1. Metagenomic dataset preprocessing – data reduction
Konstantinos Mavrommatis

2. Complexity Who is there? (phylogenetic content) What does it do?
Acid Mine Drainage
Sargasso Sea
Termite Hindgut
Cow rumen
Soil
The total metagenome is the result of a cell community. Cells belong to different organisms ranging from strains to domains.
Who is there?
(phylogenetic content)
What does it do?
(Functional content)
Why is it there?
(Comparative study)
Species complexity

3. ? Dataset processing Analysis Feature prediction QC
Sample preparation
High throughput sequencing
Assemble reads
Analysis
Feature prediction ? QC
Functional annotation
and comparative analysis
Binning

4. Dataset processing (v 3.0a)
Submitted file
Assembled contigs
Submitted file
454 reads
Submitted file
Illumina reads
Fasta/fastq
File QC.
Check character set and contig name. Remove trailing Ns.
Trimming.
Q=20
Trimming.
Q=13
Fasta
Low complexity.
Size of 80 bp
Dereplication.
Prefix = 5, identity 95%,
Clustering.
100% identity
File for gene calling
fasta

5. Dataset processing Feature prediction pipeline (v 3.0a)
File for gene calling
fasta
CRISPR detection.
crt / pilercr
RNA detection.
tRNAscan / hmmer / Blast / (isolates:Rfam)
CDS detection.
Isolates: prodigal
Metagenomes: varies
Unassembled reads + assembled contigs
Conflict resolution
Concatenation of all results.
Creation of final output file
File for IMG
IMG

6. Dataset processing Quality trimming
Courtesy Alex Copeland
Remove sequences from the ends of the reads.
lucy for 454 datasets.
Illumina (longest high quality string)

7. Dataset processing Low complexity filter
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using dust (NCBI)
Remove sequences with less than 80 informative bases

8. Dataset processing Dereplication

9. Dataset processing Sequence dereplication
atcccat
atc-cat
atcccat
atcccat
atcccat
gctacat
gctncat
gctacat
Not dereplicated
gctacat
using uclust
95% identity (global alignment).
Identical prefix (5nt)

10. Dataset processing Evaluation of processing tools
Unassembled sequences due to their small size, quality problems, and large number need to be processed with efficient pipelines.
Simulated datasets:
Using sequences extracted from finished genomes (Perfect sequences)
Using reads that have been used to assemble finished genomes (Real errors).
Evaluation and development of new tools/wrappers.

11. Dataset processing Feature prediction
Available methods:
Ab initio: Metagene, MetaGeneMark, FragGeneScan, Prodigal.
Similarity based: Blastx, USEARCH.
isolate
CORRECT
MISSED
WRONG
NEW
metagenome

12. Method performance

13. Quality effect

14. Trimming

15. 454 Ti(no errors)

16. 454Ti(with errors)

17. Illumina 115 bp

18. Illumina 74 bp

19. Contigs
frameshift
Wrong prediction

20. Why annotate unassembled reads?
Sample
Total size
102,722,384 (2x150) reads
Assembled contigs
1,375,950 contigs
5060 different pfams
Assembled reads
Mapped (by bwa)
11,778,925 reads
Genes called on unassembled reads
64,737,444 genes
7481 different pfams
8,373,641 (12%) genes
Similar to genes on contigs1
Genes with similarity to isolate genomes
40,778,854 genes
Additional information about functions and phylogeny
Assembled only
More accurate statistics based on unassembled + assembled
Unassembled + assembled +
real metagenome

21. Processing time(metagenomes)
Highlight metrics. Things that
Show what I think should be the best metric for predcition for 2012
Total submissions
Processing time
Data size (bp)
336
2.45 days (annotation)
24 days (integration)
174,719,855 (average)
58,006,992, (total)

22. Processing time(isolates)
Total submissions
Processing time
Data size (bp)
3630
10 hours(annotation)
12 days (integration)
1,658,242 (average)
4,114,099, (total)

23. Thank you for your attention