SeAMotE: a method for high-throughput motif discovery in nucleic acid sequences
© Agostini et al.; licensee BioMed Central Ltd. 2014
Received: 23 May 2014
Accepted: 16 October 2014
Published: 23 October 2014
The large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites.
Here, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature.
SeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote.
Transcriptional and post-transcriptional events involve the interplay between protein effectors and nucleic acid targets, whose physical interaction is guided by sequence motifs and specific structural elements [1–3]. Motifs are usually defined as short nucleotide sequence patterns of length k (k-mers) and represented with matrices containing the probabilities to find nucleotides in specific positions (position weighted matrices PWMs). In the past decade, the advancement of high-throughput technologies contributed to the generation of a large amount of genomic data , promoting development of computational methods to detect regulatory elements such as transcription and splicing factor binding sites . One fundamental requirement of methods for large-scale analysis is that relevant features (e.g., recognition motifs) are identified with good accuracy and in reasonable time [6, 7]. Very importantly, algorithms should be as comprehensive as possible to provide insights into the nature of regulatory elements in their real genomic context, which requires analysis of different biological sets .
DREME  restricts the search for sequence motifs to a simplified form of “regular expression” (RE) words over the IUPAC alphabet, which consists of 11 wildcard characters in addition to the standard DNA alphabet (ACGT). To save computation time, DREME estimates the significance of RE candidates by a heuristic search without scanning the whole input sequences ;
CMF (Contrast Motif Finder)  is designed to discriminate between two sets of DNA sequences through non-discrete PWMs. The method takes into account false positive sites when updating PWMs and related variables;
DECOD (DECOnvolved Discriminative motif discovery)  uses k-mer counts. To compensate for errors introduced from ignoring the context of the k-mer, DECOD uses a deconvolution method that accounts for the higher rates of k-mers containing subsets of the true motif;
XXmotif (eXhaustive, weight matriX-based motif discovery)  consists of i) a masking stage, where repeat regions, compositionally biased segments and homologous segment pairs are identified; ii) a pattern stage, where p-value enrichments are calculated for seed patterns using all 5-mers (with up to two degenerate IUPAC characters); iii) and a PWM stage, where thousands of candidate PWMs are merged.
Despite the variety of motif discrimination approaches, knowledge of programming languages [8, 15] and acquaintance with web-based bioinformatics platforms [7, 16] often limit their use among non-specialists.
In this article, we introduce SeAMotE, a web-server to perform de novo discriminative motif discovery in nucleic acid datasets. We present an approach that enables the exhaustive search of distinctive patterns in large sets of sequences, in a reasonable amount of computational time and with an easy-to-use user interface.
Generation of a pool of k-mers seed motifs using the IUPAC alphabet;
Evaluation of the coverage of each pattern in the positive and reference sets;
Determination of enriched (Fisher’s exact test) and differentially represented (Youden’s index = Sensitivity + Specificity - 1) motifs;
Extension of selected seeds by adding a IUPAC letter in the k + 1 position;
Re-iteration of steps 2-4 until the enrichement of at least one pattern remains above the coverage threshold in the positive set;
Calculation of motif significance (Fisher’s exact test) and redundancy removal (Hamming distance);
Generation of the positon weighted matrices and logo for each motif.
At least one input set (FASTA format file) should be provided for the analysis. Currently, the number of sequences is limited to 104, with a maximal length of 15 ·103 nucleotides per sequence;
- 2.A reference set is required to estimate the significance of the discovered motifs. This can be:
Provided by the user (FASTA format file), having the same size restrictions as the input set.
Automatically generated as a shuffle set, where the foreground set composition (i.e., individual nucleotide alphabet frequencies) and dimensions (i.e., number of sequences and lengths) are kept constant;
Automatically generated as a random set, where the foreground set dimensions are preserved but the internal composition is based on letter frequencies obtained from the human transcriptome/genome;
The coverage threshold (i.e. the percentage of sequences matching the searched pattern) represents a parameter that the algorithm uses internally to select the most abundant motifs in the two datasets (speed of calculation increases at low coverage threshold).
Datasets for motif finding
Nucleic acids sequences were collected from ChIP-seq and CLIP-seq experiments available in the public domain [18, 19]. ChIP-seq data comprises 351 ENCODE datasets obtained from three groups, Haib_Tfbs by HudsonAlpha (141 sets), Sydh_Tfbs by Yale and UCD (164 sets), and Uw_Tfbs by University of Washington (46 sets). This collection covers 90 unique transcription factors (TFs) and more than 50 cell-types under different treatments. Same number of low and high intensity peaks (1000 sequences) was used to select negative and positive datasets, respectively . CLIP-seq dataset contains 13 doRINA  datasets of 10 RNA-binding proteins (RBPs) [21–28]. Sequences with doRINA scores in the top 5 percentile were considered as positives (bound transcripts; more details on the definition of peaks and the calculation of associated scores can be found in doRINA paper ). For each positive set, we selected same amount of sequences in the bottom 5 percentile of doRINA scores to build the negative set (unbound transcripts).
The documentation/tutorial of the SeAMotE algorithm is available online, and it can be accessed using the links in the menu at the top of every server page. It contains a brief description of the method, a tutorial and information on the benchmark. Additionally, the web interface in the output page provides help-notes (accessible also through the “mouse-over” function) for table variables and download buttons. Online documentation and “Frequently Asked Questions” (FAQs) sections updates will be provided on a regular basis according to method improvements and users’ inquiries,respectively.
Results and discussion
Identification of TF annotated motifs
Comparison of discriminative motif finder methods
In 69 out of 351 cases (i.e. 20% of the dataset), SeAMotE identified motifs that are different from those reported in Jaspar and Jolma et al. databases. CMF and DREME identified different patterns in 74% and 67% of such cases (i.e., 51 out of 69 and 46 out of 69, respectively), which suggests that this group of TFs might display diverse binding modes. Indeed, with respect to the 282 successful hits, these motifs are predicted with significantly lower discrimination (p-value = 5.88e−5; Mann-Whitney U test on discrimination). Thus, it is possible that the discrepancy with literature data arises from lower sequence specificity of the TFs, which makes the foreground and background sets more similar and, therefore, less informative. It should be also mentioned that the 69 misassigned cases correspond to 42 TFs, and for 28 of them(66.7%) SeAMotE was able to correctly recognise the annotated binding pattern in at least one cell-type or specific treatment . We also observe that some of the unassigned patterns can be correctly attributed to literature motifs if other comparison tools are employed instead of TOMTOM. In an additional calculation, we used Matlign  to compare the similarity between literature patterns and the top-ranked motif identified by SeAMotE. In 36 out of 69 cases, we found that SeAMotE motifs have higher propensity to cluster with those of the same TF family [29, 30]. Intriguingly, we observe that in 54 out of the 69 cases (78.3%) the top-ranked motif is associated with one PWM of an interacting TF, indicating that TF binding could be mediated by other proteins.
Identification of RBP recognition motifs
Comparison of DREME  and SeAMotE
Cross-validation of the CLIP-seq data
Finally, we assessed SeAMotE performances using a 3-fold cross-validation approach introduced by Patel and Stormo : CLIP-seq sets of positive and negative sequences were randomly divided into three sets of similar sizes (P1, P2, P3) and (N1, N2, N3); two of the three were combined to form a training set and the remaining one was used as test set. By this means, three training (TR1, TR2 and TR3) and three test sets (TE1, TE2 and TE3) were generated. We then compared the most significant motifs found in the training with those present in the test set using TOMTOM  (p-value <0.01). SeAMotE was able to correctly reproduce the most enriched motifs using training and testing sets, thus confirming the robustness of our approach (Additional file 3: Table S2).
Algorithms for discriminative motif discovery are useful to identify regulatory elements in DNA and RNA sequences. Comparisons between different sets provide relevant information to rationalize sequence determinants of physical interactions and can be exploited for future experimental design. In this work, we introduced the SeAMotE algorithm for analysis of large-scale nucleic acid datasets. Through an easy-to-use interface, the SeAMotE web-server offers key features such as fast discrimination based on pattern occurrence, choice of multiple reference backgrounds (shuffle, random or custom) and identification of significant motifs in the whole span of tested pattern widths, which provides a range of practical solutions for analysis of experimental data. Indeed, as reported in recent studies, inter-positional sequence patterns and variable binding sites information are key features to identify regulatory motifs and will be used in future computational developments . We demonstrated the powerfulness of SeAMotE for a large number of TF targets, correctly reproducing the results available in literature and showing better performances than other available tools. We also proved the flexibility and robustness of the algorithm by assessing its ability to identify enriched sequence patterns in CLIP experiments and using a three-fold cross-validation. We anticipate that the use of SeAMotE and its integration into DNA/RNA-protein interaction predictors, such as catRAPID [36, 37], would greatly enhance the ability to recognise physical associations.
Availability and requirements
● Project name: SeAMotE
● Project home page:http://s.tartaglialab.com/new_submission/seamote
● Operating system(s): Platform independent
● Programming language: C and R scripts
● Other requirements: Web browser (e.g. Safari, Firefox, Explorer or Chrome)
● Restrictions: No login requirement; users behind a proxy might experience slow-down issues
The authors would like to thank Roderic Guigó (CRG), Guillaume Filion (CRG), Andreas Zanzoni (Inserm, U1090), Giovanni Bussotti (EMBL-EBI) and Samuel Francis Reid (CRG) for stimulating discussions.
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545, and from the Spanish Ministry of Economy and Competitiveness (SAF2011-26211). We also acknowledge support from the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013–2017’ (SEV-2012-0208).
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