Download Aligning reads with Galaxy

Finding genes de novo with RNA-seq
Graham Etherington
[email protected]
Today's topics
• The basics – What is RNA-seq, alternative
splicing.
• Assembly techniques
– Reference-based alignment
– De-novo assembly
• Expression analysis
Today's topics
• Tutorials in Galaxy
– Finding genes through transcript
assembly
• TopHat – Cufflinks
– Expression analysis
• Cuffcompare – Cuffdiff
RNA-seq – the basics
• Genome of interest.
– How many genes are there?
– Are some novel?
– Alternative spliced isoforms?
– Are some transcripts more abundant than others?
– Which genes are expressed under different
environmental or biological conditions (e.g. lack of
a nutrient, pathogen infection, etc)?
What is RNA-seq?
Genome
Genes
Extract mRNA (transcribed genes)
Sequence
RNA-seq basics - Alternative splicing
Reference-based Alignment
• Use when a closely-related reference is
available.
• 3 steps
① Use a splice-aware aligner (e.g. BLAT, TopHat) to
align reads to a reference genome.
② Cluster reads from each locus to build isoform
De Bruijn graphs.
③ Traverse graph to resolve isoforms. Each
different path through graph represents a
potentially different isoform.
Alignment
Seed and extend alignment (e.g. BLAST)
Query
ATCGCGTTACGATCCGTAA
Find all occurrences of ‘ATCGCG’
ATCGCGGTCGTTAATCGCGCGTTCGATCGCGTTACGATCCGTAACGCACCATCGCGTTGC
Seeds
Target
Alignment
Seed and extend alignment (e.g. BLAST)
Query
ATCGCGTTACGATCCGTAA
Extend alignments
Genome
ATCGCGTTAGTTAATCGCGTTACCGATCGCGTTACGATCCGTAACGCACCATCGCGTTAA
Alignment
• Burrow-Wheeler Transform (BWT)
– used by BWA, SOAP, Bowtie (and TopHat) aligners
• Creates a compressed index of the genome.
• Index is a sorted range of substrings from genome that
can be quickly searched.
• Stretches of sequence can be looked-up
– Like the index of a book. Words (sequences) can be looked
up in index which then points you to the pages (genomic
locations) were that word (sequence) is found.
• Narrows-down the search space (searches index instead of
genome)
• Speeds up alignment and requires less memory when compared to
older alignment algorithms.
Creating and Traversing Graphs
Aligned reads
Create graph that
represents alternative
splicing
Traverse graph to find
all possible paths
All possible splicevariants from graph
Reference-based Alignment
• Preferable where a high-quality reference exists.
• Can assemble full-length transcripts at depth of
10x.
• Advantages:
–
–
–
–
Contamination not a great problem – won’t align.
Less memory use than de novo assembly
Detection of low-abundance transcripts
Identify transcripts undiscovered in annotated
reference
Reference-based Alignment
• Disadvantages:
– Relies on the accuracy of the reference sequence
• May contain errors, deletions, missassemblies.
• Can miss divergent transcripts
– Reads often align to multiple regions
• Excluding multi-mapped reads – leaves gaps
• Randomly assign multi-mapped reads – false transcripts
– Can’t easily assemble trans-spliced genes (2 premRNAs spliced together to form 1 mature mRNA)
De-novo assembly
• Doesn’t use a reference sequence.
• Constructs De Bruijn graph by breaking reads
into k-mers and connecting overlapping
nodes.
• Graph is traversed to identify paths through it.
• Each path represents a unique sequence.
De Bruijn graphs
• All substrings of length k (k-mers) are generated from each read.
• 5-mers in this example
De Bruijn graphs
•
•
•
•
Overlapping k-mers used to create nodes in graph.
Chains of adjacent nodes in graph are collapsed into a single node
Alternative paths through graph are identified.
Isoforms identified
De-novo Assembly
• Advantages
– Doesn’t need a reference sequence.
– Sometimes better than reference-based assembly
when:
• reference is of low quality (e.g. missing bits).
• Unknown exogenous transcripts want to be detected.
• Where long introns are expected.
– Doesn’t depend on the correct alignment of reads
to splice sites.
De-novo Assembly
• Disadvantages:
– Lots of data requires lots of RAM
– Requires greater sequencing depth than
reference-based assembly (30x cf 10x).
– Highly similar transcripts are likely to be
assembled into single transcripts.
– Sensitive to read-errors. Hard to tell errors from
low-abundance transcripts.
Expression analysis
The more abundant an RNA, the more times it will be randomly selected for sequencing.
The Cufflinks tool suite assembles transcripts and calculates their abundance.
Sample 1
Gene A
(control)
Sample 2
Gene A
(infected)
expressed mRNA
sequencing
reads
Expression analysis
• Use number of mapped reads as an indicator
of expression.
Map reads back to genome
Sample 1
Gene A
(control)
Differential expression
Sample2
Gene A
(infected)
Normalisation
• 2 sequence libraries can produce different volumes of data
– transcript A present in same abundance in library X and library Y
– library X produces 3 times more reads than library Y
– transcript A in library X will appear 3 times more abundant.
• Need some way to normalise the expression data.
• Fragments Per Kilobase of exon, per Million fragments
mapped (FPKM).
– accounts for the number of reads in experiment, length of
transcript and the number of reads aligning to it.
– allows a comparisons between two datasets when there is
considerably more data in one dataset than the other.
Tutorials
• Go through the tutorial sheet.
• The task:
– Reference-based RNA-seq assembly using TopHat and
Cufflinks in Galaxy.
– RNA-seq expression analysis using Cuffcompare and
Cuffdiff in Galaxy.
http://galaxy.tsl.ac.uk