====== Review after the course ======
**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.
====== Note ======
**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,
====== Program / Programación ======
===== Day 1. =====
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. =====
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. =====
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. =====
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. =====
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. =====
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. =====
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. =====
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. =====
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)