Advancing Science with DNA Sequence Metagenome analysis Natalia Ivanova MGM Workshop February 2,...
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Transcript of Advancing Science with DNA Sequence Metagenome analysis Natalia Ivanova MGM Workshop February 2,...
Advancing Science with DNA Sequence
Metagenome analysis
Natalia IvanovaNatalia Ivanova
MGM WorkshopMGM Workshop
February 2, 2012February 2, 2012
Advancing Science with DNA Sequence
1. Metagenome definitions:1. Metagenome definitions:
a refresher course a refresher course
Advancing Science with DNA Sequence
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
Metagenome definitions
Advancing Science with DNA Sequence
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
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
ATGCAAAGGCCGCATCCAGCAGGTT
TACGTTTCCGGCGTAGGTCGTCCAA
ATGCAAAGGCCGCATCC
TACGTTTCCGGCGTAGG
AGCAGGTT
TCGTCCAANNNNNN
Metagenome definitions
Advancing Science with DNA SequenceAmplified and Unamplified
Libraries
Fragmentation (1ug)
A-tailing with Klenow exo-
End repair / Phosphorylation
DNA ChipHeat Inactivation
Double SPRI
Fragmentation (1ug)
A-tailing with Klenow exo-
Adaptor Ligation
End repair / Phosphorylation
DNA Chip
Double SPRI
SPRI Clean
SPRI Clean
SPRI Clean
PCR 10-cycle Amplification
Amplified Library Unamplified Library
Adaptor Ligation
DNA Chip
qPCR Quantification
SPRI Clean DNA Chip
qPCR Quantification
SPRI Clean
Advancing Science with DNA Sequence
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.
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.
Metagenome definitions (contd):
overlap = alignment of reads at x% identity
Advancing Science with DNA Sequence
Reasoning behind combining multiple assemblies
Advancing Science with DNA Sequence
Assembly Pipeline v.0.9
Trimming does not appear to be ideal for this process
Picking best kmer – manual process
CPU time intensive, no known metagenomic Kmer prediction algorithm
8
A snapshot of older (454-Illumina) metagenome assembly pipeline
Advancing Science with DNA Sequence
Assembly of sequences at less than 100% identity =>
population contigs and scaffolds representing a consensus sequence of species populationisolate contig species population
contigs
Metagenome definitions (contd):
overlap = alignment of reads at x% identity
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 coveragePer-base coverage => per-gene coverage2. 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
Advancing Science with DNA Sequence
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
Advancing Science with DNA Sequence
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.orgContact person: Ed Kirton, [email protected]
• IMG does not provide tools for analysis of 16S data from the metagenome itself
we do assembly -> none of assembled 16S sequences is reliable BLASTn of reads matching conserved regions is misleading we do pyrotags for every metagenome sequenced at the JGI
http://pyrotagger.jgi-psf.org
Advancing Science with DNA Sequence
2. IMG/M features:2. IMG/M features:divide and conquer divide and conquer
(see also IMG/M -> Using IMG/M -> Using IMG/M -> IMG (see also IMG/M -> Using IMG/M -> Using IMG/M -> IMG User Guide and IMG/M Addendum)User Guide and IMG/M Addendum)
http://img.jgi.doe.gov/m
http://img.jgi.doe.gov/merusername: publicusername: publicpassword: publicpassword: public
Advancing Science with DNA SequenceIMG/M User Interface MapAbout IMG/M -> Using IMG/M -> User
Interface Map
Advancing Science with DNA Sequence
Dividing the contigs by GC content or length
• StatisticsMicrobiome Details ->
Genome Statistics -> DNA Scaffolds
• SearchMicrobiome Details ->
Scaffold Search
Advancing Science with DNA SequenceDividing the genes phylogenetically:
Phylogenetic DistributionPhylogenetic Distribution of Genes
Microbiome Details -> Phylogenetic Distribution of Genes
Components: histograms Protein Recruitment Plots summary statistics tables lists of genes
histogram(phylum/
class)
gene counts
gene lists
summary statistics
histogram
(family)
histogram
(species)
counts, lists, statistics
counts, lists
recruitment plots
Advancing Science with DNA Sequence
Dividing the contigs: Scaffold Cart
• Lists of contigs or genes in Gene Cart
E. g. Microbiome Details -> Genome Statistics -> DNA Scaffolds -> scaffold counts
Scaffold CartFeatures: Scaffold Export Adding all genes to Gene
Cart Function Profile (against
functions in Function Cart) Histograms by GC content,
length and gene count Phylogenetic Distribution
Advancing Science with DNA Sequence
All Carts in IMG are interconnected
Gene Cart
Scaffold Cart
Function Cart
Advancing Science with DNA Sequence
Dividing the genes by abundance/ by function
• Abundance ProfilesCompare Genomes -> Abundance Profiles Tools
Components:
Common parameters: Normalization (none/scale for size) Type of count (raw counts/estimated gene copies) Type of protein family (COG, Pfam, Enzyme, TIGRfam)
Advancing Science with DNA Sequence
Other tools
• Phylogenetic Marker COGsFind Functions -> Phylogenetic Marker COGs
• SNP BLAST and SNP VistaGene Page -> SNP BLAST -> SNP VISTA
IMG/M exercises:http://genomebiology.jgi-psf.org/Content/MGM-11.Feb2012/agenda.html
The first 3 pages are questions without answers; the rest is a cheat sheet
Advancing Science with DNA Sequence
Life outside IMG: binning tools
Alignment-based tools• MEGAN – BLAST+LCA
http://www-ab.informatik.uni-tuebingen.de/software/megan• MTR – BLAST+ MTR
http://cs.ru.nl/gori/software/MTR.tar.gz• SOrt-ITEMS – processed BLAST best hit
http://metagenomics.atc.tcs.com/binning/SOrt-ITEMS• CARMA and Web-CARMA – MSA + neighbor-joining tree
http://webcarma.cebitec.uni-bielefeld.deCompositional tools• PhyloPythia – 6-mers, SVM
http://cbcsrv.watson.ibm.com/phylopythia.html• TACOA – 2-6 mers, k-nearest neighbor classifier
http://www.cebitec.uni-bielefeld.de/brf/tacoa/tacoa.html• Phymm and PhymmBL – Interpolated Markov models (IMMs)
http://www.cbcb.umd.edu/software/phymm/• ClaMS – DOR, DBC
http://clams.jgi-psf.org
Advancing Science with DNA Sequence
Life outside IMG: statistical analysis tools
Comparison of 2 samples• MEGAN - http://www-ab.informatik.uni-tuebingen.de/software/megan
• STAMP - http://kiwi.cs.dal.ca/Software/STAMP
Comparison of sets of samples• ShotgunFunctionalizeR – R package for statistical
analysis - http://shotgun.zool.gu.se
• METAREP – package from JCVI, includes multidimensional scaling, hierarchical clustering, etc - http://www.jcvi.org/metarep
• METASTATS – package for analysis of paired samples with replicates - http://metastats.cbcb.umd.edu/
• LEfSE – package for comparison of multiple classes of samples with replicates - http://huttenhower.sph.harvard.edu/lefse/