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Tools for Metagenomics with 16S/ITS and Whole Genome Shotgun Sequences
The document discusses computational tools for metagenomics, focusing on various types of metagenomic studies including 16S rRNA surveys and whole genome shotgun sequencing. It highlights the challenges and advantages of these methods, such as data processing workflows, amplification biases, and the need for high coverage in sequencing. Further, it emphasizes the importance of planning informatics workflows and incorporating statistical analyses early in the research process.
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Tools for Metagenomics with 16S/ITS and Whole Genome Shotgun Sequences
- 1. Computational Tools for Metagenomics SuryaSaha Twitter: @SahaSurya / LinkedIn: www.linkedin.com/in/suryasaha/ Magdalen Lindeberg Plant Pathology & Plant-Microbe Biology Microbial Friends & Foes, Sep 25, 2012
- 2. Temperton, Current Opinionin Microbiology, 2012 Impact of Technology on Metagenomics
- 3. Types of “Meta”genomics 16S rRNA survey of bacterial microbiome ITS survey of fungal microbiome Bellemain, BMC Microbiology 2010Slide: Julien Tremblay, JGI
- 4. Types of “Meta”genomics Whole genome shotgun • Varying complexity of microbial communities • High coverage sequencing • Sophisticated informatics • Host associated metagenomes – Deep sequencing of host meta-genome – Bioinformatic screening of host sequences • Environmental metagenomes – Eg. Soil samples – Requires very high depth of coverage – Complicated to assemble
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- 7. Big picture!! What userssee What users want!!
- 8. 16S/ITS community surveys •Multiple target regions in 16S gene and ITS region • Comparison of results requires amplification of same region • Advantages – Fast survey of large communities – Mature set of tools and statistics for analysis – Good for first round survey • 454 16S tags or pyrotags (~ 700 bp) have been the preferred method • Illumina Miseq (2x150bp, 2x250 bp) are the next workhorses • Depth of sampling – 2-6000 reads/sample for simple communities – 20000 reads /sample for complex soil metagenomes
- 9. 16S/ITS issues • Lackof tools for processing ITS/Fungal microbiome data sets – RDP classifier targets only ITS – No ITS reconstruction tools • Amplification bias effects accuracy and replication • Use of short reads prevents disambiguation of similar strains • 16S or ITS may not differentiate between similar strains – Clustering is done at 97% – Regions may be >99% similar • Sequencing error inflates number of OTUs • Chloroplast 16S sequences can get amplified in plant metagenomes
- 10. 16S/ITS sequence processingworkflow Filter for contaminants and low quality reads Assemble overlapping reads Reduce datasets (clustering) Perform taxonomic classification and compute diversity metrics
- 11. 16S/ITS sequence processingworkflow Filter for contaminants and low quality reads Assemble overlapping reads Reduce datasets (clustering) Perform taxonomic classification and compute diversity metrics • Quality plots and read trimming – FastQC http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ – FASTX http://hannonlab.cshl.edu/fastx_toolkit/ • Chimera removal – AmpliconNoise http://code.google.com/p/ampliconnoise/ – UCHIME http://www.drive5.com/uchime/
- 12. Impact of SequenceLength Slide: Feng Chen, JGI
- 13. 16S/ITS sequence processingworkflow Filter for contaminants and low quality reads Assemble overlapping reads Reduce datasets (clustering) Perform taxonomic classification and compute diversity metrics • Merge overlapping paired end reads – FLASH http://www.genomics.jhu.edu/software/FLASH/index.shtml – FastqJoin http://code.google.com/p/ea-utils/wiki/FastqJoin – CD-HIT read-linker http://weizhong-lab.ucsd.edu/cd-hit/wiki/doku.php?id=cd-hit- auxtools-manual
- 14. 16S/ITS sequence processingworkflow Filter for contaminants and low quality reads Assemble overlapping reads Reduce datasets (clustering) Perform taxonomic classification and compute diversity metrics • Clustering with high stringency – UCLUST/USEARCH (16S only) http://www.drive5.com/usearch/ – CD-HIT-OTU (16S only) http://weizhong-lab.ucsd.edu/cd-hit-otu/ – phylOTU (16S only) https://github.com/sharpton/PhylOTU
- 15. 16S/ITS sequence processingworkflow Filter for contaminants and low quality reads Assemble overlapping reads Reduce datasets (clustering) Perform taxonomic classification and compute diversity metrics • Composition based classifiers – RDP database + classifier http://rdp.cme.msu.edu/classifier/classifier.jsp • Homology based classifiers – ARB + Silva database (16S only) http://www.arb-home.de/ – GreenGenes database (16S only) http://greengenes.lbl.gov/cgi-bin/nph-index.cgi – UNITE database (ITS only) http://unite.ut.ee/ – FungalITSPipeline (ITS only) http://www.emerencia.org/fungalitspipeline.html
- 16. • http://www.qiime.org/ • Comprehensivesuite of tools – OTU picking – Taxonomic classification – Construction of phylogenetic trees – Visualization – Compute diversity statistics • Available as Amazon EC2 image
- 17. Whole Genome Shotgun(WGS) Metagenomics • Better classification with Increasing number of complete genomes • Focus on whole genome based phylogeny (whole genome phylotyping) • Advantages – No amplification bias like in 16S/ITS • Issues – Poor sampling of fungal diversity – Assembly of metagenomes is complicated due to uneven coverage – Requires high depth of coverage
- 18. WGS sequence processingworkflow Filter for low quality reads Assemble reads Perform taxonomic classification and compute diversity metrics
- 19. WGS sequence processingworkflow Filter for low quality reads Assemble reads Perform taxonomic classification and compute diversity metrics • Quality plots and read trimming – FastQC http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ – FASTX http://hannonlab.cshl.edu/fastx_toolkit/
- 20. WGS sequence processingworkflow Filter for low quality reads Assemble reads Perform taxonomic classification and compute diversity metrics • NGS assembly with uneven depth – IDBA-UD http://i.cs.hku.hk/~alse/hkubrg/projects/idba_ud/ – MIRA http://www.chevreux.org/projects_mira.html – Velvet / MetaVelvet http://www.ebi.ac.uk/~zerbino/velvet/ http://metavelvet.dna.bio.keio.ac.jp/
- 21. WGS sequence processingworkflow Filter for low quality reads Assemble reads Perform taxonomic classification and compute diversity metrics • Hybrid composition/homology based classifiers – FCP http://kiwi.cs.dal.ca/Software/FCP – Phymm/PhymmBL http://www.cbcb.umd.edu/software/phymm/ – AMPHORA2 http://wolbachia.biology.virginia.edu/WuLab/Software.html – NBC http://nbc.ece.drexel.edu/ – MEGAN http://ab.inf.uni-tuebingen.de/software/megan/
- 22. WGS sequence processingworkflow Filter for low quality reads Assemble reads Perform taxonomic classification and compute diversity metrics • Web based classifiers – MG-RAST http://metagenomics.anl.gov/ – CAMERA http://camera.calit2.net/ – IMG/M http://img.jgi.doe.gov/cgi-bin/m/main.cgi
- 23. MetaPhAln • Unique clade-specificmarkers for sequenced bacteria and archaea • 400 genuses/4000 genomes including HMP genomes • Species level resolution • MetaPhAln 2 in the works – Eukaryotes including Fungi – Viruses – Higher coverage of archaea • Krona and GraphAln for visualization of output • Websites – https://bitbucket.org/nsegata/metaphlan – http://huttenhower.sph.harvard.edu/metaphlan
- 24. PhyloSift/pplacer • Reference databaseof marker genes • Places reads on tree of life based on homology to reference protein • Integration with metAMOS for pre-assembling next- generation datasets • Bacterial and Archaeal classification only • Plant and Fungi marker genes are being added • Websites – http://phylosift.wordpress.com/ – https://github.com/gjospin/PhyloSift
- 25. Real cost ofSequencing!! Sboner, Genome Biology, 2011
- 26. Acknowledgements Funding Magdalen Lindeberg Cornell University DaveSchneider USDA-ARS, Ithaca Citrus greening / Wolbachia (wACP)
- 27. Thank you! Surya Sahass2489@cornell.edu Suggestions • Plan informatics workflow as early as possible • Incorporate statistics at different stages in the workflow