ArticlePloS one2018
Extension of the viral ecology in humans using viral profile hidden Markov models.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 27 citations in OpenAlex.
- The hidden genetic reservoir: structural variants as drivers of marine microbial and viral microdiversity.Environmental microbiome · 2025Article
- Article
- Compression-Complexity Measures for Analysis and Classification of Coronaviruses.Entropy (Basel, Switzerland) · 2022Article
- Explainable deep neural networks for novel viral genome prediction.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- Metagenomic Screening for Lipolytic Genes Reveals an Ecology-Clustered Distribution Pattern.Frontiers in microbiology · 2022Article
- A Primer on the Analysis of High-Throughput Sequencing Data for Detection of Plant Viruses.Microorganisms · 2021Review
- Exploring Virome Diversity in Public Data in South America as an Approach for Detecting Viral Sources From Potentially Emerging Viruses.Frontiers in genetics · 2021Article
- Marseilleviruses: An Update in 2021.Frontiers in microbiology · 2021Review
- Review
- ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples.PloS one · 2019Article
- Machine Learning for detection of viral sequences in human metagenomic datasets.BMC bioinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When human samples are sequenced, many assembled contigs are "unknown", as conventional alignments find no similarity to known sequences. Hidden Markov models (HMM) exploit the positions of specific nucleotides in protein-encoding codons in various microbes. The algorithm HMMER3 implements HMM using a reference set of sequences encoding viral proteins, "vFam". We used HMMER3 analysis of "unknown" human sample-derived sequences and identified 510 contigs distantly related to viruses (Anelloviridae (n = 1), Baculoviridae (n = 34), Circoviridae (n = 35), Caulimoviridae (n = 3), Closteroviridae (n = 5), Geminiviridae (n = 21), Herpesviridae (n = 10), Iridoviridae (n = 12), Marseillevirus (n = 26), Mimiviridae (n = 80), Phycodnaviridae (n = 165), Poxviridae (n = 23), Retroviridae (n = 6) and 89 contigs related to described viruses not yet assigned to any taxonomic family). In summary, we find that analysis using the HMMER3 algorithm and the "vFam" database greatly extended the detection of viruses in biospecimens from humans.
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.