How to deal with your RNA-seq data ?

Emilie Drouineau, Rachel Legendre & the RNA-seq team

École de Bioinformatique AVIESAN-IFB-Inserm 2022

1 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Summary

2 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Bioinformatics

Introduction and prerequisites

3 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Raw NGS data

Instrument

Flowcell

Intensities

4 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Data storage: Hiseq2500

• Text file with size between 100 to 150 Gb by lane
• Let’s compare : War and peace by Léon Tolstoï
• 1817 pages
• 6 cm width
• 4 Mb
• 1 lane :
• 25 000 times "war and peace”
• 45 Millions pages
• 1.5 km (5 Eiffel towers)
• 8 lane by flow cell => 1 Tb of raw data/ week / sequencer
• Times 2 for paired-end

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5 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Data storage: NovaSeq6000

• Text file with size between 80Gb to 3Tb (in single flowcell mode)
• Let’s compare : War and peace by Léon Tolstoï
• 1817 pages 4250
• 6 cm width
• 4 Mb
• 1 run :
• 750 000 times "war and peace”
• 1350 Millions pages
• 45 km (138 Eiffel towers)
• Times 2 for dual flowcell mode

6 | Emilie Drouineau | Bioinformatics | 15/11/2022

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RNA-seq applications

« Transcriptome analysis provides information about the identity and quantity of all RNA molecules in one cell or a population of cells »

7 | Emilie Drouineau | Bioinformatics | 15/11/2022

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RNA-seq: Why ? How

Ask right question before libraries preparation and sequencing:

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Prokaryotes

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I don’t find a ribo-depletion kit for my organism:

• Design yourself the oligos

I want to identify antisense RNA:

• Directional protocol (standard)

I’m interested in transposons:

• Longer read sequencing
• Paired-end sequencing

Eukaryotes

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I want coding genes only:

• PolyA strategy

I want non-coding genes also:

• Ribo Depletion

I’m interesting in small RNA profiling:

• Use specific protocole

I’m interesting in isoforms:

• Paired-end sequencing
• Long read technologies

8 | Emilie Drouineau | Bioinformatics | 15/11/2022

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RNA-seq: Why ? How

Regardless of your organism:

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• Complexity of your genome and the biological question paired end or single end, length of reads ?
• Sequencing depth (multiplexing rate)
• More biological replicates than more sequencing depth
• Stranded RNA-seq protocol to assigned reads to a particular strand

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9 | Emilie Drouineau | Bioinformatics | 15/11/2022

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RNA-seq: Why ? How

Regardless of your organism:

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• Complexity of your genome and the biological question paired end or single end, length of reads ?
• Sequencing depth (multiplexing rate)
• More biological replicates than more sequencing depth
• Stranded RNA-seq protocol to assigned reads to a particular strand

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For a successful experiment, it's imperative to include bioinformaticians and biostatistician before the beginning of the RNA extraction

10 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Prerequisites

RNA sample:

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• DNAse treatment
• Quantity (adapted protocole)
• Quality (RNA integrity number > 7)
• Stocked at -80°C

Reference genome:

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Complete genomic sequence in fasta format

Annotation file:

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All features (genes, CDS, intron, UTR) of genome in GFF/GTF format

11 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Where find the genome and the annotation ?

Common databases Specific databases

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Keep control on your datas

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FASTQC: explore quality scores

14 | Emilie Drouineau | Bioinformatics | 15/11/2022

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FASTQC: explore quality scores

15 | Emilie Drouineau | Bioinformatics | 15/11/2022

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FASTQC: explore quality scores

Systematic high duplication level in RNA-seq, why ?

16 | Emilie Drouineau | Bioinformatics | 15/11/2022

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How to screen contaminations ?

Different levels:

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• Ribosomal contamination from same organism
• Align reads against the ribosomal genome with a dedicated mapper

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17 | Emilie Drouineau | Bioinformatics | 15/11/2022

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How to screen contaminations ?

Different levels:

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• Ribosomal contamination from same organism
• RNA contamination from other organism
• Use dedicated or derived tools such as fastq_screen or kraken

18 | Emilie Drouineau | Bioinformatics | 15/11/2022

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How to screen contaminations ?

Different levels:

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• Ribosomal contamination from same organism
• RNA contamination from other organism
• DNA contamination
• DNAse treatment could be ineffective and for DNA to make it through into the final library.

As soon as you visualise your reads against an annotated genome the presence of DNA is normally fairly apparent as a consistent background of reads over the whole genome

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19 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Bioinformatics

From mapping to counting

20 | Emilie Drouineau | Bioinformatics | 15/11/2022

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RNA-seq mapping specificity

• Mapping on genome or transcriptome ?
• the transcriptome is currently not well characterised enough to serve as a suitable reference for RNA-Seq
• get more gene isoforms information through mapping it to the genome

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• Take account to reads that come from exon-exon junctions

Cole Trapnell & Steven L Salzberg.Nature Biotechnology 27, 455 - 457 (2009)

21 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Mapping timeline

From https://www.ebi.ac.uk/~nf/hts_mappers/

22 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Choose the good mapper

Which one is the best mapper ?

23 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Choose the good mapper

Which one is the best mapper ?

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Which mapper should I use based on my data and my analysis ?

24 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Choose the good mapper

Depends on:

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- Detection of splicing events STAR, minimap2, Hisat2

- Length of reads:

Very short read (<50) : Bowtie1

Up to 1000kb : BWA-SW, bowtie2

Long reads : Minimap2

- Allow gap on alignment STAR, BWA, Bowtie2

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Common situations: choose a mapper widely-used and well maintained

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25 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Known biases in RNA-seq

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Intron coverage: if many reads align to introns, this is indicative of incomplete poly(A) enrichment or abundant presence of immature transcripts.

Intergenic reads: if a significant portion of reads is aligned outside of annotated gene sequences, this may suggest genomic DNA contamination (or abundant non-coding transcripts).

3' bias: over-representation of 3' portions of transcripts indicates RNA degradation.

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26 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Mapping QC on RNA-seq

• Percentage of mapped reads along genome
• Human/Mouse: 70 to 90 %
• Prokaryotic: more to 90 %
• Uniformity of read coverage on exons and the mapped strand.
• Low rate of multiple mapping
• Low rate of ribosomal RNA

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27 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Mapping QC on RNA-seq

• Common :
• Samtools (flagstats)
• Bamtools (stats)
• Picardtools (CollectRnaSeqMetrics)
• RseQC
• Human and mouse :
• RNAseQC
• Qualimap

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28 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Quantify number of reads on each gene

When counting reads, make sure you know how the program handles the following:

• overlap size (full read vs. partial overlap)
• multimapping reads
• reads overlapping multiple genomic features of the same kind
• reads overlapping introns

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Two popular tools :

• Htseq-count
• featureCounts

29 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Quantify number of reads on each gene

Deschamps-Francoeur, et al. 2020. doi:10.1016/j.csbj.2020.06.014

30 | Emilie Drouineau | Bioinformatics | 15/11/2022

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RNA-seq experiment

Organism: Arabidopsis thaliana, plant and model organism.

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Genome and annotation available in TAIR10, the arabidopsis database

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Dataset: 3 biological replicates, paired-end sequencing.

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Characterization of the function of the protein arginine methyltransferase AtPRMT5 during de novo shoot regeneration in Arabidopsis by a knocking-out of AtPRMT5.

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31 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Practice

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• Change directory:

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cd /shared/projects/<PROJECT>

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• Create a new directory:

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mkdir TP_rnaseq

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• Copy the script template in your home:

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cp /shared/projects/form_2022_32/coursLinux/rna-seq/01-Bioinfo/runme.sh TP_rnaseq

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• Follow the commands on the runme

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32 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Pipeline

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Bioinformatics

Visualize your data

34 | Emilie Drouineau | Bioinformatics | 15/11/2022

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Visualize alignments

Which format ?

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• BAM
• BigWig, BedGraph (base-by-base scores)
• BED, GFF (feature-by-feature data)

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Which tools ?

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• Browser : IGV, Artemis, UCSC Genome browser, SeqMonk…
• Snapshots : Deeptools, ngs.plot,...

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Visualize alignments

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Go to AT4G31120

36 | Emilie Drouineau | Bioinformatics | 15/11/2022

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