RNA-seq: analysis of raw data and preprocessing - part 2

Raw data investigation

Joachim Jacob
20 and 27 January 2014

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Experimental setup
We have decided on:
● how many samples per condition
● how deep
This determines how reliable the statistics will be,
using experience, and tools like Scotty. A wrong
experimental design cannot be fixed. Best
approach: pilot data (3 samples per condition, 10M)
But we have other sequencing options to choose!

PE versus SE Illumina

●

Single end (SE): from each cDNA fragment only
one end is read.
Paired end (PE): the cDNA fragment is read from
both ends.

Purify and
fragment
SE

●

PE

PE versus SE Illumina
Single end (SE):
●

Gene level differential expression

Paired end (PE):
●

Novel splice junction detection

●

De novo assembly of transcriptome

●

Helps with correctly positioning reads on the
reference genome sequence.

Note: PE not the same as mate pairs.

Strandedness
●

●

Naive protocols obtain reads from cDNA
fragments. BUT the link with the sense or
antisense strand is broken.
Stranded protocols generate reads
from one strand, corresponding to the
sense or antisense strand (depending on
the protocol).

Strandedness

Not stranded

Stranded

Example of a stranded protocol
●

dUTP protocol to
generate stranded
reads.

Importance of strandedness
●

●

Strandedness can bias the read counts
compared to non-stranded protocols.
Depends on the genome whether you
should apply it, e.g. in case genes
overlap, the improved benefit of
assigning reads to correct genes can
outweigh technical variation.

Length of the reads
●

●

●

Does not matter so much (when we want
to quantify aligning to a reference
sequence): 50 bp will do.
The most important point is to be able to
accurately position the read on the
reference genome sequence, to assign it
to the correct gene.
Length can become important, if you
want to assemble the transcriptome.

For DE on the gene level
The 'cheapest' protocol for high-throughput
sequencing suffices to achieve DE detection:
●

SE

●

50bp

●

Option: strandedness.

Use the money you have left over for
increasing the number of replicates.

Raw Illumina data
The data you get arrives as...

barcode
experiment

Compressed, usually with gzip

Raw Illumina data
(this one: 87196924 lines)
@HWI-ST571:202:D1B86ACXX:2:1102:1146:2155 1:N:0:ACAGTG
CCAACATCGAGGTCGCAATCTTTTTNANCGATATGAACTCTCCAAAAAAA
+
@@@FFFDFHHDG?FFHIIJJJJJIJ#1#1:BFFIGJJJJJIJJGIJJJJA

One read (minimum 4 lines)
sequence

CGGAGCTGAAGGAGAAACTGAAATCCCTGCAATGTGAATTGTACGTTCTT
+
CCCFFFFFGGHHHIJJJJJJJIJFHIJIIIJJJJGIIIIIEFGHIFCHJI

certainty reading this base
at this position ('quality')

GTTGGCAGCCCTGGAGCCCTGCCTCGGTGGTTTAGCCAGTACTAGGGGAT
+
CCCFFFFFHHHHHJJJIJJJJJJGIJJCGHFHIGIHJJJBDHGHHJJJIE
ATTTCCTCTTATTTACGTTGCTTTAAAGCGAGACTTCAACGCCATTTGAC
+
@@CFFFFFHHFHDFGHIJIIJGIJGGEHGGJB>??FHHGFFFGHIGIECF
CATCGAAGCAAAGCATATAAAGTTANTNNTNNCTGAGTTGTACATATTGC
+
??;;D?DB6CDB+<EFE>:AFA443#2##1##11)0:0?9**0??DAGI4
GAAGTGCCCCGCTGGCAGCACACAAGGAGCAGCCCGCTGCCGGACCACTC
+
?@@DDDADFFAA:CEGHBFGAHGD?F@BE9BFF?D@F;'-8AG<B92=;;

http://wiki.bits.vib.be/index.php/.fas

Exploring the raw data
1) check whether the Fastq file is consistent
-

2) Make graphs of some metrics of the raw data

http://wiki.bits.vib.be/index.php/.fastq
http://wiki.bits.vib.be/index.php/RNAseq_toolbox#Quality_control_and_visualization_of_raw_reads

FastQC – graphical exploration

http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

FastQC – perfect example

Reads have
good quality!

Anna Karenina principle: “There is only one way
to be good, but there are many ways to be
wrong.”

We will start by showing a good sample.
Afterwards we will discuss a less good sample.

http://en.wikipedia.org/wiki/Anna_Karenina_principle


Smooth
histogram/
density line
towards the
right,


steady
nucleotide
distribution.

Bias typical
for illumina


Not strongly
fluctuating
GC content

Bias typical
for illumina


GC-content
nicely bell
shaped


No N's!
(should ring
something)


All reads have
length 50bp,


Reads are
nicely
duplicated:
some amount
of duplication
is to be
expected in
RNA-seq data.


Kmers are short
sequence
stretches.
Sometimes they
are
overrepresented.
But in RNA-seq
this is not so
important
(duplication).

FastQC – less good RNA-seq sample

A relatively large
Portion of the
reads have
mistakes at
the 3' end of
the read.


There is an overrepresentation of reads
with a low mean
quality score


Not a steady level
of different nucleotide
fractions


Fluctuates


Heavily skewed versus
AT rich reads


Apparently a mixture
of two sets of reads
with different lengths


Duplication seems a
bit on the low side
(reported figures are
from 60 -75%)


Very highly skewed
read number.
Often the
sequence of Truseq
adaptor, or multiplex identifiers
can be
found here.
BLAST can reveal
more information!


Specific patterns of
Specific kmers.
Note: A and T rich

Quality control of raw data
Proceed? Or rerun?
This QC can guide you to which preprocessing steps
you need to apply for sure. The extra time and
money needed to correct the biases can sometimes
justify a rerun of the experiment.
This QC shows which preprocessing steps have
already been made by the sequencing provider.

Preprocessing
Removing unwanted parts of the raw data so it helps as
much as possible with reaching our goal: defining
differentially expressed genes.
1) removing technical contamination
● Low quality read parts
● Technical sequences: adaptors
● PhiX internal control sequences
2) removing biological contamination
● polyA-tails
● rRNA sequences
● mtDNA sequences
After this, we run FastQC again.

Technical contamination
Our goal is to define DE expression, for this we
need to assign reads with a high confidence to the
correct genomic location.
Removal of low quality read parts: they have a
higher chance to contain errors, and cause noise in
our read counts.

Removal of adaptor sequences (and other
technical sequences, such as multiplex) as they
cannot be mapped to the reference genome.

List of technical sequences

Advised to use defaults

Removal of adaptor sequences (and other
technical sequences, such as multiplex) as they
cannot be mapped to the reference genome.

http://code.google.com/p/ea-utils/wiki/FastqMcf

Fastq-mcf output

http://code.google.com/p/ea-utils/wiki/FastqMcf

Never remove duplicate reads! Highly expressed
genes can have genuine duplicate reads, which are
not due to the PCR amplification step in the
protocol.
●

PhiX sequences: the DNA of Phi X bacteriophage
is spiked in to monitor and optimize sequencing on
Illumina machines. Your sequencing provider
should filter out those sequences before delivery.
You can filter them out by aligning your reads to the
PhiX genome.
●

http://en.wikipedia.org/wiki/Phi_X_174

Biological contamination
cell

Mitochondria contain
rRNA, mRNA and mtDNA
rRNA and non-coding (95% of RNA)
nucleus
mRNA (5% of RNA)

Mitochondrial
rRNA and nc

mRNAs are captured with
oligo-dT coated beads.
Occasionally, non-protein
coding sequences are also
captured (especially since
mtRNA and rRNA can be
relatively rich in AT).
We can remove them via
homology searching (BLAST)
with known non-protein
coding sequences.

mRNA (5% of RNA)


AAAAAAAAAAAAA

mRNAs are post-transcriptionally modified: e.g. the
addition of a poly-A tail. If
our goal is to map the reads
to a reference genome
sequence, the polyA tails
should be removed. This
can be viewed as some
source of 'biological
contamination' in our
sequences (…).

●

Get the non-protein coding sequences via
Biomart.

Mitochondrial genome sequence also.

Filter the biological contamination
Your reads
The biological reads
Imported via Biomart
We are interested in the
reads that don't map!

Doing this in Galaxy
Useful: take a sample of your reads: fastq-to-tabular,
select random lines, tabular-to-fastq
1. create a new history
2. load the sample data in
3. Run fastqMcf to remove technical sequences
4. Run bowtie to match against biological sequence
databases, and keep reads that don't match.
5. Summarize: fastqc
→ make a workflow of this sample history.
→ run the workflow on all your samples in parallel
→ store the cleaned reads in a data library.

Summary preprocessing
Your reads
…...

Format consistent? Errors in quality?

Your groomed reads
Trends in raw data? QC report

...

…....

…...

Get technical contaminants
- ….

Your groomed reads without technical contamination
Get biological contaminants
- ….
- ….
Your groomed reads without technical
and biological contamination

…....

...

…...

How does your data look now? QC

Keywords
Paired end
Stranded reads
gzip
fastq
Adapter sequence

Write in your own words what the terms mean

Exercise
→ investigating and preprocessing raw RNA-seq data

RNA-seq: analysis of raw data and preprocessing - part 2

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RNA-seq: analysis of raw data and preprocessing - part 2