ICEberg Documentation Version 3.0

Introduction

ICEberg 3.0 is a comprehensive database that focuses on Integrative and Conjugative Elements (ICEs) in bacterial genomes. ICEberg 3.0 represents a significant improvement over previous versions, which offers three major enhancements: (i) New ICE, IME and CIME data with manual curations. (ii) Categorization of ICE cargo functions. (iii) Extends its focus to encompass ICEs from the human microbiome. It will help in facilitating the understanding of bacterial evolution by capturing the diverse functions and characteristics of ICEs.

ICE dataset

• Records Update.The database now includes 1033 newly identified ICEs, 347 Integrated Mobile Elements (IMEs), and 40 Conjugative Mobile Elements (CIMEs), resulting in a total of 2065 ICEs, 607 IMEs, and 275 CIMEs. The data accuracy and reliability have been enhanced through manual curation and verification processes.
• Accessory Functional Modules. ICEberg 3.0 classifies accessory module genes into six groups, including antibiotic resistance, virulence factor, metal resistance, defense system, degradation capabilities, and symbiosis. The categorization allows researchers to gain insights into the biological traits and functionalities associated with ICEs.
• Data Records and Search. Users can search for ICE records based on ICE name, host species or functional categories of interest. The database offers advanced search filters to narrow down results based on user-defined criteria.

• Data Visualization. ICEberg 3.0 employs visual representation techniques to showcase the modular structure of ICEs and highlight their diverse accessory functions. The visual representation aids in better understanding the organization and functional roles of ICE elements.

• ICE Statistics. The distribution of various cargo functional genes within integrative and conjugative elements in the ICEberg database, encompassing antibiotic resistance, virulence factor, defense system, metal resistance, and degradation.

mICE dataset

• Data Sources and Content. The mICE section of ICEberg 3.0 focuses on ICEs identified in the human microbiome, the data in the mICE database is sourced from the Human Microbiome Project (HMP) and includes a collection of 1386 ICEs with diverse cargo genes, specifically from five different body sites: the oral cavity, nasal cavity, gastrointestinal tract, urogenital tract, and skin.
• Data Records and Search. Users can search for ICE records based on ICE family, host strains, sample source or functional categories of interest. The mICE database enables users to explore the role of ICEs within the context of the human microbiota and provides valuable insights into the functional potential of these elements.

• mICE Cluster. The presence of ICE clusters carrying various cargo functional genes, indicating the potential horizontal transfer of ICEs between different host bacteria or different human body site (the oral cavity, nasal cavity, gastrointestinal tract, urogenital tract, and skin).

• mICE Statistics. The sample source (Oral, Nasal, Gastrointestinal, Urogenital, and Skin) and host strain of integrative and conjugative elements in human microbiome, and the cargo functional genes distribution.

ICEfinder

• Features and Characteristics. ICEfinder utilizes advanced algorithms to predict ICEs and IMEs, considering gene composition, genetic context, and conjugation module characteristics. It allows researchers to simultaneously predict ICEs and explore their functional annotations, aiding in the comprehensive analysis of ICE elements in bacterial genomes.

• ICE Comparision The predicted ICEs region can be submitted for comparison with the ICEberg database. Two comparison methods are provided, namely Mash and Multigeneblast, both using default parameters. The Mash distance threshold is set to 0.005, while the parameters for Multigeneblast are as follows: hitspergene 250, minseqcov 25, minpercid 30, distancekb 10, and syntenyweight 0.5.

• mICE prediction. A specialized tool for predicting ICEs from metagenome assemblies is integrated into ICEfinder, enabling researchers to obtain metagenome species abundance, as well as identify and analyze ICEs within complex microbial communities. This integration provides a comprehensive solution for researchers to explore the presence of ICEs, understand their impact on microbial diversity, and gain insights into their functional characteristics within the context of metagenomic datasets.