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PreTIS: A Tool to Predict Non-canonical 5’ UTR Translational Initiation Sites in Human and Mouse

PreTIS: A Tool to Predict Non-canonical 5’ UTR Translational Initiation Sites in Human and Mouse

  • Kerstin Reuter, 
  • Alexander Biehl, 
  • Laurena Koch, 
  • Volkhard Helms
PLOS
x

Abstract

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.

Author Summary

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.

This is a PLOS Computational Biology Methods paper.

Introduction

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 [15]. 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 [1012]. 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.

Materials and Methods

Datasets

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 [35]. 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 [35].

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Fig 1. Example mRNA sequence showing the categorization of true positive and true negative start sites.

Suppose that a ribosome profiling experiment detected the following start sites for a given mRNA sequence: CUG at position -78 and CUG at position -120 (blue colored codons). These start sites were then assumed to be true positive start sites. In consequence, all near-cognate start sites not listed in the ribosome profiling dataset and upstream of the most downstream reported true start site were assumed to be true negatives (dark red colored codons). The light red colored codons are start sites not considered as false starts in the analyses since they are located downstream of the most downstream reported true start site. Note that the grey colored downstream part depicts the annotated CDS sequence whereas the italic (purple) upstream part marks the -99 upstream window needed to calculate some of the features (see below). All marked start sites (true positive and true negative) exhibit a surrounding window of ±99 nucleotides as well as a downstream in–frame stop codon. In total, this mRNA sequence would provide 2 true start sites and 9 false start sites out of 23 putative starts.

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Features based on mRNA sequence information

All features used here are solely based on information derived from the mRNA sequences.

Position–weight–matrix (PWM).

In mammalian cells, some codons (e.g. AUG and CUG) are more frequently used to initiate translation compared to other codons (e.g. AUA or AGG) [3, 4]. In the HEK293 dataset used here, 26.1% of the reported upstream initiation start codons are AUGs and 29.8% are CUGs [3], see S1 Fig. The start codon information was considered using position-weight-matrices (PWMs). To account for the important role of the flanking sequence context for translation initiation, we considered a window ranging from -15 to +10 with respect to the start site in a set of sequences S. First we calculated from the data in the training set a position–frequency–matrix (PFM) with the nucleotides nt ∈ {A,C,U,G} as the rows and the sequence position i as the columns. The matrix entries were filled by dividing the sum of occurrences of a nucleotide at position i by the total number of sequences contained in S. The PWM was then calculated by dividing each entry in the PFM by the respective nucleotide background frequency and taking the natural logarithm, i.e. where the background frequency bgnt is defined as the actual nucleotide frequency of the 5’ UTR in S. We calculated three PWMs, one based on the true start sites (PWMpositive), one based on the false start sites (PWMnegative), and one based on the log–ratio between true and false start sites (PWMratio) in the training set. The PWMscore for a sequence s was then computed as where nti is the nucleotide occurring at position i in sequence s. With PWMpositive, a PWMscore greater than zero indicates that the given sequence s is more likely a true start than a false start while a PWMscore less than zero suggests a higher probability of being a false start site.

Start site conservation.

To calculate the conservation of a putative start site, sequence alignments between pairs of human and mouse sequences (5’ UTR and CDS), found by applying blastn, were generated using MUSCLE [23]. For this, 5’ UTR and CDS were translated into all three possible reading frames and were aligned accordingly. We then translated the protein alignment back into the associated (gap-free) nucleotide alignment (compare with [3]). A human start site was assumed to be conserved if it shares the same codon or amino acid with the murine ortholog at the respective position. This yielded two binary features: codon and amino acid conservation. We also calculated the average degree of 5’ UTR sequence conservation, using the human–mouse mRNA sequence alignment. For this we divided the number of matching nucleotides by the length of the 5’ UTR sequence. Gaps were ignored.

Start codon flanking sequence context.

The flanking sequence context was assessed in two ways where we considered either only the positions -3R (R = purine) and +4G, which were determined to be crucial for initiation [24, 25], or experimentally determined translational start codon efficiencies [26]. In the first approach, the Kozak sequence context was discretized into strong (A or G at -3 and G at +4), intermediate (A or G at -3 and no G at +4), weak (no A and no G at -3 and G at +4) and no Kozak context. These categories were presented as the values 1 (no), 2 (weak), 3 (intermediate) and 4 (strong). In the second approach, we used the raw translation efficiency values reported by Noderer et al. (2014) as feature for the respective flanking sequence context of a start site. These authors investigated the translational efficiency of all possible 11–nt–long (position -6 to +5) flanking sequence contexts around the AUG translational start using high–throughput sequencing combined with fluorescence signaling [26]. We assumed that alternative starts behave similarly as AUG codons and therefore use the same translational efficiency values for the alternative starts.

Minimum free energy of mRNA secondary structure.

Secondary structure is an important factor for translation initiation [25, 27, 28]. Dependent on the propensity of the mRNA secondary structure downstream of a putative start codon, the ribosome scanning in downstream direction can pause and translation is initiated [28]. It was shown that a secondary structure with a minimum free energy of that starts 12–15 nt downstream of the translational start site can prevent leaky scanning and compensate for an unfavorable flanking sequence context [28, 29]. A secondary structure starting 14 nts from the translational initiation site was observed to have the largest effect [28]. Here, we considered different windows for calculating the minimum free energy of the secondary structure and then selected the most suitable one to differentiate between true and false start sites: a 60–nt window starting at position +14, a 60–nt window starting at position +20, a window from position -10 to +50 and a window from -50 to +50. Minimum free energies were calculated using RNAfold [30].

GC–content.

It was shown that the GC–content continuously decreases from 5’ UTR across the CDS to the 3’ end in human [31]. We therefore analyzed whether the GC–content differs between true and false start sites using the same windows as for the minimum free energy (see above). Note that the minimum free energy of an mRNA secondary structure and its GC–content are related to each other since G–C pairs possess a higher degree of stability than A–U pairs due to their additional hydrogen bond [32].

Open–reading–frame (ORF) length.

It appears plausible that the length of ORFs that code for functional proteins is generally longer than the ones resulting from arbitrary start sites in the mRNA sequence. Therefore, we also considered the length of the putative open reading frame.

5’ UTR nucleotide distribution.

As mentioned before, the GC–content varies between 5’ UTR and CDS. If a part of the annotated 5’ UTR is actually used as CDS this may result in a different nucleotide composition compared to the actual 5’ UTR. Therefore, we calculated the percentage of all four nucleotides (e.g. ) in the entire 5’ UTR. This resulted in four additional features.

5’ UTR length.

We also tested the 5’ UTR length with respect to significant differences between true and false start sites by defining the 5’ UTR (nucleotide) length as further feature.

K–mer search.

We counted the frequency of all possible k–mers of length k = 1 (position–specific k–mers) and k = 3 (codon and respective amino acid k–mers) in a window from -99 to +99 around the start site. k-mers were defined as all possible combinations of subsequences of length k, given an alphabet, here nucleotides {A, C, U, G}. We considered in–frame and out–of–frame k–mers as well as k–mers upstream and/or downstream of the start site as suggested in [33]. In total, this yielded 1,229 k–mers: position–specific k–mers in the predefined window of ±99 amount to 198 positions × 4 nucleotides = 792 (e.g. “K-mer: position -12 is C”), 64 codons × 5 (counted in the complete ±99 region, the upstream region, the downstream region as well as in–frame–downstream and in–frame–upstream) = 320, 20 amino acids × 5 = 100, 1 stop codon × 5 = 5, and 4 nucleotides (k = 1) × 3 (complete ±99 region, upstream region and downstream region) = 12. This sums up to 792 + 320 + 100 + 5 + 12 = 1,229 k–mers.

In total, we considered 1,252 features, with three features based on PWMs, 20 biologically–motivated features (e.g. conservation or the flanking sequence context) and 1,229 features found by a k–mer search for k = 1 and k = 3.

Regression approach

The prediction approach, shown in Fig 2, was applied to the human HEK293 [3] and mouse ES datasets [4].

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Fig 2. Flowchart of the regression approach.

Data balancing was repeated ten times to investigate model robustness. Significant features were identified by the Wilcoxon-rank sum test.

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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 , 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 outpredt 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].

In silico SNP analysis

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.

Results

In this work we used ribosome profiling data from HEK293 cells [3] and mouse ES cells [4] to analyze sequence encoded differences between true and false translational initiation sites located in the mRNA 5’ UTR. A third dataset, only containing AUG starts, was used as validation set [5]. Calculated sequence-based features were subsequently used to build a prediction model. In the following, we present the datasets used, the results of the regression approach, its application and an implementation as webservice PreTIS.

Filtered dataset

The start sites reported by ref. [35] 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.

Regression approach

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).

Best performing prediction model.

Since all models gave a very similar performance with accuracies of about 80%, we decided to choose the simple linear regression model that can be interpreted well. The best performing and nicely balanced human linear regression model was obtained in run 2 (the prediction was repeated 10 times). This model had an accuracy of 83%, a sensitivity of 84%, a specificity of 82% and a precision of 83% on the test data. It was then applied to predict unknown start sites of a gene of interest and to conduct an in silico mutation analysis. Moreover, it is embedded in the webservice PreTIS. Therefore, this model is analyzed in more detail in the following.

Fig 3 displays the predicted codon distribution when applying the best performing linear regression model of run 2 to the mRNA sequences in the test set and using the threshold t = 0.54 that gave the best overall performance.

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Fig 3. Codon distribution of the test samples in the best performing human model.

AUG, CUG and GUG were the most prevalent true positive start sites (t = 0.54).

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The distribution of predicted codons agreed with the preferences found experimentally [3, 4]: AUG and CUG are the most prevalent start codons whereas AUA or AAG are more often classified as true negatives. Nevertheless, our predictor also detects true negative AUGs and CUGs and true positive AUAs and AAGs.

The features that were used to build this prediction model are displayed in Table 3. The most significant feature is the length of the 5’ UTR (p < 10−310). The 5’ UTR was found to be shorter on average for true starts sites (414±270 nts) compared to false start sites (675±545 nts). The second most significant biologically–motivated feature with a p–value of p = 8.2 × 10−190 was the conservation of the 5’ UTR. The values of 0.4±0.16 for the true start sites and 0.33±0.16 for false start sites suggest that 5’ UTRs harboring true start sites are in general more conserved. Another highly significant feature (p = 5.1 × 10−144) was the number of upstream AUGs. Considered false start sites had more upstream AUGs (0.59±0.9) than considered true start sites (0.22±0.57), see Table 3. This can be explained as follows: if AUG is located upstream of another putative start site, the linear scanning model of Kozak [35] implies that it is more probable that the AUG is used as start site instead.

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Table 3. Mean value and standard deviation of the 44 features that were used in the best human model.

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PWMpositive was also found to be highly significant (p = 5.5 × 10−173). The PWMs were recalculated for each training sample to achieve unbiased test samples in each run. The background frequency of the best performing run 2 is bgA:0.16, bgC:0.29, bgU:0.21 and bgG:0.34, while the average background frequency of all training and test runs was calculated as bgA:0.21±0.06, bgC:0.27±0.06, bgU:0.22±0.06 and bgG:0.3±0.06. Thus, as expected [31], Guanine and Cytosine are prevalent in the 5’ UTR. Fig 4 shows the PWM scores calculated for the test samples in the run with best overall performance (run 2) based on the PWM generated using the true training samples (PWMpositive) in this run. The scores of the true (test) start sites were significantly higher (2.75 ± 1.5) than those of the false (test) start sites (−0.14 ± 2.82).

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Fig 4. Frequency distribution of PWMpositive scores for the test samples of the best performing run 2.

The PWM was established using the true start sites in the training data of run 2. The difference between TPs and TNs was found to be highly significant (p = 5.5 × 10−173, Wilcoxon–rank sum test).

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Interestingly, the distribution of the false start sites was found to be bimodal. Thus, one might speculate that some of these considered false start sites with higher PWM values (i.e. start sites not found by the ribosome profiling technique and located upstream of the most downstream reported true start, which are therefore considered as true negative starts, see also Fig 1) might be used as actual start sites in different cell types or cellular conditions. This also explains the overlap between the true positive and true negative start sites in Fig 4.

Another biologically important feature that also represents the flanking sequence context is the “Kozak sequence context” feature (see methods) with a p–value of 9.2 × 10−95. As expected from experimental findings [35], true start codons more often exhibit a strong or intermediate Kozak context compared to false start sites that often show no Kozak context at all, see Table 3 and S2 Fig. This is also in agreement with the observation that A at position -3 (p = 3.4 × 10−35) and G at position +4 (p = 2.3 × 10−25) were found (by the k–mer search) to be important for translational initiation. Similarly, the translational efficiency of the flanking sequence context, experimentally investigated in [26], was also highly significant (p = 1.1 × 10−83). The average efficiency of true start sites is, as expected, higher than the one calculated for the false start sites. Moreover, true start codons were found to be more often conserved between human and mouse sequences compared to false start sites (p = 3.2 × 10−36). Start site conservation was also mentioned in the original publication of the HEK293 dataset we used here [3].

Many significant features detected by the k–mer search contained upstream G–C–patterns (e.g. “K-mer: upstream CCG” or “K-mer: upstream CGG”) at higher frequencies for true start sites compared to false start sites. This reflects the generally higher GC–content in the 5’ UTR compared to the CDS and is in accordance with the finding that the GC–content decreases from the 5’ UTR to the CDS [31].

Consistent with the p–values of the features used in the best performing human linear regression model are the feature (weight) coefficients determined by the model training step, see S3 Fig. The highest coefficients were assigned to the PWMpositive and the number of upstream AUGs (“K-mer: upstream AUG”).

Transferability of the prediction model

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.

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Table 4. Performance of the best human HEK293 model applied to the mouse ES and human HEK293–AUG datasets.

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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 ()). 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.

Applications of the prediction model

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.

Prediction of unknown start sites.

We applied the final model to a gene of interest, GIMAP5 (ENST00000358647), that was not contained in the human ribosome profiling data. GIMAP5 codes for a GTPase binding GTP (Guanosine TriPhosphate) and is involved in the survival of T–cells [36]. The scan of GIMAP5 resulted in 27 candidate start sites with an in–frame stop codon and a surrounding window of ±99 nts to calculate the k-mer features in. Fig 5 shows the predicted initiation probabilities of the putative start sites.

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Fig 5. Alternative start codons of human gene GIMAP5.

Predicted start sites were subdivided into four confidence groups and highlighted by different colors and dashed lines: very high (hot/best candidates with c ≥ 0.9), high (0.8 ≤ c < 0.9), moderate (0.7 ≤ c < 0.8) and low (t = 0.54 ≤ c < 0.7) initiation confidence c. For this gene, we found one hot candidate with a very high confidence value of 0.92 of being a true start site (AUG at position -203).

https://doi.org/10.1371/journal.pcbi.1005170.g005

Out of these 27 candidate start sites, we found eight start codons with a confidence value above t = 0.54. Among these starts, we found one hot candidate (AUG at position -203) with a very high confidence value of 0.92 of being a true start site. Moreover, a CUG at position -36 was also predicted with a high confidence value of 0.81. We postulate that these start sites are able to initiate translation in a specific cell type or cellular condition (for instance cellular stress response).

In this manner, the webservice PreTIS can be used to visualize all putative start sites and subsequently to predict unknown translational start sites.

In silico mutation analysis.

As an outlook where this methodology could be helpful as well, we investigated the effect of fictitious SNPs on the translational initiation confidence around all start sites of gene GIMAP5. We used the same surrounding window of -15 to +10 that was used to calculate the PWMs. Fig 6 shows three possible scenarios how in silico mutations in the flanking sequence context of a putative start site affect its predicted initiation confidence.

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Fig 6. SNP analysis of gene GIMAP5.

Mutation matrix showing the impact of the flanking sequence context of four putative start sites of gene GIMAP5 on the predicted initiation confidence. In each case, only one nucleotide is mutated with respect to the reference sequence (top line). Grey means that the start was predicted as true translational start (predicted initiation confidence is greater than 0.54) whereas white means that the start was classified as false start. Mutations at the start sites itself were not considered. The numbers reflect the predicted initiation confidence values. A: CUG at position -36. B: CUG at position -44. C: AUA at position -237. D: CUG at position -160.

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In the first example, the initiation confidence value is, independent of the SNP, always above the threshold. This means that the start site is always predicted as true start site since the overall advantageous properties are not changed severely by a single SNP that is inserted (see Fig 6A).

In the second case, the predicted initiation confidence value changes dependent on the SNP that is artificially inserted into the flanking sequence (see Fig 6B and 6C). Take for instance, CUG at position -44 (Fig 6B): a C\G at position +4 increases the initiation confidence from 0.53 (see Fig 6) to 0.60. The same holds for a U\A SNP and U\G SNP at position -3 that increase the predicted initiation confidence to 0.66 and 0.57, respectively. For the AUA start site at position -237 (Fig 6C), an U\A SNP and a U\G SNP at position -3 increased the initiation confidence from 0.48 to 0.63 and 0.55, respectively. Positions -3 and +4 were mentioned beforehand to be crucial for translation initiation [24, 25]. Moreover, SNPs at position -12, also found to be significant by the k–mer search (Table 3), seem to have an important influence on the translation initiation. A G\C SNP entails a dramatic drop of the initiation confidence value to 0.33 (Fig 6C) since far less true starts contain Cs at position -12 (0.13) compared to false starts (0.3), see Table 3.

Finally, it may also happen that the initiation confidence is always below the given threshold, independent of the SNP that is inserted (Fig 6D). This is based on the overall disadvantageous properties of a start site such that a single mutation cannot “boost” the overall disability of this start to initiation translation. The results of the in silico mutation analysis of all other GIMAP5 start sites can be found in S6 Fig.

To investigate the influence on the predicted initiation confidence (IC) on a more general scale, we calculated the difference in the initiation confidence of a mutation (A, C, U and G) compared to the wildtype sequence (ICdifference = ICmutationICwildtype) for all start sites in the 3,566 genes of the HEK293 dataset. The results are shown in Fig 7. For example, if adenine or guanine are inserted at position -3, the initiation confidence value increases, with median values of 0.11 and 0.06, respectively (see Fig 7). As mentioned, position -12 seems to play an important role in translational initiation. By comparing all start sites and possible mutations, a cytosine at this position lowers the initiation confidence by 0.16 on average.

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Fig 7. In silico mutation analysis considering all 3,566 genes of the HEK293 dataset.

The flanking sequences of all possible start sites in the HEK293 dataset were mutated. Shown is the difference in the predicted initiation confidence (ICdifference = ICmutationICwildtype). Positions -3 and -12 are prevalent and seem to have the largest influence on the prediction. Positions at the start site were not mutated.

https://doi.org/10.1371/journal.pcbi.1005170.g007

Moreover, it was experimentally shown that positions +5 and +6 are important for efficient translation initiation, especially in non-AUG initiation [37]. More precisely, it was shown that the second codon (i.e. positions 4, 5 and 6) being GAU or GCU enabled an efficient translation initiation while GUA ablated initiation. Thus, an AU or CU seem to be important at position +5/+6 while UA is disadvantageous for translation initiation. This experimental finding can also be observed in Fig 7: A and C at position +5 increase and U at position +5 decreases the confidence value, while on the other hand at position +6, a U increases the confidence value.

Discussion

We were able to identify highly significant features belonging to three different feature classes (biologically-motivated features, PWM as well as k–mer features) that distinguish between true and false translation initiation sites. A simple linear regression model based on significant and uncorrelated features enabled us to reliably differentiate between true and false start sites. While a k–mer search enabled an unbiased scan, the biologically–motivated features reflect experimental observations regarding translation initiation. The PWM accounts for the flanking sequence context that is crucial to initiate translation. Also, it reflects the role of the start codon itself since it was shown that some codons (e.g. AUG and CUG) are used more often by the ribosome to initiate translation in mammals [3, 4].

Problematic was the inhomogeneous data set that most likely contains some FPs and misses some TPs. Reasons for this may be experimental drawbacks, the processing steps of the raw data, or the cell line that was used (some start sites may only be used in specific cell lines). In general, several experimental steps have an influence on and can alter the data output: cell harvesting, nuclease treatment and library generation [9]. The key idea of ribosome profiling is the inhibition of translation. This may introduce certain biases into the data. For example, if inhibition is slow, ribosomes can artificially accumulate at specific positions [9]. Moreover, RNA fragments (e.g. non-coding RNAs) can distort the translation readouts. Especially in sequence analysis, the mapping of the sequence reads from similar regions of different transcript variants is challenging. This is further complicated by the short length (about 30 nucleotides) of ribosome footprints [9]. Moreover, it is currently not possible to apply ribosome profiling to single cells, in contrast to mRNA-seq for instance [9].

Without doubt, the ribosome profiling technique is a huge innovation to understand translational initiation. However, it appears that the start codon selection based on the experimental outcome is challenging. For example, a GUG start in gene RPLP1 at position -107 determined by Lee et al. (2012) has the following flanking sequence context: GCC GCC AAG GUG CUC [3]. In the light of the findings of Kozak [25, 38, 39], one may speculate whether the upstream AAG codon would be the more appropriate start codon. Nevertheless, the deep analyses of the different datasets presented here was able to point out crucial sequence features for which a solid experimental evidence exists (for example Kozak context) that significantly differed between the considered true and false start sites. This verifies and draws confidence that the overall ribosome profiling dataset(s) are suitable for the prediction of translational start sites.

Although, we used ribosome profiling applied to a specific cell line (HEK293) for training and testing, we propose that the predicted start sites have the potential to initiate translation in other cell types as well since the features used are only based on sequence properties. As a rather extreme example, we showed that the classifier trained on human HEK293 cells works reasonably well, albeit with lower accuracy, on mouse ES cells. Interestingly, we observed that the codon distribution of predicted start sites in the test set was similar to that of the experimentally observed start sites. This provides confidence in the quality of our prediction approach. Moreover, applying regression instead of classification enabled us to provide an initiation confidence value ranging from 0.0 to 1.0 rather than a strict decision between true and false start site. Subdividing start sites into different confidence classes c (very high: c ≥ 0.9, high: 0.8 ≤ c < 0.9, moderate: 0.7 ≤ c < 0.8 and low: t = 0.54 ≤ c < 0.7) helps to identify hot candidate start sites with very high initiation confidence values. The analysis of SNPs in the start site flanking sequence context showed that mutations can have a large impact on the initiation confidence. This not only holds true in our prediction approach but also in the context of in vivo translation. Kozak found that individual mRNA positions (-3, +4) are crucial for initiation [24, 25].

Webservice PreTIS

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 (tc < 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.

Supporting Information

S1 Fig. Relative frequency of different start codons.

Distribution of different true start codons in human HEK293 cells (4,482 true starts) and in mouse ES cells (3,009 true starts).

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S2 Fig. Kozak sequence context.

The flanking sequence context of the 4,482 true starts (left columns) and the 49,520 false starts (right columns) with respect to positions -3 and +4, which were shown to be crucial for translation initiation. The definitions of different “Kozak types” are described in the methods section.

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S3 Fig. Linear regression model.

Linear regression coefficients of the best human linear regression model trained on HEK293 data.

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S4 Fig. Different prediction thresholds to classify start sites of the mouse ES dataset.

The solid line corresponds to the threshold of t = 0.54 whereas the dashed line displays results for t = 0.52. A: Unbalanced dataset. B: Balanced dataset.

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(TIFF)

S5 Fig. Different prediction thresholds to classify start sites of the HEK293–AUG dataset.

The solid line corresponds to the threshold of t = 0.54 whereas the dashed line displays results for t = 0.65 and t = 0.64, respectively. A: Unbalanced dataset. B: Balanced dataset.

https://doi.org/10.1371/journal.pcbi.1005170.s005

(TIFF)

S6 Fig. GIMAP5 in silico mutation analysis results of all start sites.

Mutation matrices showing the impact of the flanking sequence context of all putative start sites of gene GIMAP5 on the predicted initiation confidence. In each case, only one nucleotide is mutated with respect to the reference sequence (top line). Grey means that the start was predicted as true translational start (predicted initiation confidence is greater than 0.54) whereas white means that the start was classified as false start. Mutations at the start sites itself were not considered. The numbers reflect the predicted initiation confidence.

https://doi.org/10.1371/journal.pcbi.1005170.s006

(PDF)

Acknowledgments

The authors thank Christian Spaniol for his technical support during the development of the webservice, Michael Hutter, Thorsten Will and Richard Zimmermann for a careful reading of the manuscript, their valuable comments and suggestions and Christian Spaniol and Thorsten Will for testing the webservice prior to release.

Author Contributions

  1. Conceptualization: KR VH.
  2. Data curation: KR.
  3. Formal analysis: KR LK.
  4. Investigation: KR.
  5. Methodology: KR.
  6. Project administration: VH.
  7. Software: KR AB.
  8. Supervision: VH.
  9. Validation: KR.
  10. Visualization: KR AB.
  11. Writing – original draft: KR.
  12. Writing – review & editing: KR VH.

References

  1. 1. Peabody DS. Translation initiation at non-AUG triplets in mammalian cells. J Biol Chem. 1989 Mar;264(9):5031–5035. pmid:2538469
  2. 2. Kozak M. Context effects and inefficient initiation at non-AUG codons in eucaryotic cell-free translation systems. Mol Cell Biol. 1989 Nov;9(11):5073–5080. pmid:2601709
  3. 3. Lee S, Liu B, Lee S, Huang SX, Shen B, Qian SB. Global mapping of translation initiation sites in mammalian cells at single-nucleotide resolution. Proc Natl Acad Sci USA. 2012;109:E2424–2432. pmid:22927429
  4. 4. Ingolia NT, Lareau LF, Weissman JS. Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell. 2011 Nov;147(4):789–802. pmid:22056041
  5. 5. Calviello L, Mukherjee N, Wyler E, Zauber H, Hirsekorn A, Selbach M, et al. Detecting actively translated open reading frames in ribosome profiling data. Nat Methods. 2015 Dec;. pmid:26657557
  6. 6. Ivanov IP, Firth AE, Michel AM, Atkins JF, Baranov PV. Identification of evolutionarily conserved non-AUG-initiated N-terminal extensions in human coding sequences. Nucleic Acids Res. 2011;39:4220–4234. pmid:21266472
  7. 7. Ingolia NT. Ribosome profiling: new views of translation, from single codons to genome scale. Nat Rev Genet. 2014;15:205–213. pmid:24468696
  8. 8. Ingolia NT, Ghaemmaghami S, Newman JR, Weissman JS. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science. 2009;324:218–223. pmid:19213877
  9. 9. Brar GA, Weissman JS. Ribosome profiling reveals the what, when, where and how of protein synthesis. Nat Rev Mol Cell Biol. 2015 Nov;16(11):651–664. pmid:26465719
  10. 10. Hann SR, Dixit M, Sears RC, Sealy L. The alternatively initiated c-Myc proteins differentially regulate transcription through a noncanonical DNA-binding site. Genes Dev. 1994;8:2441–2452. pmid:7958908
  11. 11. Vagner S, Touriol C, Galy B, Audigier S, Gensac MC, Amalric F, et al. Translation of CUG- but not AUG-initiated forms of human fibroblast growth factor 2 is activated in transformed and stressed cells. J Cell Biol. 1996;135:1391–1402. pmid:8947560
  12. 12. Touriol C, Bornes S, Bonnal S, Audigier S, Prats H, Prats AC, et al. Generation of protein isoform diversity by alternative initiation of translation at non-AUG codons. Biol Cell. 2003;95:169–178. pmid:12867081
  13. 13. Hatzigeorgiou AG. Translation initiation start prediction in human cDNAs with high accuracy. Bioinformatics. 2002;18:343–350. pmid:11847092
  14. 14. Saeys Y, Abeel T, Degroeve S, Van de Peer Y. Translation initiation site prediction on a genomic scale: beauty in simplicity. Bioinformatics. 2007;23:i418–423. pmid:17646326
  15. 15. Sparks ME, Brendel V. MetWAMer: eukaryotic translation initiation site prediction. BMC Bioinformatics. 2008;9:381. pmid:18801175
  16. 16. Chen W, Feng PM, Deng EZ, Lin H, Chou KC. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem. 2014;462:76–83. pmid:25016190
  17. 17. Tatusov T, Tatusov R. ORF Finder; accessed 14-1-2015.
  18. 18. Min XJ, Butler G, Storms R, Tsang A. OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 2005;33:W677–680. pmid:15980561
  19. 19. Crappe J, Van Criekinge W, Trooskens G, Hayakawa E, Luyten W, Baggerman G, et al. Combining in silico prediction and ribosome profiling in a genome-wide search for novel putatively coding sORFs. BMC Genomics. 2013;14:648. pmid:24059539
  20. 20. Michel AM, Andreev DE, Baranov PV. Computational approach for calculating the probability of eukaryotic translation initiation from ribo-seq data that takes into account leaky scanning. BMC Bioinformatics. 2014;15:380. pmid:25413677
  21. 21. Kinsella RJ, Kahari A, Haider S, Zamora J, Proctor G, Spudich G, et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database (Oxford). 2011;2011:bar030. pmid:21785142
  22. 22. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. pmid:20003500
  23. 23. Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004;5:113. pmid:15318951
  24. 24. Kozak M. Compilation and analysis of sequences upstream from the translational start site in eukaryotic mRNAs. Nucleic Acids Res. 1984;12:857–872. pmid:6694911
  25. 25. Kozak M. Point mutations define a sequence flanking the AUG initiator codon that modulates translation by eukaryotic ribosomes. Cell. 1986;44:283–292. pmid:3943125
  26. 26. Noderer WL, Flockhart RJ, Bhaduri A, Diaz de Arce AJ, Zhang J, Khavari PA, et al. Quantitative analysis of mammalian translation initiation sites by FACS-seq. Mol Syst Biol. 2014;10:748. pmid:25170020
  27. 27. Kozak M. Circumstances and mechanisms of inhibition of translation by secondary structure in eucaryotic mRNAs. Mol Cell Biol. 1989;9:5134–5142. pmid:2601712
  28. 28. Kozak M. Downstream secondary structure facilitates recognition of initiator codons by eukaryotic ribosomes. Proc Natl Acad Sci USA. 1990;87:8301–8305. pmid:2236042
  29. 29. Kozak M. Structural features in eukaryotic mRNAs that modulate the initiation of translation. J Biol Chem. 1991;266:19867–19870. pmid:1939050
  30. 30. Lorenz R, Bernhart SH, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler PF, et al. ViennaRNA Package 2.0. Algorithms Mol Biol. 2011;6:26. pmid:22115189
  31. 31. Zhang L, Kasif S, Cantor CR, Broude NE. GC/AT-content spikes as genomic punctuation marks. Proc Natl Acad Sci USA. 2004;101:16855–16860. pmid:15548610
  32. 32. Gould IR, Kollman PA. Theoretical Investigation of the Hydrogen Bond Strengths in Guanine-Cytosine and Adenine-Thymine Base Pairs. J Am Chem Soc. 1994;116:2493–2499.
  33. 33. Liu H, Wong L. Data mining tools for biological sequences. J Bioinform Comput Biol. 2003;1:139–167. pmid:15290785
  34. 34. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–2830.
  35. 35. Kozak M. The scanning model for translation: an update. J Cell Biol. 1989;108:229–241. pmid:2645293
  36. 36. The UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:D204–212. pmid:25348405
  37. 37. Boeck R, Kolakofsky D. Positions +5 and +6 can be major determinants of the efficiency of non-AUG initiation codons for protein synthesis. EMBO J. 1994 Aug;13(15):3608–3617. pmid:8062835
  38. 38. Kozak M. At least six nucleotides preceding the AUG initiator codon enhance translation in mammalian cells. J Mol Biol. 1987;196:947–950. pmid:3681984
  39. 39. Kozak M. An analysis of 5’-noncoding sequences from 699 vertebrate messenger RNAs. Nucleic Acids Res. 1987;15:8125–8148. pmid:3313277