Abstract
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.
Highlights
Discovery of extremophilic enzymes has developed into a major driver for the biotech industry
Profile and Pattern Matching (PPM): PROFILE AND PATTERN MATCHING FOR FUNCTION IDENTIFICATION Analysis of the huge amount of data resulting from generation whole genome sequencing (NGS) requires modern bioinformatic tools
Compared 5164 coding sequences (CDS) of to the Broad annotation the BG7 annotation resulted in 5210 CDS with 163 (3.1%) false negatives and 271 (5.2%) false positives, and RAST annotation gave in 5446 CDS with 116 (2.1%) false negatives and 321 (5.9%) false positives (Alam et al, 2013)
Summary
Discovery of extremophilic enzymes has developed into a major driver for the biotech industry. Many industrially relevant enzymes were isolated from organisms growing at high temperature, high salt concentration, or in environments contaminated with organic solvents, significant challenges and limitations exist for bio-prospecting of extremophilic enzymes (Liszka et al, 2012). Metagenomic data have not yet lead to the expected boost of biotechnology (Chistoserdova, 2010), mostly because they suffer from short read length, a low probability to identify rare populations (below 1%) (Kunin et al, 2008), and difficulties in assembling larger contigs of genetic material for members of complex communities. Single-cell genomics (Lasken, 2007) circumvents this problem, and larger contigs from uncultured organisms can be analyzed. A major challenge in mining genomic data of uncultured organisms is a lack of homology to genes of established organisms resulting in limited reliability of gene annotation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
