Axbio Publication Alert! | Axbio Publishes MetaCONNET: A Cutting-Edge Metagenomic Polishing Tool for Long-Read Assemblies

Dec 3rd, 2024 – Researchers at Axbio in collaboration with Inner Mongolia Agricultural University and Peking University International Cancer Institute, have developed MetaCONNET, an advanced deep-learning-based polishing tool designed to enhance metagenomic long-read assemblies. Their findings were recently published in PLOS ONE under the title: "MetaCONNET: A Metagenomic Polishing Tool for Long-Read Assemblies"1. 

Long-read sequencing has revolutionized metagenomics by enabling more contiguous genome assemblies, but high sequencing error rates remain a challenge. While existing polishing tools like Medaka, CONNET, and NextPolish attempt to correct errors, most are optimized for single-species genomes and struggle with the complexity and uneven sequencing depth inherent to metagenomic datasets.

MetaCONNET is a deep-learning-powered polishing tool designed specifically for metagenomic assemblies. The AXBio team evaluated its performance against leading tools, demonstrating superior accuracy, coverage, contiguity, and resource efficiency. Key advantages of MetaCONNET include:

・Higher accuracy in error correction for long-read assemblies

・Better coverage and contiguity across diverse microbial communities

・Optimized resource consumption for large-scale metagenomic studies

With the growing applications of metagenomics in microbiome research, infectious disease diagnostics, and environmental monitoring, MetaCONNET provides a powerful new tool to improve the quality of metagenomic genome assemblies. 

This breakthrough underscores Axbio's leadership in bioinformatics innovation and commitment to advancing next-generation sequencing solutions.

Stay tuned for more cutting-edge advancements from AXBio's research team!

[1] Bingru Sun, Jian Guo, Hao Jin, Zijie Jin, Yaping Sun, Yuanchen Mao, Fuli Xie, Yun He, Zhihong Sun, Wei Li, Igor Ivanov, Hui Tian (2024). MetaCONNET: A metagenomic polishing tool for long-read assemblies. Plos One. https://doi.org/10.1371/journal.pone.0313515