzing a new DNA sequence
looks something like this (this is not exactly the set of scripts that
we use, but it's close enough):
name_sequence.pl < new.dna |
quality_check.pl |
vector_check.pl |
find_repeats.pl |
search_big_database.pl |
load_lab_database.pl
A file containing the new DNA sequence is processed by a perl script
named "name_sequence.pl", whose only job is to give the sequence a new
unique name and to put it into boulder format. Its output looks like
this:
NAME=L26P93.2
SEQUENCE=GATTTCAGAGTCCCAGATTTCCCCCAGGGGGTTTCCAGAGAGCCC......
The output from name_sequence.pl is next passed to the quality checking
program, which looks for the SEQUENCE tag, runs the quality checking
algorithm, and writes its conclusion to the data stream. The data
stream now looks like this:
NAME=L26P93.2
SEQUENCE=GATTTCAGAGTCCCAGATTTCCCCCAGGGGGTTTCCAGAGAGCCC......
QUALITY_CHECK=OK
Now the data stream enters the vector checker. It pulls the SEQUENCE
tag out of the stream and runs the vector checking algorithm. The data
stream now looks like this:
NAME=L26P93.2
SEQUENCE=GATTTCAGAGTCCCAGATTTCCCCCAGGGGGTTTCCAGAGAGCCC......
QUALITY_CHECK=OK
VECTOR_CHECK=OK
VECTOR_START=10
VECTOR_LENGTH=300
This continues down the pipeline, until at last the
"load_lab_database.pl" script collates all the data collected, makes
some final conclusions about whether the sequence is suitable for
further use, and enters all the results into the laboratory database.
One of the nice features of the boulderio format is that multiple
sequence records can be processed sequentially in the same Unix
pipeline. An "=" sign marks the end of one record and the beginning of
the next:
NAME=L26P93.2
SEQUENCE=GATTTCAGAGTCCCAGATTTCCCCCAGGGGGTTTCCAGAGAGCCC......
=
NAME=L26P93.3
SEQUENCE=CCCCTAGAGAGAGAGAGCCGAGTTCAAAGTCAAAACCCATTCTCTCTCCTC...
=
There's also a way to create subrecords within records, allowing for
structured data types.
Here's an example of a script that processes boulderio format. It uses
an object-oriented style, in which records are pulled out of the input
stream, modified, and dropped back in:
use Boulder::Stream;
$stream = new Boulder::Stream;
while ($record = $stream->read_record('NAME','SEQUENCE')) {
$name = $record->get('NAME');
$sequence = $record->get('SEQUENCE');
..[continue processing]...
$record->add(QUALITY_CHECK=>"OK");
$stream->write_record($record);
}
(If you're interested, more information about the boulderio format and
the perl libraries to manipulate it can be found at
http://stein.cshl.org/software/boulder/).
The interesting thing is that multiple informatics groups independently
converged on solutions that were similar to the boulderio idea. For
example, several groups involved in the worm sequencing project began
using a data exchange format called ".ace". Although this format was
initially designed as the data dump and reload format for the ACE
database (a database specialized for biological data), it happens to
use a tag/value format that's very similar to boulderio. Soon .ace
files were being processed by Perl script pipelines and loaded into the
ACE database at the very last step.
Perl found uses in other aspects of laboratory management. For example,
many centers, including my own, use Web based interfaces for displaying
the status of projects and allowing researchers to take actions. Perl
scripts are the perfect engine for Web CGI scripts. Similarly, Perl
scripts run e-mail database query servers, supervise cron jobs, prepare
nightly reports summarizing laboratory activity, create instruction
files to control robots, and handle almost every other information
management task that a busy genome center needs.
So as far as laboratory management went, the informatics cores were
reasonably successful. As far as development and distributing generally
useful, however, things were not so rosy.
The problem will be familiar to anyone who has worked in a large,
loosely organized software project. Despite best intentions, the
project begins to drift. Programmers go off to work on ideas that
interest them, modules that need to interface with one another are
designed independently, and the same problems get solved several times
in different, mutually incompatible ways. When the time comes to put
all the parts together, nothing works.
This is what happened in the genome project. Despite the fact that
everyone was working on the same problems, no two groups took exactly
the same approach. Programs to solve a given problem were written and
rewritten multiple times. While a given piece of software wasn't
guaranteed to work better than its counterpart developed elsewhere, you
could always count on it to sport its own idiosyncratic user interface
and data format. A typical example is the central algorithm that
assembles thousands of short DNA reads into an ordered set of overlaps.
At last count there were at least six different programs in widespread
use, and no two of them use the same data input or output formats.
This lack of interchangeability presents terrible dilemma for the
genome centers. Without interchangeability, an informatics group is
locked into using the software that it developed in-house. If another
genome center has come up with a better software tool to attack the
same problem, a tremendous effort is required by the first center to
retool their system in order to use that tool.
The long range solution to this problem is to come up with uniform data
interchange standards that genome software must adhere to. This would
allow common modules to be swapped in and out easily. However,
standards require time to agree on, and while the various groups are
involved in discussion and negotiation, there is still an urgent need
to adapt existing software to the immediate needs of the genome centers.
Here is where Perl again came to the rescue. The Cambridge summit
meeting that introduced this article was called in part to deal with
the data interchange problem. Despite the fact that the two groups
involved were close collaborators and superficially seemed to be using
the same tools to solve the same problems, on closer inspection nothing
they were doing was exactly the same.
The main software components in a DNA sequencing projects are:
a trace editor to analyze, display and the short DNA read
chromatograms from DNA sequencing machines.
a read assembler, to find overlaps between the reads and assemble
them together into long contiguous sections.
an assembly editor, to view the assemblies and make changes in
places where the assembler went wrong.
a database to keep track of it all.
Over the course of a few years, the two groups had developed suites of
software that worked well in their hands. Following the familiar genome
center model, some of the components were developed in-house while
others were imported from outside. As shown in Figure 2, Perl was used
for the glue to make these pieces of software fit together. Between
each pair of interacting modules were one or more Perl scripts
responsible for massaging the output of one module into the expected
input for another.
When the time came to interchange data, however, the two groups hit a
snag. Between them they were now using two trace editors, three
assemblers, two assembly editors and (thankfully) one database. If two
Perl scripts were required for each pair of components (one for each
direction), one would need as many as 62 different scripts to handle
all the possible interconversion tasks. Every time the input or ouput
format of one of these modules changed, 14 scripts might need to be
examined and fixed.
The solution that was worked out during this meeting is the obvious one
shown in Figure 3. The two groups decided to adopt a common data
exchange format known as CAF (an acronym whose exact meaning was
forgotten during the course of the meeting). CAF would contain a
superset of the data that each of the analysis and editing tools
needed. For each module, two Perl scripts would be responsible for
converting from CAF into whatever format Module A expects
("CAF2ModuleA") and converting Module A's output back into CAF
("ModuleA2CAF"). This simplified the programming and maintenance task
considerably. Now there were only 16 Perl scripts to write; when one
module changed, only two scripts would need to be examined.
This episode is not unique. Perl has been the solution of choice for
genome centers whenever they need to exchange data, or to retrofit one
center's software module to work with another center's system.
So Perl has become the software mainstay for computation within genome
centers as well as the glue that binds them together. Although genome
informatics groups are constantly tinkering with other "high level"
languages such as Python, Tcl and recently Java, nothing comes close to
Perl's popularity. How has Perl achieved this remarkable position?
I think several factors are responsible:
1. Perl is remarkably good for slicing, dicing, twisting, wringing,
smoothing, summarizing and otherwise mangling text. Although the
biological sciences do involve a good deal of numeric analysis now,
most of the primary data is still text: clone names, annotations,
comments, bibliographic references. Even DNA sequences are textlike.
Interconverting incompatible data formats is a matter of text mangling
combined with some creative guesswork. Perl's powerful regular
expression matching and string manipulation operators simplify this job
in a way that isn't equalled by any other modern language.
2. Perl is forgiving. Biological data is often incomplete, fields can
be missing, or a field that is expected to be present once occurs
several times (because, for example, an experiment was run in
duplicate), or the data was entered by hand and doesn't quite fit the
expected format. Perl doesn't particularly mind if a value is empty or
contains odd characters. Regular expressions can be written to pick up
and correct a variety of common errors in data entry. Of course this
flexibility can be also be a curse. I talk more about the problems with
Perl below.
3. Perl is component-oriented. Perl encourages people to write their
software in small modules, either using Perl library modules or with
the classic Unix tool-oriented approach. External programs can easily
be incorporated into a Perl script using a pipe, system call or socket.
The dynamic loader introduced with Perl5 allows people to extend the
Perl language with C routines or to make entire compiled libraries
available for the Perl interpreter. An effort is currently under way to
gather all the world's collected wisdom about biological data into a
set of modules called "bioPerl" (discussed at length in an article to
be published later in the Perl Journal).
4. Perl is easy to write and fast to develop in. The interpreter
doesn't require you to declare all your function prototypes and data
types in advance, new variables spring into existence as needed, calls
to undefined functions only cause an error when the function is needed.
The debugger works well with Emacs and allows a comfortable interactive
style of development.
5. Perl is a good prototyping language. Because Perl is quick and
dirty, it often makes sense to prototype new algorithms in Perl before
moving them to a fast compiled language. Sometimes it turns out that
Perl is fast enough so that of the algorithm doesn't have to be ported;
more frequently one can write a small core of the algorithm in C,
compile it as a dynamically loaded module or external executable, and
leave the rest of the application in Perl (for an example of a complex
genome mapping application implemented in this way, see
http://waldo.wi.mit.edu/ftp/distribution/software/rhmapper/).
6. Perl is a good language for Web CGI scripting, and is growing in
importance as more labs turn to the Web for publishing their data.
My experience in using Perl in a genome center environment has been
extremely favorable overall. However I find that Perl has its problems
too. Its relaxed programming style leads to many errors that more
uptight languages would catch. For example, Perl lets you use a
variable before its been assigned to, a useful feature when that's what
you intend but a disaster when you've simply mistyped a variable name.
Similarly, it's easy to forget to declare make a variable used in a
subroutine local, inadvertently modifying a global variable.
If one uses the -w switch religiously and turn on the "use strict vars"
pragma, these Perl will catch these problems and others. However there
are more subtle gotchas in the language that are not so easy to fix. A
major one is Perl's lack of type checking. Strings, floats and integers
all interchange easily. While this greatly speeds up development, it
can cause major headaches. Consider a typical genome center Perl script
that's responsible for recording the information of short named
subsequences within a larger DNA sequence. When the script was written,
the data format was expected to consist of tab-delimited fields: a
string followed by two integers representing the name, starting
position and length of a DNA subsequence within a larger sequence. An
easy way to parse this would to split() into an list like this:
($name,$start_position,$length) = split("\t");
Later on in this script some arithmetic is performed with the two
integer values and the result written to a database or to standard
output for further processing.
Then one day the input file's format changes without warning. Someone
bumps the field count up by one by sticking a comment field between the
name and the first integer. Now the unknowing script assigns a string
to a variable that's expected to be numeric and silently discards the
last field on the line. Rather than crashing or returning an error
code, the script merrily performs integer arithmetic on a string,
assuming a value of zero for the string (unless it happens to start
with a digit). Although the calculation is meaningless, the output may
look perfectly good, and the error may not be caught until some point
well downstream in the processing.
The final Perl deficiency has been a way to create graphical user
interfaces. Although Unix True Believers know that anything worth doing
can be done on the command line, most end-users don't agree. Windows,
menus and bouncing icons have become de rigueur for programs intended
for use by mere mortals.
Until recently, GUI development in Perl was awkward to impossible.
However the work of Nick Ing-Simmons and associates on perlTK (pTK) has
made Perl-driven GUIs possible on X-windows systems. My associates and
I have written several pTK-based applications for internal use at the
MIT genome center, and it's been a satisfying experience overall. Other
genome centers make much more extensive use of pTK, and in some places
its become a mainstay of production.
Unfortunately, I'm sad to confess that a few months ago when I needed
to put a graphical front end on a C++ image analysis program I'd
written, I turned to the standard Tcl/Tk library rather than to pTK. I
made this choice because I intended the application for widespread
distribution. I find pTK still too unstable for export: new releases of
pTK discover lurking bugs in Perl, and vice-versa. Further, I find that
even seasoned system administrators run into glitches when compiling
and installing Perl modules, and I worried that users would hit some
sort of road block while installing either pTK or the modules needed to
support my application, and owuld give up. In contrast, many systems
have the Tcl/Tk libraries preinstalled; if they don't, installation is
quick and painless.
In short, when the genome project was foundering in a sea of
incompatible data formats, rapidly-changing techniques, and monolithic
data analysis programs that were already antiquated on the day of their
release, Perl saved the day. Although it's not perfect, Perl seems to
fill the needs of the genome centers remarkably well, and is usually
the first tool we turn to when we have a problem to solve.
