Browsing by Author "Jenrette, Jeremy"
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- Improved reference genome of the arboviral vector Aedes albopictusPalatini, Umberto; Masri, Reem A.; Cosme, Luciano V.; Koren, Sergey; Thibaud-Nissen, Françoise; Biedler, James K.; Krsticevic, Flavia; Johnston, J. Spencer; Halbach, Rebecca; Crawford, Jacob E.; Antoshechkin, Igor; Failloux, Anna-Bella; Pischedda, Elisa; Marconcini, Michele; Ghurye, Jay; Rhie, Arang; Sharma, Atashi; Karagodin, Dmitriy A.; Jenrette, Jeremy; Gamez, Stephanie; Miesen, Pascal; Masterson, Patrick; Caccone, Adalgisa; Sharakhova, Maria V.; Tu, Zhijian Jake; Papathanos, Philippos A.; Van Rij, Ronald P.; Akbari, Omar S.; Powell, Jeffrey R.; Phillippy, Adam M.; Bonizzoni, Mariangela (2020-08-26)Background The Asian tiger mosquito Aedes albopictus is globally expanding and has become the main vector for human arboviruses in Europe. With limited antiviral drugs and vaccines available, vector control is the primary approach to prevent mosquito-borne diseases. A reliable and accurate DNA sequence of the Ae. albopictus genome is essential to develop new approaches that involve genetic manipulation of mosquitoes. Results We use long-read sequencing methods and modern scaffolding techniques (PacBio, 10X, and Hi-C) to produce AalbF2, a dramatically improved assembly of the Ae. albopictus genome. AalbF2 reveals widespread viral insertions, novel microRNAs and piRNA clusters, the sex-determining locus, and new immunity genes, and enables genome-wide studies of geographically diverse Ae. albopictus populations and analyses of the developmental and stage-dependent network of expression data. Additionally, we build the first physical map for this species with 75% of the assembled genome anchored to the chromosomes. Conclusions The AalbF2 genome assembly represents the most up-to-date collective knowledge of the Ae. albopictus genome. These resources represent a foundation to improve understanding of the adaptation potential and the epidemiological relevance of this species and foster the development of innovative control measures.
- Shark detection and classification with machine learningJenrette, Jeremy; Liu, Zac; Chimote, Pranav; Hastie, Trevor; Fox, Edward; Ferretti, Francesco (Elsevier, 2022-07-01)
- sharkPulse Validation MonitorJenrette, Jeremy; Chang, Gregory; Gordon, Steven; Mulgrew, Mason; Debay, Hunter (Virginia Tech, 2021-05-07)Abundance and distribution data of global shark populations is necessary for effective conservation and management. While there are operative direct methods to retrieve such data from scientific surveys and fisheries monitoring, species specific indices of population abundance coming from these sources are rare for most shark species. Yet, there is an abundance of unconventional and unstructured information within social networks that is virtually untapped and has great potential to fill the information gap characterizing shark populations. Social networks such as Flickr and Instagram provide data wells of shark sightings that can be data mined, but must be validated as genuine sightings. Despite its modern surge in popularity, there is little research that implements social media for shark conservation. Here, we show the biological importance of creating an application within sharkPulse to facilitate speedy validation by involving citizen scientists. The Monitor allows users to search a world map for potential shark sightings and fill out forms for popup balloons of these sightings. If it is indeed a shark, they may answer 'yes' and fill out the associative taxonomic information if they are familiar with the common and/or species name. They may also consult a sharkPulse identification guide if they are not familiar. The application was built with RShiny App software. The Validation Monitor can be used by anyone interested in these charismatic group of animals. The application can be found at sharkpulse.cnre.vt.edu/can-you-recognize/.