Editorial
Ten Simple Rules for Developing a Short Bioinformatics
Training Course
Allegra Via1, Javier De Las Rivas2, Teresa K. Attwood3, David Landsman4, Michelle D. Brazas5, Jack A. M.
Leunissen6, Anna Tramontano1, Maria Victoria Schneider7*
1 Biocomputing Group, Department of Physics, Sapienza University of Rome, Rome, Italy, 2 Bioinformatics & Functional Genomics Research Group, Cancer Research
Center (IBMCC, CSIC/USAL), Salamanca, Spain, 3 Faculty of Life Sciences and School of Computer Science, University of Manchester, Manchester, United Kingdom,
4 Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Maryland, United States of
America, 5 Informatics and Bio-computing, Ontario Institute for Cancer Research, MaRS Centre, Toronto, Canada, 6 Laboratory of Bioinformatics, Wageningen University,
Wageningen, The Netherlands, 7 Outreach and Training Team, EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, United
Kingdom
Introduction
This paper considers what makes a
short course in bioinformatics successful.
In today’s research environment, exposure to bioinformatics training is something that anyone embarking on life
sciences research is likely to need at some
point. Furthermore, as research technologies evolve, this need will continue to
grow. In fact, as a consequence of the
introduction of high-throughput technologies, there has already been an increase
in demand for training relating to the use
of computational resources and tools
designed for high-throughput data storage, retrieval, and analysis. Biologists and
computational scientists alike are seeking
postgraduate learning opportunities in
various bioinformatics topics that meet
the needs and time restrictions of their
schedules. Short, intensive bioinformatics
courses (typically from a couple of days to
a week in length, and covering a variety
of topics) are available throughout the
world, and more continue to be developed to meet the growing training needs.
The challenges, however, when planning,
organising, and delivering such courses,
are not trivial [1], especially considering
the heterogeneous backgrounds of participants. Here, we address such challenges
and present a consensus of rules derived
from the shared expertise of several
bioinformatics trainers. While the rules
apply broadly to bioinformatics training,
aspects addressing specific audiences are
also discussed in order to make these rules
pragmatic and applicable to a wide range
of readers. Delivering bioinformatics
training is both crucial to facilitate the
use of, and to exploit the investment in,
bioinformatics tools and resources, and an
excellent opportunity to solicit user evaluation and feedback to improve them.
One point of crucial interest to the
training course community concerns material preparation and distribution. Pre-
paring effective materials (slides, notes,
references, etc.) entails a huge effort that
would be enormously facilitated if course
developers could start from a body of
available materials, for example if they
could gain access to repositories of
materials deposited by trainers of other
courses. This was one of the reasons
motivating the Bioinformatics Training
Network (BTN) to set up the BTN website (http://www.biotnet.org/), which has
been planned as a vessel for the training
community to share and disseminate
course information and materials. Course
developers are warmly welcome to subscribe to the site and make available their
materials to the community [2].
better oriented to the expected outcomes
and are more likely to be satisfied with the
course. As most training sessions are based
on slide presentations, dedicate at least
one slide (preferably, while providing the
session overview) to the learning objectives, and mention how these will be
achieved, using specific examples whenever possible; if appropriate, also mention
how the knowledge gained and skill set(s)
will be useful for trainees’ work environments. Stating what participants will not
learn to do (e.g., to avoid over-estimation
of the depth of analysis that can be
achieved in a short course) is also important for tempering their expectations.
Rule 1: Set Practical and
Realistic Expectations
Rule 2: Verify That Trainees’
Expectations Match Course
Scope
It is critical to explicitly identify the
training objectives and expected outcomes
from the outset. Begin by devising the title
of your course and specifying the target
audience (e.g., laboratory biologists, computational scientists). This information is
not only useful for trainers to help
appropriately focus and weight the contents of their training sessions, but is also
vital for participants. By explicitly stating
the course objectives up front, trainees are
Verify that trainees’ expectations match
what will be delivered. The most effective
mechanism to ensure that expectations are
well matched is to collect information from
trainees prior to the training session itself
(e.g., via a questionnaire), or by discussions
with trainees at the start of the course.
Obtaining such information early on
allows time to alter course materials to
better meet participant expectations, for
example by adjusting case studies and
Citation: Via A, De Las Rivas J, Attwood TK, Landsman D, Brazas MD, et al. (2011) Ten Simple Rules for
Developing a Short Bioinformatics Training Course. PLoS Comput Biol 7(10): e1002245. doi:10.1371/
journal.pcbi.1002245
Editor: Philip E. Bourne, University of California San Diego, United States of America
Published October 27, 2011
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted,
modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under
the Creative Commons CC0 public domain dedication
Funding: This work was partly supported by the Intramural Research Program of the NIH, NLM, NCBI, and by
funds awarded to the EMBL-European Bioinformatics Institute by the European Commission under SLING, grant
agreement number 226073 (Integrating Activity) within Research Infrastructures of the FP7 Capacities Specific
Programme EMBL-EBI. The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: vicky@ebi.ac.uk
PLoS Computational Biology | www.ploscompbiol.org
1
October 2011 | Volume 7 | Issue 10 | e1002245
examples to reflect the audience’s interests.
Furthermore, this will make you aware of
the trainees’ different backgrounds. Read,
or listen to, and evaluate all responses,
both to discern whether the course content
matches participant expectations and to
learn what the trainees’ needs are. Such
information will also allow you to detect
clusters of trainees: e.g., those working
with a particular model organism, those
more interested in DNA than in proteins,
or more plant than animal scientists.
Useful information to collect includes their
research backgrounds and computational
skill sets, their current projects relevant to
the course, and their expectations of the
training (e.g., what reasons led them to
apply for this particular course?). Also
solicit information from trainees about the
biological problems they wish to solve by
participating in the course.
Rule 3: Plan Exercises and
Activities and Test Resources
before Delivery
Plan the course in independent units/
modules, each with an introduction, set of
aims, list of actions, and potential difficulties. When a new module is introduced,
recall the achievements of the previous
module, and state what tasks participants
will be able to additionally accomplish at
the end of the new module.
If you, the trainer, are also responsible
for the resource/tool being presented, you
are likely to be able to handle unexpected
queries or problems. However, many
trainers deliver sessions on resources/tools
built and maintained somewhere else by
someone else, using someone else’s data.
Regardless, always prepare an alternative
plan in anticipation of unforeseen difficulties. For example, at short notice, you
might not be able to use live queries, so
ensure that you have sufficient back-up
material (e.g., animations, videos, etc.) to
allow you nevertheless to deliver your
training session effectively.
To appear as prepared and experienced
as possible, try your practical exercises
beforehand. In cases where the query or
task required to a bioinformatics server
takes a long time, or is too demanding on
the service provider, either begin with
smaller query datasets, or provide the task
results after trainees have prepared the
query set-up, so that they still gain the
experience of performing the task and
class time is used more efficiently. It is
important to note that some service
providers will often hold query results for
48 hours.
Rule 4: Ensure Computational
Equipment Preparedness and
Hands-On Support Availability
Ensure (or rather, insist) that workstations (Linux, Mac, or PC) have all the
necessary software installed to allow trainees to complete the course. Make sure that
the venue provides each trainee (or, at
most, each pair of trainees) with one
computer. Where trainees are required to
bring their own workstation (e.g., laptop),
provide enough instruction and test commands to ensure that software and dependencies have been properly set up ahead
of time. Request that a system support
technologist be available, and in the room,
when starting your sessions, to ensure the
functionality of the classroom workstations
and/or of the participants’ personal computers.
Do not underestimate the trainer/trainee ratio, especially in consideration of the
trainees’ diverse backgrounds. Be prepared to provide extra hands-on support
while trainees become familiar with new
interfaces, tools, and resources. Such
support may be provided by trainers of
other modules, tutorial assistants, past
trainees, or even current trainees who
are familiar with the tool/resource basics.
Rule 5: Use the Dynamic World
of Bioinformatics Resources and
Tools as a Learning
Opportunity
Provide time references for the information you deliver, as bioinformatics
resources and tools, and stored data,
evolve continuously. Place emphasis on
the ‘‘official’’ sites, as these are most likely
to remain stable reference points for
trainees. When creating your materials
and exercises, as much as possible, avoid
screen-shots, as these date quickly—otherwise, you risk spending substantial
amounts of time updating outdated slides
rather than concentrating on developing
suitable case studies and examples relevant
to your audience. Describe the essence of
data that can be retrieved from a particular resource and the principles governing
a tool, rather than sticking to specific
releases, web interfaces, or, for example, to
tables of ranked results, which are likely
to differ from day to day, as new data
become available in the databases. Take
into account that new data may have been
added to the databases you are planning to
use, and hence the outputs of the queries
might be different from those you planned
to demonstrate. As this occurrence is
actually an integral part of bioinformatics,
PLoS Computational Biology | www.ploscompbiol.org
2
this can be beneficial for trainees to
witness—you might even want to explore
such situations extensively, to convey the
idea that resources and tools are dynamic.
Rule 6: Balance Concepts with
Practical Outcomes
Bioinformatics training encompasses a
vast amount of learned skills. Acquiring
these skills is a bit like learning to ride a
bicycle, where it is best to just start
pedalling, because watching others will
not help you learn the process! Of course,
it is important to provide trainees with
the fundamental concepts and theoretical
background to ensure that they can use
bioinformatics tools and resources meaningfully. Nevertheless, it is a good rule to
provide a balance between the theoretical/technical and contextual aspects. For
example, many trainees may not value
information on flat-files, relational schemas, APIs, and web services, but will be
more concerned about knowing which
tools and resources to use for their specific
needs, and why, and how to interpret their
outputs (just as the average cyclist is not
interested in the internal workings of the
gearbox, as long as they know how and
when to shift gear!). Discuss the limitations
of the methods without getting carried
away by the intricacies of the algorithms
or the minutiae of a tool’s capabilities.
Ensure that you cover not only those
questions that bioinformatics approaches
can answer, but also the limitations of
bioinformatics, explicitly illustrating examples that cannot be answered.
Avoid long sessions of browsing around
web interfaces or showing one screenshot
after another. Trainees will be eager to try
tools themselves and will benefit far more
from a well-planned session, with adequate time allocated to an exercise or
simple exploration, than from merely
watching someone else explore for them.
When giving a demonstration, try to get
participants to follow along with you. To
compensate for the likely diversity in
speed and computer-ease of your audience,
when possible, pair trainees of different
backgrounds together and progress activities at a speed that will allow all trainees to
keep pace. Once you have completed a
task, confirm that everyone has achieved
the result, and recapitulate the scope of
the actions to reinforce the meaning and
significance of the session. If you allow
trainees to work by themselves on specific
tasks, conclude with what you expected
them to have achieved and how! Also
consider providing this summary of steps
and expected outcomes in an electronic/
October 2011 | Volume 7 | Issue 10 | e1002245
paper version as an addendum, as trainees
might want, and would certainly benefit
from being able, to review the task again, on
their own time. Furthermore, trainees will
often be eager to share what they have learnt
when they return to their work environments, so having a set of good course
manuals/practical exercises is essential to
enable them to do so. Absolutely avoid
spending 80% of the session talking and then
rushing through the last 20% of the practical
aspects. Moreover, try to avoid telling
trainees to finish later (on their own)
whatever they did not complete, as they will
probably not do so, will feel resentful
because what they really wanted to do was
not done and, more importantly, they will
have lost the important recap and reinforcement that you can provide.
Rule 7: Reinforce Learning with
Contextual and ‘‘Real World
Experience’’ Examples
Wherever possible, provide appropriate
biological context: examples without relevant context lack meaning and fail to
engage trainees. After introducing a new
concept, allow time to put the concept
immediately into action. Begin hands-on
exercises with a short worked example
where everyone can complete contextual
learning on a common dataset. Follow this
with time for further exploration: here,
you might either provide a second dataset or, if relevant or practicable, invite
trainees to use their own. If appropriate,
illustrate examples taken from your real
world research experience. For instance,
outline biological problems that you tackled with bioinformatics and describe
resources and tools that you adopted to
solve them and to achieve your findings
and how.
Rule 8: Ensure the Methods/
Tools Have Relevance to the
Trainee Experience and
Scientific Research Needs
Design your materials such that the
examples you provide illustrate the concepts you wish to convey and, at the same
time, are relevant to the research interests
of at least some of the trainees. Whenever
prior information about trainees’ interests
is available, use it. Appreciate that a plant
biologist will not have a need for humancentric examples, nor will they find them
comparable. The more relevant you make
the examples for the trainees, the more
likely they are to retain their interest and
develop their skills! Furthermore, encourage trainees to explore the tools and
resources presented during the course not
only with the carefully prepared examples
provided, but also from the perspective of
their own research interests: nothing
motivates as much as the need to solve
one’s own problems!
The use of tools and resources from the
perspective of personal research interests,
will lead new users to take a fresh critical
look at them. From this perspective,
trainees might be able to provide a special
assessment of the tools and resources
introduced in the course which would be
different and complementary to the one
that experienced users can provide. Trainers can gain an understanding of how
easy (or hard) exploring web interfaces or
programmatically access and parse resources is, and specific comments on what
is intuitive or not to trainees can be
captured informally or formally (e.g.,
through surveys). In this regard, you may
explain to trainees that evaluation and
feedback collected during the actual training course or in a final feedback survey
can aid significantly to improve bioinformatics resources.
Rule 9: Allow for Interactivity
and Provide Time for Reflection,
Individual Analysis, and
Exploration
Ensure interactivity and time for reflection. Provide time for trainees to acquaint
themselves with the interfaces of the tools/
resources, and to understand their contents: allowing trainees to explore a tool or
resource on their own tends to promote
greater retention of concepts.
Schedule 10–15 minutes at the end of
each module to review the presented
concepts, and to stimulate questions from
the trainees, who will probably have only
just started processing the information.
Do not simply rely on a set of slides and
step-by-step tutorials to teach concepts.
Make use of flip-charts to brainstorm
together, asking trainees for ideas and
alternative ways to resolve particular
biological questions. Group sessions like
this, where trainees are encouraged to
share their thoughts and views with the
whole class, can help both to identify
common issues and aspects to be explored,
and to highlight any trainee limitations
and/or mismatched expectations. Moreover, incorporating such group discussions
directly into training sessions can often
help to instil a greater level of understanding than when trainees are left to passively
explore set examples (or to copy and paste
scripts with no explanation of what these
might achieve). Exploit such brainstorming sessions to demonstrate how bioinformatics tools and resources can help to
address, and sometimes solve, complex
problems.
Depending on the time available, include quizzes and/or problem-solving
tasks and open discussion sessions in which
participants can reflect on the skills they’ve
learned and how these might be used to
address questions of interest to them.
Provide trainees (perhaps in pairs or
groups) with a brief set of questions prior
to, and after, the training course. Questions that probe their knowledge and
understanding of bioinformatics are useful
both for trainers (to verify that the course
has been pitched correctly and to establish
what knowledge has been gained) and for
trainees. Furthermore, by asking trainees
to think about, and answer, a series of
course-relevant questions, you ensure adequate time for concept and content
digestion and reflection.
Rule 10: Encourage
Independent Thinking and
Problem Solving
Finally, teach to fish rather than give
fish! In other words, try to develop
independent thinking rather than simply
spoon-feeding trainees with slides and
step-by-step tutorials: it is more important
to learn how to tackle research questions
with bioinformatics, and to know where/
how to search for solutions, than it is to
learn about the minutiae of every available
tool and resource.
References
1. Schneider MV, Watson J, Attwood T, Rother K,
Budd A, et al. (2010) Bioinformatics training: a
review of challenges, actions and support requirements. Brief Bioinform 11: 544–551.
2. Schneider MV, Walter P, Blatter MC, Watson J,
Brazas B, et al. (2011) Bioinformatics Training
Network (BTN): a community resource for
bioinformatics trainers. Brief Bioinform. In press.
PLoS Computational Biology | www.ploscompbiol.org
3
October 2011 | Volume 7 | Issue 10 | e1002245