Making powerful science: an introduction to NGS data analysis
- 1. Making powerful science: an introduction to NGS data analysis Dr Adam Cribbs Group leader in systems biology Botnar Research Centre
- 2. Introduction PhD Prof Fionula Brennan Tregs in Rheumatoid Arthritis Postdoctoral scientist Prof Sir Marc Feldmann Prof Udo Oppermann Epigenetics of T cells MRC Career development fellowship Prof Chris Ponting Dr David Sims Systems biology MRC Career development fellowship PI position
- 4. Purpose of this section • Introduction to the concepts in NGS data analysis • Data formats and quality control • Challenges in data analysis • Software and pipelines
- 5. Application of NGS sequencing Sequencing of Genomic DNA Sequencing of DNA library Sequencing of cDNA library Whole genome sequencing • Genome re-sequencing • de novo genome sequencing • Metagenomics applications Epigenetic profiling • Methylation sequencing • Nucleosome footprinting Genomic footprinting • ChIP sequencing Targeted sequencing • PCR-amplified regions • Capture-enriched DNA Transcriptome analysis • Novel RNA classes (lncRNAs) • Novel splice variants Transcriptome expression • mRNA • Small RNA RNA footprinting • Ribosomal footprinting • RNA-IP sequencing
- 6. Bioinformatic challenges Now I have my data what do I do????
- 7. Bioinformatic challenges • 2.7 billion to hundreds of £ • NGS pushed the need for bioinformatics and big data analytics • Need for power!!
- 8. Need for computation • Need for computer power • VERY large files (10s of millions of lines) • Impossible to use familiar tools such as python • Impossible memory usage and execution time • Need for a large amount of compute power • Compute clusters • Parallel code and multi threading to speed up analysis • Need for faster software • Pipelines • Bioinformatics power! • Properly structured working
- 9. Data management issues • How to store data – very large raw data • Alternative data structures – e.g. binary storage (bam files) • Certain studies use different amounts of storage • RNA-seq per file 2Gb • WGS – 500 Gb files • Less of an issue now than it used to be 3-5 years ago – hardware improvements
- 10. Computational clusters • Multi-nodes (servers) with multi-cores • High performance storage (expensive) • In-line storage • Fast networks (50Gb Ethernet between nodes) • Located in a single data centre • Need skilled data-admin staff to monitor and fix issues
- 11. Cloud based analysis • Pros • Flexible • Pay for what you use • Don’t need to maintain a data centre • Cons • Transfer big data over internet is slow • You pay for bandwidth • Lower performance – disk IO • Privacy/data concerns • More expensive for long term projects
- 12. The future • NGS arrived in 2007/2008 • No-one predicted NGS in 2001 • How can we really predict the future? • Problems will always remain: • Software always lags behind hardware
- 13. Bioinformatics and computational biology • The term bioinformatician can mean many things • Usually little biology background but quantitative skills • Computational biologist is usually someone with a biology and quantitative background • There is definitely a massive skills shortage in both
- 14. How to learn computational skills • Introduction to Next-gen data analysis • EBI in Cambridge - https://www.ebi.ac.uk/training/online/course/functional- genomics-ii-common-technologies-and-data-analysis-methods/next-generation • OBDI program • 3 month short term training for a particular skill • https://www.imm.ox.ac.uk/research/units-and-centres/mrc-wimm- centre-for-computational-biology/training • Undertake part of your PhD in a computational group
- 15. NGS analysis
- 16. NGS data analysis Raw reads from sequencer Quality assessment of reads Mapping Pathway analysis Gene networks Data storage and visualisation
- 17. Quality control of reads • Sequencing output: • Reads + quality • Flat files – are very large – inefficient but it’s the standard • Question: is the quality of my sequencing data good?
- 18. Quality control of reads • Fastqc – babraham institute • https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
- 19. Tools to deal with read QC • Fastx-toolkit to optimize different datasets • Fastq-screen – check that all of your data is not contaminated • Trimming to improve quality • Trimmomatic • Cutadapt • There are many many more! • But beware of removing too many reads or trimming too much
- 20. Mapping reads to genome/transcriptome • Mapping data is very important to get correct • Many different mappers – make sure you use the latest software • Always treat your samples consistently
- 21. Mapping reads to genome/transcriptome • Main issues: • Number of mismatches • Number of multi-hits • Mates expected distance • Exon junction
- 22. GTF file for mapping • File format for reference sequence
- 23. Mapping reads to genome • Which one to use??? • Depends on application
- 24. Mapping reads to transcriptome • Which one to use??? • Depends on application • Don’t use tophat or hisat – use Tophat2 and hisat2
- 25. SAM/BAM format • Standard mapping output • Sequence alignment map (SAM) • Tab delimited • 11 mandatory fields 1. Read name 2. Flag 3. Reference 4. Position 5. Quality 6. Cigar 7. Ref name of mate 8. Pos of mate 10. Seq 9. Template len
- 26. SAM/BAM format • FLAG • CIGAR
- 27. SAM/BAM tools • Commandline • Samtools • view • Index • Sort • Picard • MarkDuplicates • Python • Pysam – maintained and developed by CGAT (Andreas Hager)
- 29. RNA-seq workflow for DEG • Workflow1: • Tophat2 (align) -> cufflinks (transcript assembly) -> cuffdiff (DEG) -> cuffmerge (merge assemblies) • Workflow 2: • Hisat2 (align with any spliced mapper) -> featurecounts (counting reads to transcripts) -> DESeq2 or EdgeR (DEG) Hisat2 alignment DESeq2 featurecounts General linear model that accounts for negative binomial distribution
- 30. Count data • Following featurecounts you are left with a counts table Fewer genes with large counts and more with fewer counts
- 31. DEG methods compared • Which model to use???? • My preference is DESeq2 • Well written and better support • edgeR not accounting for typeI errors as well? Microarray RNA-seq
- 32. DESeq2 model • Model overview: • First fits a GLM to the data using a sample size factor • Cooks distance for counts outlier detection • Dispersion is measured • zero-centered normal prior to shrink lower end • Wald test or LRT test
- 33. Pathway analysis • Pathway analysis helps to identify novel pathways that may be disease relevant • Skewed towards cancer • Not always informative • Paid vs public
- 34. Biological interpretation • The most important part and most difficult • Can be a problem when dealing with a company • Language barrier between biologist and bioinformatician • Visualising data helps overcome this
- 35. Developing pipelines • To speed up your analysis and make your code reproducible you need to write pipelines https://github.com/Acribbs/scflow
- 37. Further resources • Please email me • MOOCS: • Coursera : https://www.coursera.org/learn/bioinformatics- methods-1 • Edex: https://www.edx.org/micromasters/bioinformatics • Programming skills: • Codeacademy • EBI Introduction to Next-generation sequencing course - competitive