1. Transcriptomics II De novo assembly

2. Sequencing Read Processing
Trimmomatic
ILLUMINA CLIP – removes specified adapters
SLIDINGWINDOW – removes regions falling below quality threshold
LEADING/TRAILING – removes if below quality threshold
MINLEN – drops reads below length
TOPHRED33/TOPHRED64 – converts quality scores
java -jar Trimmomatic-0.32/trimmomatic-0.32.jar
SE/PE -threads 16 -phred33 -trimlog SP**trimlog
SP**.fq SP**trimmed.fq
ILLUMINACLIP:/TruSeq2-SE.fa:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 MINLEN:75

3. De novo assembly De novo assembly using Trinity software
Memory and time intensive
1GB RAM per 1M sequences
1 hour per 1M sequences (more processors!)
Consider in silico normalization
perl trinityrnaseq-2.0.3/Trinity --max_memory 240G --CPU 20 --left All_1_trimmed.fq --right All_2_trimmed.fq --SS_lib_type FR --seqType fq normalize_reads --min_contig_length full_cleanup --output Trinity_Pb_Normalized &> Trinity_Pb_Normalized.log

4. Why Trinity? -Paired reads -Isoform differentiation -Full length transcripts

5. Assembly Results Total trinity 'genes': 421044
Total trinity transcripts:
Percent GC: 44.20
Contig N50: 2160
Median contig length: 444
Average contig:
Too many assembled transcripts
Use CD-HIT/RSEM to prune

6. CD-HIT-EST and RSEM
RSEM – RNA-Seq by Expectation Maximization: Estimates gene and transcript level abundance
Prune reads with FPKM (1 per billion)
perl trinityrnaseq_r /util/filter_fasta_by_rsem_values.pl --rsem_output RSEM.isoforms.results --fasta Trinity.fasta --output FPKM_0.001.fasta --fpkm_cutoff 0.001
Total trinity genes = , Total trinity transcripts =
CD-HIT: Combines sequences based upon similarity
100% Identity
cd-hit-v /cd-hit-est -i TB_Manuscript2\ FINAL\ DATA/Trinity.fasta -c 1.0 -n 8 -o cd-hit-v /Trinity_CDHIT100 -T 20 -M
Total trinity genes = , Total trinity transcripts =

7. Annotation BLAST– Sequence homology B2G4PIPE/BLAST2GO – GO term
use computing cluster, run array job if parallel implementations not available
B2G4PIPE/BLAST2GO – GO term
Command line version/graphical version
KEGG – Pathway analysis
Available as stand alone or within BLAST2GO

9. Time to relax….not quite.

10. Map - Bowtie v. Sailfish
Bowtie – little upfront investment, but need to map millions/billions of reads
Sailfish – large upfront investment in K-mer library, but no need to map billions of reads.

11. Quantify Run RSEM on each individual sample
Use Trinity Pipeline to combine samples into a single expression table
Gene level
Transcript level
Use edgeR (Empirical analysis of digital gene expression data in R) within Trinity Pipeline

12. Trinity DGE Pipeline/Post Analysis
Data Generated
A sample to sample DGE comparison
Consensus DGE comparison
Visualizations Possible
Volcano Plots
Heatmaps
Cluster Dendrograms
Utilize Data
Use clusters and up- and down- regulated subgroups to identify genes, GO terms and pathways that experience changes in regulation.