Listeriomics: an Interactive Web Platform for Systems Biology ofListeria.
As for many model organisms, the amount ofListeriaomics data produced has recently increased exponentially. There are now >80 published completeListeriagenomes, around 350 different transcriptomic data sets, and 25 proteomic data sets available. The analysis of these data sets through a systems biology approach and the generation of tools for biologists to browse these various data are a challenge for bioinformaticians. We have developed a web-based platform, named Listeriomics, that integrates different tools for omics data analyses, i.e., (i) an interactive genome viewer to display gene expression arrays, tiling arrays, and sequencing data sets along with proteomics and genomics data sets; (ii) an expression and protein atlas that connects every gene, small RNA, antisense RNA, or protein with the most relevant omics data; (iii) a specific tool for exploring protein conservation through theListeriaphylogenomic tree; and (iv) a coexpression network tool for the discovery of potential new regulations. Our platform integrates all the completeListeriaspecies genomes, transcriptomes, and proteomes published to date. This website allows navigation among all these data sets with enriched metadata in a user-friendly format and can be used as a central database for systems biology analysis.IMPORTANCEIn the last decades,Listeriahas become a key model organism for the study of host-pathogen interactions, noncoding RNA regulation, and bacterial adaptation to stress. To study these mechanisms, several genomics, transcriptomics, and proteomics data sets have been produced. We have developed Listeriomics, an interactive web platform to browse and correlate these heterogeneous sources of information. Our website will allow listeriologists and microbiologists to decipher key regulation mechanism by using a systems biology approach.
Département de Biologie Cellulaire et Infection, Institut Pasteur, Unité des Interactions Bactéries-Cellules, Paris, France; INSERM, U604, Paris, France; INRA, USC2020, Paris, France; Institut Pasteur-Bioinformatics and Biostatistics Hub, C3BI, USR 3756 IP CNRS, Paris, France.
Christophe
C
Bécavin
Département de Biologie Cellulaire et Infection, Institut Pasteur, Unité des Interactions Bactéries-Cellules, Paris, France; INSERM, U604, Paris, France; INRA, USC2020, Paris, France.
Mikael
M
Koutero
CEA-Division of Immuno-Virology, Institute of Emerging Diseases and Innovative Therapies, IDMIT Center, Fontenay-aux-Roses, France.
Nicolas
N
Tchitchek
INRA-UR MIAT, Université de Toulouse, Castanet-Tolosan, France.
Franck
F
Cerutti
Institut Pasteur-Bioinformatics and Biostatistics Hub, C3BI, USR 3756 IP CNRS, Paris, France.
Pierre
P
Lechat
Institut Pasteur-Bioinformatics and Biostatistics Hub, C3BI, USR 3756 IP CNRS, Paris, France.
Nicolas
N
Maillet
INRA-UR MIAT, Université de Toulouse, Castanet-Tolosan, France.
Claire
C
Hoede
INRA-UR MIAT, Université de Toulouse, Castanet-Tolosan, France.
Hélène
H
Chiapello
INRA-UR MIAT, Université de Toulouse, Castanet-Tolosan, France.
Christine
C
Gaspin
Département de Biologie Cellulaire et Infection, Institut Pasteur, Unité des Interactions Bactéries-Cellules, Paris, France; INSERM, U604, Paris, France; INRA, USC2020, Paris, France.
Pascale
P
Cossart
Listeria; database; genomics; proteomics; systems biology; transcriptomics