1. Preprocessing Data
Rob Schmieder

2. Bad data analysis

3. Good data analysis New dataset Quality control & Preprocessing
Similarity
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Assembly

4. 3 Tools for metagenomic data

5. http://edwards.sdsu.edu/prinseq Quality control and data preprocessing
Rob Schmieder

6. Number and length of sequences
Bad
Reads should be approx. same length (same number of cycles)
→ Short reads are likely lower quality
Good

7. Linearly degrading quality across the read
Trim low quality ends

8. High quality throughout the sequence
Good quality through
the length of the sequence
Sequence quality
falls off quickly
→ Bad sequence data

9. Ion quality scores

10. Low quality sequence issues
Most assemblers or aligners do not take into account quality scores
Errors in reads complicate assembly, might cause misassembly, or make assembly impossible

11. What if quality scores are not available ?
Alternative:
Infer quality from the percent of Ns found in the sequence
Removes regions with a high number of Ns
Huse et al. found that presence of any ambiguous base calls was a sign for overall poor sequence quality
Huse et al.: Accuracy and quality of massively parallel
DNA pyrosequencing. Genome Biology (2007)

12. What if quality scores are not available ?
Alternative:
Infer quality from the percent of Ns found in the sequence
Removes regions with a high number of Ns
Huse et al. found that presence of any ambiguous base calls was a sign for overall poor sequence quality
Huse et al.: Accuracy and quality of massively parallel
DNA pyrosequencing. Genome Biology (2007)

13. Ambiguous bases
If you can afford the loss, filter out all reads containing Ns
Assemblers (e.g. Velvet) and aligners (SHAHA2, BWA, …) use 2-bit encoding system for nucleotides
some replace Ns with random base, some with fixed base (e.g. SHAHA2 & Velvet = A)
2-bit example: 00 – A, 01 – C, 10 – G, 11 - T

14. Quality filtering
Any region with homopolymer will tend to have a lower quality score
Huseet al. found that sequences with an average score below 25 had more errors than those with higher averages
Huse et al.: Accuracy and quality of massively
parallel DNA pyrosequencing. Genome Biology (2007)

15. Sequence duplicates

16. Real or artificial duplicate ?
Metagenomics = random sampling of genomic material
Why do reads start at the same position?
Why do these reads have the same errors?
No specific pattern or location on sequencing plate
11-35%
Gomez-Alvarez et al.: Systematic artifacts in metagenomes from
complex microbial communities. ISME (2009) 16

18. One micro-reactor – Many beads
Martine Yerle (Laboratory of Cellular Genetics, INRA, France)

19. Impacts of duplicates False variant (SNP) calling
Require more computing resources
Find similar database sequences for same query sequence
Assembly process takes longer
Increase in memory requirements
Abundance or expression measures can be wrong

20. Impacts of duplicates False variant (SNP) calling
Require more computing resources
Find similar database sequences for same query sequence
Assembly process takes longer
Increase in memory requirements
Abundance or expression measures can be wrong
Reference
...ACCACACGTGTTGTGTACATGAACACAGTATATGAGCATACAGAT...
GTGTTGTGTACATGAACACAGTATATGAGCATACAGAT...
GTGTACATGAACACAGTATATGAGCATACAGAT...
TGAACACAGTCTATGAGCATACAGAT...
TGAACACAGTCTATGAGCATACAGAT...
TGAACACAGTCTATGAGCATACAGAT...
TGAACACAGTCTATGAGCATACAGAT...
TGAACACAGTCTATGAGCATACAGAT...

21. Impacts of duplicates False variant (SNP) calling
Require more computing resources
Find similar database sequences for same query sequence
Assembly process takes longer
Increase in memory requirements
Abundance or expression measures can be wrong

22. Detect and remove tag sequences

23. No tag
MID tag
WTA tags

24. Imperfect primer annealing

25. Fragment-to-fragment concatenations

26. Data upload
Tag sequence definition

27. Tag sequence prediction

28. Parameter definition
Download results

29. Identification and removal of sequence contamination

30. Contaminant identification
Previous methods had critical limitations
Dinucleotide relative abundance uses information content in sequences  can not identify single contaminant sequences
Sequence similarity seems to be only reliable option to identify single contaminant sequences
BLAST against human reference genome is slow and lacks corresponding regions (gaps, variants, …)
Novel sequences in every new human genome sequenced*
* Li et al.: Building the sequence map of the human
pan-genome. Nature Biotechnology (2010)

31. Faster algorithms for Next-gen data

32. Principal component analysis (PCA) of dinucleotide relative abundance
Microbial metagenomes
Viral metagenomes

33. Contaminant identification
Current methods have critical limitations
Dinucleotide relative abundance uses information content in sequences  can not identify single contaminant sequences
Sequence similarity seems to be only reliable option to identify single contaminant sequences
BLAST against human reference genome is slow and lacks corresponding regions (gaps, variants, …)
Novel sequences in every new human genome sequenced*
* Li et al.: Building the sequence map of the human pan-genome. Nature Biotechnology (2010)

34. DeconSeq web interface
Two types of reference databases
Remove
Retain

35. DeconSeq web interface (cont.)

36. DeconSeq Identity = How similar is the query sequence to the
reference sequence
How much of query sequence
is similar to reference sequence
Coverage =

37. DeconSeq Blue = More similar to “retain”
Red = More similar to “remove”

38. Human DNA contamination identified in 145 out of 202 metagenomes