The Mycobacterium tuberculosis transposon sequencing database (MtbTnDB): a large-scale guide to genetic conditional essentiality
Jinich, A.; Zaveri, A.; DeJesus, M. A.; Flores-Bautista, E.; Smith, C. M.; Sassetti, C. M.; Rock, J. M.; Ehrt, S.; Schnappinger, D.; Ioerger, T. R.; Rhee, K. Y.
Show abstract
Characterizing genetic essentiality across various conditions is fundamental for understanding gene function. Transposon sequencing (TnSeq) is a powerful technique to generate genome-wide essentiality profiles in bacteria and has been extensively applied to Mycobacterium tuberculosis (Mtb). Dozens of TnSeq screens have yielded valuable insights into the biology of Mtb in vitro, inside macrophages, and in model host organisms. Despite their value, these Mtb TnSeq profiles have not been standardized or collated into a single, easily searchable database. This results in significant challenges when attempting to query and compare these resources, limiting our ability to obtain a comprehensive and consistent understanding of genetic conditional essentiality in Mtb. We address this problem by building a central repository of publicly available Mtb TnSeq screens, the Mtb transposon sequencing database (MtbTnDB). The MtbTnDB is a living resource that encompasses to date {approx}150 standardized TnSeq screens, enabling open access to data, visualizations, and functional predictions through an interactive web app (www.mtbtndb.app). We conduct several statistical analyses on the complete database, such as demonstrating that (i) genes in the same genomic neighborhood have similar TnSeq profiles, and (ii) clusters of genes with similar TnSeq profiles are enriched for genes from similar functional categories. We further analyze the performance of machine learning models trained on TnSeq profiles to predict functional annotation of orphan genes in Mtb. By facilitating the comparison of TnSeq screens across conditions, the MtbTnDB will accelerate the exploration of conditional genetic essentiality, provide insights into the functional organization of Mtb genes, and help predict gene function in this important human pathogen.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Targeted genome mining with GATOR-GC maps the evolutionary landscape of biosynthetic diversity 94%
- Deciphering the Biosynthetic Potential of Microbial Genomes Using a BGC Language Processing Neural Network Model 94%
- TransposonUltimate: software for transposon classification, annotation and detection 94%
Similar papers in this journal
- Evolutionarily stable gene clusters shed light on the common grounds of pathogenicity in the Acinetobacter calcoaceticus-baumanniicomplex 94%
- A cryptic transcription factor regulates Caulobacter adhesin development 94%
- Reassembling a cannon in the DNA defense arsenal: genetics of StySA, a BREX phage exclusion system in Salmonella lab strains 93%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.