Metagenome is the entire genetic information of microorganism at specific site/time. Analysis of metagenomic data could be achieved by two approaches; 1) amplicon (16s RNA gene) data analysis and whole genome metagenomics data analysis. Here we focus on 16S rRNA amplicon using Mothur Pipeline for analysis of metagenomics data.

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

4. Theoretical
Part I: Introduction

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

11. 3) Marine
Microbiome
Research

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

21. 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

41. Output file formats

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.

51. BIOM format
http://biom-format.org/

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

57. Make your account!
https://usegalaxy.org/

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

62. Workflows: extract workflow

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

74. https://www.mothur.org/wiki/Installation

75. Main steps

82. Let’s start!
1. Get Data

88. Functional Analysis
https://bpa-csiro-workshops.github.io/btp-manuals-md/modules/metagenomics-mo
dule-fda/fda/
http://motherbox.chemeng.ntua.gr/anastasia_dev/u/makis/w/copy-of-starting-from-
reads-1

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