Package: ilmn-pelops
PelopsDedicated caller forDUX4rearrangements from whole genome sequencing data.CitingPelops is based on a method first described in:Ryan, S.L., Peden, J.F., Kingsbury, Z. et al.
Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia.
Leukemia 37, 518–528 (2023).https://doi.org/10.1038/s41375-022-01806-8Pelops itself is described and validated in:Grobecker, P., Mijuskovic, M., et al.
Pelops: A dedicated caller forDUX4rearrangements from short-read whole genome sequencing data.
In preparation (2024)PrerequisitesPython 3.7 and above.InstallationYou can install the latest stable released version of Pelops using pippipinstallilmn-pelops--upgradeNote: pip/pypi will prefer stable versions (i.e. 0.7.0) over subsequent beta releases (i.e.
0.8.0b1). If you need to install a beta version for testing, please first
uninstall your current version of pelops.pipuninstallilmn-pelopsUsagePelops is a tool with a command line interface (cli). Discover its usage withpelops--helpCallingDUX4-rearrangementsTo call DUX4-rearrangements from a BAM/CRAM file, use thedux4rsubcommand. To
see all available options runpelopsdux4r--helpInputsThe input to Pelops is a short-read whole-genome sequencing BAM or CRAM file from a tumour sample,
aligned to the GRCh38 reference genome. The BAM/CRAM file needs to be indexed.
Pelops was tested on alignments by DRAGEN (version 4.0.3), bwa (version 0.7.17), and Isaac (version SAAC01325.18.01.29).Systematic noise BED fileTo increase specificity when calling non-IGHDUX4-rearrangements, we recommend using a systematic noise BED file.
This file contains genomic regions that will be ignored by Pelops when identifying candidate regions involved in aDUX4-rearrangement. Since such regions can be specific to the read alignment tool, reference genome,
sequencing protocol, and cancer type analysed, we recommend creating a separate systematic noise BED file
for each project. One way to obtain these genomic regions would be to run Pelops on a panel of normal samples, which are
guaranteed to have noDUX4-rearrangements, and generate a list of false-positive calls.OutputsPelops outputs results in a JSON file, and optionally exports supporting reads in SAM files.Description of JSON outputThetop levelof the JSON contains information about pelops (assumed genome reference, version, name, and CLI command).
It also contains information about the input file (number of unique and mapped reads - which can be a user input).
Finally, it contains a list of rearrangements investigated by pelops.{"reference":"GRCh38","unique_mapped_reads":1000000000,"rearrangements":[...],"program_name":"pelops","version":"0.5.0","cli_command":"pelops dux4r --total-number-reads 1000000000 --export . test.bam"}Therearrangementsconsist of a unique ID, genomic region sets "A" and "B", and the evidence for the rearrangement
between these two regions.
For the commandpelops dux4r, ID01always corresponds to rearrangements between the coreDUX4regions andIGH,
while ID02corresponds to rearrangements of the extendedDUX4regions withIGH.
IDs03and beyond are potential rearrangements of the coreDUX4region with other genomic regions (marked asUNNAMED);
there can be a variable number of them.{"rearrangements":[{"id":"01","A":{"name":"CoreDUX4"...},"B":{"name":"IGH"...},"evidence":{...}},{"id":"02","A":{"name":"ExtendedDUX4"...},"B":{"name":"IGH"...},"evidence":{...}},{"id":"03","A":{"name":"CoreDUX4"...},"B":{"name":"UNNAMED"...},"evidence":{...}}]}AandBdocument the exact set of genomic regions used for each rearrangement.
For example, the coreDUX4region is shown below.
WhileIGH,CoreDUX4andExtendedDUX4are pre-defined, eachUNNAMEDregion will be different.{"name":"CoreDUX4","regions":[{"chrom":"chr4","start":190020407,"end":190023665},{"chrom":"chr4","start":190066935,"end":190093279},{"chrom":"chr4","start":190172774,"end":190176845},{"chrom":"chr10","start":133663429,"end":133685936},{"chrom":"chr10","start":133739606,"end":133762125}]}Theevidencefor each rearrangement consists of the number of split and paired reads between region sets A and B,
and the spanning read pairs per billion (SRPB). It is calculated as
$$\text{SRPB} = 10^9 \frac{\text{paired reads} + \text{split reads}}{\text{total unique and mapped reads}}.$${"paired_reads":15,"split_reads":4,"SRPB":19.0}SAM filesOptionally, for each rearrangement a SAM file can be exported which contains all paired and split reads with their mates.
The naming convention is<id>_<name_A>-<name_B>.sam, where<id>,<name_A>,<name_B>correspond to the ID and
names of genomic region sets A and B, respectively, as documented in the JSON.ContributingWe are not accepting pull requests into this repository at this time, as the
licence currently does not allow modifications by third parties.
For any bug report / recommendation / feature request, please open an issue.CreditsSeeAuthors.