1. Transcriptome Assembly

2. Transcriptome Assembly
Goals of de novo Transcriptome Assembly
Gene discovery and sequence comparison in non-model organisms
Generation of a reference sequence for mapping and testing for differential gene expression
Contrasts with Genome Assembly
Varying levels of coverage
(Alternative) splicing and post-transcriptional modifications

3. Transcriptome Assembly
Data Sources
Illumina (by far the most common), 454, Ion Torrent
Library considerations
Strand-specific (dUTP/UDG method)
Dual-indexing
Paired-end reads and long reads
Fragment RNA not cDNA
in vitro normalization (not common)
Image: Illumina, Inc.

4. Transcriptome Assembly
Assemblers
Trinity
Widely used
High quality
Integrated with annotation and DE pipelines
SOAPdenovo-Trans
Lower CPU and RAM requirements
k-mer can be specified
Oases (Velvet)
Trans-ABySS
Newbler (454)
MaLTA (Ion Torrent)
Image: Broad Institute

5. Transcriptome Assembly
Pre-Assembly Read Processing
Trimming and quality filtering
Trimmomatic
Cutadapt
in silico normalization
Trinity (built-in)
khmer

6. Transcriptome Assembly
Computational Resources
Linux
RAM intensive (Trinity: 1 GB per 1 M reads)
Public Resources
XSEDE Greenfield Server at PSC
Data Intensive Acadmeic Grid (DIAG)
NCGAS Galaxy portal
Image: XSEDE

7. Transcriptome Assembly
Annotation
First question: Do I need to annotate?

8. Transcriptome Assembly
Annotation
Tools
TransDecoder
Trinotate
PASA
CPU-Intensive
Lots of transcripts, lots of homology searches