We are pleased to announce the release of a new addition to the Oxford Nanopore Open Data project: sequencing of the Genome in a Bottle Ashkenazi Trio. These three reference samples were sequenced with two PromethION flow cells each to yield around 200 Gbases of sequencing per sample. All sequencing was performed using the Ligation Sequencing Kit V14 sequencing chemistry.
The following cell line samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: GM24143, GM24149, GM24385
As with previous releases the new dataset is available for anonymous download from an Amazon Web Services S3 bucket. The bucket is part of the Open Data on AWS project enabling sharing and analysis of a wide range of data.
The data is located in the bucket at:
See the tutorials page for information on downloading the dataset.
Two flowcells were used to sequence each of the samples to high depth:
For each flowcell used in the sequencing the PromethION device outputs are available. All
data is present as
.fast5 files, along with associated summary files in a structured fashion.
For example results from one of the flowcells used to sequence the GM24385 (HG002) sample are found as:
$ aws s3 ls s3://ont-open-data/giab_lsk114_2022.12/flowcells/hg002/20221109_1654_5A_PAG65784_f306681d/PRE fast5_fail/PRE fast5_pass/PRE fast5_skip/PRE other_reports/2022-12-06 16:11:54 258 barcode_alignment_PAG65784_f306681d_16a70748.tsv2022-12-06 22:33:58 670 final_summary_PAG65784_f306681d_16a70748.txt2022-12-06 22:34:01 2670667 pore_activity_PAG65784_f306681d_16a70748.csv2022-12-06 22:34:00 1233022 report_PAG65784_20221109_1700_f306681d.html2022-12-06 22:34:01 764774 report_PAG65784_20221109_1700_f306681d.json2022-12-06 22:34:02 3313290 report_PAG65784_20221109_1700_f306681d.md2022-12-06 22:34:02 190 sample_sheet_PAG65784_20221109_1700_f306681d.csv2022-12-06 22:34:03 2583851309 sequencing_summary_PAG65784_f306681d_16a70748.txt2022-12-06 22:34:18 640850 throughput_PAG65784_f306681d_16a70748.csv
The data analyses presented here were performed using our end-to-end wf-human-variation workflow implemented in Nexflow. The workflow is fully integrated using containerised software to provide scalable analysis. As a brief overview the workflow is capable of performing:
The workflow was run on the combined sets of data from each pair of flowcells for each sample. For each sample we have provided results for two flavours of the basecalling algorithm: 1) hac - high accuracy and 2) sup - super accuracy. The choice is reflected in the path names in the S3 bucket.
All compute was performed using Amazon Web Services Batch compute driven by Nextflow. The end-to-end workflow including basecalling and small variant calling took around 7 hours to run. All alignments and variants were calculated with respect to GRCh38.
In addition to running variant calling have provided also results of benchmarking analysis using hap.py for small variants (HG002, HG003 HG004) and truvari for structural variants (HG002 only). The results of running these tools are shown in the tables below and can be found at:
Note that since the HG002 structural variant benchmark is with respect to the GRCh37 reference, the sequencing data was realigned to this reference before performing variant calling and benchmarking
For additional information regarding these data please contact email@example.com.
We hope that these data and analyses provide a useful resource to the community.