Not all chromosomes present in reference annotations.

[DrOllyGomez]

console looks like:

summarisedexpt <- bambu(reads = bamfiles, rcOutDir = "/home/mike.harbour/Desktop/bambuWorkingDirectory/bambuOut", annotations = bambuAnnotations, genome = fa.file)
Start generating read class files
iteration: 1not all chromosomes present in reference annotations,
annotations might be incomplete. Please compare objects
on the same reference

Error: BiocParallel errors
1 remote errors, element index: 1
11 unevaluated and other errors
first remote error: attempt to set too many names (1) on IRanges object of length 0

Resources:

1. I downloaded "gencode.v40.annotation.gtf.gz" from site:
   http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_40/
2. I downloaded "GRCh38.p13.genome.fa.gz" from SAME site. Expecting these to be compatible.

I mapped my fastq's against the genome file above to create bams.
Then in R I issued:

gtf.file <- "/home/mike.harbour/Resources/release40/gencode.v40.annotation.gtf"
bambuAnnotations <- prepareAnnotations(gtf.file)
fa.file <- "/home/mike.harbour/Resources/release40/GRCh38.p13.genome.fa"
library(Rsamtools)
bamfiles <- Rsamtools::BamFileList(c("Desktop/bambuWorkingDirectory/barcode01/aligned_reads_bam_BC01.bam", etc etc etc a further 11 bam files
summarisedexpt <- bambu(reads = bamfiles, rcOutDir = "Desktop/bambuWorkingDirectory/bambuOut", annotations = bambuAnnotations, genome = fa.file)

and got the errors reported above.

Version

packageVersion("bambu")
[1] ‘2.0.3’

Any advice, comments, fixes most welcome. Thanks in advance!
Mike

[cying111]

Hi @DrOllyGomez ,

Based on the error message, it looks like one of the internal objects is unexpectedly empty. The two files that you used look good to me as they are compatible. May I know which system are you running this on, Windows or Mac?

[DrOllyGomez]

Hiya!
I'm using Ubuntu 20, and I'm running bambu from a singularity image that I obtained from the nf-core nanoseq project (I dont run that pipeline (yet), I am using the image with bambu in it as a standalone app, via a shell into the container.)

In case it is relevant, here is some preparatory code I use to make the bams I aligned using the resources I mentioned:

minimap2 -ax map-ont -L $fullpathtogenomeindex $fullpathtomasteroutputdirectory/barcode$bcindex/master.fastq > $fullpathtomasteroutputdirectory/barcode$bcindex/mapoutBC$bcindex.txt 2>$fullpathtomasteroutputdirectory/barcode$bcindex/mapchatBC$bcindex.txt
2>&2
cat $fullpathtomasteroutputdirectory/barcode$bcindex/mapoutBC$bcindex.txt | samtools view -bh -F 2308 -q $qvaluetouse | samtools sort -O bam > $fullpathtomasteroutputdirectory/barcode$bcindex/aligned_reads_bam_BC$bcindex.bam 

I'm very grateful for your attention in this matter
Best wishes
Mike

[cying111]

Hi @DrOllyGomez , thanks for showing the code that you used to map the reads, it is very helpful. It looks to me like the parameter that you are using is for aligning genomic reads not RNA/cDNA reads, which may give you very little aligned reads and later caused the error I believe, to check that, you can run the following code on your bam file:
samtools flagstat your_bam_file
You probably will see very little mapped reads

To map RNA/cDNA reads, you should follow this section

Map long mRNA/cDNA reads
minimap2 -ax splice:hq -uf ref.fa iso-seq.fq > aln.sam # PacBio Iso-seq/traditional cDNA
minimap2 -ax splice ref.fa nanopore-cdna.fa > aln.sam # Nanopore 2D cDNA-seq
minimap2 -ax splice -uf -k14 ref.fa direct-rna.fq > aln.sam # Nanopore Direct RNA-seq
minimap2 -ax splice --splice-flank=no SIRV.fa SIRV-seq.fa # mapping against SIRV control

from minimap2 manual page, where the splice parameter will allow splicing, so the intron sequences will be skipped.

[DrOllyGomez]

Thank you so much for your help!! I apologise for making a basic error.
That's made all the difference ... many more reads, and bambu succeeds!
All the very best, .. very excited now :)

Mike

[DrOllyGomez]

Closing ticket, operator supplied wrong input.

[yangqimeng99]

Hi bambu team,

I found this issue because, like the original poster, I'm also working with cDNA long-read data from multiple platforms. My case is at a much larger scale, so I'd really appreciate your guidance on the recommended workflow.

In total I have 1000+ samples spread across three sequencing platforms: PacBio, ONT, and another nanopore-based long-read platform similar to ONT. My goal is to build a single, unified transcript annotation (one extended_annotations.gtf) from all of them, so that novel transcripts receive consistent IDs across the whole cohort.

My questions:
Mixed-platform, single run: Can I pass all of these samples — the per-sample BAMs (or precomputed rcFiles) from all three platforms — directly into one bambu() call (reads = c(...)) to produce a single combined discovery result? Or does bambu expect all inputs in one run to come from the same platform?
Effect of mixing platforms: The three platforms differ in error/read-length profiles and are aligned with different minimap2 settings — broadly, PacBio is a high-accuracy long read, while ONT and the third platform are nanopore-based. Does combining them in one run affect model training or NDR estimation? I understand bambu trains a model and scores novel transcripts per-sample by default — so is cross-platform mixing handled naturally, or would you instead recommend running discovery separately per platform (or per technology type) and then merging the annotations?
Scaling to 1000+ samples: What is the recommended workflow for discovery + quantification at this scale? Specifically:

Is the rcFile approach (generate rcFiles once per sample, then run discovery on all of them together) the intended path here?
Any guidance on lowMemory, ncore, and the expected memory/runtime at this scale?
Is there a practical upper limit on the number of samples for a single joint discovery run, or a point where you'd suggest a two-step strategy (per-batch or per-platform discovery → merge)?

The version I'm currently using: bioconductor-bambu v3.12.1

Thanks a lot for this great tool, and for any best-practice recommendations for a mixed-platform, large-cohort setup!