Chrome Extension
WeChat Mini Program
Use on ChatGLM

Phage-inclusive Profiling of Human Gut Microbiomes with Phanta

Biotechnology(2023)

Department of Genetics

Cited 5|Views26
Abstract
Due to technical limitations, most gut microbiome studies have focused on prokaryotes, overlooking viruses. Phanta, a virome-inclusive gut microbiome profiling tool, overcomes the limitations of assembly-based viral profiling methods by using customized k-mer-based classification tools and incorporating recently published catalogs of gut viral genomes. Phanta's optimizations consider the small genome size of viruses, sequence homology with prokaryotes and interactions with other gut microbes. Extensive testing of Phanta on simulated data demonstrates that it quickly and accurately quantifies prokaryotes and viruses. When applied to 245 fecal metagenomes from healthy adults, Phanta identifies ~200 viral species per sample, ~5× more than standard assembly-based methods. We observe a ~2:1 ratio between DNA viruses and bacteria, with higher interindividual variability of the gut virome compared to the gut bacteriome. In another cohort, we observe that Phanta performs equally well on bulk versus virus-enriched metagenomes, making it possible to study prokaryotes and viruses in a single experiment, with a single analysis.
More
Translated text
Key words
Genomics,Metagenomics,Life Sciences,general,Biotechnology,Biomedicine,Agriculture,Biomedical Engineering/Biotechnology,Bioinformatics
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest