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simoncchu avatar simoncchu commented on July 30, 2024

Yeah, this is in my to-do-list. I'll export a vcf file format. For the current output, each column representation could be find here: https://github.com/parklab/xTea_paper/tree/main/run_tools/xTea/HG002.
There is a intermediate file called candidate_list_from_clip.txt has the number of clipped reads (third column), but I didn't count the mapped...

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xzhuo avatar xzhuo commented on July 30, 2024

Thanks for the pipeline, it is a very useful tool.

Follow up on the xtea_long output: why do the SVA insertion positions often start from a negative value?
Like the 2nd line here: https://github.com/parklab/xTea_paper/blob/main/run_tools/xTea/HG002/HG002_hg38_Nanopore_xTea_SVA.txt

chr6 138775846 SVA None -1411:1274:+ None

Thank you very much!

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simoncchu avatar simoncchu commented on July 30, 2024

negative value indicates the insertion is started/ended from the flanking region (likely to be transduction). But maybe also the reported annotation is incorrect.

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xzhuo avatar xzhuo commented on July 30, 2024

Thanks for your swift reply! Are there ways to infer the correct consensus position for SVA?

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simoncchu avatar simoncchu commented on July 30, 2024

It's not straightforward as the reference SVA annotation is fragmented and inaccurate (because of the tandem repeats expansion). For a simple way, just consider position 0 as the start position on the consensus, but it may be inaccurate.

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evayfang2019 avatar evayfang2019 commented on July 30, 2024

Thank you, a useful tool for analyzing TEs.
Can xtea_long now generate vcf file directly? I find my output is still classified_results*.txt. I don't know if I made a mistake.

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