Let's recap what we learned yesterday:
We looked at finding differentially expressed genes when we are not interested in novel genes.
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mapping reads to the genome and transcriptome.
1. Unspliced mapping: BWA. We used BWA to map reads from two conditions C1 and C2 to our transcriptome.
- Unspliced mappers are typically faster (particularly those using BWT).
- Unspliced mappers will miss/misalign reads that span exon-exon junctions.
- Unspliced mappers are best when mapping RNA-Seq data directly to the transcriptome.
2. Spliced mapping: HISAT2/STAR. We used STAR to map reads from two conditions C1 and C2 to our genome.
- Spliced mapping is more conducive for rna-seq data.
- Spliced alignments look different from unspliced alignments in their cigar scores ("N")
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- Inputs: mapping output (sam or bam file) and annotation file (gff/gtf file)
- Bedtools- for simple counting. Any time a read overlaps a gene, it's counted towards that gene.
HtSeq - for fine tuned counting. You can choose how you want to count reads that map only partially to a gene, that map to multiple genes etc.
Code Block title Output File Example FBgn0000008 304 311 273 264 296 296 FBgn0000014 47 40 39 36 63 43 FBgn0000015 41 35 28 22 35 35Output: Gene id, following by raw counts for that gene.
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How to take the gene counts for different conditions and compare then to identify genes that are differentially expressed?
- DESeq, edgeR, DEXSeq
- normalize, calculate variance, statistical test, output genes along with fold change, p value, FDR
- R- how to install packages, run R commands and plot graphs.
We learned some unix as well!
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3. Pseudomapping using Kallisto: We used Kallisto to quantify transcripts using reads from two conditions C1 and C2.
- When you want to quantify known transcripts, kallisto is the fastest way to go. It skips the alignment step.
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