Biology is becoming increasingly
dependent on information resources. Large-scale projects generate enormous
amounts of mapping and sequencing data. Complex analytical methods are
required to work with these data. Lab groups create databases which encode
combined expertise of lab members into object-oriented knowledge bases.
The desktop-centric personal computer is an impediment, rather than an
asset, for most of these tasks because it is oriented towards a single
user on a single computer, rather than multiple users sharing resources.
BIRCH is a network-centric resource providing a comprehensive set of databases
and software for molecular biologists. BIRCH is integrated into a larger
campus-wide Unix system so that all computing, including molecular biology,
Internet access and office tasks can be done in an easy-to-use graphic
desktop, accessible anywhere on campus, at home, or when travelling. The
network-centric approach makes all resources accessible in a consistent
fashion, minimizing costs and software administration, and promoting reliability,
data integrity, security and scalability. See
http://home.cc.umanitoba.ca/~psgendb.
Starting up a new lab is
an opportunity to make a fresh, start, to do things right the first time.
This idea was at the front of my mind when I set up my lab at the University
of Manitoba in 1990. The late 1980s was the period when the PC became a
necessity for most biologists. Resources like GenBank were only available
if enough members of a department had the will and the money to keep the
databases on a minicomputer or mainframe. Among biologists, only hard-core
computer aficionados like myself knew about the Internet, and almost no
one appreciated why email was useful. Communication between PCs was by
modem. The desktop PC, thorough its relative ease of use and moderate cost
enabled biologists to take advantage of computers for data analysis, writing,
and making figures. The small group of us who had learned computing on
mainframes appreciated the "user-friendliness" of PCs. However, we also
noted that PCs lacked the discipline, consistency and reliability of mainframes.
Of fundamental importance was the fact that mainframes and minis allowed
any user to do any task from any terminal. Much like the Protestant Reformation,
in which every believer became a theologian, every PC user became a system
administrator. And, as in the Reformation, numerous denominations sprang
up, peppering labs with PCs, Macs, some UnixPCs, Amigas, which still had
to talk to the Vax and Unix minis or workstations. People learned to take
for granted that a program that would run on one PC could not run on another.
Data, documents, and drawings became scattered across these machines like
samples in a -70ºC freezer. Files accumulated with no one in charge,
most older files being of little value, but at the same time not easily
distinguishable from those few that were of great value, due to inconsistent
or incomplete labelling. Since most biologists had never used mainframes,
things such as having to reboot several times a week were just a normal
part of using a PC.
In recent years, the isolated
lab with a group leader and a few students has given way to megaprojects
with many collaborators sharing data, clones, cultures and strains. At
the same time, the data from publicly-funded projects is increasingly used
by others in the research community: complete genomic sequences, genetic
maps, and expression data. Researchers need access to complex methods,
such as gene prediction, phylogenetic analysis and recognition of patterns
among groups of sequences or expression datasets.
Because each PC is a unique
combination of hardware, software, configurations and data, it is actually
an impediment to progress. In particular, fragmentation of data across
many PCs allows for inconsistent organization of data and makes it difficult
for data to be shared, or even located. Differences in software and configuration
from machine to machine results in competition among lab workers for use
of specific computers. When expensive software is installed on a single
machine, workers can only use it on that machine. When many users use a
given machine, changes or deletions made by each user affect all other
users. Like the -70ºC freezer, no one is responsible for keeping the
PC organized. In summary, the standalone PC makes what you can do strongly
dependent on which machine you're using.
Network computing is best
summarized by the expression, "the network IS the computer", meaning that
resources such as CPUs, disk drives, printers, and mail are handled by
one or more servers connected through a network backbone (Fristensky, 1999a;
http://home.cc.umanitoba.ca/~psgendb/nc/
).
Many of these components are redundant, providing greater reliability.
Services could be provided by different hardware (e.g. Sparc, Intel, Alpha,
RS6000) running different operating systems (e.g. Unix, VMS, OS400). As
illustrated in Figure 1, four users are logged into four identical X-terminals,
which display windows generated by applications, using X11 protocols (Open
Group, 1998). The applications themselves run on any number of identically-configured
servers which do all the actual processing other than drawing windows on
the screen. The users' home directories, as well as publicly-available
files and software, reside on a central file server, mounted across the
network to all servers. Thus, any user can log into any server, from any
terminal, and perform any task. X-windows emulators for PCs allow users
to work at home, with exactly the same desktop that appears on their X-terminals
at work. Because all configuration occurs at the server, X-terminals require
no configuration or upgrades. Failure of any one server or terminal is
not a problem because servers and terminals are interchangeable. Automated
backups are done for all users. While the file server is, in principle,
a single point of failure, the fact that it serves a large number of users
makes it cost-effective to invest in highly reliable file servers, such
as RAID servers. (To put this point in perspective, the hard drive in your
PC, probably the cheapest available, is also a single point of failure.)
Performance for multiple users can be as good or better than performance
on a standard PC by making sure that network bandwidth and the server to
user ratio is adequate.
Universities and corporations
typically already have clustered servers. For example, at the University
of Manitoba, the Biological Research Computing Hierarchy (BIRCH) system
was built to take advantage of an already-existing Unix server cluster
(Fristensky, 1999b). In Figure 1, publicly-readable directories contain
software (bin), data (dat), documentation (doc), and administrative files
(admin), in addition to complete DNA (GenBank) and protein (PIR) databases.
The only setup done by the user is to run a script called `newuser', which
configure their account to read a central BIRCH configuration file each
time they login. Since administration is centralized, changes and additions
are immediately available to all users. In 9 years of administering BIRCH,
it has never been necessary to make separate configuration changes on each
user's account.
-
Figure 1. System schematic.
Sequence analysis often involves
many steps and a number of programs, often written by several different
authors. gde is unique by making it easy to chain programs together seamlessly.
Because gde takes care of conversion of file formats and the actual running
of programs, the user does not have to learn the intricacies of running
each component program. Equally important, as new methods become available,
they can be plugged into the existing .GDEmenus file, expanding the choices
available to the user.
Complete information on BIRCH,
including documentation for all programs, as well as information on how
to create your own BIRCH site, can be found at
http://home.cc.umanitoba.ca/~psgendb.
Example: Phylogenetic
analysis
Figure 2 illustrates how
BIRCH simplifies the complex analytical tasks requiring many different
programs used in concert. The gde window at top of Figure 2 contains a
set of plant chitinase III genes, whose protein coding sequences are extracted
by features (Fristensky, 1993) into a new gde window (e.g. all sequences
in this window begin with an `atg' start codon). Sequences are translated
by ribosome (Fristensky, 1993) (3rd window from top) and protein sequences
aligned using pima (Smith and Smith, 1990) (4th window). The protein alignment
is used to guide a DNA alignment of the coding regions using mrtrans (Pearson,
W.R., unpublished). (Note the correspondence between gaps in the protein
alignment with those in the DNA alignment in the 5th window.). Finally,
fastDNAml (Olsen, et al., 1994) is run to produce a maximum likelihood
tree, with a report appearing in a text editor and the tree appearing in
the treetool tree editor (Maciukenas, 1994). By storing the results of
each step in a separate gde window, the intermediate results can be independently
saved or analyzed, and the user can choose which program and parameters
to use at each step, as indicated in Figure 3.
-
click here for full-sized image
Figure 2. Screen-shot of semi-automated
phylogeny using GDE to launch programs at each step.
Any sequence analysis package is limited
in functionality to what was included by the vendor. The BIRCH approach
is to integrate new programs from many sources to provide users with choices
at each step of the analysis. In the analysis shown in Figure 3, there
is a choice of several alternative programs at most of the steps. For example,
construction of the tree from the DNA alignment could have been done by
any of 7 programs implementing parsimony, maximum likelihood or distance
methods. Choosing phylo_win (Galtier
et al., 1996) at this point
would transfer the analysis to phylo_win, which implements several of these
methods as well as providing a separate tree viewer.
Figure 3. Schematic of program choices
at each step in phylogenetic analysis.
Since the overhead involved
in installing a program can be substantial, the network-centric model means
that the job of installing each of the component programs used in this
analysis need only be done once to provide these capabilities to everyone
in the lab. In the PC model, each lab typically installs a different set
of programs, scattered across several PCs. Not every program is available
on all PCs, and each PC must be upgraded and debugged separately.
Example: Laboratory databases
Creation of a database for
an EST project is illustrated in Figure 4 with AceDB, now the most commonly
used program for genome databases (Durbin and Thierry-Meig, 1991). At top
left, cDNA MB56-1G is selected in a gde window, and a fasty (Pearson
et
al., 1997) search compares it with the GenPept protein database. The
output for MB56-1G (Fristensky
et al., 1999) is at centre left.
The main ACeDB window appears at top right, and the data for MB56-1G are
entered in the window at lower right. The ACeDB Tablemaker (center right)
is used to organize ESTs into biochemical categories (e.g. defence) and
gene families (e.g. pathogenesis-related protein gene Cxc750).
The PC model is especially
problematic for databases. As illustrated above, it would be convenient
to have both sequence database searches and the AceDB software and database
on one computer. However, on PCs, if one user is running fasty searches,
which can take substantial time, no one else can access the database. In
any case, the PC model would only allow one user to access the database
at any one time. With network-centric computing, any number of fasty searches
or ACeDB clients can be run by any number of users, simultaneously. Where
usage is heavy, the load can be spread out across different servers.
-
click here for full-sized image
Figure 4. Example of
an EST project, managed using ACeDB.
For users interested in implementing
an ACeDB database for their own lab, a sample database along with documentation
can be found at
http://home.cc.umanitoba.ca/~psgendb/acedb/acedb.html
Unifying the computing
environment
The next step in our chain
of logic was to realize that the network-centric model could simplify not
only the computation-intensive molecular biology tasks, but all of what
are now thought of as common desktop tasks. Essentially all aspects of
the Internet, such as Web browsers, the File Transfer Protocol (FTP), USENET
newsgroups, and many aspects of email were originally developed under Unix,
and later ported to PCs. More recently, office applications have been appearing
in increasing numbers for Unix. In my lab, we do our computing in a 100%
Unix environment. Every computing task, from record-keeping, data analysis,
writing papers, drawing figures, and preparing presentations are all unified
seamlessly on a single easy to use graphic desktop via X-windows. As illustrated
in Figure 5, this paper and the accompanying figures were written on the
same Sun Unix system that BIRCH resides on. The example shows that common
desktop programs, including web browsers, mailers, file managers, word
processors, and drawing programs all run in the Unix environment. It is
also worth noting that an equally comprehensive laboratory computing environment
is available at the NIH (Advanced Laboratory Workstation).
-
-
click here for full-sized image
Figure 5. Screen shot
of a typical X-windows session. A customized CDE can be opened from anywhere
on the screen. Submenus, organized by category, make it easy for users
to find and launch programs.
We have avoided Windows NT
for reasons of reliability, scaleability, and security (Kirch, 1999), and
poor support for terminal sessions. While most major Unix systems already
support 64-bit processing, NT will not be a 64-bit system for at least
several years. As well, software written for Windows is seldom configurable
for users with separate home directories. In most cases, Windows software
requires that all users have write access to central configuration directories
for each program, meaning that any user could disable the program, and
users can't have separate configuration files.
Another advantage of X11/Unix
systems is that X-windows sessions can be run remotely. Even in places
where X-terminals are not available, X-windows software packages are available
to allow PCs to emulate X-terminals. Links to various commercial X-windows
packages can be found at
http://home.cc.umanitoba.ca/~psgendb/nc/xterm.html.
A `light-weight' X11 protocol is implemented in Virtual Network Computing
(VNC), which offloads most of the screen-drawing tasks normally done at
the terminal or the PC to the server. At the server end, vncserver creates
a screen image, which is updated by vncviewer running on the remote PC.
Vncviewers exists for all PC platforms, and there is even a Java-applet
version of vncviewer that runs in any Java-enabled web browser. Given a
fast Internet connection, if vncserver is installed on your Unix system,
you can run an X-windows session on your home server from a Java-enabled
web browser, anywhere in the world. VNC is distributed free by AT&T
Research, Cambridge (
http://www.uk.research.
att.com/vnc/).
The popularity of the laptop
points to the need for people to have a consistent set of programs and
files no matter where they go. This convenience comes at the price of frequent
transfer of data between laptops and PC at home and the lab, upgrading
the software, and the fact that it is impractical to keep connecting the
laptop to the network as you travel from one place to another. As more
of what we do depends on networks, laptops become less useful. With VNC,
all you need is a web browser in an airport kiosk or hotel room to access
all the same files and applications that you use in the lab.
Finally, training of the user
base is a critical component of any computer resource. The network-centric
model allows the user to work on their own account during training sessions,
and guarantees that the system will work the same way in their lab as it
did in the session. The consistency of the computer system across all desktops
also makes it possible to have central, web-based documentation that is
up-to-date and accurate for all users.
Conclusion
Unix has made it easy to
integrate all computing, from common tasks such as word processing, to
database access and sequence analysis on a single, easy to use desktop,
accessible simultaneously to all workers in the lab from any terminal.
In a recent project, our lab sequenced 278 cDNAs from oilseed rape plants
inoculated with a fungal pathogen, to identify genes that are induced by
fungal attack (Fristensky
et al., 1999). Since this was our first
EST project, we learned by doing. As the dataset grew, we wrote Java software
to automate conversion of fasty results into database entries, submission
of the results to GenBank, integration of resultant accession numbers back
into our database, and generation of a hypertext table of ESTs for online
publication. This process also led to several cycles of refinement of the
data objects in our lab database, as we learned more about how to conceptualize
our data. The coexistence of ACeDB, gde, word processors and drawing programs
all on the same filesystem on the same server made it easy to run analytical
programs in gde windows to answer our questions about the data as we wrote
the manuscript. There is a momentum to writing a paper, or analysing data,
that is interrupted whenever you have to move from one PC to another to
work on different parts of a problem. BIRCH, and the larger Unix system
of which it is a part, allowed an uninterrupted flow of work for everyone
involved in the project. And unlike the -70 freezer, in which samples could
belong to anyone, and contain anything, each BIRCH user keeps their files
in their own accounts. When a lab worker leaves the lab, their files can
be transferred to a new account on the same system. Whoever picks up where
they left off will not have the problem that things that worked for one
person on one machine won't work for a new person on a different machine.
We so often use the expression
"don't reinvent the wheel". Yet, in practice that is exactly what each
PC owner does when installing a new PC. As we demand more sophisticated
computing resources, it makes sense to move to a more network-centric approach,
allowing biologists to simply use computers, and not manage with them.
References
Advanced Laboratory Workstation
(ALW) System, Center for Information Technology, National Institutes of
Health.
http://www. alw.nih.govFelsenstein J. (1989). PHYLIP
Phylogeny Inference Package.
Cladistics 5, 164-166.
Fristensky, B. (1993) Feature
Expressions: Creating and Manipulating Sequence Datasets. Nucleic Acids
Research. 21, 5997-6003.
Fristensky, B. (1999a) Network
computing: Restructuring how scientists use computers and what we get out
of them. In Bioinformatics Methods and Protocols [edited by Misener
S.; Krawetz, S.], Totowa, NJ, USA: Humana Press. pp. 401-412,
Fristensky, B. (1999b) Building
a multiuser sequence analysis facility using freeware. In Bioinformatics
Methods and Protocols, Totowa, NJ, USA: Humana Press. pp 131-145.
Fristensky, B.; Balcerzak, M.;
He, D.-F.; Zhang, P. (1999) Expressed sequence tags from the defense response
of Brassica napus to Leptosphaeria maculans. Molecular Plant Pathology
Online http://www.bspp.org.uk/mppol/1999/0301FRISTENSKY.
Galtier, N.; Gouy, M.; Gautier,
C. (1996) SeaView and Phylo_win, two graphic tools for sequence alignment
and molecular phylogeny. Computer Applications in the Biosciences12,
543-548.
Kirch, J. (1999) Microsoft Windows
NT4.0 Server versus Unix. http://www.unix-vs-nt.org/kirch/
Maciukenas, M. (1994) Treetool.
ftp://rdp.life.uiuc.edu/rdp/programs/TreeTool/
Olsen, G.J.; Matsuda, H.; Hagstrom,
R.; Overbeek, R. (1994). FastDNAml: a tool for construction of phylogenetic
trees of DNA sequences using maximum likelihood. Computer Applications
in the Biosciences 10, 41-48.
The Open Group (1998) X Window
System. http://www.camb.open
group.org/
Pearson, W.R.; Wood, T.; Zhang,
Z.; Miller, W. (1997) Comparison of DNA sequences with protein sequences.
Genomics46,
24-36.
Smith, R.F.; Smith, T.F. (1990)
Automatic generation of primary sequence patterns from sets of related
protein sequences. Proceedings of the National Academy of Sciences,
USA 87, 118-122.
Smith, S.; Overbeek, R.; Woese,
C.R.; Gilbert, W.; Gillevet, P.M. (1994) The Genetic Data Environment:
an expandable GUI for multiple sequence analysis. Computer Applications
in the Biosciences 10, 671-675. ftp://megasun.bch.umontreal.ca/pub/gde/
