1. Li and Dewey BMC Bioinformatics 2011, 12:323
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Kim Dong-in
테스트
Bo Li1 and Colin N Dewey1,2*
Li and Dewey BMC Bioinformatics 2011, 12:323

2. Li and Dewey BMC Bioinformatics 2011, 12:323
Abstract + Backgroud
RNA-Seq
millions read
reads end of cDNA from RNA fragment (single,pair)
transcript quantification
multiple genes or isoforms
reads count, length
Li and Dewey BMC Bioinformatics 2011, 12:323

3. Li and Dewey BMC Bioinformatics 2011, 12:323
Abstract + Backgroud
Transcript quantification - mapping reads to genome, transcript set - estimation gene, isoform abundances Major complication - Not map uniquely to a single gene or isoform
Li and Dewey BMC Bioinformatics 2011, 12:323

4. Li and Dewey BMC Bioinformatics 2011, 12:323
Abstract + Backgroud
RSEM (RNASeq by Expectation Maximization)
transcript sequences not reference genome
 de novo transcriptome assembler
Extension methodology
paired-end, length reads, length distributions, quality scores
95% credibility interval (CI)
posterior mean estimate(PME)
 maximum likelihood (ML) estimate
 abundance of each gene and isoform
Li and Dewey BMC Bioinformatics 2011, 12:323

5. Li and Dewey BMC Bioinformatics 2011, 12:323
Abstract + Backgroud
RSEM (RNASeq by Expectation Maximization)
In experiments
best quantification accuracy
 short SE reads than PE reads in gene level
same sequencing
quality scores is not significant.
Illumina error
only read sequences  quantification accuracy
Li and Dewey BMC Bioinformatics 2011, 12:323

6. Li and Dewey BMC Bioinformatics 2011, 12:323
Abstract + Backgroud
count reads number, read length (mapped uniquely gene)
 problems
- mappability not in account : biased
- alternatively-spliced genes : incorrect estimates
- isoform abundances
 developed - address
rescuing reads to multiple gene
modeling by isoform level
EM (expectation-maximization algorithm)
Li and Dewey BMC Bioinformatics 2011, 12:323

7. Li and Dewey BMC Bioinformatics 2011, 12:323
Abstract + Backgroud - Related work
similar statistical methods tools  only RSEM, IsoEM handling reads mapped ambiguously isoforms and genes RSEM (RNASeq by Expectation Maximization) - modeling RSPDs(start position distributions) - compute posterior mean estimate(PME) 95% credibility interval (CI) - designed without a whole genome sequence IsoEM - maximum likelihood (ML) estimate
Li and Dewey BMC Bioinformatics 2011, 12:323

8. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation
RSEM (RNASeq by Expectation Maximization)
1. generate reference transcript sequences
2. aligned the reference
- estimate abundances, credibility intervals
scripts
rsem-prepare-reference
rsem-calculate-expression
Li and Dewey BMC Bioinformatics 2011, 12:323

9. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation
Li and Dewey BMC Bioinformatics 2011, 12:323

10. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Reference sequence preparation
designed to transcript sequences not whole genome
1. complicated alignment to genome ( eukaryotic )
 splicing , polyadenylation
 challenging at genome level
2. transcript-level alignments
easy, faster
Li and Dewey BMC Bioinformatics 2011, 12:323

11. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Reference sequence preparation
rsem-prepare-reference
- genome database
- de novo transcriptome assembler
- EST database
- UCSC, Ensemble genome browser database
- set of preprocessed transcript sequences
append poly(A) tail sequences to reference
(disabled with–no-polyA)
Li and Dewey BMC Bioinformatics 2011, 12:323

12. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Reference sequence preparation
rsem-prepare-reference
--gtf mm9.gtf \
--transcript-to-gene-map knownIsoforms.txt \
--bowtie-path /sw/bowtie \
/mm9/chr1.fa,/data/mm9/chr2.fa,...,/data/mm9/chrM.fa \
/ref/mouse_125
-or /mm9
Li and Dewey BMC Bioinformatics 2011, 12:323

13. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Read mapping ,abundance estimation
rsem-calculate-expression
mapping(aligned) to reference
calculation of relative abundances
mapping tools: bowtie(default), sam format
mapping condition
- no single best align
- mismatches in first 25 bases
- reads > 200
Li and Dewey BMC Bioinformatics 2011, 12:323

14. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Read mapping ,abundance estimation
rsem-calculate-expression
fasta (position-dependent)
fastq (paired-end, single-end, score)
EM(expectation-maximization algorithm)
options
–strand-specific
sense or antisense directions
–fragment-length (SE)
PE learns length
Li and Dewey BMC Bioinformatics 2011, 12:323

15. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Read mapping ,abundance estimation
rsem-calculate-expression
–estimate-rspd
: highly 5’ or 3’ biased from position distributions
–calc-ci (maximum likelihood)
95% credibility intervals : capture uncertainty
posterior mean
Li and Dewey BMC Bioinformatics 2011, 12:323

16. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Read mapping ,abundance estimation
rsem-calculate-expression - output
estimate quantity
- isoform-level, gene-level
: used by edgeR, DESeq
estimate fraction transcripts
- TPM (transcripts per million)
- independent, mean expressed transcript length
TPM > RPKM, FPKM
Li and Dewey BMC Bioinformatics 2011, 12:323

17. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Visualization
rsem-calculate-expression – output –out-bam BAM file : genome browser(alignment) sem-bam2-wig BAM  wig the expected number of reads overlapping each genomic position  annotation GTF-formatted
Li and Dewey BMC Bioinformatics 2011, 12:323

18. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Visualization
Li and Dewey BMC Bioinformatics 2011, 12:323

19. Li and Dewey BMC Bioinformatics 2011, 12:323
Implementation - Visualization
rsem-plot-model  rsem-calculate-expression output to pdf report learned fragment read length distributions sequencing error parameters
Li and Dewey BMC Bioinformatics 2011, 12:323

20. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
IsoEM - transcript sequences aligned(bowtie) Cufflinks - quantification mode genome sequence aligned(tophat) rQuant - genome sequence aligned(tophat) RSEM (v0.6) - transcript sequences aligned(bowtie)
Li and Dewey BMC Bioinformatics 2011, 12:323

21. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
20 million RNA-Seq (non-strand-specific, mouse transcriptome) Paired-end reads Single-end reads throwing out the second read of each pair reference transcript RefSeq - 20,852 genes and 1.2 isoforms per gene on average Ensembl - 22,329 genes and 3.4 isoforms per gene on average
Li and Dewey BMC Bioinformatics 2011, 12:323

22. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
tested methods measured accuracy
median percent error (MPE)
error fraction (EF) – 10%
false positive (FP) statistics
Li and Dewey BMC Bioinformatics 2011, 12:323

23. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
RSEM and IsoEM outperform Cufflinks and rQuant.
1. not fully handle reads map to multiple genes
- cufflinks
“rescue"-like strategy one iteration of the EM algorithm
- rQuant
method handles gene multireads is not clear.
Li and Dewey BMC Bioinformatics 2011, 12:323

24. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
RSEM and IsoEM outperform Cufflinks and rQuant. 2. performance gap Cufflinks, rQuant – genome set RSEM and IsoEM – transcript set Cufflinks not properly short transcripts - abnormally high abundance estimates of shorter mean fragment length (280 bases) RSEM - poly(A) tail handling but not IsoEM
Li and Dewey BMC Bioinformatics 2011, 12:323

25. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
Li and Dewey BMC Bioinformatics 2011, 12:323

26. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
MPE : median percent error
EF : error fraction
FP : false positive
Li and Dewey BMC Bioinformatics 2011, 12:323

27. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
HBR: human brain reference
UHR: universal human reference
Microarray Quality Control (MAQC)
qRT-PCR : 1,000 (5%) out of a total of 19,005
- gene : 716 filterd genes
biased towards single-isoform genes
Li and Dewey BMC Bioinformatics 2011, 12:323

28. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
Li and Dewey BMC Bioinformatics 2011, 12:323

29. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Paired vs. single end reads
single-end
number of reads than length at gene level
optimal read length, around 25 bases in mouse and maize
paired-end
isoform, alternative splice genes
Li and Dewey BMC Bioinformatics 2011, 12:323

30. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
Li and Dewey BMC Bioinformatics 2011, 12:323

31. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Comparison to related tools
mouse RefSeq
empirical : training data
Profile : base-dependent
these results only for the task of quantification (We stress…)
Li and Dewey BMC Bioinformatics 2011, 12:323

32. Li and Dewey BMC Bioinformatics 2011, 12:323
Results and Discussion - Availability and requirements
Project name: RSEM
Project home page:
Operating systems: Any POSIX-compatible platform
(e.g., Linux, Mac OS X, Cygwin)
Programming languages: C++, Perl
Other requirements: Pthreads; Bowtie, R
Li and Dewey BMC Bioinformatics 2011, 12:323

33. Li and Dewey BMC Bioinformatics 2011, 12:323
Conclusions
RSEM (RNASeq by Expectation Maximization)
- preforming gene, isoform level
- not require a reference genome
- quantification with de novo transcriptome assemblies
- visualization outputs
- credibility interval (CI) estimates
- userfriendly, two commands
- reference transcript files
- single end at gene level quntification
- paired end within-gene isoform for mouse, human
Li and Dewey BMC Bioinformatics 2011, 12:323