Evidence map›Paper›PMID 38711860›Full record

ArticleNAR genomics and bioinformatics2024

DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and viruses.

Shengwei Hou, Tianqi Tang, Siliangyu Cheng, Yuanhao Liu, Tian Xia, Ting Chen, Jed A Fuhrman, Fengzhu Sun

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Shengwei HouDepartment of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.ORCID https://orcid.org/0000-0002-4474-7443
Tianqi TangDepartment of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.
Siliangyu ChengDepartment of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.
Yuanhao LiuDepartment of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Tian XiaDepartment of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Ting ChenDepartment of Computer Science and Technology, Institute of Artificial Intelligence & BNRist, Tsinghua University, Beijing 100084, China.
Jed A FuhrmanMarine and Environmental Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
Fengzhu SunDepartment of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sequence classification facilitates a fundamental understanding of the structure of microbial communities. Binary metagenomic sequence classifiers are insufficient because environmental metagenomes are typically derived from multiple sequence sources. Here we introduce a deep-learning based sequence classifier, DeepMicroClass, that classifies metagenomic contigs into five sequence classes, i.e. viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. DeepMicroClass achieved high performance for all sequence classes at various tested sequence lengths ranging from 500 bp to 100 kbps. By benchmarking on a synthetic dataset with variable sequence class composition, we showed that DeepMicroClass obtained better performance for eukaryotic, plasmid and viral contig classification than other state-of-the-art predictors. DeepMicroClass achieved comparable performance on viral sequence classification with geNomad and VirSorter2 when benchmarked on the CAMI II marine dataset. Using a coastal daily time-series metagenomic dataset as a case study, we showed that microbial eukaryotes and prokaryotic viruses are integral to microbial communities. By analyzing monthly metagenomes collected at HOT and BATS, we found relatively higher viral read proportions in the subsurface layer in late summer, consistent with the seasonal viral infection patterns prevalent in these areas. We expect DeepMicroClass will promote metagenomic studies of under-appreciated sequence types.

Identifiers

PMID38711860
PMCPMC11071121

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.