1. A critical evaluation of HTQC: a fast quality control toolkit for Illumina sequencing data Chandan Pal, PhD student Sahlgrenska Academy
Institute of Neuroscience and Physiology | Chandan Pal

2. Components Why quality control toolkit? Illumina data summary
Sources of errors in Illumina data
A bit on QC pipeline
Focus on HTQC toolkit
Focus on other QC tools
Critical analysis of HTQC
Quiz (2 questions: answer and get chocolate bars)
Institute of Neuroscience and Physiology | Chandan Pal

3. Why Quality Control is so important?
Produce poor /biased/ incorrect results
Conclusion is dependent on QC and filtering
Genome as a tool in Diagnostics
Reducing volume of dataload for the softwares and the tools used for downstream analysis
To solve it:
Identify problems in data (visualization can help)
Remove problems from the data (using filtering workflow)
Institute of Neuroscience and Physiology | Chandan Pal

4. Illumina short reads characteristics
Length
bp, 100bp most popular
Attributes
- high quality at 5’ start and lowers at 3’ end
- Indels and homopolymer erros are rare
Library types:
- Single end (low reliable)
- Paired-end (very reliable)
- Mate-pair (varies)
Quality score (Q-score): ASCII character code: offset 64 or 33)
- Q40 (error probability (P) or 1 in 10000) (Qphred=-10log10(P))
- Q30 (error probability or 1 in 1000)
- Q20 (error probability 0.01 or 1 in 100)
- Q10 (error probability 0.1 or 1 in 10)
Institute of Neuroscience and Physiology | Chandan Pal

5. Meaning of a Illumina read
Run Lane Tile X-position Y-position Index Read Number
@HWI-EAS209_0006_FC706VJ:5:58:5894:21141#ATCACG/1 TTAATTGGTAAATAAATCTCCTAATAGCTTAGATNTTACCTTNNNNNNNNNNTAG
+HWI-EAS209_0006_FC706VJ:5:58:5894:21141#ATCACG/1 efcfffffcfeefffcffffffddf`feed]`]_Ba_^__[YBBBRTT\]][]ddddd^dddadd^BBBBBBBBBBB
base call base quality
Institute of Neuroscience and Physiology | Chandan Pal

6. How are quality scores generated?
Using a model which uses a set of quality predictor values (intensity, background signal) as inputs , measure base call reliability and produce quality score as ouputs.
Quality score is assigned to each base call for every cluster in a given tile on a flow cell at a specific cycle
All quality score are recorded in base call files (.bcl) wich contain base call and quality score per cycle.
During analysis quality score are written to fastq files in ASCII coded form
Institute of Neuroscience and Physiology | Chandan Pal

7. Sources of error in Illumina
Non-random distribution of the reads in the sequenced sample over the reference (high coverage in the area of high %GC)
Certain substitution error (wrong base call are frequently preceded after G or C)
Frequencies of base substitution (vary: A to C is most frequent)
Region specific sequencing artifacts
Uncalled bases (encoded as ’B’ in quality string)
Ambigiuos bases (velevet silently convert them to random base, some sotware misbehave, some crashes)
Other Tile specific problems
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8. Tile specific problem
Base calling (problem in specific tile, chastity)
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9. QC processing of raw data
Fastq files
QC inspection
Filtering out bad data
Institute of Neuroscience and Physiology | Chandan Pal

10. Investigation on read quality
General
- Base quality distribution
- Average base quality distribution
- base counts by position
- base quality by position
Instrument specific
- Tile base quality
- Tile alignment quality
Experiement specific
- DNAseq (alignment score, mapping quality, targeted region QC, read-pair status, insert-size distribution)
- Chip-Seq (Duplicate reads)
Institute of Neuroscience and Physiology | Chandan Pal

11. Trimming reads End trimming
- clip bases from the ends till quality threshold is met (ignores low quality bases in middle of read)
Fixed trimming
- clip x bases from the end of every read (e.g., if you knew that something had gone wrong with the run after a certain cycle)
Should trim low quality sequences
- each bad base
- window moving avergare (e.g. 4 to 5)
- minimum % good per window
Institute of Neuroscience and Physiology | Chandan Pal

12. Effect of Data removal Losing lots of data (up to 99.5%)
Losing data in a certain area of the genome with very low coverage
Removal of wrong duplication as contaminant (but actually biological: mitochondrial or chloroplast sequence as common repeats in the target genome)
Problem in de novo projects for assembly (contigs breaks)
Institute of Neuroscience and Physiology | Chandan Pal

13. Comparison of QC softwares
Institute of Neuroscience and Physiology | Chandan Pal

14. HTQC toolkit components
ht_stat (QC report in text format)
Ht_stat_draw.pl (visualization)
ht_tile_filter (tile specific)
ht_trim (trim bases from both ends of the reads)
ht_qual_filter (remove low quality reads)
ht_length_filter (remove short reads)
Institute of Neuroscience and Physiology | Chandan Pal

15. Read quality assessment
Institute of Neuroscience and Physiology | Chandan Pal

16. Institute of Neuroscience and Physiology | Chandan Pal

17. Read quality assessment
Institute of Neuroscience and Physiology | Chandan Pal

18. Run-time efficiency A. Real: Elapsed time in real world
B. User: CPU cost in user mode
C. System: CPU cost in system mode
Institute of Neuroscience and Physiology | Chandan Pal

19. Time efficiency of the HTQC tool
HTQC tool claims:
Approx. 3-times FASTER than FastQC,
Approx. 30 times FASTER than BIGpre,
Approx. 40 times FASTER than solexaQA
Use a lot less memory
Institute of Neuroscience and Physiology | Chandan Pal

20. Observation FastQC HTQC 2-times SLOWER than FastQC
real 2m25.152s (24 million reads: 40 bp long)
user 2m27.437s
sys 0m2.023s
HTQC
real 5m58.007s
user 5m48.289s
sys 3m16.106s
2-times SLOWER than FastQC
Used 1.9 Gb Memory (FastQC used 180 Mb)
Institute of Neuroscience and Physiology | Chandan Pal

21. Why taking a bit time? Bad Input-Output loop
while ((curr_char = fgetc(handle)) !=EOF) (
if (curr_char == LINE_SEPARATOR) break;
buffer.push_back(curr_char); )
Means it reads the huge Fastq file character by character and adds it to std:vector.
Institute of Neuroscience and Physiology | Chandan Pal

22. Pros and Cons of HTQC toolkit
Drawbacks
ht_stat will give the quality statistics
Need to run ht_stat_draw.pl to get the plots
GnuPlot need to be installed
CMake need to be installed
Pros
Generate plots, quality statistics
Trimming, filtering by quality/length/tile
Written in C++ so faster than Perl based softwares
Institute of Neuroscience and Physiology | Chandan Pal

23. Overall quality filtering stages
B-tail trimming
removing reads containing adapter sequence prior to analysis.
removing reads that have less than two-thirds of the bases with Q ≥ 30 in the first half of the read,
reads not passing the chastity filter (specific for illumina pipeline filtering: CHASTITY >= 0.6, the ratio of the highest of the four (base type) intensities to the sum of highest two)
reads containing at least one uncalled base.
Institute of Neuroscience and Physiology | Chandan Pal

24. Summary & Suggestions
FastQC performed better in terms of time effiecinecy though no filtering facilities
HTQC can do most what a user expect from a QC toolkit but have to run individual commands to do it.
fastX toolkit can be an alternative: have to run individual commnds to perform each steps (same as HTQC). Can’t handle paired-ends reads.
Initial investigation on FastQC then jump to fastX toolkit (single end)/HTQC toolkit (paired-end)/ fastX toolkit+ own script (paired-end)
Institute of Neuroscience and Physiology | Chandan Pal

25. Quiz Illumina first came to the market in A. 2004 B. 2005 C. 2006
D. 2007
2. First author of today’s paper
A. Xi Yang
B. Feng Li
C. Jun Wu
D. Xue Xiao
Institute of Neuroscience and Physiology | Chandan Pal