Estimating Gene Count Talk - Revision history

Rf at 13:34, 9 May 2017

2017-05-09T13:34:57Z

Rf at 13:21, 9 May 2017

2017-05-09T13:21:55Z

Rf at 13:14, 9 May 2017

2017-05-09T13:14:12Z

Rf: Created page with "Estimating gene count = Estimating Gene Count = How many reads are overlapping genomic features? - or - Can we confidently assign each read to a feature/transcript/gene? Not..."

2017-05-09T12:33:05Z

Created page with "Estimating gene count = Estimating Gene Count = How many reads are overlapping genomic features? - or - Can we confidently assign each read to a feature/transcript/gene? Not..."

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Estimating gene count

= 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:
– Can account for 10-30% of reads
GeneA – chr11
GeneB – chr5

– Ignore them … (decrease sensitivity)
– Weighted assignment

= Multi mapping reads =
* Unsolved problem:
– Can account for 10-30% of reads
GeneA – chr11
GeneB – chr5

Solution is to use longer reads
– Ignore them … (decrease sensitivity)
– Weighted assignment

= Transcripts/Genes =
* Transcripts/Isoforms or Genes
T1
GeneA

= Transcripts/Genes =
* Transcripts/Isoforms or Genes
T1
T2

GeneA

T3

= Transcripts/Genes =
* Transcripts/Isoforms or Genes
T1
T2

GeneA

= Transcripts/Genes =
* Transcripts/Isoforms or Genes
T1
T2

GeneA

T3

= Transcripts/Genes =
* Transcripts/Isoforms or Genes
T1
T2

GeneA

T3

= Transcripts/Genes =
* Transcripts/Isoforms or Genes
T1
T2

GeneA

T3

= Transcripts/Genes =
* Transcripts/Isoforms or Genes
T1
T2

GeneA

T3

Gene level is aggregating transcripts
Transcript level needs longer reads

= HTSeq-count =

* Designed for RNA-Seq counting
* Simple to use (especially since v0.6.0)
* Work at gene level
* Remove multi-mapped reads
* Several modes to resolve remaining uncertainty

= HTSeq-count =

= Probabilistic approach =

Cufflink

cuffdiff

= Probabilistic approach =

Cufflinks:
Reconstruct the transcripts from the data and annotation

= Probabilistic approach =
Cufflinks:
Reconstruct the transcripts from the data and annotation

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

= Probabilistic approach =
Cufflinks:
Reconstruct the transcripts from the data and annotation
Pros:
- Better methodology
- Integrated package (ease of use)
Cons:
Cuffdiff:
- Do not support alternative experiment design
- History of heterogeneous results/versions
* Assign each read/fragment to a transcript with a probability maximum likelihood.

@@ Line 57: / Line 57: @@
 [[File:htcats.png]]
-= Probabilistic approach =
+= Probabilistic approach 1 =
-Cufflink
+* Cufflinks
-cuffdiff
+[[File:minpath.png]]
-= Probabilistic approach =
+= Probabilistic approach 2 =
-Cufflinks:
+[[File:isolik.png]]

@@ Line 30: / Line 30: @@
 = Two transcripts, another set of reads =
-[[File: t1t2.png
+[[File:t1t2.png]]
 = Aggregation to Gene-level 1 =
-[[File:tt1t2aggreg.png]]
+[[File:t1t2aggreg.png]]
 = Third transcript, another set of reads =
@@ Line 42: / Line 42: @@
 = Aggregation to Gene-level 2 =
-[[File:t1t2tt3aggreg.png]]
+[[File:t1t2t3aggreg.png]]
 = HTSeq-count =
 * Designed for RNA-Seq counting
 * Work at gene level
 * Remove multi-mapped reads
 * Several modes to resolve remaining uncertainty
-= HTSeq-count =
+[[File:htseq.png]]
 [[File:htcats.png]]
@@ Line 74: / Line 73: @@
 Cuffdiff:
-Assign each read/fragment to a transcript
+Assign each read/fragment to a transcript with a probability maximum likelihood.
 = Probabilistic approach =

@@ Line 1: / Line 1: @@
 = Estimating Gene Count =
 How many reads are overlapping genomic features?
@@ Line 17: / Line 15: @@
 = Multi mapping reads =
 * Unsolved problem:
-– Can account for 10-30% of reads
+:- this can account for 10-30% of reads
-– Ignore them … (decrease sensitivity)
+[[File:unsolved.png]]
-= Multi mapping reads =
+* Ignore them, but then again this decreases sensitivity
-* Unsolved problem:
+* Weighted assignment
-Solution is to use longer reads
+Of course, longer reads would solve this problem.
-= Transcripts/Genes =
+= One transcript, one set of reads =
-= Transcripts/Genes =
+[[File:t1.png]]
-GeneA
+= Two transcripts, another set of reads =
-T3
+[[File: t1t2.png
-= Transcripts/Genes =
+= Aggregation to Gene-level 1 =
-GeneA
+[[File:tt1t2aggreg.png]]
-= Transcripts/Genes =
+= Third transcript, another set of reads =
-GeneA
+[[File:t1t2t3.png]]
-T3
+= Aggregation to Gene-level 2 =
-= Transcripts/Genes =
+[[File:t1t2tt3aggreg.png]]
-GeneA
+= HTSeq-count =
-T3
+[[File:htseq.png]]
 * Designed for RNA-Seq counting
@@ Line 103: / Line 55: @@
 = HTSeq-count =
 = Probabilistic approach =