Download 18. Introduction to Metagenomes

Advancing Science with DNA Sequence
Metagenome definitions:
a refresher course
Natalia Ivanova
MGM Workshop
September 12, 2012
Advancing Science with DNA Sequence
Metagenome definitions
Metagenome is a collective genome of microbial community, AKA
microbiome (native, enriched, sorted, etc.).
Metagenomic library (or libraries) is constructed from isolated DNA
(native, enriched, etc.).
Metagenomic library can be single-end (AKA standard)
or paired-end
Advancing Science with DNA Sequence
Metagenome definitions
Single-end (standard) metagenomic library will produce
contigs upon assembly (i. e. longer sequences based on
overlap between reads)
Any Ns found in contigs correspond to low quality bases
ATGCAAAGGCCGCATCCAGCAGGTT
TACGTTTCCGGCGTAGGTCGTCCAA
Paired-end metagenomic library will produce scaffolds upon
assembly (non-contigous joining of reads based on read
pair information)
Ns found in scaffolds correspond either to low quality bases or
to gaps of unknown size
ATGCAAAGGCCGCATCC
AGCAGGTT
NNNNNN
TACGTTTCCGGCGTAGG
TCGTCCAA
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Amplified and Unamplified
Libraries
Amplified Library
Unamplified Library
Fragmentation (1ug)
Fragmentation (1ug)
Double SPRI
End repair / Phosphorylation
End repair / Phosphorylation
SPRI Clean
Double SPRI
A-tailing with Klenow exoSPRI Clean
A-tailing with Klenow exo-
DNA Chip
Adaptor Ligation
Heat Inactivation
DNA Chip
Adaptor Ligation
SPRI Clean
PCR 10-cycle Amplification
SPRI Clean
qPCR Quantification
DNA Chip
SPRI Clean
qPCR Quantification
DNA Chip
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Metagenome definitions (contd):
Unless the community has very low complexity (i. e.
dominated by one or a few clonal populations),
assembly at 100% nucleotide identity will be very
fragmented.
overlap = alignment of reads at x% identity
What to do with k-mer based assemblies?
Use multiple k-mer settings, combine assemblies
with an overlap-layout consensus assembler like
minimus2 using minimal % identity of 95%.
Tradeoff between overlap length and % identity.
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Reasoning behind combining multiple
assemblies
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Trimming does not
appear to be ideal for
this process
Assembly
Pipeline v.0.9
CPU time intensive, no known
metagenomic Kmer
prediction algorithm
A snapshot of older (454Illumina) metagenome
assembly pipeline
Picking best kmer – manual proces
7
Advancing Science with DNA Sequence
Metagenome definitions (contd):
overlap = alignment of reads at x% identity
Assembly of sequences at less than 100% identity =>
population contigs and scaffolds representing a
consensus sequence of species population
isolate contig
species population
contigs
Advancing Science with DNA Sequence
2 more important definitions
1.
Sequence coverage (AKA read depth)
How many times each base has been sequenced => needs to
be considered when calculated protein family abundance
Per-contig average coverage
Per-base coverage => per-gene coverage
2. Bins
Scaffolds, contigs and unassembled reads can be binned into
sets of sequences (bins) that likely originated from the
same species population or a population from a broader
taxonomic lineages
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What IMG does and doesn’t do
• Scaffolds and contigs are generated by assembly – not
provided in IMG/M
• Sequence coverage can be computed by the
assembler based on alignments it generates
(preferable) or can be added later by aligning reads
to contigs – the latter can be provided in IMG/M
• Bins are generated by binning software – not
provided in IMG/M
• Scaffolds, contigs and unassembled reads are
annotated with non-coding RNAs, repeats (CRISPRs),
and protein coding genes (CDSs); the latter are
assigned to protein families (COGs, Pfams, TIGRfams,
KEGG Orthology, EC numbers, internal clusters) – is
provided in IMG/M
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What’s the difference between IMG and
MG-RAST, IMG and CAMERA?
• We prefer to assemble the data

longer sequences -> better quality of gene prediction and functional
annotation

longer sequences -> chromosomal context and binning -> population-level
analysis
• But we don’t provide assembly services except for metagenomes
sequenced at the JGI


we may be able to help with assembly of 454
we’re not equipped to assemble massive amounts of Illumina data
http://galaxy.jgi-psf.org
Contact person: Ed Kirton, [email protected]
• IMG does not provide tools for analysis of 16S data from the
metagenome itself



we do assembly -> assembled 16S sequences are generally not very reliable
BLASTn of reads matching conserved regions is misleading
we do pyrotags or i-tags for every metagenome sequenced at the JGI
http://pyrotagger.jgi-psf.org