Cufflinks

Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Basics of RNA - seq data analysis
Revision

Drawback of Dynamic programming - Very slow
Need for - faster alignment strategies
Fast Sequence alignment strategies
• Using hash table based indexing - seed extend paradigm, space allowance
• Using suffix/prefix tree based - Suffix array, Burrows wheeler
transformation and FM index
• Merge sorting
Strategy: making a dictionary (index) – An example of 4-nt index
AAAA: 235, 783, 10083,......
AAAC: 132, 236, 832, 932, ...
TTTT: 327, 1328, 5523,......
Algorithms
Hashing reads - Eland, MAQ, Mosaik...
Hashing reference genome - BFAST, Mosaik, SOAP, ...
Hash table based indexing

Burrows wheeler transformation and FM index

Criteria for choosing an aligner
• Global or local
• Aligning short sequences to long sequences such as short
reads to a reference
• Aligning long sequences to long sequences such as long
reads or contigs to a reference
• Handles small gaps (insertions and deletions)
• Handles large gaps (introns)
• Handles split alignments (chimera)
• Speed and ease of use

Short read aligner
Aligner Purpose
Bowtie Fast
BWA small gaps (indels)
GSNAP Large gaps (introns)
Bowtie 2 Takes care of gaps
Long sequence aligner
Aligner Purpose
BLAST Many reference genome
BLAT Large gaps (introns)
BWA Small gaps (indels)
Exonerate Ease of use
GMAP Large gaps (introns)
MUMmer Align two genome

RNA
Exon 1 Exon 2
Exon read mapping
Spliced read mapping
Exon 1 Exon 2
RNA
Seed matching
K-mer seeds
Seed extend
RNA
Exon 1 Exon 2
Exon read mapping
Exon 1 Exon 2
RNA
Seed matching
K-mer seeds
Seed extend
RNA -seq alignment strategy
Exon - first approach Seed - extend approach

• Available tools:
• MapSplice, SpliceMap, TopHat
• Two step procedure
• Map reads continuously using
unspliced read aligners
• Unmapped reads are split into shorter
segments and aligned independently
• Efficient when not too many reads into
the junction
• Second step is computationally intensive
• Can miss reads across exon-intron
junctions
RNA
Exon read mapping
Exon 1 Exon 2
Exon - first approach

• Representative algorithms
• Genomic short read nucleotide
alignment program (GSNAP)
• Computing accurate spliced
alignments (QPALMA)
• Steps
• Break reads into short seeds
• Candidate regions are
combined’ (such as Smith-Waterman)
• Increased sensitivity
• One arm may not provide enough
specificity for alignment
RNA
Exon 1 Exon 2
Exon read mapping
Exon 1 Exon 2
RNA
Seed matching
K-mer seeds
Seed extend
Seed - extend approach

A software package that identifies splice sites ab initio by large- scale
mapping of RNA-Seq reads.
• A splice junction mapping algorithm must be able to identify reads that
may have only a few bases on one side of a junction, or else that
junction will be missed
TopHat
Map reads to whole genome with Bowtie
Collect initially unmappable
reads
Build seed table index
from
unmappable reads
Generate possible splices
between
neighbouring exons
Map reads to possible
splices
via seed-and-extend
Assemble consensus of
covered regions
gt ag ag
gt ag ag

Normalization of read count
R/FPKM (Mortazavi et al.,2008) - Reads/Fragment per kilobase of exon
per million mappable reads
• Corrects for: differences in sequencing depth and transcript length
• Aiming to: compare a gene across samples and different genes within
samples
TMM (Robinson and Oshlack., 2010) - Trimmed mean of M values
• Corrects for: differences in transcript pool composition; extreme outliers
• Aiming to: provide better across-sample comparability

Normalization of read count
Limma voom (LogCPM) (Law et al.,2013) - Counts per million
• Aiming to: Stabilize variance, removes dependence of variance on the
mean
TPM (Li etal 2010, Wagner et al 2012) - Transcripts per million
• Corrects for: transcript length distribution in RNA pool
• Aiming to: provide better across-sample comparability

• FPKM for paired end reads and RPKM for single end reads
• Fragment means fragment of DNA, so the two reads that
comprise a paired-end read count as one.
• Per kilobase of exon means the counts of fragments are then
normalized by dividing by the total length of all exons in the gene.
• This bit of magic makes it possible to compare Gene A to Gene B
even if they are of different lengths.
• Per million reads means this value is then normalized against the
library size.
• This bit of magic makes it possible to compare Gene A in Sample
1 to Sample 2
R/FPKM (Mortazavi et al.,2008)

A quantiﬁcation measurement for gene expression
• R: expression level of the gene
• L: length of the gene
• N: depth of the sequencing
• C: number total reads fall into the gene region
R/FPKM (Mortazavi et al.,2008)
Total exon size of a gene is 3,000-nt. Calculate the expression levels for
this gene in RPKM in an RNA-seq experiment that contained 50 million
mappable reads, with 600 reads falling into exon regions of this gene.
R = 600/(50 × 3.000) = 4.00
R = C ÷ L × N( ) L in kbs and N in Millions

Calculation of FPKM/RPKM
Genes Sample1 Sample 2 Sample 3
1 (2kb) 20 24 60
2 (4kb) 40 50 120
3 (1kb) 10 16 30
4 (10kb) 0 0 2
Total 70 90 212
Total reads for sample 1, 2 and 3 - 7M ,9M and 21.2M
(millions of reads equated to a scale of tens of reads)
Step 1. Divide the reads of each gene with the total reads of the sample
Genes Sample1(RPM) Sample 2(RPM) Sample 3(RPM)
1 (2kb) 2.86 2.67 2.83
2 (4kb) 5.71 5.56 5.66
3 (1kb) 1.43 1.78 1.42
4 (10kb) 0 0 0.09

Fragments/Reads per kilobase per million of reads
Reads are scaled for both depth and length
Step 2. Divide the values obtained after step 1 with the gene lengths
Genes Sample1 (RPKM) Sample 2
(RPKM)
Sample 3
(RPKM)
1 (2kb) 1.43 1.33 1.42
2 (4kb) 1.43 1.39 1.42
3 (1kb) 1.43 1.78 1.42
4 (10kb) 0 0 0.009
Total normalized
reads
4.29 4.5 4.5

Calculation of TPM
Step 1. Divide the reads of each gene with the length of each gene
Genes Sample 1 Sample 2 Sample 3
1 (2kb) 20 24 60
2 (4kb) 40 50 120
3 (1kb) 10 16 30
4 (10kb) 0 0 2
Total reads per kb of gene for sample 1, 2 and 3- 3M,4.05M and 9.02M
Genes Sample 1(RPK) Sample 2(RPK) Sample 3(RPK)
1 (2kb) 10 12 30
2 (4kb) 10 12.5 30
3 (1kb) 10 16 30
4 (10kb) 0 0 0.2
Total 30 40.5 90.2
(millions of reads equated to a scale of tens of reads)

Step 2. Divide the values obtained after step 1 with the gene lengths
Genes Sample1(TPM) Sample 2(TPM) Sample 3(TPM)
1 (2kb) 3.33 2.96 3.326
2 (4kb) 3.33 3.09 3.326
3 (1kb) 3.33 3.95 3.326
4 (10kb) 0 0 0.02
Total 10 10 10
Genes Sample1 (RPK) Sample 2(RPK) Sample 3(RPK)
1 (2kb) 10 12 30
2 (4kb) 10 12.5 30
3 (1kb) 10 16 30
4 (10kb) 0 0 0.2
Calculation of TPM

RPKM vs TPM
Genes Sample1 (RPKM) Sample 2
(RPKM)
Sample 3
(RPKM)
1 (2kb) 1.43 1.33 1.42
2 (4kb) 1.43 1.39 1.42
3 (1kb) 1.43 1.78 1.42
4 (10kb) 0 0 0.009
Total normalized
reads
4.29 4.5 4.5
Genes Sample1(TPM) Sample 2(TPM) Sample 3(TPM)
1 (2kb) 3.33 2.96 3.326
2 (4kb) 3.33 3.09 3.326
3 (1kb) 3.33 3.95 3.326
4 (10kb) 0 0 0.02
Total normalized
reads
10 10 10
Sums of total normalized reads

Eg : if certain genes are very highly expressed in one tissue but not another,
there will be less ‘’sequencing real estate’’ left for the less expressed genes in
that tissue and RPKM normalization (or similar) will give biased expression
values for them compared to the other sample
Equal sequencing depth -> Yellow and green will get lower RPKM in RNA population
1 although the expression levels are actually the same in populations 1 and 2
Robinson and Oshlack Genome Biology 2010, 11: R25, http://genomebiology.com /
2010/11/3/R25
RNA population 1 RNA population 2
TMM – Trimmed Mean of M Value
Attempts to correct for differences in RNA
composition between samples

Identification of Differentially expressed genes - I
(using Cufflinks)

Identification of differentially expressed genes
Quality filtered/trimmed RNA-Seq Short reads
FPKM based
strategy
Calculate transcript
abundances
(Cufflinks)
Reference Genome
(Y/N)
Mapping to the reference
(GMAP-GSNAP, Tophat,Bowtie,etc.)
Y
N De novo Transcriptome
assembly (Trinity)
Mapping and detection of
DEGs (RSEM)
Count based
strategy
Generate count data
(RSEM)
Detection of DEGs
(cuffdiff2)
Detection of DEGs
(DESeq, edgeR, EBSeq)

Identification of differentially expressed genes
Quality filtered/trimmed RNA-Seq Short reads
FPKM based strategy
Calculate transcript
abundances
(Cufflinks)
Reference Genome
Mapping to the reference
(GMAP - GSNAP)
Detection of DEGs
(cuffdiff2)
Downloading the reference
genome and gtf from UCSC
genome browser
Requirements
For running gmap-gsnap- fasta file of the genome and reads file
For running cufflinks - bam files of all samples and gtf file of the genome
For running samtools - sam file generated from gmap-gsnap

Downloading the FASTA file and GTF from the UCSC genome
browser (https://genome.ucsc.edu)

When you click on the bosTau8.fa.gz, you will be able to download a file of
866.1MB,which on clicking would give a file of 2.72 GB

Genome Mapping and Alignment using GMAP - GSNAP
Genomic Mapping and Alignment Program
• GMAP is a standalone program for mapping and aligning cDNA sequences to a
genome.
• The program maps and aligns a single sequence with minimal startup time and
memory requirements, and provides fast batch processing of large sequence sets.
• The program generates accurate gene structures, even in the presence of
substantial polymorphisms and sequence errors, without using probabilistic splice
site models.
Step 1. Command for indexing the the genome : gmap_build -d btau8
bosTau8.fa
Initially used a hashing
scheme but later used a
much more efficient
double lookup scheme

The index files created are as below in the folder btau8 
gsnap –d btau8 –t 4 control_R1.fastq> control_R1.sam
Step 2. Mapping the reads to the genome

• The end product of the GMAP aligner is a SAM file which needs to be
converted into a BAM file for further analysis in cufflinks.
• Repeat the same for the other replicate by changing the input file name.
• A total of four SAM files are generated separately.
The BAM files generated can be analysed in two ways -
1. The BAM files can be used to generate a merged assembly of transcripts
via cufflinks and cuffmerge. This merged assembly (i.e merged.gtf) is
used in cuffdiff to generate differential expressed genes.
2. Cuffdiff can be used directly to generate differentially expressed genes
using the BAM files generated.
The index files created are as below in the folder btau8

./samtools view –bsh aln.sam >aln.bam
-b: Output in the BAM format. -s: Input in the SAM format. –h: Include
header in the output
For the Control sample:
./samtools view –bsh control_R1.sam >control_R1.bam
For the Infected sample:
./samtools view –bsh infected_R1.sam >infected_R1.bam
Step 3. Converting SAM to BAM using samtools

Command for sorting:./samtools sort aln.bam aln.sorted
Example:
./samtools sort control_R1.bam control_R1_sorted
For the Infected sample:
./samtools sort infected_R1.bam infected_R1_sorted
Step 4. Sorting BAM using samtools

Command for running cufflinks on a BAM file
cufflinks -G btau8refflat.gtf -g btau8refflat.gtf -b bosTau8.fa -u -L CN
control_R1_sorted.bam
Step 5. (Option 1) Differential expression using cufflinks,
cuffmerge and cuffdiff.

For the infected sample:
cufflinks -G btau8refflat.gtf -g btau8refflat.gtf -b bosTau8.fa -u -L CN
infected_R1_sorted.bam
These commands generate transcript.gtf files for each replicate, which are
further used in cuffmerge to generate a merged assembly. This merged
assembly is then used in cuffdiff to generate differentially expressed genes.

Command for running cuffmerge
cuffmerge -g btau8refflat.gtf -s bosTau8.fa -p 8 assemblies.txt
assemblies.txt is the file with the list of all the GTFs.
This generates a merged.gtf in the merged_asm folder. This file is
used in the next cuffdiff command.
Command for running cuffdiff
cuffdiff merged.gtf control_R1_sorted.bam control_R2_sorted.bam
infected_R1_sorted.bam infected_R2_sorted.bam
This command generates many files, out of which gene_exp.diff is the file
of our concern.

CuffDiff computes differentially expressed genes in the set. For computing
differential expression at least two samples -infected and control are required.
CuffDiff should always be run on replicates - i.e., N infected vs N control.
Command:
Cuffdiff –p –N transcripts.gtf
-p: num-threads <int>. -N
Running cuffdiff for our BAM files
cuffdiff –p 3 –N bostau8reflat.gtf control_R1_sorted.bam,control_R2_sorted.bam
infected_R1_sorted.bam,infected_R2_sorted.bam –o cuffdiff_out
Step 5. (Option 2) Differential expression using CuffDiff directly

A unique identifier
describing the object
(gene, transcript, CDS,
primary transcript)
Gene ID
Gene Name
Infected
OK (test successful), NOTEST (not enough alignments
for testing), LOWDATA (too complex or shallowly
sequenced), HIDATA (too many fragments in locus), or
FAIL, when an ill-conditioned covariance matrix or
other numerical exception prevents testing
FPKM in
Sample 1
FPKM in
Sample 2
The (base 2) log
of the fold
change y/x
Genomic coordinates for easy
browsing to the genes or
transcripts being tested.
Control
The value of the test statistic
used to compute significance
of the observed change in
FPKM
The uncorrected
p-value of the test
statistic
gene_exp.diff
Log2fold change = Log2(FPKM infected/FPKM of control)
= Log2(0.576748/3.92513) = -2.76673

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