Enriquez, D. | Search bioRxiv

bioRxiv2026

Complete mitochondrial genomes of Arizona West Nile virus vectors, Culex quinquefasciatus and Culex tarsalis

Here we report a newly developed method utilizing long-range PCR and long-read Pacific Biosciences HiFi sequencing that successfully obtained two full-length and annotated mitochondrial genomes from Culex quinquefasciatus Say, 1823 and Culex tarsalis Coquillett, 1896, both from Maricopa County, Arizona, USA. Given the substantial burden of West Nile virus in Maricopa County over the past decade, and that these vectors are primarily responsible for spillover to human populations in the county, it is critical to better understand their distribution over time and space. This study begins to approach this need by contributing a novel approach that has resulted in the first West Nile virus vector mitochondrial genomes from Arizona. Our circular Cx. quinquefasciatus mitogenome is 15,587 bp in length, making it the first USA-based mitogenome sequenced through the AT-rich control region. The Cx. tarsalis mitochondrial genome is 16,416 bp long, longer than recently published California-based CTarK1 and Texas-based PQ585801 mitogenomes. The increased length of the Cx. tarsalis mitogenome is a result of a 905 bp insertion in the AT-rich control region, not present in the species publicly available mitogenomes. A maximum likelihood-based phylogenetic reconstruction supports the species designation of these newly-sequenced mitogenomes. The newly developed methodology offers a unique approach to study medically-important vector species around the globe, providing a solution to study populations through pooled vector pathogen surveillance programs.

genomics↗

bioRxiv2018

Joint analysis of matched tumor samples with varying tumor contents improves somatic variant calling in the absence of a germline sample

Archival tumor samples represent a potential rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and germline variants that are private to the individual. Archival sections often contain adjacent normal tissue, but this normal tissue can include infiltrating tumor cells. Comparative somatic variant callers are designed to exclude variants present in the normal sample, so a novel approach is required to leverage sequencing of adjacent normal tissue for somatic variant calling. Here we present LumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient. The approach is based on the concept that the allelic fraction of somatic variants, but not germline variants, would be reduced in samples with low tumor content. LumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses the model to determine expected allelic fractions for somatic and germline variants and classify variants accordingly. To evaluate using LumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high tumor content, low tumor content, and germline exome sequencing data (to define true somatic variants) available for each patient. We show that both sensitivity and positive predictive value are improved by analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or compared to in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which normal samples were not available for germline sequencing, but tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using LumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent normal as a reference. Together, these results demonstrate the potential utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional DNA sample is not available.

bioinformatics↗