part 3 of rna-seq for de analysis: the mapping process
DESCRIPTION
Third part of the training session 'RNA-seq for Differential expression analysis'. We explain the details of the mapping process of RNA-seq data that allow us to detect differential expression. Interested in following this session? Please contact http://www.jakonix.be/contact.htmlTRANSCRIPT
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RNA-seq for DE analysis training
Mapping to assign reads to genes
Joachim Jacob22 and 24 January 2014
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Goal of mapping
We want to assign reads to genes they were derived from.
The result of the mapping will be used to construct a summary of the counts: the count table.
GeneA: 12GeneB: 5...
1. Read
2. Mapping
3. Goal
Genome sequence
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2 main scenarios in RNA-seq
Reference genome sequenceavailable
NO reference genome sequence available● De novo assembly of the reads (trinity) (transcriptome construction)● Map the reads to the assembly (RSEM mapper)● Extract count table(note:no removal of polyA is required.
Computationally expensive!)
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Reads mapped to reference genome
The mapping algorithm takes reads and aligns them to the right location in the reference sequence. Some key issues with this process:
1. Reference is haplotype: mixture of alleles, leads to mismatches.
2. Reads contain sequencing errors
3. Reads derived from mRNA, genome is DNA.
35 for 2 alleles→together
If we compare samples within the same specimen, this effect is similar for all samples.
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mRNA reads: some reads span introns
● We need gapped alignment algorithms
mRNA
etc.
Many reads span introns: they need to be aligned with gaps. This can be used to
detect intron-exon junctions
exon
intron
One isoform!
http://www.ensembl.org
Reads
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Isoforms complicate things
● Isoforms are transcribed at different levels, contributing differently to the number of reads.
http://www.ensembl.org
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Gapped read mapping (1)
● Exon-first approach: TopHat (popular)
TopHat: discovering splice junctions with RNA-SeqVol. doi:10.1093/bioinformatics/btp12025 no. 9 2009, pages 1105–1111
Junction database constructed to try to map unmapped reads.
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Gapped read mapping (2)
● STAR: fast and suited for longer reads
STAR: ultrafast universal RNA-seq alignerAlexander Dobin et al. Bioinformatics
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What you need for mapping
1. A reference genome sequence (fasta), to be indexed by the mapper.
2. A genome annotation file (GFF3 or GTF), with indication of currently known annotations (you can sometimes do without: you can let the mapper detect new genes)
3. The cleaned (preprocessed) reads (fastq)
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Getting your reference genome sequence
● Genomes to be used by TopHat can be fetched from iGenomes and for STAR here
● If your genome is not
listed above, check
http://ensembl.org
and
http://ensemblgenomes.org ; and follow indexing software
● If still no luck, try a specialized species website, e.g.
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Indexing a genome
● Mapping reads is fairly fast, because the heavy lifting is done beforehand: the reference genome sequence is preprocessed by indexing (taking a lot of time), making mapping fast.
● On Galaxy, the indexing has already been performed for you. Just choose your genome from the list.
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Getting genome annotation information
● Annotation info is stored in text files formatted as GTF or GFF3 files.
● If sequencing is deep enough, the complete transcriptome structure can be derived from the mapping: splice junctions, isoforms, variants,... CuffLinks for example reconstructs the annotation from an alignment, and generates a GFF file, to be used. Potentially novel transcripts are included in this file. But remember, this is NOT OUR GOAL.
● We will use a GTF file from an respected genome database to assist the mapping of reads.
http://cufflinks.cbcb.umd.edu/
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Using genome annotation information
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GTF example
TIP: You can look at an example in Galaxy!
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Mapping in Galaxy
Mapping in Galaxy
Basic settings
!
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Mapping in Galaxy
!!!
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Mapping in Galaxy
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Mapping tip
● PRO TIP: align a subsample of reads in Galaxy. Play with the settings, and determine the best outcome.
● Set the mapping fairly liberal: map as much as possible, and let the mapper assign mapping qualities. Ideally, every read maps once ('uniquely mapped'). In the following step, we will discard reads mapped to multiple locations ('multi reads').
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Mapping quality control
The outcome of the alignment is a SAM or a BAM format, which you can visualize in Galaxy (or with a stand-alone viewer such as GenomeView or IGV.
Let's visualize
Check whether this visualization matches:- paired end- splice junctions- strandedness- ...
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Practical tips
GTF
These are the reads, 2 coloursbecause of the sense and
antisense strand. (obviouslythis library was not stranded!)
Position
Some reads span an intron
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Mapping QC
Quality control of the mapping is done by checking the SAM file.
1. is the fraction of reads mapping OK?2. is the number of uniquely mapped reads OK?3. is the number of mismatches OK?4. in case of PE reads: do the reads fall into the same gene?
Tools:RseQCBAMQC
http://rseqc.sourceforge.net/
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Mapping QC - RSeQC
After checking the mapping visually, determine more metrics with RseQC.
http://rseqc.sourceforge.net/
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Mapping QC - RSeQC
Duplication rate observed in the RNA-seq data.
http://rseqc.sourceforge.net/
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Mapping QC - RSeQC
Read quality of aligned reads
http://rseqc.sourceforge.net/
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Mapping QC - RSeQC
Sequence depth saturation
http://rseqc.sourceforge.net/
Early flattening points to saturation
Q1 Q4: from low→count genes
to high count genes
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Mapping QC - RSeQC
Sequence depth saturation
http://rseqc.sourceforge.net/
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Mapping QC - RSeQC
The 'read coverage' per gene, averaged over all genes.
http://rseqc.sourceforge.net/
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Mapping QC - RSeQC
The gene body coverage graph, also per sample.
http://rseqc.sourceforge.net/
Deviating!
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Mapping QC - BamQC
Another useful tool is BamQC of the Qualimap Suite. Be aware however: mainly useful for DNA-seq!
http://qualimap.bioinfo.cipf.es/
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Mapping QC: BamQC
http://qualimap.bioinfo.cipf.es/ Fraction of genome sequence not covered
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Mapping QC: BamQC
Some examples to watch out for.
http://qualimap.bioinfo.cipf.es/
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Mapping QC: BamQC
Some examples to watch out for.
http://qualimap.bioinfo.cipf.es/
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Mapping QC: BamQC
Some examples to watch out for.
http://qualimap.bioinfo.cipf.es/
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Keywordshaplotype
Gapped mapping
GTF
duplication
isoforms
strandedness
coverage
Write in your own words what the terms mean
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Exercise
→ Mapping exercise
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Break