Translation of mRNA sequences into proteins typically starts at an AUG triplet. In rare cases, translation may also start at alternative non–AUG codons located in the annotated 5’ UTR which leads to an increased regulatory complexity. Since ribosome profiling detects translational start sites at the nucleotide level, the properties of these start sites can then be used for the statistical evaluation of functional open reading frames. We developed a linear regression approach to predict in–frame and out–of–frame translational start sites within the 5’ UTR from mRNA sequence information together with their translation initiation confidence. Predicted start codons comprise AUG as well as near–cognate codons. The underlying datasets are based on published translational start sites for human HEK293 and mouse embryonic stem cells that were derived by the original authors from ribosome profiling data. The average prediction accuracy of true vs. false start sites for HEK293 cells was 80%. When applied to mouse mRNA sequences, the same model predicted translation initiation sites observed in mouse ES cells with an accuracy of 76%. Moreover, we illustrate the effect of in silico mutations in the flanking sequence context of a start site on the predicted initiation confidence. Our new webservice PreTIS visualizes alternative start sites and their respective ORFs and predicts their ability to initiate translation. Solely, the mRNA sequence is required as input. PreTIS is accessible at http: //service. bioinformatik. uni-saarland. de/pretis.
Translational initiation is a more complex process than reported in common textbooks. Experimental work showed that the canonical AUG–Methionine translational start is not always used to initiate eukaryotic translation [1–5]. Further alternative codons located upstream of the annotated AUG start can also serve as additional functional start sites and form additional or alternative ORFs. Those non–AUG triplets are postulated to differ from AUG by one nucleotide and hence comprise CUG, UUG, GUG, AAG, ACG, AGG, AUA, AUC and AUU [6]. Translation can proceed in–frame as well as out–of–frame relative to the main open reading frame [7]. This can, for example, lead to (small) upstream ORFs resulting in short peptides or to extended proteins when translational initiation takes place at an in–frame start codon located upstream of the canonical start. Ribosome profiling data provides information on the density of ribosomes located at different regions of the transcript upon application of small chemicals that block the elongation process [4,8]. Regions which are protected by ribosomes are not digested in the next step when the mRNA is treated with nucleases [9]. These ribosome footprints (RNA) have a length of about 30 nucleotides and are sequenced after nuclease treatment and subsequently mapped to a reference genome [9]. For example, Lee et al. (2012) applied ribosome profiling to human embryonic kidney 293 (HEK293) cells [3]. As translational inhibitors they used Cycloheximide (CHX) and Lactimidomycin (LTM), which both bind to the ribosome E-site [3]. While CHX can bind to both, initiating and elongating ribosomes, LTM prefers initiating ribosomes with a tRNA-empty E-site [3]. Thus, by combining both inhibitors, it is possible to differentiate initiating from elongating ribosomes [3]. Lee et al. identified 16,863 potential start sites out of about 10,000 transcripts whereby start sites were allowed to be located in the 5’ UTR, at the annotated start site, in the coding region, or in the 3’ UTR, respectively. Possible biological reasons underlying alternative translation initiation are the expansion of biological variety, regulatory processes as well as targeting of the proteins to different compartments [10–12]. Touriol et al. (2003) proposed that alternative translation initiation results in different proteoforms that can exhibit different functions as well as various cell localizations, which is of great importance for cell fate [12]. Some codons (e. g. AUG or CUG) are more frequently used as translation initiation starts than other codons [3,4]. So far, several bioinformatics studies have addressed the task of predicting alternative translational start sites or ORFs. The majority of these studies only considered AUG starts. Hatzigeorgiou (2002) applied an artificial neural network (ANN) embedding a linear search for AUG starts [13]. They achieved 94% accuracy and were able to predict the correct start site in 60% of human cDNAs. Saeys et al. (2007) developed a meta–tool that combines three simple AUG start site predictors that consider either position–weight–matrices, k–mer frequencies or the number of stop codons downstream of a start site. This combination of several simple predictors, named StartScan, resulted in a sensitivity of 80%, tested on human chromosome 21 [14]. Sparks and Brendel (2008) argued that when one only searches for one translational start, predicting the leftmost (i. e. the most upstream) AUG as sole correct translational start yielded specificity and sensitivity of 94%, respectively [15]. Chen et al. (2014) used a flexible window and represented human DNA as k–tupels that reflect the nucleotide composition and also integrated the physicochemical properties of amino acids [16]. For AUG codons, their method achieved an accuracy of 98%. A webservice of their algorithm is available. Besides, there also exist several web–based tools for ORF identification. ORF Finder searches for ORFs given the accession number or sequence and the genetic code [17]. ORF–Predictor provides an ab initio prediction of ORFs based on expressed sequence tag (EST) or cDNA sequences and BLASTX alignments or intrinsic sequence signals [18]. Only few studies involved ribosome profiling data or considered in– and out–of–frame start codons differing from AUG. Ivanov et al. (2011) studied annotated human 5’ UTRs via sequence alignments with orthologous species followed by a manual evaluation [6] to detect non-AUG initiation in human sequences. They predicted 42 novel genes with non–AUG upstream translation initiation. For 25 of these genes non–canonical translation initiation could be experimentally validated using Western blot as well as ribosome profiling data. They also confirmed 17 alternatively translated genes that were known at this time. Crappé et al. (2013) applied an SVM approach to ribosome profiling data to detect small conserved open reading frames (sORFs) in mouse that code for micropeptides (10 − 100 amino acids) [19]. Baranov and colleagues (2014) used ribosome profiling data to calculate translational initiation probabilities [20]. In contrast to our work, they focused on the initiation strength of a putative start site as a function of the number of ribosome footprints. To our best knowledge, no study so far has evaluated the general properties of human start codons considering both AUG and all near–cognate codons, in- and out–of–frame, based on start sites identified by applying ribosome profiling, and exploited this to predict the initiation confidence from the mRNA sequence. The aim of this work was to analyze alternative translational start sites (AUG and near–cognate codons) with respect to sequence–based features to differentiate between true and false start sites. We used start sites that were identified by applying ribosome profiling to HEK293 cells [3,5] and mouse embryonic stem cells [4] as our primary datasets. Based on mRNA sequence information we generated support vector machines as well as a linear regression model for human and mouse sequences. The learned model can then be applied to mRNA sequences not covered by ribosome profiling data or to investigate the impact of mutations in the flanking sequence context of a start site on its translation initiation confidence. Our webservice PreTIS visualizes putative alternative start sites and the predicted initiation confidence in human.
Annotated genomic mRNA sequences for human and mouse were retrieved from Ensembl biomart (Ensembl version 77 [21]). We only included curated mRNA sequences with available mRNA RefSeq identifier (starting with NM_). It was recently shown that 85% of the start sites used to initiate translation are conserved between human and mouse [3]. Thus, we used homologous pairs of human and murine sequences to calculate the conservation of putative start codons as well as the 5’ UTR sequence conservation (see below). We identified the respective murine orthologous mRNA sequences using the approach by Ivanov et al. (2011) and used the first blastn [22] hit as the respective ortholog (default blastn parameters). To identify putative start sites, each 5’ UTR was scanned for all AUGs and for alternative near–cognate start codons that differ from generic AUG by one nucleotide (CUG, UUG, GUG, AAG, ACG, AGG, AUA, AUC und AUU) and that are located either in–frame or out–of–frame with the main open reading frame. Different sequence–based features were then calculated for all putative start codons that have a downstream in–frame stop codon. To establish reliable true positive and true negative translational start site datasets for training and testing purposes, we used the findings from different ribosome profiling experiments [3–5]. Each dataset was analyzed independently. Note that the datasets used here contain translational start sites derived from ribosome profiling data by the original authors (gene accession number, position relative to annotated start site, codon). We did not include raw ribosome profiling (footprint) data in our approach. In total, we trained two start site prediction models: a human prediction model based on the HEK293 dataset [3] and a mouse prediction model based on the Mouse ES dataset [4]. The third HEK293–AUG dataset [5] was used as validation set to further evaluate the reliability and robustness of the developed prediction model. For training and testing of every classifier, we considered each start site (AUG and near-cognate) that matched a start codon found by ribosome profiling as a true start. False start sites were defined as follows: remaining candidate start sites (AUG and near-cognate) that were not detected by ribosome profiling and that are, based on the assumption of a linear scanning model, located at least 99 nts downstream of the transcription start site as well as upstream of the most downstream reported true translation initiation start. Fig 1 shows an example mRNA sequence that illustrates the grouping of true positive and true negative start sites for training and testing purposes based on ribosome profiling data. This start sites categorization was executed for each of the three datasets, each time based on the individual ribosome profiling experimental results [3–5]. All features used here are solely based on information derived from the mRNA sequences. The prediction approach, shown in Fig 2, was applied to the human HEK293 [3] and mouse ES datasets [4]. First, as mentioned, all putative start sites in the 5’ UTR were defined as true positives or true negatives based on the reported ribosome profiling data and their location in the mRNA sequence. We then balanced the size of the dataset so that it contains the same number of true and false start sites by randomly under–sampling from the larger dataset. We repeated the data balancing as well as the assignment of random training and test sets 10 times to evaluate the model robustness and reported the average performance. We applied Wilcoxon–rank sum test and Bonferroni correction (with a significance threshold of 0. 01 1,252 = 8 × 10 - 6, with the total number of features as the denominator) to test for the statistical significance of the biological, the k–mer, and the PWM features to differentiate between true and false start sites. We subsequently calculated all pairwise Pearson correlations between the significant biological and PWM features as well as for the 50 most significant k–mer features and only used uncorrelated (|r| < 0. 7) features in the training step. If two or more features were correlated, the one with the smallest p–value was used. The PWMs were calculated in each training step iteration to guarantee that the test set is independent on the calculated PWMs. All features were normalized (mean zero and unit variance) to ensure comparability. Next, we generated simple linear as well as support vector (SVR) regression models on 70% of this data and tested them on the remaining 30% of the data, using three different kernels for the SVM approach: linear, radial basis function (RBF) and polynomial. We applied 10–fold cross–validation to find the best penalty parameter C in [0. 1,1, 10,100] and ϵ-tube parameter in [0. 01,0. 1,1, 10] for the training data set when applying SVR models. The remainder of parameters were kept at default values. Since we applied a regression approach, we subsequently applied 100 classification thresholds 0. 0 ≤ t ≤ 1. 0 in steps of 0. 01 to the predicted output outpred in order to classify every start site as true or false based on its model outcome and the given threshold. These thresholds can be interpreted as initiation confidence where start sites with a regression value outpred ≥ t are predicted as true start sites and the ones with outpred < t as false start sites. If a start site is predicted with an initiation confidence > 1, we substituted this value by 1. The same holds for start sites with a predicted confidence < 0, which were substituted with zero. We then compared the predicted class with the correct class and used the common measures accuracy, specificity, sensitivity, precision and area under the curve (AUC) as metrics for model assessment. The final model for the prediction of new mRNA sequences and for a SNP analysis was subsequently determined by comparing different model performances. The implementation was done in Python (version 2. 7) and using the Scikit–learn package (version 0. 17) for the machine learning part [34]. To investigate the effect of putative single nucleotide polymorphisms (SNPs) within the flanking sequence context of the start sites (position -15 to +10), we substituted (in silico) one nucleotide position at a time by all 3 remaining nucleotides, yielding 75 different contexts (the start codon itself was not mutated). We then recalculated the needed sequence features to investigate the mutational impact and subsequently applied our final prediction model to all contexts. We then report the effect of these substitutions on the predicted initiation probabilities.
The start sites reported by ref. [3–5] based on ribosome profiling data were filtered to include only starts matching AUG and near–cognate codons in the 5’ UTR. For HEK293 cells [3], this yielded 4,482 true start sites (i. e. reported in the experimental analysis) and 49,520 false start sites in 3,566 mRNAs. For mouse ES cells [4], this gave 3,009 true start sites and 19,864 false start sites in 1,632 mRNAs. True (reported) starts were assumed to be true positives (TP) and false (not reported and upstream of the most downstream reported) starts were assumed to be true negatives (TN). For comparison, we also included a smaller dataset of Ohler and colleagues [5] who only determined AUG starts in HEK293 cells. Table 1 displays the three datasets. All reported analyses are based on these filtered datasets. Among the considered AUG and near-cognate start codons, AUG (human: 26%, mouse: 16%), CUG (human: 30%, mouse: 34%) and GUG (human: 13%, mouse: 19%) were the most prevalent translational start codons, see S1 Fig. Thus, CUG and GUG are more often used in mouse compared to human. This is in accordance with [3,4] and shows that the start codon itself is very important for translational initiation. Table 1 illustrates that the negative sets outnumber the positive sets by factors of 7 (mouse ES) and 11 (HEK293). To avoid a class size dependent bias, we randomly under–sampled the same number as true positive start sites from the true negative set. Next, we trained on 70% and tested on 30% of the data (randomly assigned). Table 2 lists the performance of human and mouse models together with the optimal thresholds t. All human models perform very similarly with accuracies of about 80% while the average performance of the mouse model is lower with average accuracies of about 76%, see Table 2. We also computed receiver operating characteristic (ROC) curves and the associated area under the curve (AUC). In accordance with the other metrics, also the AUC values were satisfactory with average values of about 80% and 76% for the human and mouse models, respectively (Table 2). To investigate the transferability of our best human prediction model, we analyzed its performance using the mouse ES data as well as the HEK293–AUG dataset, see Table 4. With the threshold of t = 0. 54 that was found to be optimal for the trained HEK293 dataset, we obtained for the mouse ES dataset an accuracy of 76%, a sensitivity of 72% and a specificity of 77%. By scanning all possible thresholds, see S4 (A) Fig, we found that t = 0. 52 yields a more balanced performance of 75%, 76% and 74% for accuracy, sensitivity and specificity, respectively. Decreasing the threshold seems to be advantageous for the mouse data set, since some true positives seem to possess weaker features for translational initiation (e. g. a weak flanking sequence context or a less common initiation codon), but are nevertheless true positive starts. We then applied our best regression model to the start sites reported in the HEK293–AUG dataset that only contains AUG starts [5]. The categorization of true positive and true negative start sites was conducted as above for the HEK293 dataset (see Fig 1), with the only difference that the HEK293–AUG dataset only contains AUG start sites instead of AUG and all near-cognate codons. Thus, we defined again the false start sites as all AUG starts located in the 5’ UTR that were not detected by ribosome profiling and are located upstream of the most downstream true start site. Differentiating only between true and false AUG start sites is particularly difficult because the AUG itself is a very strong signal for a true start site and just by random chance there might be AUGs with, for example, good flanking sequence, which are not used as translational start sites (or are not reported in the dataset). Moreover, our prediction model was trained on all possible cognate codons instead of AUG alone. Our best model with the determined threshold of t = 0. 54 detected 77% of the true AUG starts in the HEK293–AUG dataset (sensitivity of 77%). Nevertheless, the specificity of this prediction is only 44% and thus the overall accuracy is only slightly better than a random decision (58%), compare to Table 4. However, when increasing the threshold from t = 0. 54 to t = 0. 65, we were able to increase the overall accuracy to 63%. A threshold of t = 0. 65 was found to be optimal for this dataset, see S5 (A) Fig. Problematic was here the precision (i. e. the number of true positives out of all samples classified as positive (T P s T P s + F P s) ). Many starts that we assumed to be true negatives actually show properties of true positives and are therefore classified as false positives. Especially if the dataset is highly unbalanced (e. g. the number of mouse ES true starts is only 15% of the false start sites) this effect has a strong influence on the precision. When we balanced our datasets, the precision increased drastically from 31% to 75% for the mouse ES dataset and t = 0. 52 and from 56% to 63% for the HEK293–AUG dataset and t = 0. 64, see Table 4. The established prediction model can, for example, be used to predict translational start sites which are not covered by ribosome profiling experiments or to analyze the impact of mutations in the flanking sequence around the start site.
PreTIS is a webservice to predict the initiation confidence of all reading–frame independent start sites (AUG and all near-cognate codons) located in the 5’ UTR of human mRNA sequences. Thereby, the best human prediction model described here is used as underlying regression model. The webservice application PreTIS requires the mRNA sequence and is accessible at http: //service. bioinformatik. uni-saarland. de/pretis. Based on the given human mRNA sequence, all possible AUG and near-cognate start sites, with a surrounding window of at least ±99 nts (needed to calculate k-mers) and an in–frame downstream stop codon, are located in the 5’ UTR. PreTIS then calculates the required sequence features (see Table 3) for all detected start sites and subsequently predicts the initiation confidence. Based on the predicted initiation confidence value and the given prediction threshold of t = 0. 54, a start site is categorized into different initiation confidence classes. For start sites with confidence values c greater than the given threshold t, the four confidence groups were defined as follows: very high (hot/best candidates with c ≥ 0. 9), high (0. 8 ≤ c < 0. 9), moderate (0. 7 ≤ c < 0. 8) and low (t ≤ c < 0. 7) confidence, respectively. Especially start sites with very high confidence values can be considered as hot candidates for translational initiation. The predicted initiation confidence for each start site is visualized by barplots with the x-axis displaying the mRNA position (compare to Fig 5). This enables a comprehensive comparison of, for example, different flanking sequence contexts. Features calculated for each start site can also be downloaded as. csv file for further analyses. For the calculation of some features, an orthologous mouse sequence is required. This is automatically implemented by the embedded blastn [22] search. The mRNA sequence found by blastn can be inspected afterwards and replaced, if desired. Furthermore, each job is given a Session– and a Job–ID, which enables unambiguous accession to the prediction results. Thus, PreTIS is an intuitive tool that solely requires the human mRNA sequence as input. It gives access to various calculated sequence-encoded and experimentally shown important sequence properties for translational initiation. In addition, an initiation confidence value for each start site is calculated using an established regression model that is based on recent experimental data. AUG as well as alternative start codons—in and out of the main reading frame—are considered.
Ribosome profiling data and mRNA sequence features can be used to build reliable classification models with accuracies of about 80% for start codon and open reading frame prediction in human. All predicted start sites of one transcript are postulated to have the potential to initiate translation. They could, for example, be used in different tissues or in a specific cellular condition, such as stress response. Although there exist already several other approaches to predict translational initiation start sites, so far none of them considers all in– and out–of–frame AUG and near–cognate codons. The provided web service PreTIS considerably simplifies and assists the analysis of mRNA sequences in terms of prediction of possible translation start sites and their visualization.
sequencing techniques
messenger rna
mathematics
forecasting
statistics (mathematics)
untranslated regions
molecular biology techniques
cellular structures and organelles
research and analysis methods
translation initiation
sequence analysis
sequence alignment
mathematical and statistical techniques
gene expression
molecular biology
ribosomes
biochemistry
rna
cell biology
nucleic acids
protein translation
genetics
5' utr
biology and life sciences
physical sciences
statistical methods
PreTIS: A Tool to Predict Non-canonical 5’ UTR Translational Initiation Sites in Human and Mouse