1. RNAseq analyses -- methods
June 7, 2015

2. Lecture Objectives Terminology for RNA-seq Steps in RNAseq analysis
Downstream interpretation of expression and differential estimates
multiple testing, clustering, heatmaps, classification, pathway analysis, etc.

3. Steps in RNAseq workflow
Obtain raw data from sequencer
Align/assemble reads
Process alignment to quantify reads/gene feature
Conduct differential analysis
Summarize and visualize
Create gene lists, prioritize candidates for validation, etc.
Conduct gene enrichment or pathway analysis

4. Some nomenclature
Sequencing file types:
Fasta (sequence only) Fastq (contains quality scores)
>SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT +
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

5. Alignment files SAM – Sequence Alignment/MAP coordinate file
Tab-delimited ASCII file with all of the information needed for the alignment of the reads to reference
HWI-ST155_0544:7:2:7658:34048#0 16 Chr M * 0 0 GTAGAGGTAGGACCAACAAGGACCAAGTTTCCCTGTTCCAAC ghghhhhgcghh_hhhhhhhhhhhhhhhhghhhhhghhhhhh NM:i:0 NH:i:4 CC:Z:Chr10 CP:i:
HWI-ST155_0544:7:61:11040:141129#0 0 Chr M * 0 0 CTTTTCTGGCGTAACTTGGTTCCCTTTAGTTTGGAACAGATA hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhghhchfhhhh NM:i:0 NH:i:3 CC:Z:Chr10 CP:i:
HWI-ST155_0544:7:8:11214:130793#0 0 Chr M * 0 0 CAAATAGTGGTTTGAAACCTATCAATCAAGTCACTTTCAAGT gggggggggggggggggeggdffffggggggggggggdggfc NM:i:0 NH:i:3 CC:Z:Chr10 CP:i:
BAM – Binary version of SAM
Machine readable, smaller more compact version
More commonly used by viewers and analysis programs

6. Reference guided RNAseq alignment
You have a sequenced genome and a file with the gene model annotations
Genome reference file is usually a fasta file with each chromosome as a separate entry
GTF = gene transcript file
GFF = gene feature file (GFF3)
The GTF/GFF chromosome name must match the chromosome name in the genome reference file
In this case we are using the human hg19 genome build and the Refseq transcript file from

7. RNAseq alignment

8. Number of reads mapped in the sample
RPKM (FPKM)
Reads (fragments) per Kilobase Per Million
RPKM =
raw number of reads
exon length X
1,000,000
Number of reads mapped in the sample
In RNA-Seq, the relative expression of a transcript is proportional to the number of cDNA fragments that originate from it. However:
The number of fragments is biased towards larger genes
Total number of fragments is related to total library depth
Use of FPKM/RPKM normalizes for gene size and library depth

9. Alternatives to FPKM Raw read counts HTSeq (htseq-count)
Instead of calculating FPKM, simply assign reads/fragments to a defined set of genes/transcripts and determine “raw counts”
HTSeq (htseq-count)
Python code that converts aligned reads to counts
You give it an alignment file and the associated transcript file and it will output a list of counts by feature
FeatureCounts (part of Subread package)
BAM file + GTF file => # reads/feature
PGS also reports reads/feature
We can try differential expression analysis with the RPKM and the reads file to explore the different outcomes

10. How to quantify reads per feature?

11. Counts vs FPKM(RPKM)?
Count files can be analyzed by a number of different R programs
EdgeR
DESeq2
NOISeq
More robust statistical methods for differential expression
Accommodates more sophisticated experimental designs with appropriate statistical tests

12. Gene expression differences
Once you have your data in RPKM or counts format, you can use a variety of tools to compare the different conditions and determine what genes are differentially expressed
Authors used Cufflinks/CuffDiff

13. How does cuffdiff work? More replicates = more confidence
The variability in fragment count for each gene across replicates is modeled.
Fragment count for each isoform is estimated in each replicate along with a measure of uncertainty in this estimate arising from ambiguously mapped reads
transcripts with more shared exons and few uniquely assigned fragments will have greater uncertainty
The algorithm combines estimates of uncertainty and cross-replicate variability under a negative binomial model of fragment count variability to estimate count variances for each transcript in each library
These variance estimates are used during statistical testing to report significantly differentially expressed genes and transcripts.
More replicates = more confidence

14. Multiple approaches advisable

15. Multiple testing correction
As more attributes are compared, it becomes more likely that the treatment and control groups will appear to differ on at least one attribute by random chance alone.
Well known from array studies
10,000s genes/transcripts
100,000s exons
With RNA-seq, more of a problem than ever
All the complexity of the transcriptome
Almost infinite number of potential features
Genes, transcripts, exons, juntions, retained introns, microRNAs, lncRNAs, etc, etc
Bioconductor multtest

16. Lessons learned from microarray days
Hansen et al. “Sequencing Technology Does Not Eliminate Biological Variability.” Nature Biotechnology 29, no. 7 (2011): 572–573.
Power analysis for RNA-seq experiments
RNA-seq need for biological replicates
RNA-seq study design

17. Today in computer lab
Analyze the BAM files downloaded from Google drive
Do QA/Qc
Do mRNA quantification
Do comparisons between groups
The analysis will take several hours…plan accordingly