- Open Access
Assessment of transfer methods for comparative genomics of regulatory networks in bacteria
© The Author(s). 2016
- Published: 31 August 2016
Comparative genomics can leverage the vast amount of available genomic sequences to reconstruct and analyze transcriptional regulatory networks in Bacteria, but the efficacy of this approach hinges on the ability to transfer regulatory network information from reference species to the genomes under analysis. Several methods have been proposed to transfer regulatory information between bacterial species, but the paucity and distributed nature of experimental information on bacterial transcriptional networks have prevented their systematic evaluation.
We report the compilation of a large catalog of transcription factor-binding sites across Bacteria and its use to systematically benchmark proposed transfer methods across pairs of bacterial species. We evaluate motif- and accuracy-based metrics to assess the results of regulatory network transfer and we identify the precision-recall area-under-the-curve as the best metric for this purpose due to the large class-imbalanced nature of the problem. Methods assuming conservation of the transcription factor-binding motif (motif-based) are shown to substantially outperform those assuming conservation of regulon composition (network-based), even though their efficiency can decrease sharply with increasing phylogenetic distance. Variations of the basic motif-based transfer method do not yield significant improvements in transfer accuracy. Our results indicate that detection of a large enough number of regulated orthologs is critical for network-based transfer methods, but that relaxing orthology requirements does not improve results. Using the transcriptional regulators LexA and Fur as case examples, we also show how DNA-binding domain sequence similarity can yield confounding results as an indicator of transfer efficiency for motif-based methods.
Counter to standard practice, our evaluation of metrics to assess the efficiency of methods for regulatory network information transfer reveals that the area under precision-recall (PR) curves is a more precise and informative metric than that of receiver-operating-characteristic (ROC) curves, confirming similar findings in other class-imbalanced settings. Our systematic assessment of transfer methods reveals that simple approaches to both motif- and network-based transfer of regulatory information provide equal or better results than more elaborate methods. We also show that there are not effective predictors of transfer efficacy, substantiating the long-standing practice of manual curation in comparative genomics analyses.
- Comparative genomics
- Regulatory networks
- Transcription factor
- Site search
The availability of complete genome sequences for related organisms can be effectively leveraged to study transcriptional regulatory networks (TRN) . In the past decade, comparative genomics approaches have been routinely employed to study bacterial transcriptional regulatory networks, or regulons, controlled by a single transcription factor (TF). These studies have enabled the identification of core network elements and niche-specific adaptations, providing insights into the evolution of these systems [2–7]. A conventional TRN comparative genomics analysis typically involves three well defined steps [1, 8, 9]. The first step consists in the transfer of available information on the regulatory network (i.e. known TF-binding sites and/or regulated genes) to the species under analysis, in order to infer the TF-binding motif in these target species. The second step involves a genome-wide search for putative TF-binding sites in the target genomes using the inferred TF-binding motifs. In the third step, search results from multiple genomes are integrated across orthologs, based on the assumption that only orthologs of regulated genes will systematically display TF-binding sites in their promoter regions.
The power of comparative genomics arises from the aggregation of predictions in multiple genomes under the assumption of functional selection, which dramatically reduces the number of false positives . However, the effectiveness of this approach depends crucially on the success of the initial transfer step. Information from a reference TRN can be transferred though the assumption of a conserved TF-binding motif, a conserved regulon, or a combination of the two [1, 6]. As a consequence, several methods have been proposed to transfer regulatory information across species. The simplest approach, here called “direct transfer”, is to use the reference TF-binding motif to search for sites in the target genome. The target motif is hence implicitly defined as a subset of the highest scoring sites in that genome [2–4, 7]. In “direct discovery”, the direct transfer scheme is further elaborated by applying a motif discovery or optimization algorithm on a set of high-scoring sites from the target genome [9, 10]. The alternative to TF-binding motif-based transfer is to assume conservation of gene content in the TRN. Regulon or “network transfer” is typically implemented through the detection of orthologs for genes in known regulated operons. The promoter regions of the corresponding operons in target genomes are then analyzed with a motif discovery tool to elicit the TF-binding motif [11–14]. If network information is not available, a minimal network consisting only of the TF-coding gene can be postulated under the assumption of self-regulation, and the TF-binding motif can be inferred with motif discovery tools applied to promoter region of the TF-coding gene [15–17]. Lastly, motif- and network-based transfer approaches can be combined in “mixed transfer” to minimize false positives, at the expense of lowered sensitivity [6, 9, 18].
The limited availability of experimental data on TF-binding sites has hindered attempts at systematically assessing and comparing methods for transfer of transcriptional regulatory networks. Early studies on TRN transfer indicated that the efficiency of methods based on TF-binding motif transfer faced a sharp drop-off with increasing sequence divergence among the TF orthologs . Later studies exposed the limitations of network-based transfer methods, due to shortcomings in orthology detection methods and the flexible nature of bacterial TRNs [19–21]. It has been suggested that mixed transfer methods provide better results , but the scarcity and distributed nature of TF-binding site data have to date prevented systematic benchmarking of these methods. In this work, presented at the 2015 International Symposium on Bioinformatics Research and Applications , we report the mining and integration of experimental TF-binding site data from multiple databases into a unified catalog. Leveraging this resource, we performed a systematic evaluation of TRN transfer methods across pairs of species and using multiple metrics. In agreement with previous reports, our results reveal that motif-based transfer methods perform best, but decay sharply at high TF sequence divergence. In contrast, the efficiency of network-based transfer methods is poor and weakly dependent of phylogenetic distance, while mixed methods do not significantly improve the results of motif-based transfer methods. Our analysis also highlights the inadequacy of receiver-operating-characteristic (ROC) curves in heavily unbalanced settings and indicates that the precision-recall (PR) area-under-the-curve (AUC) is the most informative statistic for assessment of transfer results. We evaluate predictive measures for transfer accuracy and discuss their applicability in the context of comparative genomics analyses.
Data compilation and evaluation of metrics for the assessment of transfer methods
To perform a systematic analysis of methods for the transfer of transcriptional regulatory networks in Bacteria, we compiled data from five major databases reporting experimentally-validated TF-binding sites across the Bacteria domain. After consolidating replicates, we obtained a catalog of 7,603 TF-binding sites for 344 TFs in 166 species (Additional file 1). To analyze TRN transfer, we focused on TF/species pairs that contained at least 10 binding sites for the same TF in both species. The resulting dataset contains 179 TF-specific species pairs for 15 different TFs across 35 bacterial species and is dominated by instances of the global transcriptional regulators LexA and Fur (Additional file 2).
As it can be seen in Fig. 1, both the Euclidean distance and KL-divergence perform only moderately well at discriminating the results of simulated noisy transfers (containing 50 % and 75 % random sites) from completely random or permuted motifs. This result is partly due to the fact that the expectation for random motifs is not to yield maximum distance, narrowing the useful range of motif comparison metrics. The two other contributing factors are the high-dimensionality of TF-binding motifs, which is known to decrease the relative contrast of L-norms , and the presence of low information bearing positions in most TF-binding motifs. Low information positions are intrinsically close in PSFM space, artificially increasing the similarity between motifs for both metrics . As a result, in both cases, random and permuted motifs display a considerable spread, leading to significant overlap with the results obtained for simulated noisy transfers. In practice, transfer methods frequently generate motifs comparable to the noisy transfers simulated here, and their overlap with random controls therefore complicates the interpretation of transfer results.
Accuracy metrics based on a genome-wide search for known TF-binding sites should in principle provide a more informative metric of the effectiveness of the transfer process, since they evaluate the ability of the inferred motif to locate true binding sites in the target genome. In contrast with motif comparison methods, the expectation for accuracy metrics is hence that incorrect or random transfers should yield very low AUC values. However, this does not happen for the ROC-AUC, a widely adopted metric in bioinformatics . This result is due to the large class imbalance in the TF-binding search problem, where a handful of known true sites must be distinguished from the genome background. Even though ROC curves scale properly with class imbalance , they are ill-suited to discriminate between classifiers in a heavily imbalanced context, because the negative class dominates the computation of the ROC-AUC . The net result of this effect is a compression of AUC scores for noisy motifs into a very narrow range (0.9–1.0), making discrimination between near-optimal and noisy transfers almost impossible. This compression affects also the results obtained for random and permuted motifs, which spread all the way up to 0.95 AUC scores, further complicating the interpretation of transfer results. By focusing on the ratio between true and false positives (precision) and otherwise ignoring the negative class, the PR-AUC generates scores are not compressed by class imbalance [28, 30]. As it can be seen in Fig. 1, the PR-AUC effectively exploits its range to discriminate between noisy transfers and systematically assigns very low values to random and permuted motifs. Hence, the PR-AUC provides the most effective metric for the benchmarking of TRN transfer methods and was used in all subsequent analyses reported here.
Comparison of transfer methods
Motif-based and network-based transfer methods rely on different assumptions about the evolutionary dynamics of transcriptional regulatory networks. The former assume that the TF-binding motif is conserved to some extent, while the latter assume that the gene components of the regulon are conserved. As a result, it is presumed that motif-based methods will perform poorly at large phylogenetic distances due to expected divergence in the TF-binding motif, whereas network-based methods are expected to be more resilient to phylogenetic distance if the biological function of the regulatory network is preserved. Interestingly, there is evidence supporting and invalidating both assumptions and their corollaries. The SOS response transcriptional regulator LexA, for instance, has been shown to target widely diverging motifs in relatively close species , whereas some transcriptional regulators, like the heat-shock response repressor HrcA or the arginine repressor ArgR, are known to preserve their binding motif across Bacteria to different extents [3, 32]. On the other hand, regulon composition has been documented to vary significantly even among closely related species [4–6, 21]. Furthermore, CRP/FNR-type regulators have been shown to control completely different networks using closely related motifs across Bacteria [33, 34].
Here we tested the robustness of TRN transfer methods by evaluating the PR-AUC of inferred TF-binding motifs in 179 TF-specific species pairs, using three motif-based and three network-based methods, as well as a combination of motif- and network-based methods (Additional file 4). The motif-based transfer methods include direct transfer and direct discovery methods. In direct transfer, the reference collection of TF-binding sites is used directly to determine the inferred collection by searching promoter regions in the target genome. In direct discovery, the results of a relaxed search and their surrounding regions are used as input for a motif discovery algorithm, with the goal of generating a motif better adapted to the target genome. The network-based transfer methods evaluated differ in how they map genes regulated in the reference genome to the target genome. This mapping can be based on the detection of direct orthology for genes in regulated operons, their functional assignment using Clusters of Orthologous Groups (COGs) or orthology detection using their interacting network partners. The mixed approach combines a relaxed TF-binding motif search with the restriction that identified sites must be associated with genes mapped with any of the three network-based transfer approaches.
In contrast with network-based transfer methods, the different implementations of motif-based transfer yield very similar results (Fig. 2). Using the reference motif to search promoter regions and define the putative target motif (direct transfer) provides results comparable to those obtained with other motif-based transfer methods and robust with respect to the specific threshold used to define the motif (Additional file 6). The use of MEME in direct discovery transfers to rediscover the TF-binding motif, which has been postulated to refine and better adapt the inferred motif to the target genome , does not provide significant improvements over direct transfer. In fact, when performing motif discovery on the promoter region surrounding the identified sites, MEME may identify other genomic elements (e.g. Pribnow boxes) as the best motifs, decreasing the accuracy of the method. Performing motif inference on the identified sites surrounded by random promoter regions prevents this effect, but does not yield a systematic improvement in PR-AUC values over direct transfer. Finally, the mixed mode approach, which has been associated with enhanced specificity [6, 9], did not yield a systematic improvement over direct transfer either.
Predictive indicators of transfer accuracy
Our comparative analysis of transfer methods (Fig. 2) indicates that, even at relatively close phylogenetic distances, both motif- and network-based transfer methods may provide inaccurate results. Hence, manual curation of transfer results, which has been the de facto standard for comparative genomics of TRN in Bacteria [2, 4, 8, 35], appears to be a necessary requisite to ensure the reliability of any subsequent comparative genomics analyses. Leveraging the TF-binding site catalog compiled here, we attempted to identify predictive indicators of transfer accuracy for motif- and network-based transfer methods. Several studies have exploited sequence similarity in the DNA-binding domain of the TF as a criterion for clustering putative regulatory regions in motif discovery [15, 17, 33, 36]. The rationale for this approach is that similar DNA-binding domains will target conserved TF-binding motifs. Hence, it is plausible to assume that DNA-binding domain sequence similarity could be an efficient predictor of transfer accuracy for motif-based transfer methods.
Transferring known information about transcriptional regulatory networks from reference to target species is a critical step in comparative genomics analyses. In this work, we compiled a catalog of known TF-binding sites in Bacteria and performed a methodic evaluation of assessment metrics in order to perform the first systematic analysis of different transfer methods. Our results identify the precision-recall area-under-the-curve as the most reliable metric for transfer efficiency. We also show that motif-based transfer methods dramatically outperform network-based approaches, but their efficiency may decrease sharply with increasing phylogenetic distance. We evaluate some predictive indicators of transfer accuracy and show that they are not consistent or precise enough to enable full automation of TRN transfer. Our results hence support the long-standing practice of manual curation in comparative genomics analyses and reveal that the introduction of more elaborate methods does not clearly benefit motif- or network-based transfer approaches.
TF-binding site and genome data
Experimentally-validated TF-binding sites were compiled from CollecTF , a Bacteria domain-wide TF-binding site database, and four model-organism databases: RegulonDB, CoryneRegNet, DBTBS and MtbRegList [43–46]. Data from these databases was downloaded and merged after removal of duplicates and of data without supporting experimental evidence. To evaluate transfer methods across pairs of species, only regulons with at least 10 experimentally-validated TF-binding sites for a given TF in both species were used. Complete genome sequences and annotations for species with available TF-binding site data were downloaded from the NCBI RefSeq database . Operon predictions for all genomes and COG annotations for protein-coding genes were obtained from the DOOR database [48, 49].
TF-binding site search and motif discovery
For TF-binding site search, only the regions spanning from −300 bp to +50 bp relative to the corresponding gene translation start site were considered. Site search was implemented using custom scripts based on standard Biopython library functions . The searched regions were scanned with a sliding window, evaluating each position with a position-specific scoring matrix (PSSM) based on a uniform background mononucleotide model . Motif discovery on selected sequences was performed with MEME, using command line settings -zoops -revcomp -dna, and maximum and minimum motif widths, respectively, of 150 % and 50 % of the reference motif width .
Two main motif-based transfer methods were implemented. In direct transfer, a position-specific scoring matrix (PSSM) is built from the reference collection of binding sites and used to scan the promoter regions of the genome of interest to identify putative sites . Under the assumption that regulon size is conserved to a first approximation, the target motif is defined as composed of the highest scoring N T sites. N T = α · N R · G T /G R , where N R is the number of sites in the reference species, G T and G R are genome lengths for target and reference species, respectively, and α is used as a scaling factor to modulate specificity (α = 1.25). Direct discovery uses a relaxed scaling factor α (α = 2.50) to generate a larger collection of putative sites. These sites and their adjoining intergenic regions are used to rediscover the TF-binding motif with MEME in direct discovery with true intergenic. For direct discovery with random intergenic, the genomic intergenic regions are substituted by 100 bp stretches randomly generated following the intergenic region nucleotide frequencies.
Network-based transfer was implemented using three different criteria to map regulated genes in the reference genome to target genomes. In ortholog mode, orthologs of all genes belonging to regulated operons were detected as best reciprocal BLAST hits between species pairs using a minimum e-value threshold of 10−10 . In COG mode, all genes in the target genome mapping to the same COG as genes in regulated operons of the reference genome were considered functional orthologs. In interaction mode, direct interacting partners for regulated genes in the reference genome were identified using the STRING database . The orthologs of these genes on target genomes were detected through reciprocal BLAST and used to define putative regulatory networks. In all three cases, the promoters of all target operons containing mapped genes were then used for motif discovery with MEME.
Mixed transfer uses a relaxed (α = 2.50) TF-binding site search with the reference TF-binding motif to define a set of putative sites in the target genome. This collection of putative sites is filtered by retaining only those sites next to operons containing genes that have been mapped to the target genome by any of the network-based transfer methods.
Two main types of assessment metrics were used to gauge the efficacy of TF motif-based transfer methods: motif comparison and performance metrics. Motif comparison methods involve the direct comparison of the TF-binding motifs inferred by the transfer method and the motif generated from the known collection of regulated genes in the target genome. To compare motifs of different lengths, the two collections of binding sites are shifted with respect to each other to obtain the gapless alignment that maximizes the information content (IC) of the joint collection . The aligned region of each collection is then used to compute its position-specific frequency matrix (PSFM). The similarity between the two PSFM is evaluated using either the Euclidean distance or the Kullback–Leibler (KL) divergence of the inferred motif from the known target motif . Performance metrics evaluate the ability of the inferred motif to retrieve the known target sites when searching an equal number of promoter regions from the target genome containing, and not containing, known sites. To assess true positives, the maximum IC alignment between target and inferred collections is used to compute the offset between predicted and known sites. The accuracy of the search using the inferred TF-binding motif is then evaluated as the area-under-the-curve (AUC) of the receiver-operator-characteristic (ROC) or precision-recall (PR) curve, computed with the scikit-learn Python library functions [24, 25].
Control experiments for TRN transfer were generated in two different ways. Noisy transfers were simulated by defining the inferred motif as a mixture pseudoreplicate of the known target collection and sequences from the promoter region of the target genome. Given a mixture weight ω, the pseudoreplicate is obtained by sampling, with replacement, (1-ω) · N sites from the known target collection and ω · N sequences of length L from the promoter region of the target genome (where N and L are, respectively, the number and width of sites in the known target collection) (Additional file 3). Noisy transfers were generated for 0.1, 0.25, 0.5 and 0.75 values of ω, simulating increasingly inefficient transfers. Permuted transfers were obtained by randomly sampling, without replacement, the columns of the known target motif. A transfer was considered successful if its PR-AUC was larger than two standard deviations above the mean PR-AUC value observed for permuted transfers.
AUC, area under the curve; BLAST, basic local alignment search tool; COG, clusters of orthologous groups; CRP, camp receptor protein; FNR, fumarate and nitrate reduction protein; IC, information content; KL, kullback–leibler; MEME, multiple EM for motif elicitation; PR, precision-recall; PSFM, position specific frequency matrix; PSSM, position specific scoring matrix; ROC, receiver-operating-characteristic; TF, transcription factor; TRN, transcriptional regulatory network
The authors wish to thank Patrick O’Neill for insightful discussions and assistance in the data mining process, and Dinara Sagitova and the CollecTF team for their annotation work.
This article has been published as part of BMC Bioinformatics Volume 17 Supplement 8, 2016. Selected articles from the 11th International Symposium on Bioinformatics Research and Applications (ISBRA '15): bioinformatics. The full contents of the supplement are available online https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-17-supplement-8.
This work, including the publication, was funded by the U. S. National Science Foundation award MCB-1158056.
Availability of data and materials
The data sets and Python scripts supporting the results of this article are available in the Erill Lab GitHub repository (http://github.com/ErillLab/Transfer_method_analysis; ).
SK compiled the data, wrote scripts for data analysis and analyzed results. IE conceived of the study, and participated in its design, coordination and evaluation of results. IE and SK drafted and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Babu MM. Computational approaches to study transcriptional regulation. Biochem Soc Trans. 2008;36(Pt 4):758–65.View ArticlePubMedGoogle Scholar
- Tan K, Moreno-Hagelsieb G, Collado-Vides J, Stormo GD. A comparative genomics approach to prediction of new members of regulons. Genome Res. 2001;11:566–84.View ArticlePubMedPubMed CentralGoogle Scholar
- Makarova KS, Mironov AA, Gelfand MS. Conservation of the binding site for the arginine repressor in all bacterial lineages. Genome Biol. 2001;2:RESEARCH0013.PubMedPubMed CentralGoogle Scholar
- Erill I, Jara M, Salvador N, Escribano M, Campoy S, Barbe J. Differences in LexA regulon structure among Proteobacteria through in vivo assisted comparative genomics. Nucleic Acids Res. 2004;32:6617–26.View ArticlePubMedPubMed CentralGoogle Scholar
- Venancio TM, Aravind L. Reconstructing prokaryotic transcriptional regulatory networks: lessons from actinobacteria. J Biol. 2009;8:29.View ArticlePubMedPubMed CentralGoogle Scholar
- Baumbach J. On the power and limits of evolutionary conservation—unraveling bacterial gene regulatory networks. Nucleic Acids Res. 2010;38:7877–84.View ArticlePubMedPubMed CentralGoogle Scholar
- Rodionov DA, Rodionova IA, Li X, Ravcheev DA, Tarasova Y, Portnoy VA, Zengler K, Osterman AL. Transcriptional regulation of the carbohydrate utilization network in Thermotoga maritima. Front Microbiol. 2013;4:244.View ArticlePubMedPubMed CentralGoogle Scholar
- Gelfand MS, Novichkov PS, Novichkova ES, Mironov AA. Comparative analysis of regulatory patterns in bacterial genomes. Brief Bioinform. 2000;1:357–71.View ArticlePubMedGoogle Scholar
- Baumbach J, Rahmann S, Tauch A. Reliable transfer of transcriptional gene regulatory networks between taxonomically related organisms. BMC Syst Biol. 2009;3:8.View ArticlePubMedPubMed CentralGoogle Scholar
- Habib N, Wapinski I, Margalit H, Regev A, Friedman N. A functional selection model explains evolutionary robustness despite plasticity in regulatory networks. Mol Syst Biol. 2012;8:619.View ArticlePubMedPubMed CentralGoogle Scholar
- McCue L, Thompson W, Carmack C, Ryan MP, Liu JS, Derbyshire V, Lawrence CE. Phylogenetic footprinting of transcription factor binding sites in proteobacterial genomes. Nucleic Acids Res. 2001;29:774–82.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang T, Stormo GD. Combining phylogenetic data with co-regulated genes to identify regulatory motifs. Bioinformatics. 2003;19:2369–80.View ArticlePubMedGoogle Scholar
- Panina EM, Mironov AA, Gelfand MS. Comparative genomics of bacterial zinc regulons: enhanced ion transport, pathogenesis, and rearrangement of ribosomal proteins. Proc Natl Acad Sci U A. 2003;100:9912–7.View ArticleGoogle Scholar
- Zhang S, Xu M, Li S, Su Z. Genome-wide de novo prediction of cis-regulatory binding sites in prokaryotes. Nucleic Acids Res. 2009;37:e72.View ArticlePubMedPubMed CentralGoogle Scholar
- Francke C, Kerkhoven R, Wels M, Siezen RJ. A generic approach to identify Transcription Factor-specific operator motifs; Inferences for LacI-family mediated regulation in Lactobacillus plantarum WCFS1. BMC Genomics. 2008;9:145.View ArticlePubMedPubMed CentralGoogle Scholar
- Cornish JP, Matthews F, Thomas JR, Erill I. Inference of self-regulated transcriptional networks by comparative genomics. Evol Bioinform Online. 2012;8:449–61.PubMedPubMed CentralGoogle Scholar
- Ravcheev DA, Khoroshkin MS, Laikova ON, Tsoy OV, Sernova NV, Petrova SA, Rakhmaninova AB, Novichkov PS, Gelfand MS, Rodionov DA. Comparative genomics and evolution of regulons of the LacI-family transcription factors. Front Microbiol. 2014;5:294. doi:10.3389/fmicb.2014.00294.
- Yu H, Luscombe NM, Lu HX, Zhu X, Xia Y, Han J-DJ, Bertin N, Chung S, Vidal M, Gerstein M. Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genome Res. 2004;14:1107–18.View ArticlePubMedPubMed CentralGoogle Scholar
- Madan Babu M, Teichmann SA, Aravind L. Evolutionary dynamics of prokaryotic transcriptional regulatory networks. J Mol Biol. 2006;358:614–33.View ArticlePubMedGoogle Scholar
- Lozada-Chávez I, Janga SC, Collado-Vides J. Bacterial regulatory networks are extremely flexible in evolution. Nucleic Acids Res. 2006;34:3434–45.View ArticlePubMed CentralGoogle Scholar
- Price MN, Dehal PS, Arkin AP. Orthologous transcription factors in bacteria have different functions and regulate different genes. PLoS Comput Biol. 2007;3:1739–50.View ArticlePubMedGoogle Scholar
- Kılıç S, Erill I. Assessment of Transcription Factor Binding Motif and Regulon Transfer Methods. In: Harrison R, Li Y, Măndoiu I, editors. Bioinformatics Research and Applications. Switzerland: Springer International Publishing; 2015. p. 440–1 [Lecture Notes in Computer Science, vol. 9096].Google Scholar
- Gupta S, Stamatoyannopoulos JA, Bailey TL, Noble WS. Quantifying similarity between motifs. Genome Biol. 2007;8:R24.View ArticlePubMedPubMed CentralGoogle Scholar
- Lewis DD. Evaluating Text Categorization. In: Proceedings of the Workshop on Speech and Natural Language. Stroudsburg: Association for Computational Linguistics; 1991. p. 312–8 [HLT’91].View ArticleGoogle Scholar
- Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39:561–77.PubMedGoogle Scholar
- Aggarwal CC, Hinneburg A, Keim DA. On the Surprising Behavior of Distance Metrics in High Dimensional Space. In: Bussche JV d, Vianu V, editors. Database Theory — ICDT 2001. Springer: Berlin Heidelberg; 2001. p. 420–34 [Lecture Notes in Computer Science, vol. 1973].View ArticleGoogle Scholar
- Zhang S, Zhou X, Du C, Su Z. SPIC: A novel similarity metric for comparing transcription factor binding site motifs based on information contents. BMC Syst Biol. 2013;7 Suppl 2:S14.View ArticlePubMedPubMed CentralGoogle Scholar
- Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS One. 2015;10:e0118432.View ArticlePubMedPubMed CentralGoogle Scholar
- Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27:861–74 [ROC Analysis in Pattern Recognition].View ArticleGoogle Scholar
- Davis J, Goadrich M. The Relationship Between Precision-Recall and ROC Curves. In: Proceedings of the 23rd International Conference on Machine Learning. New York: ACM; 2006. p. 233–40 [ICML’06].Google Scholar
- Erill I, Campoy S, Barbe J. Aeons of distress: an evolutionary perspective on the bacterial SOS response. FEMS Microbiol Rev. 2007;31:637–56.View ArticlePubMedGoogle Scholar
- Gelfand MS. Recognition of regulatory sites by genomic comparison. Res Microbiol. 1999;150(9-10):755–71. ISSN 0923-2508.View ArticlePubMedGoogle Scholar
- Dufour YS, Kiley PJ, Donohue TJ. Reconstruction of the core and extended regulons of global transcription factors. PLoS Genet. 2010;6:e1001027.View ArticlePubMedPubMed CentralGoogle Scholar
- Matsui M, Tomita M, Kanai A. Comprehensive Computational Analysis of Bacterial CRP/FNR Superfamily and Its Target Motifs Reveals Stepwise Evolution of Transcriptional Networks. Genome Biol Evol. 2013;5:267–82.View ArticlePubMedPubMed CentralGoogle Scholar
- Novichkov PS, Rodionov DA, Stavrovskaya ED, Novichkova ES, Kazakov AE, Gelfand MS, et al. RegPredict: an integrated system for regulon inference in prokaryotes by comparative genomics approach. Nucleic Acids Res. 2010;38(Web Server issue):W299–307.View ArticlePubMedPubMed CentralGoogle Scholar
- Sahota G, Stormo GD. Novel sequence-based method for identifying transcription factor binding sites in prokaryotic genomes. Bioinformatics. 2010;btq501.Google Scholar
- Pohl E, Haller JC, Mijovilovich A, Meyer-Klaucke W, Garman E, Vasil ML. Architecture of a protein central to iron homeostasis: crystal structure and spectroscopic analysis of the ferric uptake regulator. Mol Microbiol. 2003;47:903–15.View ArticlePubMedGoogle Scholar
- Zhang APP, Pigli YZ, Rice PA. Structure of the LexA-DNA complex and implications for SOS box measurement. Nature. 2010;466:883–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Schneider TD, Stormo GD, Gold L, Ehrenfeucht A. Information content of binding sites on nucleotide sequences. J Mol Biol. 1986;188:415–31.View ArticlePubMedGoogle Scholar
- Schneider TD. Reading of DNA sequence logos: prediction of major groove binding by information theory. Methods Enzymol. 1996;274:445–55.View ArticlePubMedGoogle Scholar
- Dorfman R. A Formula for the Gini Coefficient. Rev Econ Stat. 1979;61:146–49.View ArticleGoogle Scholar
- Kiliç S, White ER, Sagitova DM, Cornish JP, Erill I. CollecTF: a database of experimentally validated transcription factor-binding sites in Bacteria. Nucleic Acids Res. 2014;42(Database issue):D156–160.View ArticlePubMedGoogle Scholar
- Jacques P-É, Gervais AL, Cantin M, Lucier J-F, Dallaire G, Drouin G, Gaudreau L, Goulet J, Brzezinski R. MtbRegList, a database dedicated to the analysis of transcriptional regulation in Mycobacterium tuberculosis. Bioinformatics. 2005;21:2563–5.View ArticlePubMedGoogle Scholar
- Sierro N, Makita Y, de Hoon M, Nakai K. DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information. Nucleic Acids Res. 2008;36(Database issue):D93–96.PubMedGoogle Scholar
- Pauling J, Röttger R, Tauch A, Azevedo V, Baumbach J. CoryneRegNet 6.0--Updated database content, new analysis methods and novel features focusing on community demands. Nucleic Acids Res. 2012;40(Database issue):D610–614.View ArticlePubMedGoogle Scholar
- Salgado H, Peralta-Gil M, Gama-Castro S, Santos-Zavaleta A, Muñiz-Rascado L, García-Sotelo JS, Weiss V, Solano-Lira H, Martínez-Flores I, Medina-Rivera A, Salgado-Osorio G, Alquicira-Hernández S, Alquicira-Hernández K, López-Fuentes A, Porrón-Sotelo L, Huerta AM, Bonavides-Martínez C, Balderas-Martínez YI, Pannier L, Olvera M, Labastida A, Jiménez-Jacinto V, Vega-Alvarado L, Del Moral-Chávez V, Hernández-Alvarez A, Morett E, Collado-Vides J. RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more. Nucleic Acids Res. 2013;41(Database issue):D203–213.View ArticlePubMedGoogle Scholar
- Pruitt KD, Tatusova T, Brown GR, Maglott DR. NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res. 2012;40(Database issue):D130–135.View ArticlePubMedGoogle Scholar
- Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28:33–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Mao F, Dam P, Chou J, Olman V, Xu Y. DOOR: a database for prokaryotic operons. Nucleic Acids Res. 2009;37(Database issue):D459–463.View ArticlePubMedGoogle Scholar
- Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJL. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinforma Oxf Engl. 2009;25:1422–3.View ArticleGoogle Scholar
- Stormo GD. DNA binding sites: representation and discovery. Bioinforma Oxf Engl. 2000;16:16–23.View ArticleGoogle Scholar
- Bailey TL, Elkan C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol. 1994;2:28–36.PubMedGoogle Scholar
- Moreno-Hagelsieb G, Latimer K. Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinforma Oxf Engl. 2008;24:319–24.View ArticleGoogle Scholar
- Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013;41(Database issue):D808–815.View ArticlePubMedGoogle Scholar
- Kılıç S, Erill I. Transfer method analysis methods & data release 1.0. Zenodo. 2016. doi:10.5281/zenodo.50506.Google Scholar