ReviewJournal of bacteriology2017
Implementation and Data Analysis of Tn-seq, Whole-Genome Resequencing, and Single-Molecule Real-Time Sequencing for Bacterial Genetics.
Review in Journal of bacteriology, 2017. 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.
- The role of bacterial metabolism in human gut colonization.International microbiology : the official journal of the Spanish Society for Microbiology · 2025Review
- Review
- Role of the CrcB transporter of Pseudomonas putida in the multi-level stress response elicited by mineral fluoride.Environmental microbiology · 2022Article
- Next-generation microbiology: from comparative genomics to gene function.Genome biology · 2021Review
- Learning from -omics strategies applied to uncoverComputational and structural biotechnology journal · 2021Review
- Inherently and Conditionally Essential Protein Catabolism Genes of Porphyromonas gingivalis.Trends in microbiology · 2021Review
- Stress-Responsive Periplasmic Chaperones in Bacteria.Frontiers in molecular biosciences · 2021Review
- A metabolite binding protein moonlights as a bile-responsive chaperone.The EMBO journal · 2020Article
- Resistance Toward Chlorhexidine in Oral Bacteria - Is There Cause for Concern?Frontiers in microbiology · 2019Review
- Article
- Genome-wide discovery of novel M1T1 group A streptococcal determinants important for fitness and virulence during soft-tissue infection.PLoS pathogens · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Few discoveries have been more transformative to the biological sciences than the development of DNA sequencing technologies. The rapid advancement of sequencing and bioinformatics tools has revolutionized bacterial genetics, deepening our understanding of model and clinically relevant organisms. Although application of newer sequencing technologies to studies in bacterial genetics is increasing, the implementation of DNA sequencing technologies and development of the bioinformatics tools required for analyzing the large data sets generated remain a challenge for many. In this minireview, we have chosen to summarize three sequencing approaches that are particularly useful for bacterial genetics. We provide resources for scientists new to and interested in their application. Here, we discuss the analysis of data from transposon mutagenesis followed by deep sequencing (Tn-seq) to determine gene disruptions differentially represented in a mutant population and Illumina sequencing for identification of suppressor or other mutations, and we summarize single-molecule real-time (SMRT) sequencing for de novo genome assembly and the use of the output data for detection of DNA base modifications.
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.