Our dataset came from 58 Bacteria (49 Gram-negative and 9 Gram-Positive), one
Archaea and 11 plasmids, downloaded from the NCBI ftp server . Starting with these genome sequences, we looked for orthologous genes from a bi-directional best hit (BBH) relationship in a pairwise genome Pevonedistat in vivo comparison . Therefore, the orthologs were identified as BBH with BLASTP , in all-by-all comparisons of 70 genomic sequences. We extracted only target clusters, by using some keywords regarding the NCBI product or gene name related to T4SSs. Consequently, the final dataset contains 134 ortholog clusters totaling 1,617 predicted proteins encoding T4SS proteins. Database construction and annotation The AtlasT4SS database runs on a SUN-OS web server hosted by The National Laboratory for selleck chemical Scientific Computing (LNCC), Brazil. We used MySQL (v. 3.23.46) as a supported Relational Database Management System (RDBMS) to develop a database schema for storing Selleck Captisol sequence data, features, and annotation (Figure 1). The sequences, features and annotations are introduced into the database using Perl-based scripts with a web interface (HTML/CGI). Currently, the access to the database is done through the Web Perl-based Catalyst Framework. Figure 1 Entity–relationship diagram of T4SS database. Entities are represented by boxes
and relationships by lines joining the boxes. The general information of the genes found in the ORF entity. Each entity ORF is related to information from biological database (InterPro, Swiss-Prot, Kegg, etc.) and tools (Psort, Phobius, etc.). Gene annotations and annotator entities are described in Annotation and User, respectively. The identified clusters are described by the entity Clusters_Names. For annotation
analysis, we applied the software SABIA (System for Automated Bacterial Integrated Annotation)  and ran several programs, including BLAST , CLUSTAL W Multiple Sequence Alignments package , MUSCLE (v. 3.6)  and Jalview (v. 2.3) . Also, each T4SS record was submitted to several databases, such as InterPro Sodium butyrate  for protein domain and family annotation, KEGG (Kyoto Encyclopedia of Genes and Genomes) , COG (Clusters of Orthologous Groups of proteins) , gene onthology GO  and UniProtKB/Swiss-Prot  for functional classification, PSORT  for protein localization and Phobius  for protein topology features. Finally, we manually processed all automatic information obtained, including PubMed reference articles, in order to reach a final high quality annotation for each T4SS record (Figure 2). Figure 2 Overview of annotation page of T4SS database. The image provides an example of the main data page for a T4SS entry.