NGS Transcriptomic Workflows Hugh Shanahan & Jamie al-Nasir Royal Holloway, University of London.

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NGS Transcriptomic Workflows Hugh Shanahan & Jamie al-Nasir Royal Holloway, University of London

 Transcriptome – total sequence and abundance of RNA generated by a cell  RNA is transcribed from DNA  Genome is fixed for a organism  Transcriptome is dynamic  Variation between tissues  Variation over time  RNA transcripts are 1,000’s-10,000 bases in length Setting the scene

 Interested in  How many copies of a particular transcript are there  What is the sequence  - sequence comes from genome but alternative splicing means a transcript may not just be a contiguous block of DNA

Size of transcriptome will vary between species

 Fragment transcripts into shorter pieces (reads)  bases longs  Have many overlapping reads  Amplify (make lots of copies of) the short reads  Can sequence these short reads and then assemble them to reconstruct transcripts.  Size of data set depends on size of transcriptome but also amount of fragmentation (sequencing depth)  Can either assemble with a reference genome or de novo (very hard) Sequencing steps

NGS Workflow

 File formats have been updated to binary – used to use flat text so sizes were huge (Reference Genome – 39 Gbyte -> 0.8 Gybte)  Raw image data is actually discarded  Discussions focusses on assembly and down-stream analysis  Much of this data is deposited in the Sequence Read Archive (SRA)  We’ve papered over everything that happens before sequencing – i.e. the biochemical steps carried out  This is highly variable  These steps are not properly annotated Final points