1. MCB Lecture #21 Nov 20/14
Prokaryote RNAseq

2. Today:
Building off last lecture, we will use reference alignment methods to understand differential gene expression in prokaryotes
Use Bowtie2 for alignment
Use Edge-pro for determining transcript abundance

3. Experiment:
Compare E.coli K-12 grow in glucose minimal medium aerobically vs. anaerobically
Aerobic datasets: SRR922260
Anaerobic datasets: SRR922265
All sequenced using Illumina GAIIx, 2x36bp PE

4. Basic idea of RNAseq
One way to analyze a transcriptome (i.e., all the mRNA molecules) is to count the number of transcripts from each gene
More transcripts implies more activity of that gene
Improvement over previous technology (microarrays) that required some knowledge of what genes to look for and were less sensitive

5. Problems: How to compare short genes to long ones?
Short genes will have fewer reads mapping to them by random chance
How to compare genes from different genomes with different sampling intensity?
Transcripts sampled more deeply will have more reads mapping to them

6. RPKM "Reads per kilobase per million"
RPKM normalizes for both gene length and sampling intensity
RPKM = [# of mapped reads]/[length of transcript in kb]/[million mapped reads]
Allows genes to be compared to each other
Allows transcription to be compared between transcriptomes

7. RNAseq software Many packages exist for comparing transcriptomes
Most are tailored towards eukaryotes
Emphasis on finding splice variants (not in bacteria)
Do not account for overlapping genes (common in bacteria, rare in eukaryotes)

8. Generalized scheme for RNAseq
Map reads to reference genome
Count reads mapping to each gene
Normalize for gene length and sampling depth (i.e., calculate RPKM)
Statistically compare test and control sample sets (a topic in itself, not covered in depth here)

9. EDGE-pro The software we will use is EDGE-pro
Installed on server in /opt/bioinformatics/EDGE_pro_v1.3.1/
Tailored for prokaryotes
Magoc et al. (2013) Evolutionary Bioinformatics 9:

10. EDGE-pro outline Use Bowtie2 to map reads Calculate per base coverage
Assign per gene coverage
Disambiguate overlapping genes
Calculate RPKM for each gene

11. Running EDGE-pro
syntax: $ perl /opt/bioinformatics/EDGE_pro_v1.3.1/edge.pl -g [.fna name] -p [.ppt name] -r [.rnt name] -u [.fastq 1 name ] -v [.fastq 2 name] -s /opt/bioinformatics/EDGE_pro_v1.3.1/
-g: reference .fna file name
-p: reference .ptt file name
-r: reference .rnt file name
-u: .fastq file name to map
-v: .fastq file pairing with that specified by -u, if exists
-s: location where program lives
e.g.: $ perl /opt/bioinformatics/EDGE_pro_v1.3.1/edge.pl -g NC_ fna -p NC_ ptt -r NC_ rnt -u SRR922260_1.fastq -v SRR922260_2.fastq -s /opt/bioinformatics/EDGE_pro.v1.3.1/

12. EDGE-pro: results
One nice thing about EDGE-pro is that it runs many scripts all by itself
A "wrapper" or "pipeline" is something that bundles different programs altogether
Many of the output files are from bowtie2, some are from EDGE-pro itself
Note: make sure that you have enough space in your account for these files
The RPKM data are located in "out.rpkm_0", which is a tab-delimited table listing the reads mapped to each predicted transcript

13. Comparing conditions
There are many different ways to compare test and control conditions
This is outside of the scope of this class
The RPKM values generated by EDGE-pro can be reformatted to be input
EDGE-pro contains a script that will do this for DESeq, one of the most popular
Generally multiple replicates should be considered for each condition

14. EDGE-pro comparison
The EDGE-pro paper suggests an easy heuristic for transcriptome comparison:
Average RPMK values from treatment replicates
Determine the RPMK fold change between test and control treatments using simple division
Only keep results >4-fold different

15. A reference genome quirk:
EDGE-pro requires the standard .fna genome file and .ptt and .rnt files that list gene locations on the chromosome
Unfortunately only available from the old version of the NCBI ftp server
Location for today: ftp://ftp.ncbi.nlm.nih.gov/genomes/ Bacteria/Escherichia_coli_K_12_subs tr__MG1655_uid57779/

16. Today's assignment
Use EDGE-pro to calculate RPMK values for the E.coli K-12 RNAseq transcriptomes generated under aerobic (SRR922260) and anaerobic (SRR922265) conditions
Write a short perl script to calculate the recommended EDGE-pro comparison
Only one replicate so no averaging needed
Report 4-fold overrepresented genes in aerobic treatment
Report 4-fold overrepresented genes in anaerobic treatment