Ureaplasma Genome Paper 02/12/16
Annotation of the U. urealyticum Genome
Annotation of the U. urealyticum genome was performed by utilizing a combination of
programs for gene prediction, similarity searching, and functional assignment. The
information from these analyses was imported into a relational database based upon
Microsoft SQL Server. The user interface for this database was a series of web pages that
accessed the SQL Server database and allowed us to query available analysis data.
Additional pages allowed us to directly hand-annotate the individual gene records thus
allowing us to refine start sites and add functional descriptions and notes. This web-based
client interface was developed using Microsoft Active Server Pages technology to directly
query and update the database records. Basic sequence analysis tools were provided by
the Genetics Computer Group package of programs (Wisconsin Package Version 10.0,
Genetics Computer Group (GCG), Madison, Wisc.).
ORF Identification. Determination of potential protein-coding sequences utilized
both Genemark1 and Glimmer2 to create organism-specific open reading frame (ORF)
models that could then be used to search the entire genome for ORFs matching the
predictive models. The genome was first arranged such that the initial base of the ATG
start codon of the putatively-identified dnaA gene was base number 1 of the forward
strand. Genemark was run using the predictive model developed from Mycoplasma
genitalium to search the U. urealyticum genome for putative ORFs. To ensure that we
identified all possible U. urealyticum ORFs, we then utilized Glimmer to create a U.
urealyticum-specific model based upon the gene set initially predicted using Genemark,
and then derived a second set of putative U. urealyticum ORFs by using Glimmer to
search the U. urealyticum gneome a second time with this new model. Each predicted
ORF was then assigned a U. urealyticum identification number. ORF UU001 was
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Ureaplasma Genome Paper 02/12/16
assigned to the putative dnaA gene, and each subsequent ORF was then numbered
consecutively according to their left-most base (the start codon for ORFs on the forward
strand, and the stop codon for genes on the reverse strand). Subsequent annotation
resulted in the identification of a few additional ORFs not originally included in the
numbering scheme. These were assigned .1 and .2 designations according to their
genomic positions following previously identified ORFs.
Gene assignments. BLAST searches were performed on all predicted ORFs using
a blastp search of amino acid similarities to sequences in the Genbank non-redundant
protein database. The BLAST data was parsed using the blast modules of the BioPerl
toolkit (http://www.bioperl.org), and then imported into SQL server tables for analysis. In
addition to BLAST similarity searching, we also tentatively identified functional domains
within the U. urealyticum ORFs by searching for similarities to the Prosite motif library3,
and the Blocks database of protein families4. Programs from the GCG package provided
composition and hydrophobicity analysis along with scanning for potential signal peptide
domains. The results of these additional analyses allowed us to refine the gene
assignments initially made with BLAST. Furthermore, alignments with known proteins
provided assistance with start-codon prediction.
The results of all of these searches were used to provide putative identification of
each U. urealyticum ORF when a significant hit between the U. urealyticum sequence and
Genbank sequence was found. A combination of computer-aided gene prediction along
with human inspection of each gene record was then used to finalize gene assignments for
each U. urealyticum ORF and place each putative gene in one of five categories:
Positive assignment. There were published reports that biochemically
characterized several ureaplasma genes.
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Putative assignment. There was sufficient sequence similarity with existing genes
to suggest functional similarities.
Borderline assignment. Sequence similarity with existing genes and/or functional
domains existed, but at a borderline significance.
Conserved Hypothetical. There was significant similarity with a gene in the
database that has been classified as being of unknown function or hypothetical.
Unique Hypothetical. There was no significant similarity with any other sequence
in the database.
RNA identification. To identify genomic sequences that code for tRNAs, the set
of programs that encompass the software package, tRNAscan-SE5 was used. Ribosomal
RNAs were identified by similarity to the corresponding genes in the Ribosome Database
Project sequence database6. The sequences for tmRNA7, the 4.5S signal recognition
particle8, and ribonuclease P9 were also identified based upon sequence similarity with
known representatives of these RNA genes.
Metabolic pathways. Following assignment of coding regions and gene
identification, we placed each putatively identified gene into its functional compartment
within the metabolic pathways required for the biological functioning of the organism. To
facilitate the assignments, we utilized the EcoCyc and MetaCyc packages of metabolic
pathway tools10 as a guide.
1.
Lukashin, A. V. & Borodovsky, M. GeneMark.hmm: new solutions for gene
finding. Nucleic Acids Res. 26, 1107-1115 (1998).
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Ureaplasma Genome Paper 02/12/16
2.
Salzberg, S. L., Delcher, A. L., Kasif, S. & White, O. Microbial gene
identification using interpolated Markov models. Nucleic Acids Res. 26, 544-548 (1998).
3.
Hofmann, K., Bucher, P., Falquet, L. & Bairoch, A. The PROSITE database, its
status in 1999. Nucleic Acids Res. 27, 215-219 (1999).
4.
Henikoff, S., Henikoff, J. G. & Pietrokovski, S. Blocks+: a non-redundant
database of protein alignment blocks derived from multiple compilations. Bioinformatics
15, 471-479 (1999).
5.
Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of
transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955-964 (1997).
6.
Maidak, B. L. et al. A new version of the RDP (Ribosomal Database Project).
Nucleic Acids Res. 27, 171-173 (1999).
7.
Williams, K. P. The tmRNA Website. Nucleic Acids Res. 28, 158-161 (2000).
8.
Zwieb, C. & Samuelsson, T. SRPDB (Signal Recognition Particle Database).
Nucleic Acids Res. 28, 171-172 (2000).
9.
Massire, C., Jaeger, L. & Westhof, E. Derivation of the three-dimensional
architecture of bacterial ribonuclease P RNAs from comparative sequence analysis. J.
Mol. Biol. 279, 773-793 (1998).
10.
Karp, P. D. et al. The EcoCyc and MetaCyc databases. Nucleic Acids Res. 28, 56-
59 (2000).
1.
Lukashin, A. V. & Borodovsky, M. GeneMark.hmm: new solutions for gene
finding. Nucleic Acids Res. 26, 1107-1115 (1998).
4
Ureaplasma Genome Paper 02/12/16
2.
Salzberg, S. L., Delcher, A. L., Kasif, S. & White, O. Microbial gene
identification using interpolated Markov models. Nucleic Acids Res. 26, 544-548 (1998).
3.
Hofmann, K., Bucher, P., Falquet, L. & Bairoch, A. The PROSITE database, its
status in 1999. Nucleic Acids Res. 27, 215-219 (1999).
4.
Henikoff, S., Henikoff, J. G. & Pietrokovski, S. Blocks+: a non-redundant
database of protein alignment blocks derived from multiple compilations. Bioinformatics
15, 471-479 (1999).
5.
Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of
transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955-964 (1997).
6.
Maidak, B. L. et al. A new version of the RDP (Ribosomal Database Project).
Nucleic Acids Res. 27, 171-173 (1999).
7.
Williams, K. P. The tmRNA Website. Nucleic Acids Res. 28, 158-161 (2000).
8.
Zwieb, C. & Samuelsson, T. SRPDB (Signal Recognition Particle Database).
Nucleic Acids Res. 28, 171-172 (2000).
9.
Massire, C., Jaeger, L. & Westhof, E. Derivation of the three-dimensional
architecture of bacterial ribonuclease P RNAs from comparative sequence analysis. J.
Mol. Biol. 279, 773-793 (1998).
10.
Karp, P. D. et al. The EcoCyc and MetaCyc databases. Nucleic Acids Res. 28, 56-
59 (2000).
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