Estimating Gene Count Talk

Estimating Gene Count

How many reads are overlapping genomic features? - or - Can we confidently assign each read to a feature/transcript/gene? Not so simple.

We also have:

• Multi mapping reads
• Overlapping genes/transcripts

Two approaches:

• Focus on what’s known with certainty
• Probabilistic

Multi mapping reads

• Unsolved problem:

- this can account for 10-30% of reads

• Ignore them, but then again this decreases sensitivity
• Weighted assignment

Of course, longer reads would solve this problem.

One transcript, one set of reads

Two transcripts, another set of reads

Aggregation to Gene-level 1

Third transcript, another set of reads

Aggregation to Gene-level 2

HTSeq-count

• Designed for RNA-Seq counting
• Work at gene level
• Remove multi-mapped reads
• Several modes to resolve remaining uncertainty

HTSeq-count modes

Probabilistic approach 1

• Cufflinks

- Reconstruct the transcripts from the data and annotation

Probabilistic approach 2

• Cuffdiff:

- Assign each read/fragment to a transcript with a probability maximum likelihood.