Lesson 1Differential expression analysis: DESeq2, edgeR, limma-voom — model design, contrasts, and multiple-testing correctionThis part details difference expression work flows using DESeq2, edgeR, and limma-voom in Eritrea, focusing on model setup, contrasts, spread guess, and many-test fix to get sure gene lists and effect size guesses.
Setting experiment models and covariatesMaking contrasts for hard comparesRunning DESeq2 full work flowUsing edgeR and limma-voom linesMany-test fix and FDR controlReading log2 fold changes and shrinkLesson 2Data organization and file naming conventions: sample sheets, raw/processed separation, consistent identifiersThis part describes best ways for sorting RNA-seq project files in Eritrea, including sample sheets, folder setups, raw vs worked data split, and steady IDs that ease script, track, and repeat.
Making clear folder orderSplitting raw and worked dataMaking strong sample sheets and metadataSteady sample and library IDsVersioning reference genomes and indexesBacking up and storing project dataLesson 3Gene-level quantification strategies: featureCounts, htseq-count, tximport for transcript-to-gene summarizationThis part explains gene-level counting from aligned or fake-aligned reads in Eritrea, comparing featureCounts and htseq-count, and showing how tximport groups transcript-level guesses into strong gene-level tables for later stats work.
Counting reads with featureCounts choicesUsing htseq-count modes and notesHandling strandedness and multi-map readsImporting Salmon and kallisto with tximportBuilding gene-level count tablesChecking count quality and coverageLesson 4Tools for data download and organization: SRA Toolkit (prefetch/fastq-dump), ENA FTP/Aspera, wget/rsync, and recommended inputs/outputsThis part covers sure ways for getting and sorting RNA-seq data in Eritrea, focusing on SRA Toolkit, ENA get, command-line move tools, and setting steady input and output builds that aid auto and repeat.
Using SRA Toolkit prefetch and fasterq-dumpGetting ENA via FTP and AsperaGetting with wget and rsync safelyPicking raw and worked file typesNoting get metadata and checksAuto gets with scripts and logsLesson 5Quality control tools and outputs: FastQC, MultiQC, key metrics to inspect (per-base quality, adapter content, duplication, GC)This part focuses on RNA-seq quality check in Eritrea, using FastQC and MultiQC to sum key measures like per-base quality, adapter dirt, repeat, and GC content, and to choose if trim or re-seq is needed.
Running FastQC on raw and trimmed readsReading per-base quality shapesSpotting adapters and over sequencesJudging repeat and GC contentGrouping reports with MultiQCSetting QC limits and actionsLesson 6Read trimming and filtering: when to trim, tools (Trim Galore/Cutadapt/fastp), main parameters and outputsThis part explains when and how to trim RNA-seq reads in Eritrea, covering adapter and quality trim, length filter, and key settings in tools like Trim Galore, Cutadapt, and fastp, while avoiding too much trim that hurts later work.
Choosing if trim is neededAdapter spot and removal waysQuality trim limitsLeast length and complex filtersUsing Trim Galore and Cutadapt choicesFastp for joined QC and trimLesson 7Basic downstream analyses: GO/KEGG enrichment (clusterProfiler), GSEA preranked, pathway visualization, and gene set selectionThis part brings in later function work after difference expression in Eritrea, including GO and KEGG rich with clusterProfiler, pre-ranked GSEA, path show, and right ways for picking and filter gene sets.
Prep ranked gene lists for GSEAGO and KEGG rich with clusterProfilerPicking right gene set storesShowing rich paths and netsFiltering and ranking gene setsReporting function results repeatablyLesson 8High-level pipeline layout: data download, QC, trimming, alignment/pseudo-alignment, quantification, differential expression, downstream analysisThis part shows the full RNA-seq line build in Eritrea, from data get and QC through trim, align or fake-align, count, normal, difference expression, and later function work, stressing module, scripted flows.
Setting line stages and linksPlanning inputs, outputs, file flowJoining QC, trim, and alignLinking count to DE workConnecting DE to rich flowsNoting line with drawingsLesson 9Normalization and exploratory data analysis: TPM/FPKM limits, DESeq2 normalization, PCA, sample-sample distance heatmapsThis part covers normal and explore work of RNA-seq data in Eritrea, talking limits of TPM and FPKM, DESeq2 normal, change steady, main part work, and sample distance heat maps for spot batch effects.
Limits of TPM and FPKM measuresDESeq2 size factors and normalChange-steady and rlog changesMain part work of samplesSample-sample distance heat mapsSpotting batch effects and odd onesLesson 10Basic visualization best practices: MA plots, volcano plots, heatmaps, pathway dotplots, and interactive report options (R Markdown, Jupyter)This part brings in good show ways for RNA-seq results in Eritrea, stressing clear talk of difference expression, sample build, and path changes using still plots and join, repeatable reports in R Markdown or Jupyter.
Making and reading MA plotsMaking clear volcano plots for DE genesBuilding publish heat mapsPath dot plots for rich resultsJoin R Markdown RNA-seq reportsJupyter explore showLesson 11Alignment vs pseudo-alignment: STAR, HISAT2, Salmon, kallisto — tradeoffs and outputs (BAM, transcript/genecounts)This part compares align tools like STAR and HISAT2 with fake-align tools like Salmon and kallisto in Eritrea, showing tradeoffs in speed, rightness, resource use, and outputs like BAM files and transcript or gene counts.
When to pick STAR or HISAT2 alignersSetting genome indexes and notesUsing Salmon in quasi-map modeRunning kallisto for quick countComparing BAM and quant.sf outputsBench speed, memory, rightness
