MK20200117

• Misunderstanding of technical term
  • Metagenome != microbiome analysis by targeted amplicon sequencing (amplicon analysis of rRNA gene; meta16S/18S; community analysis)
  • Metagenome = ‘meta’ + ‘genome’; Genome = ‘gen’ + ‘ome’
  • Amplicon of rRNA gene = part of single gene, not genome!
  • Amplicon of rRNA gene of environment = ‘meta’ + rRNA gene, not 'meta' + 'genome'
• We need to improve basic knowledge of students about molecular biology and gene analysis (before genome analysis).
  • Course of basics of gene and genome
  • Course of basics of web tools
• It is appropriate to held the same couse “Introduction to Microbial Genomic and Metagenomic Analyses” annually, to update knowledge of genomics and metagenomics
  • This type of comprehensive course is more desirable than more specfic course of microbiome analysis, which is directly-related to their own research.
    • During this course for genomics/metagenomics, participants may have learned from more global aspects, and still they can apply what they learned to their amplicon studies.
  • In depth course might be held in future (but maybe such situation will not happen soon…)
    • Course of “Microbial Genomic Analyses” (note that without “Introduction of”)
    • Course of “Metagenomic Analyses” (note that without “Introduction of”)
• Lecture organization was appropriate
  • Introduction of real studies (SF and MK)
    • We may emphasize clarification of microbiome amplicon studies and genomic/metagenomic studies more clearly
  • Introduction of algorithm
  • Practices of basics (MK and GL)
  • Introduction of statistics (GL)
  • Introduction of R
  • Lecture of knowledge of pangenome and related issues (MA)
  • Introduction of tools with graphics (MA)
• Lecture based on real reasearch about statistical analysis will contribute to improvement of the effect of the course
  • Such as the ones provided at the beginning course by SF and MK and the one provided by MA
• We may update contents of the course (MK, GL)
• To avoid situation that participants (who are not motivated enough) get bored and leave the course mid-way, we may improve the contents by either of the following ways.
  • State at the beginning that they are better to attend through the end.
  • Improve practice of basics of linux to keep their motivation, whereas more concise introduction of command line.

To prepare

• No call for participants!?
• Can we fix the number of participants? max 30, because of the number of notebook at cmcc practice room.
• Can we select simply those 15 who applies first?
• Those with some motivation letter or sentence attached will be regarded primarily.
• bowtie
• tutorial data download
• wifi at the room?

What computer will we use?

• Linux server, soroban
• How accounts are created
• How accounts info are distributed to participants

• For R tutorial (3rd day), use CMCC 1st floor room, with 30 notebook, where Andrés has installed Rstudio on 2019/07.

Questions MK200103

• When network becomes down, what will we do alternative

Necessity of total hours

• 64hr = 8 days x 8hr
• Instead of 8 days, we will do 9 days, with the first and the last day of half days (i.e. 4hr), and 7-days for 8hr.
• Lab work e.g. 5hr

Time course of 1 day

• Morning: 9:00-13:00, 4hr
• Afternoon: 14:30-18:30, 4hr
• In total, 8hr / day
• 1h45min-30min break-1h45min / 4hr

Room reservation

• depend on the number of participants
• Windows computer at CMCC computer room

About document

• Share documents, MK to Giovanni,

Day 1. Monday 6 14:30-18:30 (4hr)

KAWAI, FUJIYOSHI

Lecture 1-1. (60-min) MK

• Opening remarks
• Outline of this course
• Brief review of introduction to Genomic, Metagenomic and Transcriptomic Analyses MK*

Lecture 1-2. (60-min) SF

• Microbial community analysis (Amplicon analysis, Metagenomics)SF*

Lecture 2. (120-min) MK

• Statistical mind to find more concrete association with phenomena
• What is your question / How to test your question / Importance of experimental design

Day 2. Tuesday 7 9:00 ÁVILA

Lecture 3. (120-min) AA

• Sequencers, sequencing platform (Illumina, long read)
• Basic data format of basic sequence data (FASTA, GENBANK)
• Basic data format of 'next-generation' sequence (NGS) data (FASTQ)
• Quality information of positions of reads

Lab session 1. (120-min)

Lecture 4. 14:30 (120-min) AA

• Sequence alignment, sequence similarity search

• Two basic approaches, mapping and assembly

Lab session 2. (120-min)

Day 3. Wednesday 8 9:00 KAWAI, LARAMA

Practice 1. (120-min) GL

• UNIX basic command, to sniff around sequence files (cd, pwd, ls etc.)
• Examine file contents of MiSeq
• File organization, concept of Path

Practice 2. (120-min) GL

• Text processing (grep, less, tail, head,..)
• To get familiar with NGS sequences #1 (examine NGS sequence files)
• Check contents of fastq

Practice 3. (120-min) MK

• Text processing (wc, cut, sed, awk, redirect (> , » ) etc.), gzip
• To get familiar with NGS sequences #2 (sequential commands by pipe)
• count how many of reads of fastq

Practice 4. (120-min) MK

• Shell script (Batch processing)
• Editor (vim)

Day 4. Thursday 9 9:00 KAWAI, LARAMA

Practice 6. (120-min) GL

• R statistics and graphics language #1

Practice 7. (120-min) GL

• R statistics and graphics language #2
• Goal: * Do (small) analysis and make report of statistical test (apa format)

Lab session 3. (240-min)

Day 5. Friday 10 9:00 KAWAI, LARAMA, ÁVILA

Lecture 6. (120-min) GL

• Quality control of raw sequence data, basic tools for sequence and NGS data

• Commands for NGS analyses
• Commands for sequence analyses
• Brief review of online resources / web services
• NGSToolkit (instead of FastQC)

Practice 8. (120-min) MK

• To get familiar with NGS sequences #3
• make your own pipeline for custom analysis and record of analysis* screen command* Sequence similarity search against a genome at hand (Do BLAST search locally)

Lab XX (240-min)

Day 6. Monday 13 9:00 ÁVILA , (LARAMA)

Lecture 7 and Practice 9. (240-min)

• Public sequence database
• Local mirror of databases
• Usage of information of public database
• e.g. Mapping (bowtie2)

Lecture 8 and Practice 10. (240-min)

• Genome assembly

Day 7. Tuesday 14 9:00 KAWAI

Lecture 9 and Practice 11. (120-min) MK

* Protein-coding gene prediction, RNA-coding gene prediction, gene annotation → MK

• Muliple sequence alignment
• protein domain search

Lecture 10 and Practice 12. (120-min) MK

• Gene contents of genome
• Concept of ortholog
• Concept of conserved single-copy genes

Lab session 2. (240-min)

Day 8. Wednsday 15 9:00 - 13:00ABANTO

Lecture 10 and Practice 12. (240-min)

• Introduction of analyses

• Pangenome
• Phylogenetic tree

Lab session 2. (240-min)

Day 9. Thursday 16 9:00 KAWAI, LARAMA

Lab session 2. (240-min)

• Discuss plans
• groups work
• proposal
• paper introducce
• apply what they learned

Internal meeting (120-min)