--- title: "RNA-Seq Data Analysis Workshop Contents (Theory)" canonical: "https://help.biobam.com/space/BTP/621936645/RNA-Seq%20Data%20Analysis%20Workshop%20Contents%20(Theory)" format: markdown --- ## Day 1 ### [Course presentation](https://docs.google.com/presentation/d/1aXLm1u_gZ7ExOUl0Y7cZbwFxqXCaUhdHvEDjTprHZZk/edit#slide=id.p) (Stefan) 10 min - Introduction to the course. - Lecturers and BioBam. - Scope and contents. - Course schedule. ### [Introduction to sequencing technologies, RNA-Seq and de-novo transcriptomics for non-model organisms](https://docs.google.com/presentation/d/1fjtCcUlVwldmLSjPcFozrrQeEnBLfa7Aqj3Czvo5r-k/edit#slide=id.p) (Quino) 1:15h - Introduction to NGS technologies. - NGS generals and applications in bioinformatics. (DNA-seq, RNA-seq, Metagenomics...) - NGS basics: methods, devices, coverage... (Sanger, Illumina, Nanopore...) - Transcriptomics analysis overview. Goals and applications ### [Introduction to Bioinformatics](https://docs.google.com/presentation/d/1D5zpeBFMpu2XHiaDEuiReppGoOwZs7vjLbqLxtU9zoc/edit) (Carlos) 45 min - Introduction. - Origins of bioinformatics. - Trends and challenges in bioinformatics. - Application of bioinformatics. - Bioinformatics NGS pipelines. - Biological databases. - Bioinformatics file formats. ### [Preprocessing of raw reads: quality control (FastQC), adapter clipping, quality trimming](https://docs.google.com/presentation/d/1YMUG_dFafiVSfmJFRe1zEU_M_vMJAoAW5Cze6rbD3SU/edit) (Quino) 1h - Introduction to FASTQ format. Differences according to technology, type of sequencing (single-end vs paired-end) ... - Importance of quality control of raw sequencing reads. Common problems and detection. Effects in downstream analysis. - FASTQ Quality Check. Objectives, the meaning of each metric and chart, interpretation of the results. About FASTQC - FASTQ Preprocessing procedure. Improving the quality of raw sequencing data. About Trimmomatic. - Preprocessing steps: Adapter clipping, trimming and filtering. ### **Hands - On 2h** Introduction to Omicsbox and Biobam (Stefan) 15 min - Overview of OmicsBox and BioBam - Provide Key and Download Link Intro of Dataset (10 min) (Blog de Jesus) - Show Website and PDFs - Explain Use Case and Experimental Design - Which Files to Use Explain Exercise (10min)  - Download files - Explain Exercise Steps: - Download files, Run QC, Check results - Remove Adapters and Run QC with two diff. configurations - Trimming and Run QC - Filtering and Run QC - Design WF, Run and Save Report as PDF Hands On (60 min) Carlos - Questions - Whats our read length and mean quality score - Do we have any quality issues detected in the QC and which issues? - Adapters: Which strategy  was more effective - Number of final reads and percentage of removed reads - Extra Question: ....... Resolve Exercises (15min) Summary of the Day (5 min) ## Day 2 ### [RNA-Seq de-novo assembly. Understand analysis outputs, statistics, and visualization](https://docs.google.com/presentation/d/1pLwdpfjr36zGns1scba9yKODdDNtPnqyNBKNauKB2_Y/edit#slide=id.p) (Carlos) 1:30h  - Introduction. - Transcriptome assembly strategies. - Overview of the Trinity RNA-Seq assembler. - RNA-Seq de novo assembly in OmicsBox. - Results and statistics. - Transcriptome assembly quality assessment. - `Conclusions` ### [Functional annotation of novel genomes (Blast2GO Methodology)](https://docs.google.com/presentation/d/1bRTTkeG-qeakLQS-PGkco_Vwyw2ko6uJZjmYeZdLbC0/edit#slide=id.p) (Stefan) 1:30h - Introduction to gene/transcript functional annotation. - Gene Ontology Consortium and other function descriptors. - Blast2GO Functional Annotation Methodology. - Blast. Parameters and databases. Custom Blast searches. - Mapping. - Annotation. - Other resources: InterProScan, GOSlim, EggNOG... - Exploration, statistics and visualiaztion. ### **Hands - On Day 2 **(2h) Explain Exercise (20min)  - Files from day1 and assembled dataset, blast and Interpro XMLs - Explain Exercise Steps: - Assembly, learn about parameters (takes time) - Review Report and charts (read representation) - Blast, InterPro, Mapping, Annotation Strategy. Hands On (1h 20min) Carlos - Questions - Assembly - What is the N50, how long is the longest/shortest Contig, number of transcripts - Number of genes and isoforms. - Read representation: what is it good for? - Annotation - Transcripts without Blast? Why? - How much did we annotate functionally? - Check for transcripts of a specific function or biological role Resolve Exercises (15min) Summary of the Day (5 min) ## Day 3 ### [Read mapping and read expression quantification (RSEM, HTseq)](https://docs.google.com/presentation/d/1B1Z7bFu_VMG-gp8NfWglwtWUozMJtw1ePrFSxUqH38c/edit) (Quino) 1:15h - Introduction to Read Expression Quantification. - Mapping procedure. Algorithms and strategies. Bowtie2 and STAR. - SAM/BAM format. - Count reads and estimates expression levels. HTSeq and RSEM. - Count table. - Differences between gene-level and transcript-level. - Reference-based scenario: RNA-seq alignment + create count table (gene-level quantification). - *De novo *scenario. Create Count Table (transcript-level quantification). - Visualization and quality assessment. ### [Statistical Analysis of differential gene/isoform expression, pairwise and time course (EdgeR, NOISeq, maSigPro)](https://docs.google.com/presentation/d/1WBzV3N-eqCiaSrkF4exNiM9wB-oIHY1M8AUfaXDRMUQ/edit#slide=id.p) (Carlos) 1:15h - Introduction to differential expression analysis. - Previous steps: quality assessment, normalization, and filtering. - Statistical concepts and software. R Bioconductor and statistical concepts (distribution, statistical tests...) - Pairwise differential expression analysis with edgeR. - Pairwise differential expression analysis without replicates (NOISeq). - Time Course Expression Analysis (maSigPro). - Understanding the results and visualization. ### [Functional analysis via Enrichment Analysis (Fisher's and GSEA)](https://docs.google.com/presentation/d/1fjpLjfwjMHgXzKFDlN1KT5Lbp2ep1MSXFrSFriWmiAc/edit) (Stefan) 30 min - Introduction to Enrichment Analysis. - Enrichment analysis as a strategy to obtain biological insights from differential expression results. - Fisher's Exact Test. Statistical concept and parameters. - Gene Set Enrichment Analysis. Statistical concept and parameters. - Understanding results. - Visualization ### **Hands - On Day 3 **(2h) Explain Exercise (10min)  - Use the following tools to perform: - Quantification with RSEM (Quino) Data: Fastq of day 2 (reduced), Fasta of Transcripts (good one) → Count Table - Differential Expressions (Carlos) Data: Experimental design + Count Table .... Pairwise ... - Differential Expression TimeCourse ... - Functional Analysis (Stefan) GSEA and Fisher : Hands On (1h 14min) Quino - Questions - Quantification - Differential Expressions - Functional Analysis Resolve Exercises (15min) Questions: 15 min Summary of the Day (5 min) Feedback Forms Online (5 min) (Stefan) Course Summary (10 min) (Stefan)