16S rRNA Analysis using Mothur Pipeline
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The document provides a comprehensive guide on 16S rRNA analysis using the Mothur pipeline, emphasizing the importance of microbiome studies in healthcare, environmental, and marine research. It covers both theoretical and practical aspects, including analysis pipelines, file formats, and the use of Galaxy as a web-based platform for data analysis. Additionally, it explains the differences between amplicon and shotgun metagenomics, highlighting various bioinformatics tools and workflows for effective data processing.
16S rRNA Analysis using Mothur Pipeline
- 1. 16S rRNA analysis using Mothur pipeline Eman Abdelrazik Bioinformatics Research Assistant, Center of Informatics Science, Nile University H3ABioNet Teaching Assistant
- 2. Before we start! ● Slides reproduced from Galaxy tutorials & H3ABioNet tutorials ● For Questions: https://bit.ly/2N4mlv2 ● Make sure you have a Galaxy account: https://usegalaxy.org.au/ https://usegalaxy.org
- 3. Our Journey ^^ 1) Theoretical: a) Introduction b) Analysis pipelines 2) Practical a) File formats b) Introduction to Galaxy c) Mothur workflow d) Let’s do it together e) Do it by yourself
- 5. What is the difference? ● Microbiome: Entire set of microorganism at given site, and time ● Metagenome: entire genetic information of microorganism at specific site/time ● Meta-Transcriptome ● Meta-Proteome
- 6. Why to study microbiome? 1) Health Care research ● Humans are full of microorganisms ● Skin, gut, oral cavity, nasal cavity, eyes, .. ● Affects health, drug efficacy, etc referred to as your second genome ● ~10 times more cells than you ● ~100 times more genes than you ● ~1000s different species
- 7. Cesarean Vs. Vaginal Delivery
- 9. 2) Environmental Research 2) Environmental Research ● Microbes in the soil affect plants and animals ● improve agriculture
- 12. Global Ocean Sampling Expedition Ocean Exploration Genome Project (Pacific Ocean)
- 13. Shotgun vs. Amplicon! ● Sequence only specific gene ● No functional information ● Less complex to analyse ● Cheaper ● Sequence all DNA ● More information ● Higher complexity ● Higher cost
- 14. Amplicon (16S rRNA gene) ● Targeted approach (e.g. 16S/18S rRNA gene) ● Amplifies bacteria, not host, or environmental fungi, plants. ● Present in all living organisms (viruses?!) 16S rRNA Secondary Structure
- 15. Amplicon
- 16. Amplicon With variable regions: distinguish between genus ● Pros ○ Well-established ○ Inexpensive ● Cons ○ V-region choice can bias results ○ Is based on a very well conserved gene, making it hard to resolve species and strains
- 17. Shotgun metagenomics Aims to sequence the "whole" metagenome ● Pros: ○ Not biased by amplicon primer set ○ Not limited by conservation of the amplicon ○ Can also provide functional information ● Cons: ○ Environmental contamination, including host ○ More expensive ○ Complex data analysis ○ Requires high performance computing, high memory.
- 18. What sequencing technologies offer for metagenomics!
- 19. Bioinformatics
- 20. Theoretical Part II: Analysis Pipelines
- 22. 1. Pre-processing ● There are a lot of ways to filter and trim your data ● Trade-off between quality and amount of information retained
- 23. Quality Control: Phred Score
- 25. 2. Chimera Removal ● During PCR multiple sequences can combine to form a hybrid ● Must be removed from your data for better results
- 26. 3. OTU Clustering ● Operational Taxonomic Unit: a cluster of similar sequences, represented by a single consensus sequence ~ one species. ● OTU clusters are defined by a 97% identity threshold of the 16S gene sequence variants at genus level. 98% or 99% identity is suggested for species separation.
- 28. Search marker database and taxonomy assignment
- 29. Alignment
- 30. Results: Visualizations 1. Krona ● interactive exploration of sample taxonomy / per-sample groups ● Illustrate abundance
- 31. Results: Visualizations 2. Phinch ● explore the community structure ● BIOM file input ● various visualizations ● multi-sample data
- 32. Diversity
- 33. Pipelines: 1. QIIME Quantitative Insight Into Microbial Ecology
- 34. QIIME.2 -plugins
- 35. Pipelines: 2. DADA2 DADA2 stands for - Divisive Amplicon Denoising Algorithm
- 36. Other available pipelines ● UPARSE: http://www.drive5.com/uparse ● IM Tornado: https://github.com/pjeraldo/imtornado2 ● FROGS: https://github.com/geraldinepascal/FROGS ● VSEARCH: https://github.com/torognes/vsearch
- 37. Summary
- 39. Practical Part I: File Formats
- 40. Important terms
- 42. FASTA Format
- 45. Sequence Alignment/Map Format (SAM)
- 46. Header
- 47. Records
- 48. CIGAR (Compact Idiosyncratic Gapped Alignment Report) strings. The CIGAR string is the result of the sequence alignments, defining the sequence of matches/mismatches and deletions (or gaps) compared to the reference sequence. CIGAR strings, together with the allele sequences, are used to generate a visualization of the loci alignment. https://samtools.github.io/hts-specs/SAMv1.pdf
- 49. SAM vs. BAM ● Binary format ● Better than fastq file in data storage especially from different samples as it adds extra annotation to reads (where they come from?) uBAM
- 50. BIOM format ● The Biological Observation Matrix (BIOM) format ● a general-use format for representing biological sample by observation contingency tables ● command line interface (CLI) for working with BIOM files, including converting between file formats, adding metadata to BIOM files, and summarizing BIOM files.
- 52. Practical Part II: Introduction to Galaxy
- 53. Before we start!
- 54. What is Galaxy? ● Web-based platform for biological data analysis. ● Command-line tools >> wrapped >> Galaxy ● Retain histories of analysis: re-run and share.
- 55. Galaxy Servers
- 56. Courses in Higher Education that use Galaxy
- 58. Galaxy Interface Center Panel Tools Panel History Panel
- 59. Dataset status job is completed job is executing job is queued job is paused job has failed
- 60. 1. Refresh history 2. View file 1 2 3 3. History setting
- 61. How to get Data? ● The maximum size limit is 50G (uncompressed). ● Most individual file compression formats are supported, but multi-file archives are not (.tar, .zip). ENA ID: PRJEP5480
- 63. Your workflow
- 66. Edit workflow
- 67. Import workflow
- 68. Practical Part III: Mothur workflow https://mothur.org/
- 69. Mothur ● A collection of tools combined together. ● Mothur project, initiated by Dr. Patrick Schloss, at The University of Michigan. ● most cited bioinformatics tool for analyzing 16S rRNA gene sequences. ● process data generated by Sanger, PacBio, IonTorrent, 454, and Illumina (MiSeq/HiSeq).
- 70. Mothur wiki
- 71. Manual
- 72. File types
- 73. Download: Latest version “1.43.0” https://github.com/mothur/mothur/releases/tag/v.1.43.0
- 75. Main steps
- 82. Let’s start! 1. Get Data
- 89. Wrap up!
- 90. Resources ● Soil Tutorial: https://galaxyproject.github.io/training-material/topics/metagenomics/tutorials/general-tutorial/tutorial.html ● Gut Tutorial: https://galaxyproject.github.io/training-material/topics/metagenomics/tutorials/mothur-miseq-sop-short/tutoria l.html ● https://galaxyproject.github.io/training-material/topics/metagenomics/ ● https://galaxyproject.github.io/training-material/topics/introduction/ ● https://moin.galaxyproject.org/Support#Dataset_status_and_how_jobs_execute ● https://docs.qiime2.org/2019.7/tutorials/ ● https://benjjneb.github.io/dada2/tutorial.html ● https://www.coursera.org/learn/galaxy-project