1. De novo assembly of RNA Steve Kelly www.stevekelly.eu
Slides are available at:
Example data is available at:

2. What is a de novo transcriptome?
Set of nucleotide sequences obtained by sequencing reverse transcribed RNA
Poly A selected
Ribsomal RNA depleted
Random hexamer primed
Long reads/short reads
Strand specific
Sequencing technology: Read size: Throughput per run:
Illumina ~120bp ~800Gbp
~1000bp ~700Mbp
PacBio ~2700bp ~100Mbp
GridION ~100kbp ~100Gbp/day
Select for mRNA

3. Why do de novo transcriptomes
Good way of generating new data from non-model species
- It can tell you about gene presence
- It can tell you about gene expression
- It can tell you about evolution
Cheaper than you might think!
2Gb of paired end sequence data is sufficient to get 90% of expressed genes!
A de novo plant transcriptome from Illumina sequence: ~£300
Typical computing resources (standard PC desktop)
Linux desktop: cost £
at least 1 quad core processor
at least 16gb of ram
at least 3 TB of disk space

4. Why do de novo transcriptomes
Example data for this talk is available at:
Paired end Illumina sequence data from Sorghum bicolor leaves
R1.fq is a file containing the first end reads.
R2.fq is contains the second end reads.
The first read in R2.fq is the pair of the first read in R1.fq so
the order of the reads is very important in paired-end read files!
All software used is available for Linux
- Web addresses for current versions are given
- Software takes many hours to run of large datasets
All software is command line only
- Command line examples for every step are given

5. Command line examples The name of the software being used
Where you can get it for free
Example: Using fastx toolkit
Command line:
~/fastx_quality_stats –i R1.fq –o R1_output.txt
~/fastx_nucleotide_distribution_graph.sh –i R1_output.txt –o R1_nuc.png
How to use software to generate the data shown on the slide
Shorthand for the full path to the directory where the program is located

6. De novo assembly protocol outline
1) Pre-assembly read processing
Remove poor quality data before assembly
2) Assembly
Using different parameters and merging
3) Post-assembly processing
Identifying protein sequences and orthologous protein groups
4) Quantification
Mapping raw reads back onto assembly to determine expression level

7. What do sequence files look like?
Each read has a name that starts with symbol
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/1
GAGTTTTTGTCATCGTTGATGCCAAGGAACAGTATACATGAAG… +
Fefefggggggfggggggggfgggegggggggaggggggggdg…

8. What do sequence files look like?
Each read has a name that starts with symbol
If the reads are paired, then the first in pair will end with a /1 and the second with a /2
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/1
GAGTTTTTGTCATCGTTGATGCCAAGGAACAGTATACATGAAG… +
Fefefggggggfggggggggfgggegggggggaggggggggdg…
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/2
GTTCAACTTGATGAATCATCTAAGGAGTGATCCAACAAAACAAA…
gggggegggggggggggggggfgggcfbgfgggeccd[Q^d^]]…

9. What do sequence files look like?
Each read has a name that starts with symbol
If the reads are paired, then the first in pair will end with a /1 and the second with a /2
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/1
GAGTTTTTGTCATCGTTGATGCCAAGGAACAGTATACATGAAG… +
Fefefggggggfggggggggfgggegggggggaggggggggdg…
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/2
GTTCAACTTGATGAATCATCTAAGGAGTGATCCAACAAAACAAA…
gggggegggggggggggggggfgggcfbgfgggeccd[Q^d^]]…
The read sequence is on the line after the read name

10. What do sequence files look like?
Each read has a name that starts with symbol
If the reads are paired, then the first in pair will end with a /1 and the second with a /2
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/1
GAGTTTTTGTCATCGTTGATGCCAAGGAACAGTATACATGAAG… +
Fefefggggggfggggggggfgggegggggggaggggggggdg…
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/2
GTTCAACTTGATGAATCATCTAAGGAGTGATCCAACAAAACAAA…
gggggegggggggggggggggfgggcfbgfgggeccd[Q^d^]]…
The sequence read is followed by a + symbol on a new line
The read sequence is on the line after the read name

11. What do sequence files look like?
Each read has a name that starts with symbol
If the reads are paired, then the first in pair will end with a /1 and the second with a /2
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/1
GAGTTTTTGTCATCGTTGATGCCAAGGAACAGTATACATGAAG… +
Fefefggggggfggggggggfgggegggggggaggggggggdg…
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/2
GTTCAACTTGATGAATCATCTAAGGAGTGATCCAACAAAACAAA…
gggggegggggggggggggggfgggcfbgfgggeccd[Q^d^]]…
The sequence read is followed by a + symbol on a new line
The read sequence is on the line after the read name
The base-by-base qualities are on line #4

12. Understanding read quality scores
Quality scores are the probability that the base is called incorrectly
Q = -10 Log10(p)
where p is the probability that base is incorrect
Quality score Probability of sequencing error Accuracy
10 1 in %
20 1 in %
30 1 in %
Read quality is more important for assembly than for read mapping!

13. Example of read quality scores
Reads in Illumina 1.5+ encoding (Quality )
What does quality score of B mean?
ASCII value for B = 66 (
Illumina 1.5+ offset = 64
B = 66 – 64 = 2
2 = -10 Log10(p)
p = 10^(-2/10)
p = 0.63
i.e. 63% chance that the base call is wrong

14. Visualising read quality data
Important to visualise/summarise the raw data before using it!
Most common errors can be spotted in general summary statistics!
Use FASTQC for nice visual output:
Use FASTX or Trimmomatic for simple processing tasks:

15. Pre-assembly read processing
Example: Using fastx toolkit
Command line:
~/fastx_quality_stats –i R1.fq –o R1_output.txt
~/fastx_nucleotide_distribution_graph.sh –i R1_output.txt –o R1_nuc.png

16. Pre-assembly read processing
Example: Using fastx toolkit
Normal 5’ composition bias caused by non-random hexamer used during cDNA synthesis
Command line:
~/fastx_quality_stats –i R1.fq –o R1_output.txt
~/fastx_nucleotide_distribution_graph.sh –i R1_output.txt –o R1_nuc.png

17. Pre-assembly read processing
Example: Using fastx toolkit
If there is unusual composition bias at either end it is likely to cause problems. It’s best to remove these bases!
Command line:
~/fastx_trimmer –f 6 –i R1.fq –o trim_R1.fq

18. Pre-assembly read processing
Example: Using fastx toolkit
Non-uniform composition in the reads due to sequencing adaptor sequences.
Remove the appropriate adapter sequence using fastx_clipper
Command line:
~/fastx_clipper –a GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG –i R1.fq –o filt_R1.fq
java -jar trimmomatic-0.30.jar PE -threads 4 -phred64 R1.fq R2.fq trimmed_R1.fq unpaired_R1.fq trimmed_R2.fq unpaired_R2.fq ILLUMINACLIP:all_adaptors.fasta:2:30:10 LEADING:10 TRAILING:10 SLIDINGWINDOW:5:15 MINLEN:50

19. Pre-assembly read processing
Example: Using fastx toolkit
Command line:
~/fastq_quality_boxplot_graph.sh –i R1_output.txt –o R1_box_plot.png

20. Pre-assembly read processing
Example: Using fastx toolkit
Normal 3’ degradation of read quality scores.
Sequence at 3’end has higher proportion of errors
Before assembly good idea to remove poor quality sequences!
Command line:
~/fastq_quality_boxplot_graph.sh –i R1_output.txt –o R1_box_plot.png

21. Understanding read quality scores
Quality scores are the probability that the base is called incorrectly
Q = -10 Log10(p)
where p is the probability that base is incorrect
Quality score Probability of sequencing error Accuracy
10 1 in %
20 1 in %
30 1 in %
Read quality is more important for assembly than for read mapping!
Quantification
SNP calling or assembly

22. Pre-assembly read processing
Three ways to deal with poor quality reads:
Discard reads based on quality threshold
Most stringent
Lose lots of data
2) Remove poor quality sections from reads
Middle stringency
Keep most of the read data, just loosing the unreliable bits
Try to find and fix the errors in poor quality reads
Least stringent
Error correction may introduce errors
Keep all the data

23. Pre-assembly read processing
Example: Using fastx toolkit
Removed reads which have quality < 30 for more than 50% of the read length
22.5 million -> 14.3 million (64%)
Command line:
~/fastq_quality_filter –q 30 –p 50 –i R1.fq –o filt_R1.fq
~/fastx_quality_stats –i filt_R1.fq –o filt_R1_output.txt
~/fastq_quality_boxplot_graph.sh –i filt_R1_output.txt –o filt_R1_box_plot.png

24. Pre-assembly read processing
Example: Using fastx toolkit
Clip reads from both ends which have quality until quality > 30. Discard if read length < 30
22.5 million -> 22.2 million reads (99%)
Command line:
~/fastq_quality_trimmer –t 30 –l 30 –i R1.fq –o trim_R1.fq
~/fastx_quality_stats –i trim_R1.fq –o trim_R1_output.txt
~/fastq_quality_boxplot_graph.sh –i trim_R1_output.txt –o trim_R1_box_plot.png

25. Pre-assembly read processing
Example: Using SortMeRNA
Even after polyA selection or ribosomal RNA depletion you will have 1-30% ribosomal RNA contamination in your RNAseq. Best to remove it before assembly!
You will need to interleave your read files to run SortMeRNA.
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/1
GAGTTTTTGTCATCGTTGATGCCAAGGAACAGTATACATGAA +
Fefefggggggfggggggggfgggegggggggaggggggggd
@FCC00CKABXX:2:1101:1248:2120#CAGATCAT/2
GTTCAACTTGATGAATCATCTAAGGAGTGATCCAACAAAACA
gggggegggggggggggggggfgggcfbgfgggeccd[Q^d^
One file containing all read pairs interleaved consecutively.
Command line:
~/sortmerna --I interleaved_trimmed_reads.fq -n 4 --db “the full paths to the four included ribosomal RNA libraries” --paired-in --accept ribosomal_RNA_reads --other non_ribosomal_reads --log rRNA_filter_log -a 4

26. Pre-assembly read processing
Example: Using ALLPATHS-LG
- Splits all reads into all possible sub-sequences of length k
Makes stacks of near identical sub-sequences
Bigger the value of k the more conservative
Small value of k can introduce more errors than it fixes!

27. Pre-assembly read processing
Example: Using ALLPATHS-LG
- Splits all reads into all possible sub-sequences of length k
Makes stacks of near identical sub-sequences
Bigger the value of k the more conservative
Small value of k can introduce more errors than it fixes!

28. Pre-assembly read processing
Example: Using ALLPATHS-LG
- Splits all reads into all possible >25bp sub-sequences (p < 1e-6)
- Makes stacks of near identical sub-sequences (no gaps)
Command line:
~/ErrorCorrectReads.pl PHRED_ENCODING=64 READS_OUT=corrected_reads PAIRED_READS_A_IN=R1.fq PAIRED_READS_B_IN=R2.fq FILL_FRAGMENTS=True

29. Pre-assembly read processing
Example: Using ALLPATHS-LG
- Splits all reads into all possible >25bp sub-sequences (p < 1e-6)
Makes stacks of near identical sub-sequences (no gaps)
Correct singletons and low frequency bases
Keep all 22.5 million reads (100%)
Command line:
~/ErrorCorrectReads.pl PHRED_ENCODING=64 READS_OUT=corrected_reads PAIRED_READS_A_IN=R1.fq PAIRED_READS_B_IN=R2.fq FILL_FRAGMENTS=True

30. Pre-assembly read processing
Example: Using ALLPATHS-LG
Join paired end reads into long reads prior to assembly.
Assembly contains up to 50% less reads
Assembly program doesn’t incorrectly guess insert size!
Prevents some common assembly artefacts
Command line:
~/ErrorCorrectReads.pl PHRED_ENCODING=64 READS_OUT=corrected_reads PAIRED_READS_A_IN=R1.fq PAIRED_READS_B_IN=R2.fq FILL_FRAGMENTS=True

31. Pre-assembly read processing
Example “good practice” pre-assembly processing
- Visually inspect nucleotide composition and quality scores
Clip off 5’ or 3’ ends containing unexpected/unusual nucleotide bias
Filter out any reads containing adaptor sequences
Trim back reads from both ends based on quality score
Filter out ribosomal RNA reads
Error correct and join overlapping remaining reads using k >= 25bp
- (Optional step here would be to normalise reads to even out coverage depth for example see program called “khmer”)
Assemble!

32. Which assembler to use? Assembly packages:
All use the same underlying idea of assembling using de Bruijn graphs
1) Velvet/Oases - Good all round and very fast
2) Trinity - Good at getting full length transcripts
3) TransAbyss - Good at getting lots of splice variants
4) SOAPdenovo-Trans - Very fast with low memory footprint
Results of all assembly programs are quite similar.
As each of them comes up with a new idea they tend to be built into the others quickly too so that they don’t get left behind!
I recommend you start with velvet/oases as its easy to install and use!

33. Simplified overview of assembly process

34. Simplified overview of assembly process

35. Assembling the transcriptome
Example: Using Velvet/Oases
&
Assembly only needs two parameters
- Need to specify the insert size
Need to specify the k-mer size for constructing the de Bruijn graph
Larger k more specific
Smaller k more sensitive
Best to use a range of k-mer sizes and merge the results
- For example k = 31, 41, 51 and 61
Set a minimum transcript length
Set a minimum coverage cut off
Command line example:
~/oases_pipeline.py –m 31 –M 61 –s 10 –g 41 –o merged –d “ –fastq –shortPaired –separate R1.fq R2.fq“ –d –ins_length 170 –cov_cutoff 6 –min_pair_count 4 –min_trans_lgth 200

36. Assessing the transcriptome
Effect of kmer size of transcript length part 1

37. Assessing the transcriptome
Effect of kmer size of transcript length part 2

38. Assessing the transcriptome
Effect of kmer size on number of transcripts

39. Quantifying your transcriptome
Example: Using RSEM & bowtie
&
Use processed reads for quantification
Cant use programs such as cufflinks to estimate abundance from transcripts
Current best method is RSEM
gene_id
transcript_id(s)
length
effective_length
expected_count
TPM
FPKM
transcript_1
2021
519.77
transcript_100
887
802.59 96
transcript_101
880
795.59
transcript_102
876
791.59
370
transcript_103
173.95
transcript_104
875
790.59 53
1593.4
Use expected count for DE testing
Command line example:
~/rsem-prepare-reference --bowtie-path “path-to-bowtie” transcripts.fa
~/rsem-calculate-expression --bowtie-path “path-to-bowtie” --paired-end R1.fq R2.fq transcripts.fa

40. Summary A de novo plant transcriptome from Illumina sequence: ~£300
Important to check the raw data before assembly
Correcting and removing errors before assembly improves quality
Most assembly methods have similar performance
Try it!

41. Assemble your own mini-transcriptome
Download and extract some pre-trimmed read data from:
Command line:
wget ./
unzip reads.zip
These reads come from 5 genes detected in a sorghum RNAseq experiment
Sb09g020820, Sb02g034570, Sb07g000980, Sb07g023920, Sb06g016090
Error correct and join overlapping paired end reads:
Command line:
ErrorCorrectReads.pl PHRED_ENCODING=64 READS_OUT=corrected_reads PAIRED_READS_A_IN=R1.fq PAIRED_READS_B_IN=R2.fq FILL_FRAGMENTS=True

42. Assemble your own mini-transcriptome
Assemble uncorrected (trimmed) reads
Command line:
velveth kmer_ fastq -shortPaired -separate 1.fq 2.fq
velvetg kmer_61 -read_trkg yes -scaffolding yes -ins_length 170
oases kmer_61 -ins_length 170 -scaffolding yes
Assemble corrected reads
Command line:
velveth kmer_61ec 61 -fastq -shortPaired -separate corrected_reads.paired.A.fastq corrected_reads.paired.B.fastq
velvetg kmer_61ec -read_trkg yes -scaffolding yes -ins_length 170
oases kmer_61ec -ins_length 170 -scaffolding yes

43. Assemble your own mini-transcriptome
Assemble corrected “joined” reads (*MUCH* better for larger read datasets)
Command line:
velveth kmer_61ecj 61 -fastq –short corrected_reads.filled.fastq
velvetg kmer_61ecj -read_trkg yes
oases kmer_61ecj
Look in the “transcripts.fa” output files to see what transcripts you have assembled
BLAST transcripts online at NCBI BLAST
Are each of the 5 expected genes assembled?
Sb09g020820, Sb02g034570, Sb07g000980, Sb07g & Sb06g016090
If you assemble at k = 21 do you generate any chimeric transcripts?
Try the oases pipeline script on slide 35.