RNA seq Flashcards
1
Q
What are the objectives of RNA seq?
A
- Study gene regulation and expression variation; * (e.g. compare different tissues, time points, disease states)
- Understand the structure, function and organization of information
within the genome - and many more - sub-classifying cancer, spatial transcritptomics, host pathogen interaction
2
Q
Describe microarrays
A
- quick and cost effective
- based on hybridization to complementary sequence
in affy you need to chips per experiment - for control and for the actual experiment
very noisy!
3
Q
What are some limitations of micrarrays?
A
- The data is very “noisy”
- Expression levels are determined by a spot of light against a noisy background
- Probes are not available for all genes - Affy probes are only present for approx 75-80% of human genes
- Genes with very low expression may not be detected
- The data requires a large degree of statistical manipulation
- Result only shows a gene is expressed but gives no information about which transcript
4
Q
Outline the workflow used in RNA seq
A
5
Q
Compare RNA sequencing and Microarrays
A
- Method works as it can be assumed every mRNA present will be sequenced the same number of times
- If experiment shows twice as much mRNA for a particular gene as control then gene expression is 2 fold greater
- RNA-seq gives more accurate quantification and has better dynamic range (ability to quantify genes expressed at low and high levels)
- Not limited by microarray probe sequences and availability
- RNA-seq can potentially identify novel transcripts (e.g. new splice sites)
- RNA-seq can be used to study alternative splicing
6
Q
Outline the RNA seq analysis procedure
A
7
Q
Describe library preparation
A
- Total RNa extraction and target RNA enrichment
- Poly(A) capture
- Ribosomal RNA deplaetion
- Fragment RNA and reverse transcribe
- Ligate adapters and PCR amplify
- indexes/barcodes allow multiplexing
8
Q
What should you consider in your experimental design for RNA seq?
A
- Single vs paired end (latter helps identify e.g. isoforms)
- Sequencing depth (deeper sequencing detects more transcripts)
- Biological replicates (important for differential expression) - you need to have many samples to identify eny errors
- Spike-in RNAs (can help with normalization and quality control) - you add a known amount of RNA and then you can normalize your data
- Multiplexing (pool barcoded samples, then split across lanes)
- Batch design (randomize samples across experimental batches, cannot correct for batch effects if technical and experimental factors are confounded)
9
Q
Describe the quality control step in RNA seq
A
- asses the quality and trim the reads if needed
- Quality control is an essential step in the analysis as poor quality reads can significantly impact results
10
Q
What are some problems you can face during QC?
A
- Low-quality sequences (low confidence bases)
- Sequencing artefacts (duplicate reads, sequence bias)
- Sequence contamination (reads from another organism)
11
Q
How can you solve the problems of low quality in QC?
A
- FastQC for simple QC reads on raw reads - helps you remove the low reads or trim them down
- Discard low quality reads, and trim adapter sequences & poor quality
bases (e.g. using Trimmomatic
- Discard low quality reads, and trim adapter sequences & poor quality
12
Q
Describe read alignment
A
After quality control, reads are aligned to a reference genome or transcriptome.
Method depends on experiment aims and availability of suitable references.
13
Q
What will you have to do if you’re not confident in your mRNA reads?
A
if you are not confident in your mRNA reads then you will probably have to map against the genome - more difficult because then you will have to map through the exon boundaries
14
Q
What methods of alignment can you have?
A
-alignemnt to reference genome
-alignment to reference transcriptome
alignment to de novo assembled genome
15
Q
Describe alignment to reference genome
A
- requires splice-aware aligners (e.g. STAR, HISAT2)
- use known splice junctions, but can also discover new ones
- computational challenge is to accurately align reads that span splice junctions
- you can give it an excel sheet with the exons so it is aware of where they are
16
Q
Describe alignment to reference transcriptome
A
- unspliced alignment (e.g. Bowtie2)
- generally faster, but requires comprehensive reference transcriptome
- main challenge is dealing with multi-mapping (reads that map to several transcripts)
17
Q
Describe alignment to de novo assembled transcriptome
A
if no suitable reference genome, first assemble reads into contigs, and align reads to this de novo transcriptome (e.g. for novel genome, cancer samples)
18
Q
Describe reference based mapping
A
- If the reference genome is well annotated e.g. human, the reads can be mapped to known genes using the GTF file
- For less well defined annotations or where more accurate mapping is required then the mapped transcripts need to be assembled
- Novel genome
- To identify variable transcripts/isoforms
- Cancer samples
- Tools to produce these assembled transcripts include Cufflinks and StringTie
- Read mapping aligns reads to the reference genome, marking reads that align with and without splice junctions.
- Those that map unspliced must be exons, any that jump regions must span introns
- Cufflinks/StringTie look at the distribution of reads and estimates a transcript and transcript/gene read counts
19
Q
Do we have alignment free methods? What are the benefits f them?
A
- Recent methods (e.g. Kallisto, Salmon) avoid full alignments of each read, and instead use ‘pseudoalignments’ that identify which transcripts are compatible with a given read (but not exactly where that read aligns).
- Very fast, accurate and computationally efficient
- Assume well annotated transcriptomes and cannot identify novel transcripts
- Pseudo aligners have been developed to provide faster and more efficient read mapping
- Kallisto “can quantify 30 million human reads in less than 3 minutes on a Mac
desktop computer using only the read sequences and a transcriptome index
that itself takes less than 10 minutes to build”
20
Q
How can you quantify expression?
A
-direct fragment counting
- transcript level quantification
-alignment-free quantification
21
Q
Describe direct fragment counting
A
- Count fragments that overlap each gene (use e.g. featureCounts,
HTSeq) - Simple and fast
- How to deal with multi-mapping reads?
- No information on relative transcript abundances
22
Q
Describe transcript level quantification
A
- Assign fragments to specific transcripts
- Can aggregate over all possible isoforms to obtain gene-level count
- Use a statistical model to handle multi-mapping reads, and assign 1. these fragments probabilistical
- Can observe e.g. changes in isoform usage
23
Q
Describe alignment free quantification
A
Bypass full alignment, fast and accurate (see later slides)
24
Q
How can you estimate transcript abundances?
A
- Many methods have been developed to deal with multimapping reads.
- These use statistical models to link the probability of observing a given fragment to the relative transcript abundances.
- Optimisation algorithms are then used to infer transcript abundances given the observed reads and the assumed model.
- Cufflinks is an example which combines transcript assembly with abundance estimation. It uses fragment length information to help assign reads.
25
Q
What are pseudo aligners?
A
- FM indexing illustrates one of the challenges with mapping reads to the entire genome
- Pseudo aligners have been developed to provide faster and more efficient read mapping
- Use k-mers rather than aligning full reads
- Examples include Salmon and Kallisto
- Kallisto “can quantify 30 million human reads in less than 3 minutes on a Mac desktop
computer using only the read sequences and a transcriptome index that itself takes
less than 10 minutes to build”
26
Q
Explain how Kalisto works
A
- For each read determines which transcripts it is from rather than where it aligns
- Therefore, not necessary to do a full alignment of the reads to the genome
- Raw sequence reads are directly compared to transcript sequences and then used to quantify transcript abundance
- The comparison of the sequencing reads to the transcripts is done using a transcriptome de Bruijn graph (T-DBG)
- T-DBG constructed from the k-mers present in an input transcriptome as opposed to reads which is done normally for genome/transcriptome assembly.
- Transcripts converted into a T-DBG
- Each node/vertex is a k-mer in the T-DBG and associated with transcript(s) - a k- compatibility class
- Left most node has k-compatibility class of all 3 transcripts
- Once T-DBG built kallisto stores a hash table mapping each k-mer to the linear stretches (e.g. the first 3 nodes) it is contained in as well as the position
- Called the “kallisto index”
- Reads are also split int k-mers and matched to transcripts using the hash table
- The black nodes represent the k-mers of the read, where they match transcript k-mers
- To identify the transcript(s) a read is from identify all associated k-compatibility classes
- The k-compatibility classes of all black nodes for this read are the blue and pink transcripts
- Can be extended to paired-end reads using all the k-compatibility classes along both reads
27
Q
What do you need to use Kalisto?
A
only works with a good transcriptome
28
Q
How does Kalisto improve efficiency?
A
- Kallisto improves efficiency by utilising redundancy - the 3 left most nodes have the same k-compatibility class - the same equivalence class
- When a read k-mer is hashed the k-compatibility class of the node is identified and jumps to the node after the last one in the same equivalence class
- Once the left most k-mer of the read is hashed kallisto ignores the next 2 nodes as they are redundant and hash only the 4th k-mer of the read
- For most reads kallisto only performs a hash lookup for only two k-mers
29
Q
What are the advantages and disadvantages of using pseudo aligners?
A
- Pseudo-aligners provide a highly efficient method for mapping reads to transcripts
- Benefits include speed and computational resources required
- Disadvantage is that reads can only be mapped to known transcripts
- Unable to identify and quantify unknown or novel transcripts
- Quantification is at the transcript and not gene level - slight disadvantage - we uaually work with genomesnot transcripts
- R library available to convert to gene level quantification for input to DESeq2 etc - tximport
- Kallisto has a companion R library for transcript differential expression analysis - sleuth
30
Q
What is normalisation for?
A
Normalisation adjusts the read count to compensate for within sample analysis
31
Q
What is within sample normalisation?
A
- Several units normalize counts by feature length to allow comparison of features WITHIN a sample: RPKM, FPKM, TPM
- They also normalize by total read count but this is generally NOT sufficient for comparison between samples (see later slide)
32
Q
What is TPM?
A
Very similar to RPKM and FPKM but difference is the order of operations
- FPKM and TPM (transcripts per million( are both measures of the relative abundance of a transcript in
your pool of transcripts.
- TPM is now generally preferred over FPKM (as the proportionality constant for FPKM is experiment specific).
33
Q
What are the problems we can face with relative abundance measures?
A
- FPKM & TPM both normalize for total read count, but this is generally not sufficient to make comparisons between samples.
- They can only tell us the relative proportion of transcripts in a sample.
- If we make further assumptions, we can develop suitable methods to compare gene abundances between samples.
- Ballgown further processes e.g. allowing for total read count per sample.
- YOU CAN’T COMPARE SAMPLES WITH THIS METHOOD, ONLY WITHIN SAMPLE OPTIMISATION
Assume 4 genes in genome, each equally expressed in condition 1. Genes C and D down regulated in condition 2 but other 2 have same expression. Both have 8000 reads mapped - As the number of reads are the same in both conditions the RPKM for genes A and B would be higher in 2 than 1 suggesting they are up regulated
- This is the RNA composition effect
- Tools that use this method, such as ballgown, also maintain extra information about samples (e.g. the total number of reads)
- This allows for the proper comparison of these normalized measures across samples
34
Q
How do you do between sample normalisation?
A
- Highly and differentially expressed (DE) genes can distort gene abundance measures, so total read count is not an accurate normalization factor.
- Instead, we want to find a ‘control’ set of transcripts that are not DE and use these to estimate ‘size factors’ that enable meaningful comparisons between samples.
- Several methods exist (all assume most genes are not DE), including: TMM and Median of Ratios
35
Q
Describe TMM
A
- Uses a trimmed weighted mean. Excludes genes with large log-fold ratios
between samples, and those with extreme abundance values before calculating
a weighted mean of log-fold changes. - anything extreme is exluded - Used by edgeR
36
Q
Describe Median of ratios
A
- Uses the median of ratios of observed counts. For each gene, calculate the
(geometric) mean of its expression across all samples and treat this as a pseudo-
reference. For each sample, calculate the ratios of observed gene counts to
these pseudo-references, and take the median value as the size factor. - Used by DESeq2
37
Q
What is batch correction?
A
- As well as normalization, we may need to correct for batch effects.
- These are confounding factors that cause unwanted variation in gene abundances between samples, due to technical factors that differ across batches (samples that are processed in parallel).
- For example, differences in reagents, equipment, or date of library preparation or sequencing may cause batch effects.
- Various tools (e.g. COMBAT, SVAseq) enable batch correction, assuming suitable experimental design so technical and experimental factors are not confounded.
38
Q
What do you need for identification of differentially expressed genes?
A
- More replicates increases our power to detect DE genes.
- Minimum of 3 biological replicates recommended.
- RNA-seq experiments often have few replicates, so specialized statistical
methods require
- RNA-seq experiments often have few replicates, so specialized statistical
39
Q
How do you get DE genes?
A
- Testing for a statistically significant change in expression
- Many individual statistical hypothesis tests are performed (for each of 100s- 1000s of genes) so p-values need to be corrected for multiple testing
- These methods generally require unnormalized data, as they perform an
integrated normalization step - use raw or estimated read counts
40
Q
What is multiple-testing correction and why doo we need it?
A
- Calculated p-values need to be adjusted when repeating multiple independent statistical tests to reduce the false discovery rate (FDR)
- This applies to differential expression calculations where multiple genes are being compared
- DESeq2 implements Benjamini Hochberg multiple test correction and reports a q-value along with the p-value
- The q-value is the adjusted p-value and the significance value that should be used
41
Q
What would be next steps in differential expression analysis?
A
- you can use RNA seq to narrow down the pool of genes and then do qPCR on the subset of genes
- RNA seq is a starting point - then you can take the genes to further analysis if you see anything interesting
42
Q
What is RNA seq roles in alternative splicing?
A
- RNA-seq has applications beyond quantifying gene expression.
- One gene may give rise to several different mRNAs (and protein isoforms) due to alternative splicing
- RNA-seq allows us to study changes in isoform expression.
- indentification of mutations
- Mutations may affect RNA cis- regulatory elements, spliceosomal components, or trans-acting regulatory factors.
43
Q
What are gene fusions and how can we link RNA se-q to it?
A
- Chromosomal rearrangements can lead to fused transcripts.
- RNA-seq allows us to detect these fusion events.
- Gene fusions are commonly reported in many types of cancer, and may be used for diagnosis and prognosis.- A fusion junction is a section of transcribed RNA that maps to an exon from one gene followed by an exon from another gene
- It might occur as result of a translocation, deletion or chromosomal inversion
- Example - PML-RAR protein associated with Acute Promyelocytic Leukaemia
- These types of structural rearrangements can also be identified by direct sequencing using paired end reads
