ArticleBioinformatics (Oxford, England)2010
cBar: a computer program to distinguish plasmid-derived from chromosome-derived sequence fragments in metagenomics data.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The microbial dark matter and "wanted list" in worldwide wastewater treatment plants.Microbiome · 2023Pooled it
- plsMD: a plasmid reconstruction tool from short-read assemblies.BMC bioinformatics · 2026Article
- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- Article
- Circling in on plasmids: benchmarking plasmid detection and reconstruction tools for short-read data from diverse species.Briefings in bioinformatics · 2025Article
- DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and viruses.NAR genomics and bioinformatics · 2024Article
- Enhancing Clinical Utility: Utilization of International Standards and Guidelines for Metagenomic Sequencing in Infectious Disease Diagnosis.International journal of molecular sciences · 2024Review
- Diverse plasmid systems and their ecology across human gut metagenomes revealed by PlasX and MobMess.Nature microbiology · 2024Article
- Article
- Forecasting the dynamics of a complex microbial community using integrated meta-omics.Nature ecology & evolution · 2024Article
- PLASMe: a tool to identify PLASMid contigs from short-read assemblies using transformer.Nucleic acids research · 2023Article
- Plasmer: an Accurate and Sensitive Bacterial Plasmid Prediction Tool Based on Machine Learning of Shared k-mers and Genomic Features.Microbiology spectrum · 2023Article
- Metagenomic analysis reveals gut plasmids as diagnosis markers for colorectal cancer.Frontiers in microbiology · 2023Article
- plASgraph2: using graph neural networks to detect plasmid contigs from an assembly graph.Frontiers in microbiology · 2023Article
- The Establishment and Application of a Kraken Classifier forChina CDC weekly · 2022Article
- Article
- Precise genotyping of circular mobile elements from metagenomic data uncovers human-associated plasmids with recent common ancestors.Genome research · 2022Article
- Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes.Nucleic acids research · 2022Article
- Plasmids of the urinary microbiota.Access microbiology · 2022Article
- RFPlasmid: predicting plasmid sequences from short-read assembly data using machine learning.Microbial genomics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
summaryHuge amount of metagenomic sequence data have been produced as a result of the rapidly increasing efforts worldwide in studying microbial communities as a whole. Most, if not all, sequenced metagenomes are complex mixtures of chromosomal and plasmid sequence fragments from multiple organisms, possibly from different kingdoms. Computational methods for prediction of genomic elements such as genes are significantly different for chromosomes and plasmids, hence raising the need for separation of chromosomal from plasmid sequences in a metagenome. We present a program for classification of a metagenome set into chromosomal and plasmid sequences, based on their distinguishing pentamer frequencies. On a large training set consisting of all the sequenced prokaryotic chromosomes and plasmids, the program achieves approximately 92% in classification accuracy. On a large set of simulated metagenomes with sequence lengths ranging from 300 bp to 100 kbp, the program has classification accuracy from 64.45% to 88.75%. On a large independent test set, the program achieves 88.29% classification accuracy. AVAILABILITY: The program has been implemented as a standalone prediction program, cBar, which is available at http://csbl.bmb.uga.edu/~ffzhou/cBar.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.