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Allele-specific telomere analysis from long reads


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A method for measuring allele-specific TL and characterizing telomere variant repeat (TVR) sequences from long reads. Telogator2 was designed to work with long reads that are capable of representing telomere regions without high sequencing error rates, and has been tested primarily on PacBio HiFi reads and ONT reads basecalled with Dorado.

If this software has been useful for your work, please cite us at:

Stephens, Z., & Kocher, J. P. (2024). Characterization of telomere variant repeats using long reads enables allele-specific telomere length estimation. BMC bioinformatics, 25(1), 194.


Telogator2 dependencies can be easily installed via conda:

# create conda environment
conda env create -f conda_env_telogator2.yaml

# activate environment
conda activate telogator2

Running Telogator2:

Telogator2 can be run in single command:

python -i input.fq \ 
                     -o results/ \ 
                     --muscle /path/to/muscle \ 
                     --minimap2 /path/to/minimap2

-i accepts fa / fa.gz / fq / fq.gz / bam. Multiple arguments can be provided (e.g. -i reads1.fa reads2.fa).

Telogator2 requires that muscle v3.8 is installed in order to produce consensus TVR sequences. If the dependencies were installed via conda then muscle should be found in the envs/telogator2/ directory. Additionally, Telogator2 requires a path to an aligner, via either the --minimap2, --winnowmap, or --pbmm2 input parameters.

Output files

The primary output files are:

  • tlens_by_allele.tsv allele-specific telomere lengths
  • all_final_alleles.png plots of all alleles (TVR + telomere regions)
  • violin_atl.png violin plot of ATLs at each chromosome arm

The main results are in tlens_by_allele.tsv, which has the following columns:

  • chr anchor chromosome arm
    • subtelomeres that could not be aligned are labeled chrU for 'unmapped'
  • position anchor coordinate
  • ref_samp the specific T2T reference contig to which the subtelomere was aligned
  • allele_id ID number for this specific allele
    • ids ending in i indicate subtelomeres that were aligned to known interstitial telomere regions. These alleles should likely be excluded from subsequent analyses.
  • TL_p75 ATL (reports 75th percentile by default)
  • read_TLs ATL of each supporting read in the cluster
  • read_lengths length of each read in the cluster
  • read_mapq mapping quality of each read in the cluster
  • tvr_len length of the cluster's TVR region
  • tvr_consensus consensus TVR region sequence
  • supporting_reads readnames of each read in the cluster

Recommended settings

Sequencing platforms have different sequencing error rates (and error types), as such we recommend running Telogator2 with different options based on your input data type:

PacBio Revio HiFi reads (30x) - -r hifi -tt 0.400 -ts 0.250 -n 4
PacBio Sequel II reads (10x) - -r hifi -tt 0.250 -ts 0.250 -n 3
Nanopore R10 reads (30x) - -r ont -tt 0.300 -ts 0.300 -n 5

Older Nanopore data might not be usable, as reads basecalled with Guppy have extremely high rates of sequencing errors in telomere regions. Additionally, Revio data generated prior to SMRTLink13 will likely not have sufficient telomere reads. For Revio reads sequenced with SMRTLink13 and onward, we advise including both the "hifi" BAM and "fail" BAM as input to Telogator2.

For very low coverage data, consider lowering the minimum number of reads per allele down to -n 2 or even -n 1, but expect that this will also lead to false positives.

Test data

Telomere reads for HG002 can be found in the test_data/ directory.

HiFi reads (~70x): hg002-telreads_pacbio.fa.gz
ONT reads  (~25x): hg002-telreads_ont.fa.gz

Telogator reference

The reference sequence used for telomere anchoring currently contains the first and last 500kb of sequences from the following T2T assemblies:

More will be added as they become available.


Allele-specific telomere analysis from long reads







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