Transcript levels do not faithfully predict protein levels, due to post-transcriptional regulation of gene expression mediated by RNA binding proteins (RBPs) and non-coding RNAs. We developed a multivariate linear regression model integrating RBP levels and predicted RBP-mRNA regulatory interactions from matched transcript and protein datasets. RBPs significantly improved the accuracy in predicting protein abundance of a portion of the total modeled mRNAs in three panels of tissues and cells and for different methods employed in the detection of mRNA and protein. The presence of upstream translation initiation sites (uTISs) at the mRNA 5’ untranslated regions was strongly associated with improvement in predictive accuracy. On the basis of these observations, we propose that the recently discovered widespread uTISs in the human genome can be a previously unappreciated substrate of translational control mediated by RBPs.
High throughput technologies such as RNA-sequencing (RNA-seq) and mass-spectrometry-based protein analyses provide transcriptomic and proteomic profiles, which are the basis to draft a comprehensive picture of gene expression regulation [1], [2], [3]. Several studies have reported a lack of concordance between transcriptome and the proteome profiles [3], [4], [5], [6], [7], both at the steady state [8], [9], [10] and dynamically [11], [12], [13]. Even though this phenomenon is partially accounted for by technical factors such as noise [14], biased detection [15] and limited and variable coverage of mRNA and protein measurements [16], the discrepancy is so considerable that undoubtedly it implies an unresolved complexity in the regulation of gene expression downstream of transcription. Several studies have sought to examine the extent to which specific levels of regulation contribute to determine protein abundance at the steady state [17], [13], [8]. It was initially estimated that in mouse fibroblasts transcription explains 34% of variance in protein abundance, mRNA degradation 6%, translation 55% and protein degradation 5% [8]. Employing additional statistical efforts to account for the influence of measurement error on mRNA/protein correlation, recent studies proposed a correction of the initial estimates and brought back the role of translation to 30% [18]. Several studies highlighted the strong influence of translation on differential protein abundance during dynamic responses [19], [20], [21], [22]. The regulatory mechanisms by which the various post-transcriptional processes exert their effects on protein abundance are not well understood. Regulatory features associated with these processes have been identified not only in the coding regions but also in the 5’ and 3’ untranslated regions (UTRs) of mRNAs in multiple species [23], [24]. After their synthesis, processing, and export to the cytoplasm, mRNAs are broadly engaged in two activities: they may serve as templates for translation or as substrates for degradation pathways. Translational control, principally involving the initiation stage, can occur on a global basis by changes in the amounts and activation state of components of the translational machinery: translation factors [25], tRNAs [26] and ribosomes [27], [28]. Transcript-specific control of translation is less understood. The mechanisms of selective translation through recognition of target mRNAs by trans-acting factors, such as non-coding RNAs [28], [29], [30] and RNA-binding proteins (RBPs) [31], [32], are still subject of investigation [33], [34], [35], [36], [37], and are known only in a limited number of cases [38], [39], [40], [27], [41], [42]. Here, we developed a model of post-transcriptional control of gene expression by using multivariate linear regression to estimate protein levels from transcript levels. The model is empirically developed from two types of primary data: quantitative transcriptome assays matched with proteome assays, and post-transcriptional regulatory annotations of mRNA untranslated regions (UTRs) obtained by scanning for occurrences of in vitro experimentally determined RBP binding sites [31]. Including RBP levels and binding sites resulted in a statistically significant improvement of accuracy in protein abundance estimates of a fraction of the total modeled mRNAs in three panels of tissues and cells. We showed this improvement to be associated with the presence of upstream translation initiation sites (uTISs). This observation suggests the possibility that RBP could influence protein abundance by modulating alternative translation initiation, a mechanism of translational control still not experimentally described.
To devise a model of protein levels from transcript levels including a quantitative description of the contribution of RBP-mediated post-transcriptional control, we selected three data panels consisting of matched transcript and protein profiles: twelve normal human tissues [43], 59 cancer cell lines (the NCI-60 panel) [44], and 87 colorectal cancer tissues (the CPTAC CRC panel) [45]. The normal tissue panel contains the widest physiological variability, therefore it was used for determining model predictiveness. The NCI-60 and CPTAC CRC panels were used to show repeatability of the major findings in independent panels, and to assess cross-panel transferability of protein abundance models. The depth of proteome coverage in the normal tissue panel was substantially lower than of the transcriptome (Table A in S1 Text), confirming previous reports [46]. We avoided genes whose transcripts and proteins were not reliably measured in a substantial number of samples in each panel (S1 Fig). This filtering resulted in the selection of more highly abundant genes than the overall pool at either the mRNA or protein level (S2 Fig). This effect was expected, considering the low frequency at which lowly abundant peptides could be selected for peptide sequence analysis and subsequent protein quantification. Filtering for adequately measured proteins introduced a bias in the genes we were able to study, highlighted by depletion and enrichment of several Gene Ontology (GO) categories (S3 Fig). The NCI-60 and CPTAC CRC panels also showed partial proteome coverages (Table A in S1 Text, S1 Fig), and consequent biases (S2 Fig, S3 Fig). When measuring gene expression, multiple biological and technical factors can interact to produce the variability in average mRNA/protein levels, which we observed across the samples of each panel (S4 Fig, S5 Fig). To eliminate the possibility that average protein levels could help in predicting protein abundance (S6 Fig), mRNA and protein data were mean-centred per sample in each panel (Supporting Information). No sample turned out to be systematically associated with outlier measurements in any of the three panels (Supporting Information, S7 Fig). We used RNA-binding motifs in linear regression modeling to infer models of RBP post-transcriptional regulation for all genes where transcripts, proteins, and RBPs were measured in a sufficient number of samples in a panel. The compendium of RNA-binding motifs was derived for 85 human RBPs by RNAcompete [29], an in vitro method for rapid and systematic analysis of RNA sequence preferences of RBPs shown to be predictive of in vivo binding [47]. We scanned the 5’ and 3’ UTRs of the mRNAs to identify sequences matching to the RNA-binding motifs, and detected RBP binding sites for 50 RBPs within the 5’ and 3’ UTRs of the 1,109 genes modeled in the normal tissue panel (q < 0. 20). For genes modeled in the NCI-60 panel we identified binding sites for 40 RBPs on 1,327 mRNAs; in the CPTAC CRC panel for 66 RBPs on 1,825 mRNAs. The inferred RBP-mRNA interactions confirmed the previously reported tendency of multiple mRNAs to be regulated by multiple RBPs [47], [48], [49], with the number of RBPs per mRNA ranging from 1 to 38 based on inferred RBP binding sites in mRNA UTRs. This observation was independent of the stringency in statistical significance used for predicting RBP binding sites (S8 Fig). We assessed the accuracy of the RBP-inclusive models to predict the protein abundance of modelled mRNAs by cross-validation and cross-panel validation. Finally, the relevance of RBPs in transcript/protein coupling was tested for association with regulatory features of the modelled mRNAs. We then explored the features associated with the improvement in accuracy of protein abundance prediction achieved by the RBPplus model over the RNAonly one, as quantified by the difference in their R2 values (R2RBPplus–R2RNAonly). For this purpose, we analysed the association of the improvement in predictive accuracy with the major gene-specific sequence and structure annotations of the genes modelled in the normal tissue panel. We considered annotations which have been associated with post-transcriptional regulation of protein abundance [17], [10], [51], and which can be loosely classified by their demonstrated impact mostly on transcript stability and/or translation efficiency (Table 1). Spearman’s correlation with most of the tested characteristics was very low (Table 1). Interestingly, the only statistically significant correlation was observed between the improvement in accuracy of predicted protein abundance and the number of upstream Translation Initiation Sites (uTISs), as shown in Fig 4A. Our results indicate that the presence of uTISs is a common feature of those mRNAs where RBPs included in the RBPplus model improved predictive accuracy compared to the RNAonly model. Even if this association does not mean a biological link between RBPs and uTISs, it suggests that translational regulation of the main ORF could be exerted by some of the considered RBPs through an uTISs. A potential, direct mechanism for this regulation could be steric control of uTIS elements by local RBP binding. We adopted this hypothesis to attempt an initial prioritization of RBPs. In case of steric control, RBP binding sites need to be in the proximity of a uTIS. No demonstrated example of such a control is present, at the best of our knowledge, in the literature. A functional proximity between uTISs and RBP binding sites has been reported only in one study involving the Drosophila SXL protein, but in this case the uTIS defines a uORF [61]. We selected the closest RBP binding site to each uTIS identified in a gene where the RBPs in the RBPplus model improved the accuracy in predicted protein abundance relative to the RNAonly model (p < 0. 05 by randomization of proteins). We then ordered the RBPs according to the proportion of genes where they were inferred to recognize the binding sites located nearest to the uTISs. This analysis led us to prioritize the 15 RBPs inferred to bind the identified mRNAs (Fig 5). Of them, PCBP2 has been previously implicated in translational control by an internal ribosomal entry site (IRES) [62].
Although transcriptomic and proteomic assays are rarely integrated in large-scale studies, such integration provides a still unexploited instrument to study post-transcriptional control in a large-scale perspective. We performed an integrative analysis of matched RNAseq-based transcript and MS-based protein profiles to assess potential interaction between RBPs and mRNAs to determine protein abundance, beyond the contribution of transcript abundance. The pool of adequately measured proteins, as expected, was a fraction of the transcriptome coverage and was functionally biased for certain GO themes. RNAonly and RBPplus model were fitted for each mRNA/protein pair employing linear regression. To define the extent to which the RBPplus model improves the accuracy in predicted protein abundance over the RNAonly model, we harmonized our regression approaches for the RNAonly and RBPplus models, so that if RBPs are useless covariates, the RBPplus model is expected to converge to RNAonly one. We carefully checked the extent to which the effect produced by the RBPs, which were inferred to bind the modelled mRNAs, can be recapitulated by randomly sampled predictors, assessing statistical significance of improvement in predictive accuracy by empirical randomization tests. Our analysis suggested a large room for improvement over the RNAonly models, but the improvement in accuracy of predicted protein abundance achieved by the RBPs included in the RBPplus models could be reconstructed by randomly sampled proteins in the largest majority of the genes that we could model. Indeed, gene-level randomization tests identified a small fraction of genes where the impact of inferred RBP-mRNA interaction on improved predictive accuracy was statistically significant. Measuring the association of the improvement in accuracy of predicted protein abundance with mRNA features led to identify uTISs as a common feature of the genes where RBPs were shown to be informative. Recently, allele-specific translational efficiency in an F1 hybrid mouse was determined by transcriptome and polysome profiling, and an analysis of sequence features of mouse genes with biased allelic translation revealed that out-of-frame uTISs could affect translational efficiency [63]. The impact of RBPs on the improvement in accuracy of predicted protein abundance was limited to a fraction of mRNAs, and it was dependent on the number of uTISs present in mRNA 5’ UTRs but not on the strength of the downstream aTISs. Our analysis cannot provide for a potential mechanism or decide for a direct versus an indirect effect, but given these features one of the possibilities is that some of the informative RBPs could modulate translation initiation of the downstream ORFs by simply either repressing or promoting alternative, uTIS-based, translation initiation. Regulation of translation initiation in mammalian cells by interaction of RBPs with mRNA 5’ UTRs has been rarely documented, with a few examples involving the interaction between RBPs and internal ribosomal entry sites (IRESs) of specific stress-related mRNAs (reviewed in [64]), or the interaction between the IRP-1 RBP and the iron-responsive element (IRE) of the ferritin mRNA [35]. But no uTIS-dependent effect has been found in these well-studied cases. The presence of uORFs is known to regulate translation of primary downstream ORFs by operating via decay, re-initiation, or peptide-mediated ribosomal stalling during uORF translation [55], [65], [25]. Although uORFs can regulate protein levels without involving RBPs [65], an already cited previous study in Drosophila offers an example where the SXL RBP promotes translation initiation at the uORF of the msl-2 and Irr47 transcripts [61], which thus results in translational repression. More recently, the DENR-MCT-1 complex has been identified as a regulator of eukaryotic uORF-dependent translation re-initiation of a specific group of mRNAs [34]. But to our knowledge no RBP-induced, non uORF-mediated translational control mechanism in uTIS-endowed loci has yet been identified. Based on the hypothesis of a direct mechanism of RBP control of uTISs, such mechanism could be sensitive to changes in the position and spacing between RBP binding sites within the mRNA 5’ UTR. We therefore used the criterion of spatial proximity to prioritize the RBPs which were shown to help in predicting protein abundance. Of course, we cannot exclude that this control could be due to 3’ UTR binding, considering also that 3’ UTR-acted and RBP-mediated translational initiation controls are an established model. Yet, this model has been proposed [25] on the basis of few notable cases in Drosophila [66], [67], [68], [69] and Xenopus [70] translational control always during development and differentiation. In these examples, the RBP ensures specificity to the regulation of translation by binding sites within the 3’ UTR of the mRNA and contributes to the formation of a closed loop which precludes formation of the initiation complex eIF4F, therefore exerting an inhibitory effect on translation. It is worth considering that, again, in this “classical” model no role is attributed to uTISs. With the limitations highlighted in mind, the study presented here allowed us to estimate the impact of RBP-mRNA interactions on quantitative relationships between mRNA and protein abundances. RBPs were shown to help in predicting protein abundance relative to an RNAonly model, but not relative to randomly selected proteins, in the majority of considered mRNAs. Nonetheless, our analysis identified genes for which inferred RBP-mRNA interactions were informative. The association between the improvement in accuracy of predicted protein abundance and uTISs suggests that RBPs could modulate the expression of these genes by mediating alternative translation regulation. The usefulness of RBPplus models need to be further tested as soon as suitable datasets are produced by RNAseq and MS-based technologies. The pervasive presence of conserved uTISs in the human transcriptome, which has been recently revealed by ribosome profiling and related approaches [52], [62], [63], awaits a clarification of their functional role.
Matched transcriptome and proteome profiles were downloaded in the processed form provided by three independent datasets: 1) a panel of twelve human normal tissues [43], 2) the 59 samples from the US National Cancer Institute (NCI) -60 dataset [44], and 3) 87 colorectal cancer (CRC) samples profiled by The Cancer Genome Atlas (TCGA) in combination with the Clinical Proteomic Tumour Analysis Consortium (CPTAC) [45]. Processed data derived from gene expression analysis in the normal tissue panel were downloaded from the online Supplementary Information of the study [43]. Normalized transcriptome data for NCI-60 cell lines were obtained from the Gene Expression Omnibus (series accession number GSE32474), while processed proteome data were downloaded from http: //wzw. tum. de/proteomics/nci60. Processed proteome data for TCGA colorectal cancer samples were downloaded from the online Supplementary Information of the study [45], while processed transcriptome data were downloaded from TCGA (http: //cancergenome. nih. gov/). In the normal tissue panel and CRC panel, transcript abundance data were obtained by RNA sequencing (RNA-seq) and expressed as Fragments Per Kilobase per Million, log-base-10 FPKM. NCI-60 transcriptome profiles were obtained by microarray. Normal tissue proteome profiles were obtained by the intensity-based Absolute protein Quantification method, and expressed as log-base-10 iBAQ. NCI-60 and CPTAC CRC proteome profiles were based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) -based shotgun proteomic analysis. Intensity- and spectral count-based label-free quantifications were used to obtain protein abundance in the NCI-60 cell lines and in the CPTAC CRC specimens, respectively. In the normal tissue panel we excluded genes and proteins below the detection limit in more than three out of twelve tissues at either transcriptome or proteome level; in the NCI-60 and CRC panels we excluded genes below the detection limit in more than five out the total number of specimens at either the transcriptome or proteome level. Genes below the detection limit were assigned zero values or Not Available (NA) labels in the files processed data were acquired from. Within each panel, we applied inter-sample normalization by mRNA and protein mean-centring per sample. We scanned non-redundant 5’ and 3’ untranslated region (UTR) sequences of the genes profiled at both the transcript and protein levels with positional weight matrices (PWMs), which represent RNA sequence binding specificities of RBPs derived from RNAcompete [31] data and which are available through the cisBP-RNA database (http: //cisbp-rna. ccbr. utoronto. ca/). In each panel of matched transcriptome/proteome datasets, the inference of RBP binding sites in mRNA UTRs was restricted to the subset of RBPs which were detected both at the transcript and at the protein level. For each considered RBP, RBP binding sites as well as corresponding q-values were obtained using the FIMO algorithm [71] of the MEME toolkit (http: //meme-suite. org/) and retained at the false discovery rate of 20%. We built two models for each considered gene: a basic (RNAonly) model, where the abundance of protein j in sample i (PROTij) was predicted by the corresponding mRNA level only in a simple linear regression model: RNAonly: PROTij=β0j+βmRNA, jmRNAij+εij; εij∼iidN (0, σi) where β0j is the intercept term, βmRNA, j is the regression coefficient for the mRNA predictor and the error term εij is an independent and identically distributed (iid) random variable following a normal distribution of mean 0 and standard deviation σ. This model was fit for each mRNA/protein pair. An RBP-inclusive (RBPplus) multiple linear regression model was also fitted for each mRNA/protein pair: RBPplus: PROTij=β0j+βmRNA, jmRNAij+βRBP, jkRBPijk+εij; εij∼iidN (0, σi) where βRBP, jk is the regression coefficient for the kth RBP of mRNA j. This model was fitted by maximum penalized likelihood with Ridge or LASSO penalty applied to RBPs but not to mRNA measurements, using the pensim R package [72], which acts as a wrapper providing nested cross-validation to the penalized R package [73]. In the nested cross-validation scheme, test samples are held out for accuracy estimation in the outer layer of cross-validation, and penalty parameters are tuned in the inner layer of cross-validation within training samples only. In the outer layer of cross-validation, we used 5-fold for the three panels. By not penalizing mRNA measurements, the model can be expected to converge to the RNAonly model in the absence of informative RBP protein measurements. Both Ridge and LASSO penalty help control of over-fitting of high-dimensional data; LASSO additionally provides feature selection by setting the coefficients of most covariates to exactly zero. We fitted these two models, independently for each gene inferred to be bound by an RBP and in each tissue/cell panel where both transcript and corresponding protein met the missingness requirements described above. The change in accuracy of predicted protein abundance obtained by the RBPplus model relative to the RNAonly model of each considered gene was quantified by the difference in the R2 coefficients between the RBPplus and RNAonly models. This analysis used the following R2 coefficient definition: R2=1−∑i (yi−fi) 2∑i (yi−〈y〉) 2 where yi the i-th observation, <y> is the mean of the observations, and fi is the i-th prediction. We evaluated the statistical significance of the improvement in accuracy of predicted protein abundance attained by the RBPplus model relative to the RNAonly model across the genes considered in each separate panel by Wilcoxon signed-rank test. Empirical randomization tests were used to determine whether the accuracy in predicted protein abundance achieved by the RBPplus model of an individual gene was statistically significantly better relative than that expected for randomized RBPplus models of the gene. For each considered gene, we obtained 1000 randomized versions of the RBPplus model by 1) randomly sampling a number of protein predictors equal to the number of actual RBPs inferred to bind the mRNA UTRs, and 2) by permuting the protein levels of inferred RBPs across samples. As the actual RBPplus models, each randomized RBPplus model (by sample permutation or randomly sampling of protein predictors) was fitted by maximum penalized likelihood with Ridge penalty applied to RBPs but not to mRNA measurements in nested cross-validation scheme. Fig 3 illustrates the two randomization schemes. The p-value of the R2 value observed for the actual RBPplus model of each considered gene was defined by the probability of sampling a R2 value from the null distribution of R2 values that is higher than the observed R2. The RBPplus model of a gene was deemed to improve the accuracy in prediction of protein abundance if the RBPplus model accuracy was higher than that of the RNAonly model and if the probability of attaining accuracy higher than that of the RBPplus model by randomly sampling protein predictors was < 0. 05. Since our analysis involved multiple hypotheses testing, we reported false discovery rate by the Storey’s q-value method implemented in the qvalue R package [50]. We studied cross-panel model transferability of models trained using only the RBPs profiled in both of each pair of panels. For each considered mRNA, we developed the RNAonly and RBPplus models using all samples in a training panel, and tested them using all samples in the testing panel. The procedure was repeated for all possible combinations of training and test panels. We estimated model transferability computing Spearman’s correlation coefficient of protein predictive accuracies between the RBPplus models trained in a chosen panel and the RBPplus models trained in each of the other two panels. Functional enrichment/depletion analysis was based on the Biological Process categories of the generic Gene Ontology (GO) slim, a cut-down version of the Gene Ontology annotations (http: //geneontology. org/) and used hypergeometric test. Functional analysis was used 1) to assess over-/under-representation of GO themes in the genes which turned out to be adequately measured relative to the total of genes which were profiled at the mRNA/protein levels, and 2) to assess over-/under-representation of GO themes in the genes where RBPplus models were found to be informative relative to the total of modelled genes. We surveyed appropriate data sources to gather several gene annotations relevant to post-transcriptional regulation of gene expression in mammalian cells (Table 1). We quantified the selected features in the mRNAs modelled in the normal tissue panel as follows. Normalized lengths of the coding sequence as well as of the 5’ and 3’ UTRs were calculated for each mRNA according to the sequence annotations (hg38 assembly) available at the UCSC Genome Browser (https: //genome. ucsc. edu/). Local folding energy was computed within a window of 30 nucleotides upstream and downstream of the annotated translation initiation site of the modelled mRNAs using the RNAfold algorithm of the Vienna RNA package (www. tbi. univie. ac. at/RNA/). Transcript half-life measures were acquired by two distinct studies which relied, respectively, on biosynthetic labelling of newly transcribed RNA and estimation of newly/total RNA ratio in human B cells [74], and on transcription blocking in HepG2 and Bud8 cell lines [75]. A measure of efficiency of start codon recognition of primary ORFs was derived from a quantitative analysis of translation initiation sites by FACS-seq, Fluorescence-Activated Cell Sorting and high-throughput DNA sequencing [52]. The tRNA adaptation index (tAI), an estimate of the translational optimality of a coding sequence to cellular tRNA pools was computed by the codonR software [76]. Annotation of upstream translation initiation sites (uTISs) was derived by Global Translation Initiation sequencing (GTI-seq) in HEK293 cells and downloaded from the TISdb database [77]. We included an additional mapping of upstream translation initiation sites which was obtained by Quantitative Translation Initiation sequencing (QTI-seq) in HEK293 cells [58]. Upstream Open Reading Frames (uORFs) were defined by: (i) an uTIS out-of-frame at the 5’ UTR, with a stop-codon, in the same frame, downstream of it, and with a minimal length of nine nucleotides, (ii) an uTIS in-frame at the 5’UTR with a stop codon in frame after the main stop codon or before the main start codon. We used Spearman’s correlation coefficient to estimate the correlation of the change in accuracy of predicted protein abundance with each aforementioned feature. Furthermore, we used Fisher’s test to assess the enrichment of the genes where the RBPplus model was found to be informative in uTIS-containing genes as well as in uORF-containing genes. Testing was performed for uTISs identified by GTI-seq and QTI-seq technologies and for each panel of paired mRNA/protein datasets. RBPs were prioritized by an analysis of the frequency at which the binding sites of an RBP occur in the proximity of uTISs of mRNAs. We identified the closest RBP binding site to each uTIS present in the 5’ UTR of each mRNA. We then quantified the frequency of the binding sites of each RBP in the binding sites situated nearest to the uTISs overall mRNAs. RBPs were ordered according to the number of genes where they were found to recognize the binding sites closest to the uTISs. In the contexts where multiple tests were performed, raw P-values were adjusted by the Benjamini-Hochberg method for controlling false discovery rate at 5%.
Gene expression is a dynamic program by which the information stored in the genome is rendered functional by production and degradation of two types of macromolecules, RNAs and proteins. mRNAs are templates for proteins; therefore we expect correspondence between quantities of mRNAs and proteins. Genome-wide studies instead indicate a marked discrepancy between them, when considering their steady-state levels or their variations across different conditions. We employed linear regression approaches with paired mRNA/protein datasets in order to develop a model predicting the protein level of a gene from both the mRNA level and the protein levels of RBPs inferred to bind the mRNA untranslated regions. The results of our analyses restricted the utility of RBPs to improve accuracy of predicted protein abundance to a small fraction of the total modelled genes, and identified a novel association of the improvement induced by RBPs with the presence of upstream translation sites. This finding suggests a new avenue of experimental studies aimed at exploring the hypothesis that RBPs could influence protein abundance by changing the preference for certain translation initiation sites.
rna-binding proteins
medicine and health sciences
messenger rna
cancers and neoplasms
protein abundance
oncology
mathematics
forecasting
statistics (mathematics)
genome analysis
research and analysis methods
translation initiation
genomics
proteins
mathematical and statistical techniques
gene expression
proteomics
biochemistry
rna
colorectal cancer
nucleic acids
protein translation
genetics
transcriptome analysis
biology and life sciences
physical sciences
computational biology
statistical methods
Control of Gene Expression by RNA Binding Protein Action on Alternative Translation Initiation Sites