Transcriptomics

transcriptome

the set of all RNA molecules, including mRNAs, rRNAs, tRNAs, and other non-coding RNAs

Bioinformatics analysis in RNA-seq

to align reads to a reference genome and quantify gene expression

Differential gene expression analysis in RNA-seq

comparing gene expression between different cell types or conditions

RNA-seq and alternative splicing

enables detection and quantification of alternative splicing events by capturing the full spectrum of mRNA transcripts present in a sample

What algorithm does the FM-index rely on for efficient searching

Backward search algorithms

Role of RNA-seq normalization

to ensure comparability between samples by correcting for sequencing depth and RNA composition effects

Method used for differential gene expression analysis in RNA-seq data

DESeq2

sequence-specific biases

certain sequences are preferentially selected or amplified due to their nucleotide composition

fragment-level GC Bias

the GC content of a fragment affects its likelihood of being sequenced

strand-specific protocols

some RNA-seq protocols are strand-specific, meaning they distinguish between the sense and antisense strands of RNA (the specificity introduces bias in the data)

fragment length distribution

shorter fragments might be overrepresented in the sequence data due to the selection process during library preparation

TPM(transcripts per million)

normalizes for differences in transcript length; allows for direct comparison of transcript abundances between samples

FPKM(fragments per kilobase of transcript per million mapped reads)

adjusts for differences in sequencing depth and gene length by scaling the raw read counts to the total number of mapped reads and the length of the gene.

Step 1: RNA seq

get transcripts, make it cDNA, then make it fragments/”short reads”

Step 2: read mapping

taking those fragments and mapping them to our reference transcript

T/F: read mapping can tell you how much of each gene is being transcribed relative to the others

False

What does Salmon algorithm solve for?

How many transcripts we have

First parameter of salmon

n: transcript abundance. How many of each transcript do we have?

Second parameter of salmon

Z: binary matrix that tells you where each transcript is in a fragment?

Third parameter of salmon

T: set of all of our transcripts

solution of salmon

F: set of all of our fragments

transcript abundance

gene expression levels: how much of this trnascript is coding

What tool do you use for differential gene expression

DESeq2