Rapid, accurate, computational discovery of Rho-independent transcription terminators illuminates their relationship to DNA uptake
© Kingsford et al.; licensee BioMed Central Ltd. 2007
Received: 14 September 2006
Accepted: 21 February 2007
Published: 21 February 2007
In many prokaryotes, transcription of DNA to RNA is terminated by a thymine-rich stretch of DNA following a hairpin loop. Detecting such Rho-independent transcription terminators can shed light on the organization of bacterial genomes and can improve genome annotation. Previous computational methods to predict Rho-independent terminators have been slow or limited in the organisms they consider.
We describe TransTermHP, a new computational method to rapidly and accurately detect Rho-independent transcription terminators. We predict the locations of terminators in 343 prokaryotic genomes, representing the largest collection of predictions available. In Bacillus subtilis, we can detect 93% of known terminators with a false positive rate of just 6%, comparable to the best-known methods. Outside the Firmicutes division, we find that Rho-independent termination plays a large role in the Neisseria and Vibrio genera, the Pasteurellaceae (including the Haemophilus genus) and several other species. In Neisseria and Pasteurellaceae, terminator hairpins are frequently formed by closely spaced, complementary instances of exogenous DNA uptake signal sequences. We quantify the propensity for terminators to include these sequences. In the process, we provide the first discussion of potential uptake signals in Haemophilus ducreyi and Mannheimia succiniciproducens, and we discuss the preference for a particular configuration of uptake signal sequences within terminators.
Our new fast and accurate method for detecting transcription terminators has allowed us to identify and analyze terminators in many new genomes and to identify DNA uptake signal sequences in several species where they have not been previously reported. Our software and predictions are freely available.
Rho-independent (also known as intrinsic) terminators are sequence motifs found in many prokaryotes that cause the transcription of DNA to RNA to stop. These termination signals typically consist of a short, often GC-rich hairpin followed by a sequence enriched in thymine residues . The importance of Rho-independent termination varies across bacteria. In some bacteria, such as Escherichia coli, a large fraction of termination is mediated by the Rho protein or its homologs (reviewed in ). In others, such as Bacillus subtilis, Rho homologs play a smaller role, and Rho-independent termination is the norm.
Detection of transcription termination sites is key to understanding the operon structure of bacterial genomes. Understanding the operons, in turn, gives us strong hints about gene function. Computational detection of termination signals is the only practical means of identifying large numbers of terminators today, and few experimentally verified terminators exist outside of B. subtilis and E. coli. Several previous computational methods [3, 4] have relied on simple decision boundaries to separate terminators from non-terminators after training on experimentally known terminating and non-terminating sequences. Other studies have considered only the hairpin portion of potential terminators [5, 6]. Due to lack of sequence data, previous systems (for example, [4, 7]) have tended to focus on E. coli or on only a portion of the now-available genomes.
We describe here TransTermHP, a computational method for the rapid and accurate detection of these signals in genomic DNA. TransTermHP searches genomic DNA for terminators and assigns each candidate terminator a score related to the likelihood that it arose by chance. We assess sensitivity and specificity of our predictions using a set of experimentally verified operons . Our method achieves accuracy comparable with a recent method  while at the same time being much faster and not dependent on a training set of known terminators.
A previous system  by one of the authors of the current study predicted terminators within intergenic regions by contrasting candidates with terminator-like structures that occur within genes. Since the intragenic sequences were used as a background signal, the system could not assign scores to structures inside genes and its scores depended on the distribution of relatively rare terminator-like sequences inside genes. TransTermHP uses a completely new, more general model of background signal and a new scoring function that makes it less sensitive to the accuracy of the genome annotation. TransTermHP is built around a new search algorithm that employs dynamic programming to search genomes ten times faster than the system of Ermolaeva et al.  and hundreds of times faster than the method of de Hoon et al. . The algorithm can scan a 4 megabase genome in under 50 seconds on a single commodity processor.
Using the accuracy and speed of the new method we have made predictions of Rho-independent terminators for 343 bacterial and archaeal genomes (comprising the complete collection of finished prokaryotic genomes available from GenBank). These predictions are available on the web and represent the largest and most varied collection of putative terminators available to date.
A recent paper  considered Rho-independent terminators in the Firmicutes division of bacteria extensively and found within them a large number of intrinsic termination signals, a finding we corroborate. Outside the Firmicutes, we find that 15 organisms (from the Neisseria, Vibrio, and Psychrobacter genera, the Pasteurellaceae, as well as Pseudoalteromonas haloplanktis, Desulfovibrio desulfuricans, and Fusobacterium nucleatum) also appear to employ intrinsic termination for a large fraction of their termination signals.
It has been noted [9–11] for some organisms in the Neisseria genus and the Pasteurellaceae that transcription terminators often include DNA uptake signal sequences (USSs). These uptake signals are short (approximately 9-11 nucleotides (nt)), highly conserved sequences that occur repeatedly (generally ≥ 1,000 times) in a bacterial genome and that facilitate the incorporation of exogenous DNA into the cells of naturally transformable bacteria (see [12, 13] for reviews). Many of the termination signals predicted by TransTermHP in these organisms include USS motifs within their hairpins. We expand past analysis of this phenomenon to additional organisms and present evidence that this is not an artifact of the prediction method nor a result of some other preference for a hairpin configuration of USS sites: these organisms appear to have co-opted the uptake signals for use in transcription termination. In addition, we propose a new USS motif for H. ducreyi that has been overlooked by previous studies. Finally, we discuss a bias in the configuration of USS motifs that form hairpins.
Results and discussion
Algorithm to search for candidate terminators
Every window of DNA of length six that contains at least three thymines is examined to see whether it is the hairpin-proximal end of a tail of a potential terminator. The position adjacent to the 5' side of such a window is a candidate 'reference position' for the 3' end of a terminator hairpin and the sequence downstream of this position is the potential tail. The first 15 bases of the potential tail sequence are scored using a heuristic function from d'Aubenton Carafa et al. , which places more weight on thymines near the hairpin:
for n = 1...15 and x0 = 1.
The energy of potential hairpin configurations adjacent to a reference position can be found efficiently with a dynamic programming algorithm similar to classic RNA folding algorithms [14, 15]. The recurrence equation for this algorithm is given below for completeness. The table entry hairpin_score[i,j] gives the cost of the best hairpin structure for which the base of the 5' stem is at nucleotide position i and the base of the 3' stem is at position j. The entry hairpin_score[i,j] can be computed recursively as follows:
Parameters used to evaluate hairpins
1·(n - 2)
Since terminators are small structures, we are only interested in values of hairpin_score[i,j] for which j - i + 1 is a small constant. Hence, we need to keep only a small portion of the table and thus the space used is constant relative to the size of the genome. Because we fill at most a constant-sized table for each nucleotide position the running time is asymptotically linear in the number of candidate reference positions. For the predictions below, we require the total extent of the stem-loop structure to be less than 59 nt long. If j is a candidate reference position then j + 1 is likely to be one as well. By appropriately arranging the values of the hairpin_score[i,j] table in memory, we can reuse the values computed for index j when considering index j + 1, resulting in a further practical increase in speed. We refer the reader to the available source code for implementation details (Additional data file 1).
The computed hairpin_score[i,j] gives the energy of the best hairpin with endpoints i and j. Fixing j at the reference position, we try all possible 5' endpoints i to find the extent of the best hairpin adjacent to the reference position j. In other words, the 5' endpoint is taken to be argmin i hairpin_score[i,j] where i ranges over the possible endpoints within a bounded distance of j. To reduce the number of candidates that we must store, we keep only candidates with tail score ≤ -2.5 and hairpin scores ≤ -2. In addition, we discard any candidates with stems of length ≤ 4.
The above search procedure will find many terminators that overlap. While some overlapping terminators may be of interest (as candidates for terminator/antiterminator pairs, for example), others clearly are not. In particular, we remove from consideration any terminator that is a subsequence of another terminator with better hairpin and tail scores. Because the hairpins of bidirectional terminators tend to be flanked by stretches of adenines on one side and complementary thymines on the other, we also remove any terminator for which the bottom half of the stem appears to be a hybridization between the two tails of a bidirectional terminator. Specifically, we discard any terminator that is a super-sequence of another terminator and has five or more consecutive adenines in the 5'-most half of the 5' side of the hairpin stem and five or more consecutive thymines in the 3'-most half of the 5' side of the hairpin stem. To further discourage the A-tail and T-tail regions of potential bidirectional terminators from pairing, we require that the first base in the hairpin base closest to the T-tail not be a thymine.
In practice, our search procedure is very fast. It can search the complete 4.2 megabases of the B. subtilis genome (including intragenic regions) using a standard desktop machine in under one minute. In contrast, on the same machine, a recent method  took over two days to scan only the regions immediately downstream of genes. The speed of our search algorithm facilitates interactive experimentation and refinement and permits predictions for many genomes to be undertaken. Our specialized, dynamic-programming-based hairpin search algorithm is the basis for TransTermHP's speed. While this algorithm does not have the biophysical accuracy of more advanced RNA folding programs (for example, [16, 17]), such accuracy is likely unnecessary for prediction of the short, generally high-quality hairpins that constitute transcription terminators.
Function to evaluate the quality of terminators
The search process assigns a hairpin score H and a tail score T to every potential terminator. From these values and genomic context we compute a combined score as a measure of the overall quality of the putative terminator. Previous methods [3, 4] have distilled a single score from the hairpin and tail scores by using a linear combination of the two values, where the weights in the linear combination are the result of a training process. Here, we take a different approach to avoid training with limited data. We assign a score C gc (H,T), defined below, that is related to the probability of finding a terminator of equal or better quality in a random sequence and is a measure of how unlikely the structure is to have arisen by chance.
Let R gc (H,T) be the number of terminators with hairpin score ≤ H and tail score ≤ T found in a random sequence of length L with GC content gc. We define the combined score as:
Thus, if many terminator-like structures in random sequences have both better tails and better hairpins than the terminator under consideration, the terminator will receive a low score. R gc (H,T) is computed empirically using randomly generated DNA sequences of length L = 20 Mb where each base is drawn from a distribution so that the generated sequence has expected GC content gc. The maximization in the numerator ensures that the expression remains well-defined even if no terminator-like structure with hairpin score ≤ H and tail score ≤ T is seen in the random sequence. The -100/log(L) factor normalizes the scores to range between 0 and 100. Ignoring this normalization, the combined score is the log of an estimate of the probability that a terminator as good or better would occur at a given position in a random sequence. In contrast to the scheme used in a predecessor system , this combined score allows assignment of scores to structures inside genes, is monotonically decreasing in each dimension, and smoothly handles candidates that occur in the real data less often than expected by chance.
The function C gc depends on the genome's GC content because the expected distribution of hairpin energies and the likelihood of a good T-tail vary with the frequency of G and C nucleotides. We use the empirically computed intra- or intergenic GC-bias for the value of gc in C gc depending on whether or not the putative terminator occurs inside an annotated coding region. To improve efficiency, C gc is calculated approximately from a set of pre-computed tables.
Validation of predictions in B. subtilis
We ran TransTermHP on the complete genome of B. subtilis, an organism for which Rho-independent termination is suspected to play a predominant role. For each gene, we take the highest scoring terminator (according to equation 3) in the region beginning 25 nt upstream of its stop codon continuing until either the start of a gene on the same strand, or 500 nt downstream of the stop codon, whichever is shorter. In the case of terminators with tied scores, we choose the terminator closest to the stop codon of the upstream gene.
To determine the sensitivity and specificity of our search procedure and scoring scheme and to facilitate comparisons, we follow the testing methodology of de Hoon et al. , who provide a set of operons with experimental support  that they use to derive examples of terminating and non-terminating regions in B. subtilis. They take as positive examples those regions annotated in this data set as having terminators. No terminators should be found following genes that are internal to an experimentally validated operon and do not have read-through terminators following them. Any such region containing a predicted terminator is considered a false positive. Using the most recent GenBank annotation, this yields 458 positive and 562 negative examples (which differs slightly from the 463 positive and 567 negative regions reported by , due to differences in annotations).
de Hoon et al.  introduced a method to predict terminators in which they learn a decision rule (line) separating terminators from non-terminators in the two-dimensional plane where one axis marks hairpin energy and the other measures the quality of the terminator tail. They report 93.95% sensitivity at 94.36% specificity after fitting the parameters of their decision rule to optimize performance on this data set. TransTermHP performs comparably with no real training: TransTermHP achieves 93.0% sensitivity at 93.6% specificity. The slightly higher numbers for the decision rule method may result from over-fitting, and its performance may not generalize outside of the training set. Indeed, the 13 terminators predicted by the decision rule method for regions in which TransTermHP finds no terminator at this specificity tend to have higher hairpin energies than other predictions, suggesting that their classification may be more affected by slight changes in the learned hyperplane. (The average reported hairpin energy of these 13 is -7.7 kcal/mol, while the average hairpin energy of all terminators reported by de Hoon et al.  in B. subtilis is -14 kcal/mol.)
It is interesting to note the apparent accuracy that TransTermHP achieves without significant training. One may have assumed that other palindromic sequences (such as certain classes of transcription factor binding sites) often found in intergenic regions would contribute to a high false positive rate. While some false positives may come from such signals, their number does not seem to impact performance significantly. This makes sense from a mechanistic viewpoint: whatever other functions such a sequence has, if it looks like a terminator, it will interrupt termination, and so such motifs will be selected against in regions in which termination is undesirable. While more advanced machine learning techniques may be helpful for improving performance, they do not seem essential for eliminating false positives.
At comparable false positive rates, TransTermHP and the decision rule method  make similar predictions following 87% of B. subtilis genes. For these genes, either both methods predict no terminator is present in the downstream region or the terminator predicted by de Hoon et al.  is contained within the terminator predicted by TransTermHP (including 15 flanking nucleotides on either side of the hairpin). For 3% of the genes, TransTermHP predicts a terminator where none is predicted by the decision rule method, and for 5.8% of genes each method predicts a different terminator. Hence, while the methods agree on a large core of terminator predictions, they also complement each other with differing predictions for terminators following 544 genes.
At a false positive rate of 10%, TransTermHP is unable to find any structures distinguishable from those found in random sequence in 5% of the positive regions, and for any false positive rate < 100% there remain 3.3% of the positive regions that do not contain a predicted terminator. It is possible that these regions are incorrectly labeled, rely on other methods of termination (for example, Rho-mediated), or have terminators outside of the search region (that is, far from the stop codon of the final gene of an operon). Alternatively, terminators in these regions may be functionally constrained to be weak terminators to enable occasional read-through, or it may be that there are structures within these regions that function well as terminators even though they cannot be distinguished from random sequences.
We label as 'high confidence' those terminators with a score greater than or equal to the cutoff necessary to achieve a 2.5% false positive rate in B. subtilis (yielding an 88% true positive rate). In the rest of this paper, we analyze high-confidence terminators that we predict in organisms without experimentally determined terminators.
Terminator predictions for 343 organisms
We used TransTermHP to predict terminators for all the complete bacterial and archaeal genomes currently available from GenBank (343 genomes at the time of this study). Because of the greatly improved speed of TransTermHP, this requires less than four CPU hours. Complete predictions and the best terminator following each gene in each of the organisms are available online .
Due to a lack of large-scale experimental evidence, we are not able to assess the sensitivity/specificity tradeoff for TransTermHP on genomes other than that of B. subtilis as discussed above. However, as previously noted , those genes that are followed by at least two convergently transcribed genes (that is, → ← ←) are likely final genes in a transcription unit, and so we expect to find a termination signal situated downstream of them. We label these regions 'robust' tail-to-tail regions. We use the percentage of such regions in a genome that contain a high-confidence terminator as a statistic that measures both TransTermHP's sensitivity and the importance of Rho-indepenent termination in an organism. Unfortunately, we cannot untangle the two: the discovery of few terminators in these tail-to-tail regions may indicate a low importance of the hairpin/uracil-tail motif in termination, or it may indicate that the terminators are not sufficiently different from random sequences under our scoring scheme. Because of the accuracy of TransTermHP in B. subtilis, we would expect the former is true, but can not rule out the latter. The terminators predicted in robust tail-to-tail regions for each of the organisms are available in Additional data file 2.
The apparent importance of Rho-independent termination varies across the taxa of prokaryotes. A few previous studies considered sufficiently many organisms from a phylogenetic grouping to assess the relative importance of Rho-independent transcription termination for species in that grouping. TransTermHP's predictions are in line with these previous studies. Among our predictions, archaea generally do not contain high-confidence terminators: averaged over the 27 archaeal genomes, only 11% of the robust tail-to-tail regions contain them. This is in agreement with previous studies that showed that hairpins are not over-represented following genes in archaea [5, 6]. That TransTermHP finds few terminators among these organisms is further evidence that it has a low false positive rate at the high-confidence threshold.
A recent study  found intrinsic termination to be the dominant termination mechanism among Firmicutes, a finding that our predictions confirm. Across the 56 Bacilli, on average 79% of the considered tail-to-tail regions contained a high-confidence terminator. Among the 7 Clostridia and 16 Mollicutes, fewer terminators were found (on average, 65% and 59%, respectively). Only 9 Firmicutes had high-confidence terminators in fewer than 60% of their robust tail-to-tail regions. Eight of these are Mollicutes; the ninth is the Clostridium Moorella thermoacetica. In agreement with , terminators were rarely found in Mycoplasma genitalium (20%) and Mycoplasma pneumoniae (24%). However, no Rho homolog is known to be present in these genomes , and so either a novel terminator mechanism is used, or the termination signals present in these organisms are weaker and more similar to random sequences.
Bacteria outside of the Firmicutes with many high-confidence terminators
Neisseria meningitidis Z2491
Neisseria meningitidis MC58
Neisseria gonorrhoeae FA 1090
Vibrio fischeri ES114
Vibrio vulnificus CMCP6
Vibrio vulnificus YJ016
Haemophilus influenzae 86 028NP
Mannheimia succiniciproducens MBEL55E
Haemophilus ducreyi 35000HP
Psychrobacter cryohalolentis K5
Psychrobacter arcticum 273-4
Pseudoalteromonas haloplanktis TAC125
Desulfovibrio desulfuricans G20
Relationship to DNA uptake
Prevalence of DNA uptake signals in terminators
Hundreds of copies of short, highly conserved DNA segments (called USSs) aid some bacteria such as N. meningitidis and Haemophilus influenzae in selectively incorporating homologous exogenous DNA [12, 13, 20–22]. These uptake signal sequences have been found to frequently occur within transcription terminator hairpins [9–11, 22]. Smith et al.  examined N. meningitidis, N. gonorrhoeae, and H. influenzae and note that, on small scales, the distance between copies of the USS is not randomly distributed but rather there is an excess of copies of the motif on opposite strands within a small distance of one another, thus forming a sequence that may fold into a hairpin when transcribed into RNA.
USS motifs in the best high-confidence terminators following genes
N. meningitidis Z2491
N. meningitidis MC58
N. gonorrhoeae FA 1090
H. influenzae 86 028NP
M. succiniciproducens MBEL55E
H. ducreyi 35000HP
Among Pasteurellaceae, the percentage of terminators containing known USSs is lower (between 17% and 29%). Though P. multocida has a larger genome than H. influenzae (2.26 Mb versus 1.83 Mb), it has 37% fewer instances of the USS motif, and fewer high-confidence terminators (17%) contain the motif. Interestingly, the most common motif within high-confidence terminators in H. ducreyi (AAGCGGT) matches the USS for the other Pasteurellaceae (AAGTGCGGT) except for an excision of a GT dinucleotide sequence. This shorter motif occurs 1,371 times in the genome (expected frequency is 140 occurrences), suggesting that it may also function as a USS. Previous studies [10, 23] failed to find any USS in H. ducreyi due to this difference from the expected H. influenzae-derived motif. No previous study has reported on the uptake signal sequences for Mannheimia succiniciproducens. Thus, in addition to quantifying their involvement in intrinsic terminators, we report the first evidence of these signals in these species.
In N. meningitides Z2491, 81% of the USS instances within intergenic regions that have a high-confidence terminator are contained within a high-confidence terminator (though perhaps not the best one). Percentages are similar in the other Neisseria. In H. influenzae, on the other hand, only 34% of such USS sites are contained within a terminator. The other Pasteurellacaea have similar low percentages (29% to 42%).
Extended USS motifs
Evidence that paired USS motifs function as terminators
Analysis of all USS hairpins in N. meningitidis Z2491
Average no. of A/Ts
No. of reg
No. of USS
No. of hairpins
Orientation of USS motifs and closely spaced USS pairs
N. meningitidis Z2491
N. meningitides MC58
N. gonorrhoeae FA 1090
H. influenzae 86 028NP
M. succiniciproducens MBEL55E
H. ducreyi 35000HP
The bias in genomic context, conservation pattern of the extended motifs, and the preference for an orientation that creates good A- and T-tails support the notion that USS hairpin configurations are maintained to promote transcription termination, and Table 3 quantifies the propensity for terminators to include USS signals. Evolutionary expediency may be the reason for the co-location of these signals. The prevalence of so many copies of a short motif and its complement (as required to ensure selective uptake) likely facilitates the creation of many hairpins that can be co-opted for termination simply by point mutations to create a strong T-tail. By reusing the USS, it is no longer necessary for there to be a series of coordinated, complementary mutations for the development of hairpin structures.
We have described a highly efficient, accurate computational system for predicting Rho-independent transcription termination in bacterial genomes, and we have used this system to predict terminators in hundreds of genomes for which no such predictions were previously available. Our predictions for 343 organisms are available on the web , and can be downloaded in bulk, by organism, or they can be searched based on score, genomic context, and features of their sequence. Terminators downstream of specified genes can be found as well. They represent the most complete set of terminator predictions yet available.
The new system is over 10 times faster than an earlier algorithm by one of the authors and thousands of times faster than a recently described system , with comparable sensitivity and specificity.
The speed and accuracy of TransTermHP has facilitated the discovery of a likely DNA uptake motif in H. ducreyi where none was previously known. Though most USS signals are not involved in termination, we have given more evidence that some USS pairs within Neisseria and Pasteurellacaea are maintained in hairpin configurations in order to affect transcription termination, and we have quantified the tendency for terminators to include USS motifs.
TransTermHP is designed to detect the common, classic intrinsic terminator motif: a hairpin stem followed by a poly-U tail. The method may be less accurate in detecting those terminators that deviate from this motif, for example, by lacking the uracil-rich tail. Given the high sensitive and specificity of TransTermHP, if it is unable to find many high-quality terminators in an organism that lacks a Rho homolog, this may be a hint that some variant of the intrinsic terminator motif is used.
In addition to its immediate utility for accurately finding Rho-independent terminators, we hope the method and accompanying software will be a useful starting point for developing other improved systems for terminator prediction, investigations of structural properties of intrinsic terminators (in the spirit of ), as well as related problems such as discovery of anti-termination structures .
Materials and methods
Genomic data for the 343 organisms was downloaded on 6 June 2006 from . Annotations were taken from the .ptt file accompanying the genomic sequence. Accession and version numbers for the sequences used are available as Additional data file 3. TransTermHP was run using the default search parameters and the -p scoring parameter to produce predictions for the best terminator following each gene in these organisms (available on the web , --bag option to TransTermHP) as well as the best terminators in each robust tail-to-tail region (Additional data file 2, --t2t-perf option to TransTermHP). The former were used when comparing performance to , while the latter was used to assess the importance of Rho-independent termination within an organism. Experiments were run on a 3.2 GHz Intel Xeon processor running Linux. High-confidence terminators are those with scores ≥ 76.
The C++ source code for TransTermHP is available (Additional data file 1) under an open source license.
Additional data files
The following additional data are available with the online version of this paper. Additional data file 1 is the complete C++ source code for the TransTermHP system. Additional data file 2 is a zipped archive of plain text files listing the highest-scoring terminators in robust tail-to-tail regions for each of the 343 genomes studied. As described above, the entire robust tail-to-tail region is searched; if the region is less than 500 nt, then the 500 nt downstream of the stop codon is searched (stopping if a gene start is encountered). In the case of ties, all terminators with the high score are output. Additional data file 3 is a plain text file listing, for each genome, the accession and version numbers for the sequences used. Additional data file 4 is a zipped archive of text files listing the best terminators found downstream of each of the genes of the 343 organisms studied.
This work was supported in part by grants R01-LM06845 and R01-LM007938 from the National Institutes of Health. Thanks also to Art Delcher, Jessica Fong and the referees for careful comments on the manuscript.
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