Comments (4)
Questions about Kipoi output are best directed to the leaders of that project; I’m not familiar with how they’re using Basenji.
For predictions relative to a gene annotation, see this tutorial https://github.com/calico/basenji/blob/master/tutorials/genes.ipynb
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thanks for your reply. I have tried the GitHub basenji prediction with basenji_sed.py. It successfully gave me the expression results. However, I'm not sure what's the definition of gene expression. I'm curious that in the publication it seems the training results are the 128 bin coverage for different cell types. So how does basenji link the coverage with the expression? Does this method use TSS coverage to predict the expression? I suppose here the gene expression level is not the FPKM score.
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Yes, it’s using predicted activity in the 128 bp bin that covers the gene’s TSSs to describe its expression. I have a preprocessing step that takes in a GTF to determine those positions.
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Thank you for the explanation.
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Related Issues (20)
- is it still unfinished? HOT 1
- No checkpoints found. HOT 3
- "When the bin size needs to be adjusted (e.g. from 128 to 100), how should I design the optimal pooling kernel size?" HOT 2
- Can I apply Basenji's series of functions to the Enformer model? HOT 2
- fasta file of saluki_predict_fasta.py HOT 1
- train/test/valid sequences for predetermined regions HOT 2
- failed running "basenji_sat_bed.py" in tutorials HOT 4
- Basenji Features used for Whole Blood Fine-Mapping HOT 3
- Issue running basenji_data.py HOT 2
- Blacklist file & unmappable regions HOT 2
- question about clip threshold in basenji/enformer. HOT 2
- Training with multiple directories HOT 1
- Problem with multigenome training with > 2 genomes HOT 1
- Tensorflow records generated with basenji_data.py differ from original HOT 1
- akita can't train HOT 1
- Question about Gene Expression Training Preprocessing HOT 3
- pygene installation HOT 2
- Question about Basenji2 receptive field HOT 2
- How to prepare TFRecords files for saluki training HOT 1
- error with akita_data. py HOT 3
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