Estimating Gene Count Talk
Contents
- 1 Estimating Gene Count
- 2 Multi mapping reads
- 3 One transcript, one set of reads
- 4 Two transcripts, another set of reads
- 5 Aggregation to Gene-level 1
- 6 Third transcript, another set of reads
- 7 Aggregation to Gene-level 2
- 8 HTSeq-count
- 9 HTSeq-count modes
- 10 Probabilistic approach 1
- 11 Probabilistic approach 2
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.
