Greetings to Cufflinks users,
I am trying to interpret outputs from cuffcompare and cuffdiff to answer the following questions: is there some alternative splicing going on in an experiment (3 time points), and if yes, what is the absolute (or relative) abundance of the alternative transcripts and of the genes themselves (by summing up the abundance of their isoforms, or by considering only the major transcript).
Despite my reading of the Trapnell et al. 2010 paper about Cufflinks I am still unsure how to read the files cuffcompare and cuffdiff are generating.
For example, I understand the various identifiers (gene_id, p_id, transcript_id, tss_id) used by these tools track the objects I am interested in; however, I don't understand how they relate to each other. The long threads on SEQanswers about identifier missing or appearing when one forgot options (such as -s for cuffcompare) only add to the confusion.
Hence, can somebody come up with a simple explanation about how these identifiers relate to each other? I was hoping to find some figure in the Cufflinks documentation, but couldn't.
Out of curiosity I wrote a script to evaluate the cardinality of the mapping between each of these identifiers. E.g., if two objects in the combined.gtf file produced by cuffdiff have the same gene_id but two p_id then the mapping from gene_id to p_id is 1 to 2. Here is the result I obtain with my samples (all.combined.gtf file produced by CuffDiff):
The number before the ':' is the cardinality of the mapping and the number after it is how many of these do we find in the file. Hence, for example, there are 3 cases where one gene_id is linked to two p_id.
As you can see, it seems that most identifiers map to each other with a 1:1 mapping, which does not help me.
EDIT
I performed the same kind of mapping analysis on various identifiers of the isoforms.fpkm_tracking file produced by cuffdiff, which appear to be the one file I am interested in. Here is the result:
Is it right to interpret that I indeed have 14 genes with two isoforms (i.e., splicing variant), and one gene with four isoforms? tss standing for transcription starting sites, I would think that some isoforms have the same tss_id but different exons. How are these second type of isoforms reported?
Best,
Aurelien
I am trying to interpret outputs from cuffcompare and cuffdiff to answer the following questions: is there some alternative splicing going on in an experiment (3 time points), and if yes, what is the absolute (or relative) abundance of the alternative transcripts and of the genes themselves (by summing up the abundance of their isoforms, or by considering only the major transcript).
Despite my reading of the Trapnell et al. 2010 paper about Cufflinks I am still unsure how to read the files cuffcompare and cuffdiff are generating.
For example, I understand the various identifiers (gene_id, p_id, transcript_id, tss_id) used by these tools track the objects I am interested in; however, I don't understand how they relate to each other. The long threads on SEQanswers about identifier missing or appearing when one forgot options (such as -s for cuffcompare) only add to the confusion.
Hence, can somebody come up with a simple explanation about how these identifiers relate to each other? I was hoping to find some figure in the Cufflinks documentation, but couldn't.
Out of curiosity I wrote a script to evaluate the cardinality of the mapping between each of these identifiers. E.g., if two objects in the combined.gtf file produced by cuffdiff have the same gene_id but two p_id then the mapping from gene_id to p_id is 1 to 2. Here is the result I obtain with my samples (all.combined.gtf file produced by CuffDiff):
gene_id -> p_id
1: 10019 2: 3
gene_id -> transcript_id
1: 10119 2: 5
gene_id -> tss_id
1: 4547 2: 2
p_id -> gene_id
1: 10025
p_id -> transcript_id
1: 10025
p_id -> tss_id
1: 4551
transcript_id -> gene_id
1: 10129
transcript_id -> p_id
1: 10025
transcript_id -> tss_id
1: 4551
tss_id -> gene_id
1: 4551
tss_id -> p_id
1: 4551
tss_id -> transcript_id
1: 4551
1: 10019 2: 3
gene_id -> transcript_id
1: 10119 2: 5
gene_id -> tss_id
1: 4547 2: 2
p_id -> gene_id
1: 10025
p_id -> transcript_id
1: 10025
p_id -> tss_id
1: 4551
transcript_id -> gene_id
1: 10129
transcript_id -> p_id
1: 10025
transcript_id -> tss_id
1: 4551
tss_id -> gene_id
1: 4551
tss_id -> p_id
1: 4551
tss_id -> transcript_id
1: 4551
The number before the ':' is the cardinality of the mapping and the number after it is how many of these do we find in the file. Hence, for example, there are 3 cases where one gene_id is linked to two p_id.
As you can see, it seems that most identifiers map to each other with a 1:1 mapping, which does not help me.
EDIT
I performed the same kind of mapping analysis on various identifiers of the isoforms.fpkm_tracking file produced by cuffdiff, which appear to be the one file I am interested in. Here is the result:
gene_short_name -> nearest_ref_id
1: 9884 2: 47 3: 1 4: 1
gene_short_name -> tss_id
1: 4491 2: 14 4: 1
nearest_ref_id -> gene_short_name
1: 9985
nearest_ref_id -> tss_id
1: 4551
tss_id -> gene_short_name
1: 4523
tss_id -> nearest_ref_id
1: 4551
1: 9884 2: 47 3: 1 4: 1
gene_short_name -> tss_id
1: 4491 2: 14 4: 1
nearest_ref_id -> gene_short_name
1: 9985
nearest_ref_id -> tss_id
1: 4551
tss_id -> gene_short_name
1: 4523
tss_id -> nearest_ref_id
1: 4551
Is it right to interpret that I indeed have 14 genes with two isoforms (i.e., splicing variant), and one gene with four isoforms? tss standing for transcription starting sites, I would think that some isoforms have the same tss_id but different exons. How are these second type of isoforms reported?
Best,
Aurelien
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