introduction to metagenomics
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An introduction to metagenomics with a focus on some of the latest methods, but also on some user friendly methods.
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introduction to metagenomics
- 1. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Introduction to metagenomics Thomas Haverkamp Thhaverk@ibv.uio.no Twitter: @Thomieh Thhaverk@ibv.uio.no
- 2. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Overview • Introduction • Sequence classification for metagenomes • Megan in brief • Oilwell metagenome • The exercise… 2
- 3. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA The bacterial tree of life Lasken & McLean., Nature Rev. Genetics, 2014
- 4. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Ultra-small bacteria 4 OP11, DO1 was detected using metagenomic analysis of 0.2 μm filtered water. The genome size is < 1 Mbp Bacteria rely on other community members For basic resources.
- 5. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Metagenomics Metagenome: the collective genome of all the microorganisms in an environment. (Handelsman et al., 1998) Metagenomics is the study of genetic material recovered from an environmental sample. 5
- 6. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Metagenomics 6 Who is there? What are they doing?
- 7. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Methods for microbial communities Amplicon based analysis • SSU rRNA (e.g. 16S, ITS) • protein coding genes: rpoB, nifH, IRS, cytC, … fungene.cme.msu.edu Microarrays – requires knowledge of the community in advance. • PhyloChip (taxonomic) • Geochip (metabolic) Shotgun sequencing – complete community analysis. 7
- 8. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Shot gun Metagenomics 8Venter et al., 2004 1.2 Million unknown genes
- 9. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA High throughput sequencing 9Source: https://flxlexblog.wordpress.com/ Newest Illumina HiSeq X 10 > 1 Tb of sequene data
- 13. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Sequence Classification Sequence classification (binning) is the process of separating sequence data using specific information creating bins Sequence classification by: 1) sequence composition Tetranucleotide frequency (kmer counting) Clustering of reads. (e.g. swarm, cd-hit) Sequence (co-) assembly (MetaHit, Metavelvet) Differential coverage of contigs (GroopM, Concoct) Advantage : read with unknown origin can be classified into a bin Disadvantage: impossible to determine taxonomy or function of the reads. 13
- 14. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GroopM workflow 14Imelfort et al., PeerJ, 2014
- 15. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Binning of synthetic contigs 15Imelfort et al., PeerJ, 2014 PCA - Tetranucleotide binning GroopM coverage binning Input data: 1159 genomes
- 16. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Sequence Classification Sequence classification (binning) is the process of separating sequence data using specific information creating bins Sequence classification by: 2) sequence similarity Compare sequences to reference database (e.g. Blast, bwa, bowtie) Use phylogenetics to classify sequences. Advantage: One can determine taxonomy and function of reads. Disadvantage: reads with no similarity to databases sequences, can not be classified. 16
- 17. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA 17 Using the best blast hit Blog of Nick Loman: http://nickloman.github.io/
- 18. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Sequence Classification Nucleotide composition: CompostBin , PCA-analysis of k-mer frequencies, Self-Organizing Maps (different variants), MetaCluster, PhyloPythia, Naïve Bayes classifier (NBC), etc Sequence similarity: MEGAN*, SorT-Items, Threephyler, COMET, Metaphlan, PhyloSift, Kraken, etc Both: Phymm / PhymmBL, Phylophytia, RAIphy, Metaxa2*(rRNA), PhyloOTU (rRNA), MLTreeMap, RITA, STAMP, WGSQuikr. Differential Coverage: GroopM, Concoct, Blobology 18See also: Logares et al., 2012 / * In this course
- 19. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA MetaPhlAn vs Phylosift 19 MetaPhlAn: Metagenomic Phylogenetic Analysis Uses a database of taxon specific marker genes Works well with known ecosystems: e.g. gut communities Phylosift: Uses a database of 37 universal proteins & rRNA genes. Designed to classify using phylogenies Both databases are smaller than NCBI NR Depending on your ecosystem, one will work better
- 22. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA MEGAN 22(Huson et al., Genome Research, 2007) • Developed for characterization of metagenomic shotgun reads • LCA assignment based on BLAST bitscore • Support for paired-end reads and comparison of datasets. • Latest version can analyze RDP files / QIIME OTU files • Analysis of metabolism via SEED, KEGG or COG maps • Comparison of multiple metagenomes (> 2)
- 23. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Why use Megan? Easy to work with on a desktop / laptop computer: Extra things needed: Java, a BLAST server MEGAN gives a visualization of BLAST results • Study diversity • Compare samples • Contamination filtering • Special gene of interest • Extraction of sequences based on taxonomic /metabolic information. 23
- 24. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA The basics of MEGAN MEGAN uses BLAST, a database and a taxonomy file • BLAST N : nucleotides against a nucleotide database. • BLAST X : Translated nucleotide against a protein database. • Which database? one of the many available database like the NCBI-non- redundant database, or a your own custom database. • Taxonomy: NCBI taxonomy, or your own custom taxonomy BLAST output file is used to bin sequences using the LCA assignment algorithm into specific taxons. 24
- 25. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA The basics of MEGAN • The LCA algorithm = “Lowest Common Ancestor” algorithm “In this approach, every read is assigned to some taxon. If the read aligns very specifically only to a single taxon, then it is assigned to that taxon. The less specifically a read hits taxa, the higher up in the taxonomy it is placed. Reads that hit ubiquitously may even be assigned to the root node of the NCBI taxonomy.” (the MEGAN manual) 25
- 26. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA 26 multiple samples Comparison between reads assigned to Phosphorus metabolism and Nitrogen metabolism Reads were annotated using MG-RAST
- 27. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Metagenomics servers • MG-RAST* (http://metagenomics.anl.gov/) • IMG/M (http://img.jgi.doe.gov/) • WebMGA (http://weizhong-lab.ucsd.edu/metagenomic-analysis/) • METAgen assist* (http://www.metagenassist.ca/METAGENassist/faces/Home.jsp) • Real-Time metagenomics (https://edwards.sdsu.edu/RTMg/) *Can also be used for amplicon sequences 27
- 29. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Sampling an oil field Sampling site Pressure 27.3 bar 0 m Reservoir sediment Temperature 85C Pressure 253 bar Seal sediment 2950 m 2850 m Subsea sediments 350 m Xpand Pressure Flask
- 30. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA The metagenome data 454 sequencing: - raw reads: 702 607 (492 bp) - clean reads: 362 562 (415 bp) Newbler assembly - Assembled contigs: 13 400 (longest 50 Kbp) 94% of reads assembled. 30Kotlar et al., 2011
- 32. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Taxonomy and metabolism alpha-Proteobacteria gamma-Proteobacteria delta/epsilon-Proteobacteria unclassified Proteobacteria Firmicutes Thermotogae Synergistaceae other/unclassified Bacteria Methanococcales Thermococcales other/unclassified Archaea Eukaryota not assigned no hits sulfur-reducing bacteria methanogens others a b Figure 2 groups with more than 2000 reads assigned -Delta/epsilon-Proteobacteria -Methanococcales -unclassified bacteria Based on known metabolism annotations -Sulfur reducing bacteria -Methanogenes -others Kotlar et al., 2011
- 33. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA 33 > 50 % < 50 % MEGAN classifications Major taxa are separated by different GC content Adaptation to environment Kotlar et al., 2011
- 35. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA a b c Figure S7 Missing Crispr genes anti-viral defence no virusses? Genome comparisons Kotlar et al., 2011
- 36. ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA GACTG CTGACT CTGAC ACTGA Testing an metagenome assembly -1 0 1 2 3 4 5 6 7 8 30 40 50 60 70 80 90 temperature (°C) relativeactivity pNTA1 pGS-21a Figure S6 Blue: Pelobacter carbinolicus enolase Green: E.coli enolase Expression of both proteins in E.coli The Pelobacter enolase is less temperature sensitive Kotlar et al., 2011
Hinweis der Redaktion
- I will start of with the bacterial tree of life taken from the publication Of Lasken and McLean. This phylogeny contains all detected bacterial phyla that we know off. What is striking in this figure is the large amount of red. These are all the phyla that are only known from sequence data. In the paper where this phylogeny was shown, they isolated single cells from several of these “unknown” taxa. This shows us that an improtant part of bacterial diversity in the environment is not known. The same goes as well for the domains with microbial Eukaryota and the Archaea. All the major branches of life contain complete phyla that have not been cultivated. Part of this is because they do not Kill us, but part of it is also because we fail te recognize how to look for them. There are several groups that have recently been shown to be more interesting that we expected. For instance the OD1 and OP11 phyla.
- In the paper from Luef at al, they did something most of would not do. 0.2 um Filtration is in the hospital used to keep fluids sterile. Here they filtered groundwater to which acetate was added, through a 0.2 um filter and collected the cells on a 0.1 um filter. They obtained DNA and that was used for amplicon 16S sequencing and shot gun sequencing or metagenomics. The main groups of bacteria that were identified belonged to the taxa OP11 and OD1. The metagenomic analysis indicated that the bacteria have a limited metabolic potential and they are probably relying on other bacteria to obtain basic resources /compounds for their metabolism. So this shows that metagenomics can be really powerful when it comes to understanding diversity. But what is metagenomics exactly?
- The term metagenome was first coined by Handelsman in 1998. The metagenome is the collective genome of all the microorganisms in an environment. Then metagenomics, is the study of genetic material recovered from an environmental sample. With this technique we can investigate and compare microbial communities. The techinque that is used here is shot-gun sequencing, not amplicon sequencing of a target gene.
- The two main questions in metagenomics are: Who is there? What are they doing? These questions are about the potentail of a communities. With DNA we only get to see what a community is capable off, but we do not see the actual processes that are active, for that we need to add metatranscriptomics and metaproteomics. Nonetheless,metagenomics can help us with a way of generating hyptothesis, and even testing hypothesis on different communities. To understand microbial communities, we first want to understand who is there. This will give us unbaised information on the diversity and the complexity of the ecosystem. Most people use amplicon sequencing for this, but with the current sequencers present, we can do this on raw shot gun data, or an assembled data. The second question is often even more interesting, but is also a lot harder to do and understand. You need to get yourself familiar with biochemistry and biogeochemistry. Now we know what metagenomics is, but how did the field of metagenomics start.
- To understand communitieis we can use a lot of different techniques. We have seen during the course so far, Amplicon sequencing of ssu rRNA, or ITS. But proteins coding genes can be used as well. Another apporach which is quick is the use of special microarrays. For instance the geochip. That microarray was used to understand microbial metabolism changes in the Deepwater Horizon accident in the gulf of mexico. But the most interesting method is Shot-gun sequecing. So why is this technique interesting.
- Shot gun metagenomics did not start with this paper, but this was the paper that really put shot gun sequencing of the metagenome on the map. The data for this paper was created using sanger sequencing. And what they did, was filter 200 Liters of seawater from the sargasso sea, which is very ologothrophic with very little boimass. Than they sequenced the extracted DNA with Sanger sequedcing. It showed that most of the genes were unknown, and it double the ncbi NR database is one go. Now we do not use sanger sequence anylonger for metagenomics, but any of the other platforms.
- As Gregor already explained there are quite a lot of platforms that are used for high throughput sequenceing. This is reminder of the different platforms. And just see what has changed in the 12 years since Craig venter published his metagenome of the sargasso sea sample. Now the throughput of the machines has become pretty extensive and a lot more people have joined the field using either amplicon sequencing or shot gun sequencing. All these people produce tons of data. Which is also showoing when you start look at the databases.
- For instance MG-RAST, is a database for doing metagenomic analysis. We can play with it tomorrow afternoon for those who like that. When I made this slide in 2012 there were already 74.462 datasets present in MG-rast 23 TBp. Which was already quite a good load of sequence data.
- Three years later, there are of 160.000 datasets in MG-rast and the number of basepairs has gone up to almost 70 Tb. It is not a dramatic increase Sadly though, most of these these are not all shot gun dataset, mostly it is amplicon datasets that are analysed. And each dataset is one sample. But still, This is quite an impressive amount of data. So how does a typical shot gun sequences workflow look like?
- Here we compare two of the main sequencing methods in microbial ecology. Amplicon sequencing and Shot gun sequencing, or whole sample sequencing. With amplicon sequencing you are either doing a taxonomic assesment of the communities or a functional diversity study of on or more protein coding genes. With whole shot gun sequencing. We start of with raw reads, that can be used directly for taxonomic and functional profiling, or we can use assembly to make larger sequences or contigs. With contigs we have the advantage that we can find complete open reading frames for proteins, while that is not the case for single reads. Any idea what the average nucleotide length is of a typical bacterial gene? It is a 1000 basepairs. So a read might only have a small part of it. Both methods needs sequence classification for community profiling to asnwer who is there. With shot gun sequencing we add the functional profiling as well. When we have contigs or reads we need to annotate those. And we can do that by sequence classification and subsequent annotation of those sequences. So how does that work?
- Basically, Sequencing classification is the process of separating sequence data using specific information. We create bins In theory there are two methods we can use to do sequecing classification. The first one is classification using sequence composition. There are several different methods for metagenomic analysis using sequence composition. We can look at the nucleotides frequencies, and especially at stretches of sequence for instance tetra nucleotides. Than there is clustering of reads, and even assembly of reads. The last method is differentia coverage of contigs. This method appeared in 2013 and this is using differences between samples to bin contigs derived from a sequence pool of all samples. One of the methods is groopM which I will explain a bit more.
- GroopM needs one assembly. This assembly can be generated with a metagenomic assembler, or just a normal whole genome assembler. The catch is we pool the reads of all samples in one big assembly run, and generated a big set of contigs. After assembly, we then map for each sample the reads to the assembly and determine the coverage of each contig per sample. This information is than used to create a a set if high confidence bin cores of long 1Kbp contigs. This dataset is than screened for contamination or completeness using a reference dataset with 111 marker genes. This can be a step for manual curation. And finally small contigs are then recruited to the core bin. So at the end we end up with fasta files for each bin. So to give you an impression of the GroopM method we can take a look at the analysis of a large synthethic metagenome
- In the GroopM paper they created a synthetic shotgun metagenome using 1159 genomes for reference and gave them real abundances based on a OTU table from a soil amplicon study. After creating the shotgun data they assembled the data and got lots of contigs. On the left you see what happens when you start by binning all these contigs using tetranucleotides On the right is the binnen after GroopM binning has been applied. Size of the circle is proportional to the length of the contig.