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ESSENTIALS: Software for Rapid Analysis of High Throughput Transposon Insertion Sequencing Data

Overview of attention for article published in PLOS ONE, August 2012
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Title
ESSENTIALS: Software for Rapid Analysis of High Throughput Transposon Insertion Sequencing Data
Published in
PLOS ONE, August 2012
DOI 10.1371/journal.pone.0043012
Pubmed ID
Authors

Aldert Zomer, Peter Burghout, Hester J. Bootsma, Peter W. M. Hermans, Sacha A. F. T. van Hijum

Abstract

High-throughput analysis of genome-wide random transposon mutant libraries is a powerful tool for (conditional) essential gene discovery. Recently, several next-generation sequencing approaches, e.g. Tn-seq/INseq, HITS and TraDIS, have been developed that accurately map the site of transposon insertions by mutant-specific amplification and sequence readout of DNA flanking the transposon insertions site, assigning a measure of essentiality based on the number of reads per insertion site flanking sequence or per gene. However, analysis of these large and complex datasets is hampered by the lack of an easy to use and automated tool for transposon insertion sequencing data. To fill this gap, we developed ESSENTIALS, an open source, web-based software tool for researchers in the genomics field utilizing transposon insertion sequencing analysis. It accurately predicts (conditionally) essential genes and offers the flexibility of using different sample normalization methods, genomic location bias correction, data preprocessing steps, appropriate statistical tests and various visualizations to examine the results, while requiring only a minimum of input and hands-on work from the researcher. We successfully applied ESSENTIALS to in-house and published Tn-seq, TraDIS and HITS datasets and we show that the various pre- and post-processing steps on the sequence reads and count data with ESSENTIALS considerably improve the sensitivity and specificity of predicted gene essentiality.

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Mendeley readers

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Geographical breakdown

Country Count As %
United Kingdom 2 <1%
Belgium 2 <1%
Spain 2 <1%
New Zealand 2 <1%
Sweden 1 <1%
Brazil 1 <1%
Netherlands 1 <1%
Taiwan 1 <1%
Argentina 1 <1%
Other 5 2%
Unknown 220 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 63 26%
Student > Ph. D. Student 56 24%
Student > Master 26 11%
Student > Bachelor 23 10%
Student > Doctoral Student 9 4%
Other 28 12%
Unknown 33 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 101 42%
Biochemistry, Genetics and Molecular Biology 47 20%
Immunology and Microbiology 18 8%
Medicine and Dentistry 9 4%
Computer Science 5 2%
Other 21 9%
Unknown 37 16%