DIVE: a reference-free statistical approach to diversity-generating and mobile genetic element discovery

Genome Biology

Method

Jordi Abante¹  Peter L. Wang²  Julia Salzman³ 

[1] Biomedical Data Science, Stanford University, 1265 Welch Rd, 94305, Palo Alto, CA, USA;Center for Computational, Evolutionary and Human Genomics, Stanford University, 327 Campus Drive, 94305, Stanford, CA, USA;Current address: Department of Biomedical Sciences, Universitat de Barcelona, Casanova 143, 08036, Barcelona, Spain;Biomedical Data Science, Stanford University, 1265 Welch Rd, 94305, Palo Alto, CA, USA;Department of Biochemistry, Stanford University, 279 Campus Drive, 94305, Stanford, CA, USA;Biomedical Data Science, Stanford University, 1265 Welch Rd, 94305, Palo Alto, CA, USA;Department of Biochemistry, Stanford University, 279 Campus Drive, 94305, Stanford, CA, USA;Department of Statistics, Stanford University, 390 Serra Mall, 94305, Stanford, CA, USA;

关键词: Horizontal gene transfer;    Mobile genetic elements;    Diversity-generating mechanisms;    CRISPR;    Transposable elements;    Integrative and conjugative elements;    Non-coding RNA;   

DOI  :  10.1186/s13059-023-03038-0

 received in 2023-05-21, accepted in 2023-08-14,  发布年份 2023

来源: Springer

【 摘 要 】

Diversity-generating and mobile genetic elements are key to microbial and viral evolution and can result in evolutionary leaps. State-of-the-art algorithms to detect these elements have limitations. Here, we introduce DIVE, a new reference-free approach to overcome these limitations using information contained in sequencing reads alone. We show that DIVE has improved detection power compared to existing reference-based methods using simulations and real data. We use DIVE to rediscover and characterize the activity of known and novel elements and generate new biological hypotheses about the mobilome. Building on DIVE, we develop a reference-free framework capable of de novo discovery of mobile genetic elements.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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