Using HPV-meta for human papillomavirus RNA quality detection

Abstract

In the era of cervical cancer elimination, accurate and validated pipelines to detect human papillomavirus are essential to elucidate and understand HPV association with human cancers. We aimed to provide an open-source pipeline, “HPV-meta”, to detect HPV transcripts in RNA sequencing data, including several steps to warn operators for possible viral contamination. The “HPV-meta” pipeline automatically performs several steps, starting with quality trimming, human genome filtering, HPV detection (blastx), cut-off settlement (10 reads and 690 bp coverage to make an HPV call) and finishing with fasta sequence generation for HPV positive samples. Fasta sequences can then be aligned to assess sequence diversity among HPV positive samples. All RNA sequencing files (n = 10,908) present in the cancer genome atlas (TCGA) were analyzed. “HPV-meta” identified 25 different HPV types being present in 488/10,904 specimens. Validation of results showed 99.98% agreement (10,902/10,904). Multiple alignment from fasta files warned about high sequence identity between several HPV 18 and 38 positive samples, whose contamination had previously been reported. The “HPV-meta” pipeline is a robust and validated pipeline that detects HPV in RNA sequencing data. Obtaining the fasta files enables contamination investigation, a non very rare occurrence in next generation sequencing.

The Swedish Cervical Screening Cohort

“…Pseudo-anonymised data can furthermore enrich datasets to train and validate machine learning models to classify or predict cervical cancer and all metagenomics analysis pipelines developed by studies on the Cervical Screening Cohort 17 , 18 are open source. These pipelines can thus enrich other datasets with evidence based on metagenomics approaches.…”

“…Sequencing data and metadata are uploaded interactively or through Jupyter notebooks to facilitate metadata uploading and use. The published bioinformatics pipelines for metagenomics analyses, available through GitHub 17 , 18 , are also implemented in the Hopsworks platform at Karolinska Institutet. In-house developed JAVA-based management tools for self-testing, reporting and data extraction for research.…”

“…The following are the main components of the software platform used to support the screening program and manage the produced data: www.nature.com/scientificdata www.nature.com/scientificdata/ uploaded interactively or through Jupyter notebooks to facilitate metadata uploading and use. The published bioinformatics pipelines for metagenomics analyses, available through GitHub 17,18 , are also implemented in the Hopsworks platform at Karolinska Institutet.…”