How to Design a good RNA-Seq experiment in an interdisciplinary context? ,

How to Design a good RNA-Seq experiment in an
interdisciplinary context?
Pôle Planification Expérimentale, PEPI IBIS
1
RNA-seq technology is a powerful tool for characterizing and quantifying transcriptome. Upstream careful experimental planning is necessary to pull the maximum of
relevant information and to make the best use of these
experiments.
1
,
INRA, France
Be aware of different types of bias
Why increasing the number of biological
replicates?
• To generalize to the population level
• To estimate to a higher degree of accuracy variation in
individual transcript (Hart, 2013)
• To improve detection of DE transcripts and control of
false positive rate: TRUE with at least 3 (Sonenson
2013, Robles 2012)
An RNA-seq
experimental
design
using Fisher’s
principles
More biological replicates or increasing
sequencing depth?
Rule 1: Share a minimal common
language
Keep in mind the influence of effects on results:
lane ≤ run ≤ RNA library preparation ≤ biological
(Marioni, 2008), (Bullard, 2010)
RNA-seq experiment analysis: from A to Z
It depends! (Haas, 2012), (Liu, 2014)
• DE transcript detection: (+) biological replicates
• Construction and annotation of transcriptome: (+)
depth and (+) sampling conditions
• Transcriptomic variants search: (+) biological
replicates and (+) depth
A solution: multiplexing.
Decision tools available: Scotty (Busby, 2013),
RNAseqPower (Hart, 2013)
Some definitions
Biological and technical replicates:
Rule 2: Well define the biological
question
Sequencing depth: Average number of a given position in a
genome or a transcriptome covered by reads in a sequencing run
Multiplexing: Tag or bar coded with specific sequences
added during library construction and that allow multiple
samples to be included in the same sequencing reaction
(lane)
Blocking: Isolating variation attributable to a nuisance variable (e.g. lane)
From Alon, 2009
• Choose scientific problems on feasibility and interest
• Order your objectives (primary and secondary)
• Ask yourself if RNA-seq is better than microarray
regarding the biological question
Adapted from Mutz, 2013
Conclusions
Make a choice
• Identify differentially expressed (DE) genes?
Rule 4: Make good choices
• Detect and estimate isoforms?
• All skills are needed to discussions right from project
• Construct a de Novo transcriptome?
How many reads?
Rule 3: Anticipate difficulties with
a well designed experiment
Prepare a checklist with all the needed elements to be
collected,
2 Collect data and determine all factors of variation,
3 Choose bioinformatics and statistical models,
4 Draw conclusions on results.
1
• Clarify the biological question
• 100M to detect 90% of the transcripts of 81% of
human genes (Toung, 2011)
• 20M reads of 75bp can detect transcripts of medium
and low abundance in chicken (Wand, 2011)
• 10M to cover by at least 10 reads 90% of all (human
and zebrafish) genes (Hart, 2013)...
construction
• Prefer biological replicates instead of technical
replicates
• Use multiplexing
• Optimum compromise between replication number and
sequencing depth depends on the question
• Wherever possible apply the three Fisher’s principles of
randomization, replication and local control (blocking)
And do not forget: budget also includes cost of biological data acquisition, sequencing data backup, bioinformatics and statistical analysis.
Who are we?
julie.aubert@agroparistech.fr, anne.delafoye@clermont.inra.fr, cyprien.guerin@jouy.inra.fr, christelle.hennequet@tours.inra.fr,
frederique.hilliou@sophia.inra.fr, fabrice.legeai@rennes.inra.fr, delphine.labourdette@insa-toulouse.fr, nmarsaud@insa-toulouse.fr, brigitte.schaeffer@jouy.inra.fr
ECCB14, 7 - 10 September 2014, Strasbourg, France
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