Transcription of human papillomavirus oncogenes in head and neck squamous cell carcinomas.

Some head and neck cancers are caused by human papillomavirus (HPV). As HPV vaccination can prevent infection, an estimation of which HPV types have an active viral oncogene transcription in what proportion of tumors might allow estimation of the proportion of head & neck cancers preventable by HPV vaccination. We used all RNA sequencing data from primary tumors of head and neck squamous cell carcinomas from the Cancer Genome Atlas (n = 500 patients).

ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples.

Despite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as "unknown" since many of the sequences are not similar to known genomes. In this work, we developed ViraMiner, a deep learning-based method to identify viruses in various human biospecimens.

NordScreen - an interactive tool for presenting cervical cancer screening indicators in the Nordic countries.

Quality assurance and improvement of cancer screening programs require up-to-date monitoring systems and evidence-based indicators. National quality reports exist but the definition and calculation of indicators vary making comparisons between countries difficult. The aim is to stimulate collaborative research and quality improvements in screening through freely available, comparable and regularly updated quality indicators.

Increasing participation in cervical screening by targeting long-term nonattenders: Randomized health services study.

High screening participation in the population is essential for optimal prevention of cervical cancer. Offering a high-risk human papillomavirus (HPV) self-test has previously been shown to increase participation. In this randomized health services study, we evaluated four strategies with regard to participation.

Machine Learning for detection of viral sequences in human metagenomic datasets.

Background: Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as "unknown", as conventional methods find no similarity to known sequences. We wished to explore whether machine learning algorithms using Relative Synonymous Codon Usage frequency (RSCU) could improve the detection of viral sequences in metagenomic sequencing data.

Viremia preceding multiple sclerosis: Two nested case-control studies.

Infections have been suggested to be involved in Multiple Sclerosis (MS). We used metagenomic sequencing to detect both known and yet unknown microorganisms in 2 nested case control studies of MS. Two different cohorts were followed for MS using registry linkages.

Extension of the viral ecology in humans using viral profile hidden Markov models.

When human samples are sequenced, many assembled contigs are "unknown", as conventional alignments find no similarity to known sequences. Hidden Markov models (HMM) exploit the positions of specific nucleotides in protein-encoding codons in various microbes. The algorithm HMMER3 implements HMM using a reference set of sequences encoding viral proteins, "vFam".

ViraPipe: scalable parallel pipeline for viral metagenome analysis from next generation sequencing reads.

Motivation: Next Generation Sequencing (NGS) technology enables identification of microbial genomes from massive amount of human microbiomes more rapidly and cheaper than ever before. However, the traditional sequential genome analysis algorithms, tools, and platforms are inefficient for performing large-scale metagenomic studies on ever-growing sample data volumes. Currently, there is an urgent need for scalable analysis pipelines that enable harnessing all the power of parallel computation in computing clusters and in cloud computing environments.

Viruses in cancers among the immunosuppressed.

Most cancer forms known to be caused by viruses are increased among the immunosuppressed, but several cancer forms without established viral etiology are also increased, notably nonmelanoma skin carcinoma (NMSC). We followed all 13,429 solid organ transplantation patients in Sweden for cancer occurrence after transplantation. We requested these tumor specimens and sequenced the first 89 specimens received (62 NMSCs, 27 other cancers).

Viruses in case series of tumors: Consistent presence in different cancers in the same subject.

Studies investigating presence of viruses in cancer often analyze case series of cancers, resulting in detection of many viruses that are not etiologically linked to the tumors where they are found. The incidence of virus-associated cancers is greatly increased in immunocompromised individuals. Non-melanoma skin cancer (NMSC) is also greatly increased and a variety of viruses have been detected in NMSC.