RNA-seq and Transcriptomics:

How to experimentally determine gene expression?

​

​

​

Based on slides of Ivan Antonov

Why do we sequence RNA?

Functional studies

• Genome is the same for the organism but different cells have different genes expressed;
• Experiment conditions may have a pronounced effect on gene expression (e.g. Drug treated vs. untreated cell line; wild type versus knock out)

Predicting transcript sequence from genome sequence is impossible at the current stage

• Alternative splicing, RNA editing, etc.

Some molecular features exist only for RNA

• Alternative isoforms, fusion transcripts, RNA editing, etc.

RNA-seq

• RNA (quite often poly A+) converted to a library of cDNA fragments with adaptors
• Molecules sequenced from one end (Single End) or both ends (Pair End)
• Reads of 30-400bp depending on sequence technology

Challenges

• Samples
• Purity, quantity, quality
• RNA is fragile compared to DNA (easily degraded)
• Most eukaryptic RNAs are spliced (consist of exons separated by introns in DNA)
• Mapping reads to genome is a problem
• The relative fraction of particular RNAs vary wildly
• 1 – 10^7 molecules per cell (lncRNA vs ribosomal RNA)
• As sequencing is based on sampling, highly expressed genes covered by majority of reads (ribosomal genes)
• Cannot estimate the overall decrease/increase of transcription
• RNAs come in a wide range of sizes
• Small RNAs must be captured separately

rRNA depletion methods

Типичный параметр: RIN (RQI), DV200

Common aims of RNA-Seq analysis (what can you ask of the data?)

• Gene expression and differential expression
• Isoforms expression, alternative splicing
• Novel transcripts discovery and annotation
• Allele specific expression
• Link to known SNPs or mutations
• SNP/mutation discovery
• Fusion detection
• RNA editing

Какие требования к эксперименту?

Сколько нам нужно чтений?

​

Expression Profiling / Differential expression 5-10 Million

Alternative splicing, quantifying cSNPs 50-100 Million

De Novo Transcriptome Assembly 100-1000 Million

​

​

Какие могут быть ошибки и как от них избавиться?

Replicates

Multiple isolations of cells showing the same phenotype, stage or other experimental condition (Environmental Factors, Growth Conditions, Time)

Correlation Coefficient 0.9

Типы реплик

DOI:10.11613/BM.2021.010502

RNA-seq data analysis

Quality control

Good vs bad quality

Duplicated reads

Bowtie/Tophat/Cufflinks/DESeq2 �RNA-seq Pipeline

Gene annotation

(.gtf file)

Reference genome

(.fa file)

Raw sequence data

(.fastq files)

Inputs

Bioinformatics challenges

• Splice junctions
• Several overlapping transcripts from one gene
• Non-uniquely mapped reads
• Transcripts of different length

Alignment (Tophat)

Tophat results

How do we quantify expression from RNA-seq?

RPKM: Reads per Kb million (Mortazavi et al. Nature Methods 2008)

• Longer and more highly expressed transcripts are more likely be represented among RNA-seq reads
• RPKM normalizes by transcript length and the total number of reads captured and mapped in the experiment
• Sequencing depth can alter RPKM values

Differential Gene Expression Analysis

CuffDiff:

-t-test (one can set a threshold)

-replicates encouraged but not needed

-can provide differential splicing and promoter usage

​

DESeq:

- counts reads as following a negative binomial distribution

- fit a generalized linear model (GLM): more then two groups can be tested; Wald test

- models variability between replicates