1. MGmapper A tool to map MetaGenomics data
A tool to map fastq files against one or
more reference sequence databases
Make output that is “fairly” easy to
understand
Give an overview of tax, abundance, depth
and coverage in relation to ref seq
Estimate insert size
Make assembled contig fasta files

2. https://cge.cbs.dtu.dk/services/MGmapper/

3. Command line versions module load mgmapper/2.2
sets path to: bwa, samtools, cutadapt, bedtools & perl/python scripts
MGmapper_PE.pl –h or MGmapper_SE.pl –h
MGmapper_classify.pl –h
Classifier to read .annot files from MGmapper (in misc/ dir)
Start the paired-end mapping now:

4. only the read pairs with the best sum of alignment scores.
Pre-processing of
reads
Adaptor removal/trimming
Identify paired reads
Reads don’t map to phiX genome
Bwa mem mapping
of all reads against
reference databases
and remove bad hits
AS=28
AS=45
AS=90
AS=45
AS=90
Filter hits based on
alignment criteria
Alignment Score >=30
Best mode: Re-arrange
database hits and keep
only the read pairs with
the best sum of
alignment scores.
full mode: keep all hits
even if present in several
databases.
Bacteria pair1 forward AS=55 reverse AS=60
Bacteria pair2 forward AS=90 reverse AS=100
Human pair1 forward AS=60 reverse AS=60
Fungi pair1 forward AS=50 reverse AS=55
Bacteria pair2 forward AS=90 reverse AS=100
Human pair1 forward AS=60 reverse AS=60
Fungi pair1 forward AS=50 reverse AS=55
Abundance and read count statistics, fasta contigs
Taxonomy annotation, post-processing (confidence)
Final output

5. Properly paired reads Ref Seq1 5´ 5´
YES: InsertSize within upper and lower
boundaries determined by bwa
| InsertSize |
NO: Paired but mapped to different
ref sequence entries
Ref Seq2
Ref Seq1

6. Inferred InsertSize
All reads that are mapped to databases in ‘bestmode’ are used to make
an inferred insert size distribution (5’ to 5’ distance)

7. Annotation of fastq reads via MGmapper
Local alignment of fastq reads against reference sequences via bwa
Best alignment but not all aligments
Taxonomy annotation
Ref seq names have been assigned full taxonomy (.tax and .kch in )

8. MGmapper options command line version

9. databases.txt

10. MGmapper options command line version

11. MGmapper options command line version

12. Command
xqsub –V –d workDir –l nodes=1:ppn=8,mem=40gb,walltime=2:00:00 –de
MGmapper_PE.pl –c 8
–i /home/databases/metagenomics/fastq/China_2_500k_R1.fastq.gz
–j /home/databases/metagenomics/fastq/China_2_500k_R2.fastq.gz
–C 1,3,4,5,11 –F 6 –d outputDir -1 ‘touch yt.PE’
Abundance.databases.txt

13. Output files

14. Post-processing *.annot files

15. Output files

16. Numbers Strain abundance (paired-end)
Abundance (%) = 100*readCount/size*2
Strain abundance (Single-end)
Abundance (%) = 100*readCount/size
Abundancespecies (%)= S Abundancestrain
Covered_positions
Number of posistions in a ref seq that are observed at >= 1X
Coverage=covered_positions/size
Depth=nucleotides/size
ReadCountUniq= reads where AS > XS, where
AS is the alignment score and
XS is second best hit
Size = number of bp’s in reference sequence

17. Benchmark dataset: 11 bacterial species spiked in distilled water
15 classifications methods tested
No abundance threshold => many false positives

18. Abundance thresholds FW in-vitro dataset, <233bp> (table 3, species)
Peabody et al. BMC Bioinformatics (2015) 16:363

19. Abundance thresholds FW in-silicio dataset <250bp> (table 4, species)

20. Reliability What criteria should be used to extract reliable results?
refSeq db Size nucleotides reads reads_uniq nm
Name Bacteria

21. Exercise Run MGmapper_PE.pl against small air-toilet sample (China)
Learn to use MGmapper_classify.pl to extract reliable hits
Collapse hits between different databases
Collapse hits at different taxonomy levels
Abundance criteria
uniqReadCount fraction
misMatch fraction

22. Abundance thresholds FW in-vitro dataset, <233bp> (table 3)
MGmapper_SE
(uniq reads > 10)
Peabody et al. BMC Bioinformatics (2015) 16:363

23. Abundance thresholds FW in-silicio dataset <250bp> (table 4)
MGmapper_PE
(uniq reads > 10)
* Nocardioides sp. JS614 included by MGmapper_PE benchmark, but not in table

24. Fastq files A_L01_R1.fq A_L02_R1.fq A_L01_R2.fq A_L02_R2.fq
Options: -I listF –J listR
Options: –i A_L01_R1.fq –j A_L01_R2.fq

25. Trimming Raw Fastq Trim, adaptor removal Trimmed cutadapt
Cutadapt –f fastq –q 30 –m 30 –b “Illumina adaptors” infile > outfile
Infile.R1
Outfile.R1
=>
Outfile.R2
Option: -S : skip cutadapt [off]

26. Reads In Common Only in Pair-end mode
Trimmed
Fastq
Identify pairs
readsInCommon
Trimmed & paired
ReadsInCommon –i all.R1 –j all.R2 –a InCommon.R1 –b InCommon.R2
all.R1
=>
all.R2
InCommon.R1
InCommon.R2

27. Remove positive control reads
Fastq
ReadsIn
Common
Bwa mem
notPhix
bam
PhiX database
bwa mem –t cores phiXdb fastqF fastqR |
samtools view -f1 -f12 -Sb - | cat > notPhiX.bam
-f1 (read paired)
-f4 (read unmapped)
-f8 (mate unmapped)
PhiX: bacteriophage PhiX 174