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

Genome Biol

Biomedical Data Science, Stanford University, 1265 Welch Rd, Palo Alto, 94305, CA, USA.

Published: October 2023

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589994PMC
http://dx.doi.org/10.1186/s13059-023-03038-0DOI Listing

Publication Analysis

Top Keywords

mobile genetic
12
dive reference-free
8
diversity-generating mobile
8
genetic elements
8
dive
5
reference-free statistical
4
statistical approach
4
approach diversity-generating
4
genetic element
4
element discovery
4

Similar Publications

Carbapenem-resistant Acinetobacter baumannii (CRAB) is an emerging threat to healthcare settings in many countries, principally in South Asia. The current study was aimed to identify, evaluate whole-genome and characterize the prophages in genome of CRAB strain, recovered from patients of Lahore General Hospital, Lahore. More than 200 samples were collected and identified by morphological and biochemical tests.

View Article and Find Full Text PDF

Metagenomic deciphers the mobility and bacterial hosts of antibiotic resistance genes under antibiotics and heavy metals co-selection pressures in constructed wetlands.

Environ Res

January 2025

Chinese Academy of Sciences Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China; Hubei Key Laboratory of Wetland Evolution & Ecological Restoration, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China. Electronic address:

Both antibiotics and heavy metals exert significant selection pressures on antibiotic-resistance genes (ARGs). This study aimed to investigate the co-selection effects of doxycycline (DC) and cadmium (Cd) on ARGs in constructed wetlands (CWs). The results demonstrated that under antibiotic and heavy metal co-selection pressures, single high concentration DC/Cd or double high, relative abundances of metagenomics assembled genomes all reached 55.

View Article and Find Full Text PDF

The gram-negative, facultative anaerobic bacterium Morganella morganii is linked to a number of illnesses, including nosocomial infections and urinary tract infections (UTIs). A clinical isolate from a UTI patient in Bangladesh was subjected to high-throughput whole genome sequencing and extensive bioinformatics analysis in order to gather knowledge about the genomic basis of bacterial defenses and pathogenicity in M. morganii.

View Article and Find Full Text PDF

Mobile genetic elements help drive horizontal gene transfer and bacterial evolution. Conjugative elements and temperate bacteriophages can be stably maintained in host cells. They can alter host physiology and regulatory responses and typically carry genes that are beneficial to their hosts.

View Article and Find Full Text PDF

RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning.

Front Neurorobot

January 2025

Hebi Institute of Engineering and Technology, Henan Polytechnic University, Hebi, Henan, China.

Introduction: Path planning in complex and dynamic environments poses a significant challenge in the field of mobile robotics. Traditional path planning methods such as genetic algorithms, Dijkstra's algorithm, and Floyd's algorithm typically rely on deterministic search strategies, which can lead to local optima and lack global search capabilities in dynamic settings. These methods have high computational costs and are not efficient for real-time applications.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!