ArticleGenome biology2017
MICRA: an automatic pipeline for fast characterization of microbial genomes from high-throughput sequencing data.
Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- Application of Pathogen Genomics to Outbreak Investigation.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.Functional & integrative genomics · 2022Review
- Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scores.Microbial genomics · 2020Article
- MICRA: an automatic pipeline for fast characterization of microbial genomes from high-throughput sequencing data.Genome biology · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increase in available sequence data has advanced the field of microbiology; however, making sense of these data without bioinformatics skills is still problematic. We describe MICRA, an automatic pipeline, available as a web interface, for microbial identification and characterization through reads analysis. MICRA uses iterative mapping against reference genomes to identify genes and variations. Additional modules allow prediction of antibiotic susceptibility and resistance and comparing the results of several samples. MICRA is fast, producing few false-positive annotations and variant calls compared to current methods, making it a tool of great interest for fully exploiting sequencing data.
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Registered trials
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