Mining a database of single amplified genomes from Red Sea brine pool extremophiles-improving reliability of gene function prediction using a profile and pattern matching algorithm (PPMA).
Reliable functional annotation of genomic data is the key-step in the discovery of novel enzymes. Intrinsic sequencing data quality problems of single amplified genomes (SAGs) and poor homology of novel extremophile's genomes pose significant challenges for the attribution of functions to the coding sequences identified. The anoxic deep-sea brine pools of the Red Sea are a promising source of novel enzymes with unique evolutionary adaptation. Sequencing data from Red Sea brine pool cultures and SAGs are annotated and stored in the Integrated Data Warehouse of Microbial Genomes (INDIGO) data warehouse. Low sequence homology of annotated genes (no similarity for 35% of these genes) may translate into false positives when searching for specific functions. The Profile and Pattern Matching (PPM) strategy described here was developed to eliminate false positive annotations of enzyme function before progressing to labor-intensive hyper-saline gene expression and characterization. It utilizes InterPro-derived Gene Ontology (GO)-terms (which represent enzyme function profiles) and annotated relevant PROSITE IDs (which are linked to an amino acid consensus pattern). The PPM algorithm was tested on 15 protein families, which were selected based on scientific and commercial potential. An initial list of 2577 enzyme commission (E.C.) numbers was translated into 171 GO-terms and 49 consensus patterns. A subset of INDIGO-sequences consisting of 58 SAGs from six different taxons of bacteria and archaea were selected from six different brine pool environments. Those SAGs code for 74,516 genes, which were independently scanned for the GO-terms (profile filter) and PROSITE IDs (pattern filter). Following stringent reliability filtering, the non-redundant hits (106 profile hits and 147 pattern hits) are classified as reliable, if at least two relevant descriptors (GO-terms and/or consensus patterns) are present. Scripts for annotation, as well as for the PPM algorithm, are available through the INDIGO website.
Division of Physical Sciences and Engineering, KAUST Catalysis Center, King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia.
Stefan W
SW
Grötzinger
Division of Biological Sciences and Engineering, Computational Bioscience Research Center, King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia.
Intikhab
I
Alam
Division of Biological Sciences and Engineering, Computational Bioscience Research Center, King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia.
Wail
W
Ba Alawi
Division of Biological Sciences and Engineering, Computational Bioscience Research Center, King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia.
Vladimir B
VB
Bajic
Division of Biological Sciences and Engineering, Red Sea Research Center, King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia.
Ulrich
U
Stingl
Division of Physical Sciences and Engineering, KAUST Catalysis Center, King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia.
Jörg
J
Eppinger
GO-terms; PROSITE IDs; bioinformatics; extermophile; functional genomics; halophiles; protein sequence consensus patterns; single amplified genomes