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Misleading bioinformatics: Mistakes, Biases, Mis-interpretations and how to avoid them Festival of Genomics 2017 Course Exercise Material: http://tinyurl.com/FOG-2017.

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Presentation on theme: "Misleading bioinformatics: Mistakes, Biases, Mis-interpretations and how to avoid them Festival of Genomics 2017 Course Exercise Material: http://tinyurl.com/FOG-2017."— Presentation transcript:

1 Misleading bioinformatics: Mistakes, Biases, Mis-interpretations and how to avoid them
Festival of Genomics 2017 Course Exercise Material:

2 https://creativecommons.org/licenses/by-nc-sa/2.0/uk/legalcode
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) @babraham_bioinf @simon_andrews @drrshamilton #GenomicsFest

3 Who we are Simon Andrews, Head of Bioinformatics, Babraham Institute,
Cambridge, UK Russell Hamilton, Bioinformatics Core Facility Manager, Centre for Trophoblast Research, Cambridge University, UK Christel Krueger, Bioinformatician, Babraham Institute, Cambridge, UK Malwina Prater, Bioinformatician, Centre for Trophoblast Research, Cambridge University, UK

4 And why do we care about misleading bioinformatics?
We all work in Bioinformatics Core Facilities We see lots of different bioinformatics projects and people We see the kinds of mistakes people commonly make Collate a collection of common problems, biases and mistakes

5 Why now? Automation in analysis increasingly common Raw sequences
(FASTQ) List of Differentially Expressed Genes

6 Why now? Automation in analysis increasingly common Raw sequences
Duplication Rates Stranded Vs Unstranded Alignment Stats Biases featureCounts Raw sequences (FASTQ) List of Differentially Expressed Genes Sample Mix Ups Read Error Rates Read Counts BAM

7 Workshop Outline Introduction Russell 15 mins
Un-quantitated Data Exercise Christel 30 mins Expectations Simon 30 mins Biases Simon 30 mins Software Russell 30 mins Summary Simon 15 mins Coffee Coffee

8 Bioinformatics in a typical NGS project
Experimental Design and Planning Library Preparation Sample Tracking Sequencing Quantitation Comparison Statistics Functional Analysis Results

9 Bioinformatics in a typical NGS project
Experimental Design and Planning Library Preparation Sample Tracking Sequencing Quantitation NGS Pipeline automation increasingly common Many advantages, but errors, bias and bug need to be checked for Comparison Statistics Functional Analysis Results

10 Impact on Project Time “Bowtie of NGS” time Experimental Design
Library Preparation Sample Tracking Sequencing Quantitation Comparison Statistics Functional Analysis Results “Bowtie of NGS” time

11 Impact on Project Time “Bowtie of NGS” Bug / Error / Mistake time
Experimental Design Library Preparation Sample Tracking Sequencing Quantitation Comparison Statistics Functional Analysis Results “Bowtie of NGS” Bug / Error / Mistake time

12 Impact on Project Time “Bowtie of NGS” Lost time
Experimental Design Library Preparation Sample Tracking Sequencing Quantitation Comparison Statistics Functional Analysis Results “Bowtie of NGS” Lost time Time could be spend on other projects Follow on work may be dependent on preliminary results time

13 Impact on Project Time “Bowtie of NGS”
Experimental Design Library Preparation Sample Tracking Sequencing Quantitation Comparison Statistics Functional Analysis Results “Bowtie of NGS” Issues can lead to significant time delays Can sometimes lead to interesting discoveries Can also make bioinformaticians cry time

14 Impact on Costs $ $ cumulative Experimental Design and Planning
Library Preparation Sample Tracking Sequencing Quantitation Comparison Statistics Functional Analysis Results

15 Impact on Costs $ cumulative Experimental Design Bug / Error / Mistake
Library Preparation Sample Tracking Sequencing Quantitation Comparison Statistics Functional Analysis Results Bug / Error / Mistake

16 Impact on Costs $ cumulative Experimental Design Bug / Error / Mistake
Library Preparation Sample Tracking Sequencing Quantitation Comparison Statistics Functional Analysis Results Bug / Error / Mistake Half the project budget could be lost

17 Workshop Goals Un-quantitated data exercise:
Experimental Design Library Preparation Sample Tracking Sequencing Quantitation Comparison Statistics Functional Analysis Results Un-quantitated data exercise: Look at the raw data before you analyse Expectations What assumptions and expectations should you have for your data Bias How to look for and identify them (examples and cases) Software How to select bioinformatics tools

18 Course Exercise Material: http://tinyurl.com/FOG-2017
Workshop Outline Introduction Russell 15 mins Un-quantitated Data Exercise Christel 30 mins Expectations Simon 30 mins Biases Simon 30 mins Software Russell 30 mins Summary Simon 15 mins Coffee Course Exercise Material: Coffee

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