Lecture 6 : smallRNA-seq and
IP methods
Modern methods for genome
analysis
(PřF:Bi7420)
Vojta Bystry
vojtech.bystry@ceitec.muni.cz
NGS data analysis
22
Raw data
.fastq
Genome/Transcriptome
Reference Mapping
.bam
Interaction
analysis
CHIP-seq
Expression
analysis
RNAseq
Variant
analysis
WES
de-multiplexing
Not known
reference
QC
QC
Experiment
design
Not ”classic”
reference
Metagenomics
Reference
assembly
Immunogenetic
VDJ-genes
CRISPR
sgRNA
Methylation
Bisulfide-seq…
Small RNA-seq
2
• Next-generation sequencing of short RNAs allows for profiling of various short (non-coding)
RNAs (microRNAs, piRNAs, tRNAs,…)
• Widely used method for identification of disease biomarkers => cancer research
• Special interest is in small RNAs that are part of circulatory system (biofluids) because these
can serve as non-invasive biomarkers
Sequencing Bioinformatic analysisBlood collection
miR-26a
low high
Small RNAs pool - microRNAs
3
• ~22nt long, regulate expression of other RNAs
• Mature miRNA binds to the 3’ UTR of coding
RNA (mRNA) -> degradation
• ~2,000 mature miRNA known for human
• isomiRs = sequence variants of miRNA
No protein
miRNA biogenesis
canonical 5’ UAGCUUAUCAGACUGAUUGA
3’
AGCUUAUCAGACUGAUUGA
GUAGCUUAUCAGACUGAUUGA
UAGCUUAUCAGACUGAU
UUAGCUUAUCAGACUGAUUGA
CUAGCXUAUCAGACUGAUUG
A
5’ isomiRs
3’ isomiRs
polymorhpic
isomiRs
seed
precursor
miRNA AAGCCGUAGCUUAUCAGACUGAUUGACGAGCGC
Small RNAs pool – tRNA fragments
4
• ~14-45nt long
• Participate in various biological processes -> research ongoing!
Small RNAs pool – other small RNAs
5
• PIWI-interacting RNAs (piRNAs) -> ~30nt long, most expressed in germinal cells where
we know what they do; found to be expressed in the somatic cells as well but functions
mainly unknown
• Small nucleoler RNAs (snoRNAs) -> chemical modification of other RNAs
• Small nuclear RNAs (snRNAs) -> pre-processing of coding RNAs in the nucleus
• Y RNA-derived small RNAs -> DNA replication?
• mRNA fragments -> random or not?
CEITEC at Masaryk University 7
Module 1: First QC
• Quality control of raw sequencing data
• Scans FASTQ files for presence of adapters
Results:
• List of detected adapters (exact sequences)
• Html/PDF report with plots and tables summarizing
the quality of raw data
Module 2
8
Module 2: Pre-processing
• Adapters trimming
• Trimming of low-quality bases, discarding of
short reads
• Read collapsing based on UMIs (if present)
Results:
• Cleaned FASTQ files
• Html/PDF report with plots and table
summarizing number of reads after each
pre-processing step nucleotide position
numberofreads
miRNA
piRNA
too short reads
trimmed reads
untrimmed reads
Length distribution for sample METSEQ-T03
9
Module 3: RNA quantification
• Complicated due to complex nature of different short RNAs
• Requires individual approach for each class of short RNAs
• miRNA identification VS isomiRs identifications (3’/5’ additions, precursor ambiguity,…)
10
Module 3: RNA quantification
• Complicated due to complex nature of different short RNAs
• Requires individual approach for each class of short RNAs
• piRNAs nested within other coding/non-coding RNAs
A)A A) B)
CEITEC at Masaryk University 12
Module 3: RNA quantification
• Complicated due to complex nature of different short RNAs
• Requires individual approach for each class of short RNAs -> most problematic are
tRFs and piRNAs
NGS data analysis
1212
Raw data
.fastq
Genome/Transcriptome
Reference Mapping
.bam
Interaction
analysis
CHIP-seq
Expression
analysis
RNAseq
Variant
analysis
WES
de-multiplexing
Not known
reference
QC
QC
Experiment
design
Not ”classic”
reference
Metagenomics
Reference
assembly
Immunogenetic
VDJ-genes
CRISPR
sgRNA
Methylation
Bisulfide-seq…
ChIP-seq
IP methods
RNA IP methods
RNA IP methods
Primary analysis + QC
● Alignment – standard DNA (RNA for CLIP)
● RNA-seq like QC
○ Check sequencing quality
○ RSeQC – Read Dstribution
Primary analysis + QC
● IP experiment quality control
○ Sample correlation
■ Replicates control treatment
○ Strand cross-correlation
■ Shift of strand mapping
■ Shift should correlate with
expected fragment size
Primary analysis + QC
● IP experiment quality control
○ Sample correlation
■ Replicates control treatment
○ Strand cross-correlation
■ Shift of strand mapping
■ Shift should correlate with
expected fragment size
Primary analysis + QC
● Fingerprint profile
○ profile of cumulative read coverages
○ how evenly are the reads distributed over the genome
Peak calling
● Read extension
Peak calling
● Statistical assessment of peaks against background
● Background
○ Control sample – recommended
○ Model background from overall coverage of the sample
● Peak calling annotation
● Differential peak calling
Post peak calling QC
● FRIP score = fraction of reads in peaks
○ High number is good
○ However can be low in specific experiments and still the experiment be OK
● Average peak profile
24
Peak calling results
25www.ceitec.eu
CEITEC
@CEITEC_Brno
Vojta Bystry
vojtech.bystry@ceitec.muni.cz
Thank you for your attention!