Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,260 bioRxiv papers from 306,813 authors.
Accurate detection of HIV transmission clusters from phylogenetic trees using a multi-state birth-death model
HIV transmission networks are highly clustered, and accurate identification of these clusters is essential for effective targeting of public health interventions. This clustering affects the transmission dynamics of the HIV epidemic, which affects the pathogen phylogenies reconstructed from patient samples. We present a new method for identifying transmission clusters by detecting the changes in transmission rate provoked by the introduction of the epidemic into a new cluster. The method employs a multi-state birth-death (MSBD) model where each state represents a cluster. Transmission rates in each cluster decrease exponentially over time, simulating susceptible depletion in the cluster. This model is fitted to the pathogen phylogeny using a Maximum Likelihood approach. Using simulated datasets we show that the MSBD method is able to reliably infer both the cluster repartition and the transmission parameters from a pathogen phylogeny. In contrast to existing cutpoint-based methods for cluster identification, which are dependent on a parameter set by the user, the MSBD method is consistently reliable. It also performs better on phylogenies containing nested clusters. We present an application of our method to the inference of transmission clusters using sequences obtained from the Swiss HIV Cohort Study. The MSBD method is available as an R package.
- Downloaded 335 times
- Download rankings, all-time:
- Site-wide: 35,040 out of 70,262
- In bioinformatics: 4,345 out of 6,888
- Year to date:
- Site-wide: 61,069 out of 70,262
- Since beginning of last month:
- Site-wide: 67,983 out of 70,262
Downloads over time
Distribution of downloads per paper, site-wide
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!