Integrating Chemical Footprinting Data into RNA Secondary Structure Prediction
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{"title"=>"Integrating Chemical Footprinting Data into RNA Secondary Structure Prediction", "type"=>"journal", "authors"=>[{"first_name"=>"Kourosh", "last_name"=>"Zarringhalam", "scopus_author_id"=>"23669720300"}, {"first_name"=>"Michelle M.", "last_name"=>"Meyer", "scopus_author_id"=>"55463372600"}, {"first_name"=>"Ivan", "last_name"=>"Dotu", "scopus_author_id"=>"8965072400"}, {"first_name"=>"Jeffrey H.", "last_name"=>"Chuang", "scopus_author_id"=>"7201692555"}, {"first_name"=>"Peter", "last_name"=>"Clote", "scopus_author_id"=>"6603587138"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84867593489", "pmid"=>"23091593", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "scopus"=>"2-s2.0-84867593489", "issn"=>"19326203", "pui"=>"365872742", "doi"=>"10.1371/journal.pone.0045160"}, "id"=>"c8fcfafe-78ca-3c70-9f7a-9281bd7aefa1", "abstract"=>"Chemical and enzymatic footprinting experiments, such as shape (selective 2'-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the [Formula: see text]-hydroxyl is reactive at flexible (loop) regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints), which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be 'correct', in as much as the shape data is 'correct'. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.", "link"=>"http://www.mendeley.com/research/integrating-chemical-footprinting-data-rna-secondary-structure-prediction", "reader_count"=>56, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Researcher"=>17, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>1, "Other"=>3, "Student > Master"=>10, "Student > Bachelor"=>5}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Researcher"=>17, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>1, "Other"=>3, "Student > Master"=>10, "Student > Bachelor"=>5}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>16, "Agricultural and Biological Sciences"=>27, "Medicine and Dentistry"=>1, "Business, Management and Accounting"=>1, "Chemistry"=>2, "Computer Science"=>8, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Chemistry"=>{"Chemistry"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>27}, "Computer Science"=>{"Computer Science"=>8}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>16}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>3, "United Kingdom"=>1, "France"=>2, "Spain"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/557474"], "description"=>"<p>Normalized (blue circles) and raw (red diamonds) shape values. Gray bars indicate the missing shape values. The subplots shows the piecewise normalization map.</p>", "links"=>[], "tags"=>["Computational biology", "physics", "Biochemistry", "mathematics"], "article_id"=>227968, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.g002", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Normalization_/227968", "title"=>"Normalization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:12:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/558050"], "description"=>"<p>A comparison of three secondary structure prediction algorithms, using shape data from Deigan et al. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045160#pone.0045160-Deigan1\" target=\"_blank\">[15]</a> for the three RNA molecules, yeast aspartyl tRNA (asp-tRNA), hepatitis C virus internal ribosomal entry site (HCV IRES), and the P546 domain from the bI3 group I intron (P546), along with shape data from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045160#pone.0045160-Kladwang1\" target=\"_blank\">[26]</a> for three additional RNA molecules, <i>E. coli</i> phenylalanine tRNA (phe-tRNA), <i>E. coli</i> 5S ribosomal RNA (5S rRNA), and <i>F. nucleatum</i> glycine riboswitch (glycine). The benchmark results are tabulated for (A) RNAsc+shape, (B) RNAstructure+shape, and (C) RNAstructure (with no shape data). Sensitivity is abbreviated by sens., positive predictive value is abbreviated by ppv. The average pointwise entropy, Morgan-Higgs structural diversity, and the expected distance of the computed probabilities to the probing data are abreviated by ave ent., str. div., and edist., respectively. Not shown: results for medloop and <i>V. vulnificus</i> adenine riboswitch (1Y26), for which all three methods have optima sensitivity and ppv values of 1.0.</p>", "links"=>[], "tags"=>["Computational biology", "physics", "Biochemistry", "mathematics"], "article_id"=>228544, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.t001", "stats"=>{"downloads"=>4, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Benchmark_results_/228544", "title"=>"Benchmark results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-16 02:22:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/557896"], "description"=>"<p>The figure shows a plot of the expected distance between normalized experimental shape values and the low energy Boltzmann ensemble, as computed by RNAsc. The -axis depicts increasing values of RNAsc parameter , while the -axis depicts expected distance . The curves confirm the statement of Theorem 2, which states that as increases, the expected distance decreases. The figure also shows that for higher values of , can be made to agree very closely . The expected distances of the predicted probabilities with <i>u</i>nnormalized shape values for RNAstructure are , , and for asp-tRNA, HCV, and P546 respectively using optimal parameter values ( and ).</p>", "links"=>[], "tags"=>["probabilities", "normalized"], "article_id"=>228393, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.g008", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expected_distance_of_predicted_probabilities_with_normalized_shape_data_/228393", "title"=>"Expected distance of predicted probabilities with normalized shape data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:19:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/557755"], "description"=>"<p>Heat maps illustrating differences between in-line probing <i>(left)</i> and shape <i>(right)</i> analysis of the yeast asp-tRNA. Nucleotides are colored corresponding to cumulative activities described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045160#pone-0045160-g003\" target=\"_blank\">Figure 3</a>, where the least reactive of bases are black ( of bases are paired in the crystal structure), the most reactive of bases are red, and the next most reactive are yellow. Gray bases are bases for which there is no data available.</p>", "links"=>[], "tags"=>["maps", "in-line", "probing"], "article_id"=>228250, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.g006", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heat_maps_of_in_line_probing_and_shape_/228250", "title"=>"Heat maps of in-line probing and shape.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:17:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/557419"], "description"=>"<p>Spontaneous cleavage pattern resulting from in-line probing of yeast asp-tRNA, nucleotides with larger backbone flexibility will have higher rates of cleavage and thus bands of greater intensity. Lanes for no reaction, T1 RNase (cleavage following only guanosines), and partial hydroxyl cleavage (-OH, cleavage after each base) are indicated. Due to the high resolution of the gel, double bands appear for nucleotides 2–9. These bands correspond to RNA molecules where the cyclic phosphate intermediate has hydrolyzed to leave either no phosphate, or a mixture of - and -phosphate products which migrate more quickly on the gel. Quantifcation of these positions combined the bands corresponding to both products. The precursor RNA and T1 RNase cleavage products are marked. Not all guanosines show cleavage due to retention of secondary structure at 5 M urea and elevated temperature.</p>", "links"=>[], "tags"=>["Computational biology", "physics", "Biochemistry", "mathematics"], "article_id"=>227912, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.g001", "stats"=>{"downloads"=>2, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_In_line_probing_/227912", "title"=>"In-line probing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:11:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/557835"], "description"=>"<p>Pointwise entropy of yeast asp-tRNA, computed from RNAsc using shape data (red squares), in-line probing (blue diamonds), and using no probing data (black circles). Average pointwise entropies: 0.210 (shape data), 0.267 (in-line probing), 0.269 (no data). As expected, by integrating either shape or in-line probing data into RNAsc, the variability (entropy) decreases; however, it appears that variability (entropy) is decreased more by shape than by in-line probing data – again, suggesting that shape data is more robust than in-line probing data when used with RNAsc.</p>", "links"=>[], "tags"=>["Computational biology", "physics", "Biochemistry", "mathematics"], "article_id"=>228326, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.g007", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pointwise_entropies_/228326", "title"=>"Pointwise entropies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:18:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/557657"], "description"=>"<p>The plots show heat maps displaying ppv () as a function of parameter for RNAsc with data from shape and in-line probing <i>(asp-tRNA</i><i>)</i>. Note the much larger area for good parameter choices when using shape data, rather than in-line probing data. This data suggests that shape data is more robust than in-line probing data, when used in computing MFE structure with RNAsc. Computations were done at 37C.</p>", "links"=>[], "tags"=>["parameter"], "article_id"=>228156, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.g005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimal_parameter_value_/228156", "title"=>"Optimal parameter value.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:15:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/557539"], "description"=>"<p>Distribution of shape discrepancies in yeast asp-tRNA <i>(top)</i> and <i>E. coli</i> phe-tRNA <i>(bottom)</i>. shape data for asp-tRNA [resp. phe-tRNA] from the Weeks Lab <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045160#pone.0045160-Wilkinson4\" target=\"_blank\">[25]</a> [resp. Das Lab <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045160#pone.0045160-Kladwang1\" target=\"_blank\">[26]</a>]. Using crystal structure as ‘gold standard’, red squares indicate locations where the absolute value of the difference of shape data and crystal structure (1 unpaired, 0 paired) exceeds 0.5. The plots on the right show the distribution of the discrepancy in shape as well as the error rate.</p>", "links"=>[], "tags"=>["Computational biology", "physics", "Biochemistry", "mathematics"], "article_id"=>228025, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Shape_discrepancies_/228025", "title"=>"Shape discrepancies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/296249"], "description"=>"<div><p>Chemical and enzymatic footprinting experiments, such as shape (selective 2′-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the -hydroxyl is reactive at flexible (loop) regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (<em>RNA soft constraints</em>), which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is <em>self-consistent</em>, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be ‘correct’, in as much as the shape data is ‘correct’. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.</p> </div>", "links"=>[], "tags"=>["integrating", "footprinting", "rna"], "article_id"=>118364, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160", "stats"=>{"downloads"=>21, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Integrating_Chemical_Footprinting_Data_into_RNA_Secondary_Structure_Prediction/118364", "title"=>"Integrating Chemical Footprinting Data into RNA Secondary Structure Prediction", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-16 02:19:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/557598"], "description"=>"<p>Distribution of reactivities of data from in-line probing <i>(A)</i> and shape <i>(B)</i>. In-line probing reactivities were determined using SAFA <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045160#pone.0045160-Das1\" target=\"_blank\">[24]</a> and then normalized to range , in order to be comparable with shape reactivities. Histograms suggest that in-line probing signal is more diffuse than that from shape. The fraction of base-pairs in asp-tRNA is which could be used to estimate the threshold shape moderate reactivity.</p>", "links"=>[], "tags"=>["in-line", "probing"], "article_id"=>228090, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.g004", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_In_line_probing_and_shape_/228090", "title"=>"Comparison of In-line probing and shape.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:14:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/557989"], "description"=>"<p>On the -axis, nucleotide positions are displayed, where the algorithm predicts the structure incorrectly. The -axis represents the shape distance to the native structure at the given nucleotide. A shape distance with absolute value indicates an error.</p>", "links"=>[], "tags"=>["glycine", "riboswitch"], "article_id"=>228486, "categories"=>["Biochemistry", "Mathematics", "Biological Sciences", "Physics"], "users"=>["Kourosh Zarringhalam", "Michelle M. Meyer", "Ivan Dotu", "Jeffrey H. Chuang", "Peter Clote"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045160.g009", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Errors_in_the_prediction_of_the_secondary_structure_of_glycine_riboswitch_by_RNAsc_/228486", "title"=>"Errors in the prediction of the secondary structure of glycine riboswitch by RNAsc.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:21:26"}

PMC Usage Stats | Further Information

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Relative Metric

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