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BiocParallel error #551

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@sophiachen1

Hi @cying111 ,

The error below returned when I run bambu 3.12.
My command is
se <- bambu(reads = samples.bam, annotations = annotations, genome = fa.file, ncore = 64, rcOutDir="./rcOutput/")

The error is :
--- Start generating read class files ---
Error: BiocParallel errors
1 remote errors, element index: 1
0 unevaluated and other errors
first remote error:
Error in .Call2("C_find_overlaps_in_groups_NCList", start(q), end(q), : negative length vectors are not allowed
Execution halted

Do you have an idea how to troubleshoot? I ran on a different sample without problem.

Thanks,
Sophia

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  1. sophiachen1 commented on Apr 9, 2026

    @sophiachen1
    Author

    I solved this issue by adding lowMemory option.

  2. sophiachen1 commented on Apr 16, 2026

    @sophiachen1
    Author

    Hi team,

    I am sorry to reopen this again. The same error comes back for some other samples even with the lowMemory option on. Do you have any suggestion to troubleshoot it?

    Thank you so much,
    Sophia

  3. sophiachen1 commented on Apr 20, 2026

    @sophiachen1
    Author

    Hi @cying111,
    Have you seen this error before? I think it happens to my bam files more than 50 Gb. Any idea to troubleshoot?

    Thank you ahead,
    Sophia

  4. yinshiyi commented on Oct 12, 2026

    @yinshiyi

    @sophiachen1 ,
    I think it is not a pure negative error, it possibly indicate that your sample is rich.
    Possibly root cause: in a deep dataset, thousands of distinct high-confidence spliced read classes appears, findOverlaps() call returns more than 2³¹ (about 2.1 billion) hits, and IRanges can’t store that many.
    In C behavior, hit count goes past INT_MAX, the length wraps to a negative number. R’s allocVector then fails with “negative length vectors are not allowed”.
    i think looking into the design of getUnsplicedReadClassByReference to improve the split worker design.

    in lowMemory, the work is splits by chromosome. that fixes samples where the total classes across the genome went over 2³¹. but when the dataset got even bigger, split by chromosome will still crash.

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