Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Aug 19;44(14):6614-24.
doi: 10.1093/nar/gkw569. Epub 2016 Jun 24.

NCBI Prokaryotic Genome Annotation Pipeline

Affiliations
Free PMC article

NCBI Prokaryotic Genome Annotation Pipeline

Tatiana Tatusova et al. Nucleic Acids Res. .
Free PMC article

Abstract

Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.

Figures

Figure 1.
Figure 1.
Cumulative number of protein clusters (Y) is defined for a given X (%) as the number of clusters containing proteins from fraction x ≥ X of all members of the clade. Data are presented for the four well studied clades.
Figure 2.
Figure 2.
A fragment of the PGAP execution graph: prediction of structural RNA genes (ncRNA, tRNA, 5S-, 16S-, 23S- rRNA).
Figure 3.
Figure 3.
Flowchart of PGAP. The red dotted line indicates separation between pass one and pass two (see text for details).
Figure 4.
Figure 4.
A region in the Deinococcus radiodurans R1 genome assembly (GCA_000008565.1) contains three overlapping ORFs predicted ab initio as CDSs in the first pass of PGAP. Automatic evaluation of the cross-species protein evidence through the second pass of PGAP reveals proteins bearing homology to all three fragments. Alignment of the proteins to the genome reveals otherwise unpredicted frameshifts. Green bars represent genes, red bars – coding regions; grey bars – alignments with red vertical bars indicating mismatches. (A) A region of Chromosome 1 of D. radiodurans (AE000513.1) containing the three CDS features is displayed alongside the six-frame translation. (B) The same region, updated to include final annotation markup with a frameshifted CDS as well as supporting proteins that demonstrate a consistent pattern and location of two frameshifts (marked by arrows at positions 100 733 and 100 959).
Figure 5.
Figure 5.
Annotation of genome of Salmonella enterica subsp. enterica serovar Typhimurium str. LT2 (NC_003197). Protein alignment provides support for gene start selection. See legend to Figure 4 for description of the meaning of green, red and gray bars. (A) the first round of alignments of protein representatives from the ‘core’ protein clusters doesn't give enough evidence for gene start selection. (B) the second round of alignments clearly supports a shorter gene model which does not overlap with the upstream gene.
Figure 6.
Figure 6.
A summary of PGAP genome annotation process is provided in the COMMENT section of GenBank and RefSeq records. The example is given for Listeria monocytogenes strain CFSAN010068, complete genome NZ_CP014250.1.
Figure 7.
Figure 7.
Frequency histogram of genomes with respect to the fraction of the whole complement of genes supported by similarity to proteins in RefSeq. In about 50% of the total set of genomes in consideration, mostly from highly populated clades, more than 95% of protein-coding genes are supported by protein sequence similarity.

Similar articles

See all similar articles

Cited by 948 articles

See all "Cited by" articles

References

    1. Besemer J., Lomsadze A., Borodovsky M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001;26:1107–1115. - PMC - PubMed
    1. Delcher A.L., Harmon D., Kasif S., White O., Salzberg S.L. Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 1999;23:4636–4641. - PMC - PubMed
    1. Tatusov R.L., Natale D.A., Garkavtsev I.V., Tatusova T.A., Shankavaram U.T., Rao B.S., Kiryutin B., Galperin M.Y., Fedorova N.D., Koonin E.V. The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res. 2001;29:22–28. - PMC - PubMed
    1. Klimke W., Agarwala R., Badretdin A., Chetvernin S., Ciufo S., Fedorov B., Kiryutin B., O'Neill K., Resch W., Resenchuk S., et al. The National Center for Biotechnology Information's Protein Clusters Database. Nucleic Acids Res. 2009;37:D216–D223. - PMC - PubMed
    1. Nawrocki E.P., Eddy S.R. Infernal 1.1: 100-fold faster RNA Homology searches. Bioinformatics. 2013;29:2933–2935. - PMC - PubMed

Publication types

Substances

Feedback