Systematic prediction of functionally linked genes in bacterial and archaeal genomes

Abstract

Functionally linked genes in bacterial and archaeal genomes are often organized into operons. However, the composition and architecture of operons are highly variable and frequently differ even among closely related genomes. Therefore, to efficiently extract reliable functional predictions for uncharacterized genes from comparative analyses of the rapidly growing genomic databases, dedicated computational approaches are required. We developed a protocol to systematically and automatically identify genes that are likely to be functionally associated with a 'bait' gene or locus by using relevance metrics. Given a set of bait loci and a genomic database defined by the user, this protocol compares the genomic neighborhoods of the baits to identify genes that are likely to be functionally linked to the baits by calculating the abundance of a given gene within and outside the bait neighborhoods and the distance to the bait. We exemplify the performance of the protocol with three test cases, namely, genes linked to CRISPR-Cas systems using the 'CRISPRicity' metric, genes associated with archaeal proviruses and genes linked to Argonaute genes in halobacteria. The protocol can be run by users with basic computational skills. The computational cost depends on the sizes of the genomic dataset and the list of reference loci and can vary from one CPU-hour to hundreds of hours on a supercomputer.

Fig. 1 |. The pipeline for the identification of gene families associated with a set of baits.

Seven stages of the pipeline are shown as boxes; each box contains information on the main action and output for the stage.

Fig. 2 |. A detailed, step by step schematic of the protocol.

Each stage of the protocol is represented by a gray box. Stage 1: contigs are shown as gray lines and the baits as red stripes within the contigs. Stage 2: ORFs in the contigs are shown as gray polygons. Stage 3: clustering procedure; the color of each ORF reflects the cluster assignment. Stage 4: profile construction from a set of proteins; PSIBLAST hits are shown as red rectangles within ORFs; sorting and filtering of proteins in clusters is performed. Stage 5: strict clustering procedure, Icity calculation and 3D metrics space: Icity, abundance in the genomic database and distance to the baits (red crosses denote clusters that contain Cas proteins, green dots denote clusters containing predicted ancillary CRISPR-linked proteins and blue circles denote clusters that do not include any CRISPR-related proteins). Stage 6: approaches to classify metrics space. Stage 7: methods of manual curation.

Fig. 3 |. The space of relevance metrics.

Protein clusters characterized by their Icity, effective abundance and effective distance to the baits are shown. Annotation for each cluster was performed by using PSIBLAST to classify the clusters into categories: ‘Cas’, a known Cas protein; ‘Associated’, predicted ancillary Cas proteins; and ‘Non-Cas’, no CRISPR-related proteins.

Fig. 4 |. Dissection of the space of relevance metrics.

The yellow area shows the sector with the maximum F score (optimized recall/precision). Annotation for each cluster was performed by using PSIBLAST to classify the clusters into categories: ‘Cas’, a known Cas protein; ‘Associated’, predicted ancillary Cas proteins; and ‘Non-Cas’, no CRISPR-related proteins.