dataesr/scientific-paragraphs-categorization huggingface dataset:
Non-canonical base pairs were removed, and a non-pseudoknotted secondary structure was extracted as the maximum subset of non-crossing base pairs  (46). Probing data were experimentally generated, as described in the next section, using a comprehensive set of conditions covering some of the popular probing reagents and SHAPE technologies. We also considered the presence/absence of Mg 2+ , both to assess the capacity of IPANEMAP to recover tertiary interactions, and to assess the induced discrepancy on probing profiles and pseudo-Boltzmann ensembles. Cheng dataset. Starting from the assumption that a functional structure should be preserved during evolution, we wanted to assess the agreement that might exist between probing data profiles for a set of RNA mutants. We considered DMS probing data, generated by (  47 ) through systematic pointwise mutations, for the Lariat-capping ribozyme (equivalent to DMS-MAP MG ILU in the nomenclature below).
5 pH, 100 mM KCl, in presence or absence of 5 mM MgCl 2. After a 10 minutes incubation at 37°C, RNAs were treated with 2 mM of SHAPE reagent or DMSO (negative control) and incubated for 2 (BzCN), 5 (1M7), 30 (NMIA) or 60 (NAI) minutes at 37°C. Modified or unmodified RNAs were purified by ethanol precipitation and pellets were resuspended in 10 µl of water. Modifications were revealed by reverse transcription using 5' fluorescently labelled primers (D2 or D4 WellRED, Sigma Aldrich 5' CTG-TGA-ACT-AAT-GCT-GTC-CTT-TAA 3') and M-MLV RNAse (H-) reverse transcriptase (Promega) as previously described  (48) , with only minor modification of the originally described SHAPE protocol  (5). cDNAs were separated by capillary electrophoresis (Beckman Coulter, Ceq8000). Data were analyzed using the QuSHAPE (49) software.
5' CTT-CAT-AGC-CTT-ATG-CAG-TTG-CTT-TTT-TTT-TTT TTT-TTT-TTT-GAT-TGT-CTT-GGG-ATA-CCG-GAT 3' For 1M7-MAP MG IL -3D, the denaturation step was repeated three times : After a 1 minute incubation at 95°C, 10 mM of 1M7 was added and the mix was incubated 1 minute at 95°C. This step was repeated 3 times for the samples labeled 3D. For NMIA-MAP IT and NMIA-MAP MG  IT , experiments were conducted as described above, except that the NGS library preparation was adapted to Ion Torrent sequencing. Sequences were mapped and analyzed with ShapeMapper2 (50) for 1M7-MAP MG  IL -3D and 1M7-MAP MG IL -3D. For the other conditions they are mapped with a script based on ShapeMapper  (27) , but adapted to Ion Torrent output files. The corresponding data sets are named after the probe followed by MAP. The presence of MG as a superscript indicates the presence of MgCl 2 , while the subscript IL (Illumina) or IT (Ion Torrent) specifies the sequencing technology.
Assessment of statistical significance Following Xu et al. (52) , we assessed the statistical significance of observed changes in predictive performances using a two-tailed Student t-test (unequal variance) with usual type I error rate α = 5%. When comparing performances within a given dataset, we used the paired version of the test. Intuitively, the t-test estimates the probability that an observed change in performance can be attributed to chance alone. It considers the hypothesis H 0 that the two distributions have equal mean and computes a P-value, i. e. the likelihood of the observed data under H 0. If the P-value is lower than α, then H 0 is deemed refuted, and the observed difference in performances is considered significant.
Implementation IPANEMAP was implemented in Python 2. 7+, and mainly depends on the RNAsubopt software in the Vienna package  (31) , and scikit-learn  (39). IPANEMAP is free software distributed under the terms of the MIT license, and Table  1. Dot-bracket representations of the native structure and IPANEMAP predictions for the glycine riboswitch sequence of the Cordero et al. (53)  data set, supplemented with an increasing collection of probing conditions. Please note that, throughout the manuscript, by "conditions" we mean a combination of the nature of the probe, the probing technology, the ions present, the temperature, or any other "environmental" variation Positions with green and red backgrounds indicate correctly and incorrectly predicted base pairs. can be freely downloaded, along with a collection of helper utilities, from: https://github.
Considering multiple probing conditions appears to improve the quality of predictions Since IPANEMAP supports any number of probing profiles, we considered the Cordero et al. (19)  dataset, further described in the previous section. It consists of 6 RNAs of known structures, for which reactivity profiles are available for the CMCT, NMIA and DMS reagents. We executed IPANEMAP with default parameters on each sequence and any subset of the three available conditions. The centroid secondary structure associated with the largest probability cluster was considered as the final prediction. Predictions were evaluated in term of the geometric mean (GM) metric, for the sake of comparison with previous studies  (33). As a control experiment, we also report RNAfold predictions in presence/absence of probing data, both in energy minimization (MFE) and maximum expected accuracy (MEA) modes.
5%), while CMCT+NMIA greatly outperforms CMCT alone (70% → 76. 9%), almost matching the performance of the NMIA alone (77. 1%). It is also worth mentioning that NMIA+CMCT, combining the best and worst conditions, achieves a better combined performance than DMS+NMIA, the two best mono-probing conditions. Remarkably, the combination of the three conditions leads to the best overall predictions, averaging 78. 5% GM. This seemingly improves by 8. 5%, 2. 7% and 1. 4% over the average predictions achieved using CMCT, DMS and NMIA respectively. Table  1  illustrates the incremental refinement of IPANEMAP predictions for a glycine riboswitch upon increasing the number of considered probing conditions. However, these observations merely suggest an improvement, as statistical significance cannot be established on such a small dataset.
Still, they suggests some level of complementarity between probing conditions, as already suggested by recent analyses  (13, 14, 16, 54)  and supported by further analyses in this paper. IPANEMAP performs comparably to state-of-the-art predictive methods when informed by a single profile We assessed the predictive capacities of IPANEMAP in a classic setting where a single probing condition is available, and compared its performances against RSample software recently introduced by Spasic et al. (33) , RSample relies on a sampling/clustering method developed independently from the current work. It was shown to perform comparably as a comprehensive collection of state-of-the-art methods in probing-guided structure prediction, including RME  (55) , RNAprob  (56) , RNAprobing (3), RNAsc  (29)  and the fold utility from the RNAStructure suite  (57).
We considered the Hajdin dataset  (43) , which consists of 24 RNAs of lengths ranging from 47 to 500 nts, all believed to fold into a unique documented conformation. We used the default numbers of sampled structures for RSample and IPANEMAP, namely 10 000 to compute correction factors and 1 000 for the clustering phase. For all RNAs, a single structure was returned by both software. Note that the Pareto-optimality always implies a single returned cluster/structure for IPANEMAP in a mono probing setting. We computed and report in Figure  3  the individual Geometric Means (GM) of predicted structures. Sensitivity and PPV can be found in Supp. Table  S4 , while Supp. Table  S5  reports the stability of predictions over 10 independent runs of IPANEMAP. IPANEMAP appears to perform similarly, or even slightly favorably in comparison with RSample.
It averages 80. 1% GM, and reports more accurate structural models than its competitor for 14 RNAs in the Hajdin dataset  (43) , returns structures equally as good on 2 RNAs, and is dominated by RSample for 8 RNAs. However, its modest average improvement of approximately 1. 5% over the dataset is not statistically significant, according to a two-tailed paired T-test. Those results support the notion that IPANEMAP represents a competitive alternative to state-of-the-art methods for single reactivity profiles.
For each pair and triplets of conditions, we executed IPANEMAP using 1 000 structures per condition, and considered the first returned structure. We then computed the associated MCC, and compared it with the MCC of the worst performing condition (Min), best-performing condition (Max) and average MCC over the single conditions experiments. A summary of the results over pairs is reported in Figure  7  (details in Supp. Table  S6 ). A superficial inspection of the resulting MCCs appears to confirm the conclusions reached on the Cordero et al. (53)  dataset. Indeed, considering two conditions led to predictions whose quality fall between the worst and the best one, while being generally close to the average. More precisely, the average MCC value of pair-informed predictions remained at 70. 7%, improving only by 0.
Supplementing wild-type profiles with random single mutants increases prediction quality In this final analysis, we tested the capacity of IPANEMAP to extract and exploit complementary information produced by the systematic probing of single-point mutants using the Mutate-and-Map (MaM) protocol  (23). The joint analysis of several mutants may appear error-prone, as single point mutants are expected to adopt different structures than the wild-type sequence (WT), an expectation that is at the core of downstream analyses  (23). However, such changes are typically local, and the robustness of IPANEMAP to outlier conditions nourishes the hope that the benefits of including independently-produced conditions will outweigh the cost of noise introduced by local changes. We generated 100 uniformly-distributed subsets consisting of 1, 2, 3 and 10 mutants extracted from the Cheng dataset (47), which we each supplemented with the WT sequence.
CONCLUSION AND DISCUSSION We introduce IPANEMAP, an integrative method for the prediction of secondary structure models from multiple probing profiles. Based on the simple premise that good structural models should be thermodynamically stable and supported by most probing experiments, it uses a combination of stochastic backtrack in the pseudo-Boltzmann ensemble, coupled with a structural clustering to elect dominant conformations. This strategy is fast, generally stable, and leads to predictions that clearly benefit from the availability of multiple probing profiles, as demonstrated by detailed analyses performed of four datasets. Our analyses reveal that integrating multiple profiles of at least three conditions allows to mitigate the effect of outlier conditions and, at times, to produce better secondary structure models than the one inferred only from the most accurate profile.
e. no compatible structure) across conditions, while conservative cutoffs will lead to sparse constraints, practically wasting most of the probing-derived information. In fact the tediousness, and suboptimality, of the manual determination of those cutoffs while modeling HIV-1 structural elements  (48) , was one of the main motivation behind the present work. Current limitations of IPANEMAP include the lack of an explicit support for pseudoknots, which will be addressed in a future version, using polynomial-time dynamic programming  (58) , or a iterative heuristic alternative  (43). The inherently stochastic nature of the sampling scheme may also be challenging for certain types of analysis. However, despite its stochastic foundations, IPANEMAP is typically stable in its predictions, as can be seen in Figure  9  for all probing datasets produced over our DiLCrz case study.
Interestingly, the most notable exception to the general stability is observed for the probing-free prediction, consistent with the widespread interpretation of SHAPE-induced pseudo-potentials as focusing the predictions onto a subset of the Boltzmann ensemble. A similar behavior is observed for the other datasets considered in our study (see Supp. Tables  S2  and S5 ). Finally, the current clustering method induce a growth of time and memory requirements that scales quadratically with the number of samples. While this allows a joint analysis of a dozen of reactivity profiles in less than an hour on a personal computer, we expect the ability to push the number of samples will lead to better, even more reproducible, predictions. To that purpose, we will explore embedding techniques and linear-time community detection algorithms as alternatives to MBkM  (38).
and b = -0. 4. Those values were halved in comparison to those recommended by Deigan et al. (28), following a grid search optimization on the Cordero dataset, and based on the rationale that lower absolute values for pseudo-energy bonuses increase the expected overlap between pseudo-Boltzmann ensembles. Those pseudo energy contributions effectively guide our predictions towards a subset of structures that are in good agreement with probing data. For each condition d in D, we use the soft constraints framework (26) of the RNAfold software (31) to produce a random (multi)set S d of M structures in the pseudo-Boltzmann ensemble.
ACKNOWLEDGEMENTS The authors wish to express their gratitude to:  Ronny Lorenz  for developing a probing-informed version of RNAsubopt, allowing to sample the pseudo-Boltzmann ensemble;  Nathalie Chamond  for helpful suggestions;  Benoit Masquida  for the gift of DiLCrz plasmid and helpful discussions about protocols and DiLCrz structure; and an anonymous reviewer for helping us detect an error in our execution of RSample.
3 3 and b = -0. 4. Those values were halved in comparison to those recommended by Deigan et al. (28), following a grid search optimization on the Cordero dataset, and based on the rationale that lower absolute values for pseudo-energy bonuses increase the expected overlap between pseudo-Boltzmann ensembles. Those pseudo energy contributions effectively guide our predictions towards a subset of structures that are in good agreement with probing data. For each condition d in D, we use the soft constraints framework (26) of the RNAfold software (31) to produce a random (multi)set S d of M structures in the pseudo-Boltzmann ensemble.
Stochastic variability of IPANEMAP predictions for DiLCrz dataset. MCC of predicted structures over 10 runs of IPANEMAP, in presence/absence of Mg 2+ , using both SHAPE CE and MaP protocols for a collection of reagents. SHAPE CE SHAPE MaP Mg 2+ free With Mg 2+ Mg 2+ free with Mg 2+ 90% 80% 70% 60% Reagents No exp (-) No Probing 1M7-CE BzCN-CE NAI-CE NMIA-CE 1M7-CE MG BzCN-CE MG CMCT-CE MG DMS-CE MG NAI-CE MG NMIA-CE MG 1M7-MaP IL 1M7-MaP IL -3D NMIA-Map IT 1M7-MaP IL MG 1M7-MaP IL -3D MG NMIA-Map IT MG Figure 9.
Motivation: Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA-target regulatory interactions in the different network models.
We built two different collections of networks, each based on a prior obtained from a popular resource of miRNA target predictions, either TargetScan  (Agarwal et al. , 2015)  or miRanda  (John et al. , 2004) -two resources that have been widely used due to their user-friendliness, maintenance and options to directly download all target predictions for a given species  (Peterson et al. , 2014). We extracted tissue-specific gene regulation by miRNAs, as well as miRNA functions from these two collections of networks. We found that PUMA consistently captures tissue-specific gene regulation by miRNAs, even when using different input sources of target predictions. Finally, we provide a new resource of tissue-specific functions of miRNAs identified with PUMA and validate predicted tissue-specific functions in a database of disease-associated single nucleotide polymorphisms (SNPs) in miRNA target sites.
GTEx RNA-Seq data We downloaded RNA-Seq data from the GTEx project (version 6. 0, phs000424. v6. 1, released October 5, 2015) from dbGap (approved protocol #9112) and used our previously described method YARN  (Paulson et al. , 2017)  to perform quality control and data normalization. YARN removed samples with sex-misidentification and merged related sub-tissues, resulting in a dataset of 9435 gene expression profiles in 38 tissues from 549 individuals. We used default settings in YARN to perform gene filtering for mRNAs (retaining genes with >1 CPM in at least 18 samples), but included expression values for all pre-miRNAs. We then used YARN to perform tissueaware normalization using qsmooth  (Hicks et al. , 2018). For more information see Supplementary Methods.
Regulatory network reconstruction We downloaded miRNA target predictions from TargetScan  (Agarwal et al. , 2015)  and miRanda  (John et al. , 2004). We selected miRNAs and target genes that were present in both datasets, for which expression levels were available in the GTEx RNA-Seq data (see Supplementary Methods). We used the MATLAB version of PUMA to integrate target predictions from TargetScan and miRanda with gene expression data from each of the 38 GTEx tissues. In total, we modeled 76 gene regulatory networks, two for each tissue. The reconstructed networks are available on Zenodo (doi: 10. 5281/zenodo. 1313768; https://tinyurl. com/puma-gtex).
Community structure analysis to identify sets of related tissue/miRNA-GO terms We selected highly significant (FDR < 0. 001) and positively enriched (ES > 0. 65) associations from these analyses and converted these scores into a binary matrix. We then used fast-greedy community detection  (Clauset et al. , 2004)  on this matrix to cluster the data and to identify communities or network modules that share tissuespecific regulatory patterns. We used the Jaccard index to compare nodes (miRNA/tissues and GO terms) that belonged to communities that included at least five GO terms in either the TargetScan or the miRanda networks. We used word clouds to visualize the tissuespecific functions of miRNAs in these communities (see Supplementary Methods).
Tissue-specific gene regulation by miRNAs We started by reconstructing miRNA-target gene genome-wide regulatory networks for a large collection of human tissues. We downloaded RNA-Seq data for 54 different tissues (including three different cell types) using Bioconductor package YARN  (Paulson et al. , 2017). Within YARN, we performed quality control and normalization of the data, merging tissues with similar expression profiles (see Section 2). This resulted in a gene expression dataset that included 9435 samples across 38 tissues. We limited network reconstruction to only those genes and miRNAs that were expressed and which appeared in the TargetScan and miRanda prior, leaving 16 161 genes and 621 miRNAs; these 621 target miRNAs corresponded to 643 regulators in the prior networks (see Supplementary Methods).
miRNA functions correspond to disease-associated SNPs To further validate our findings, we integrated the tissue-specificity scores with miRdSNP, a database of SNPs in the 3 0 UTR of human genes  (Bruno et al. , 2012). To do this, we downloaded the miRdSNP database, converted and matched miRNA names, and intersected miRNAs and target genes present in miRdSNP with those present in our regulatory networks. We then matched diseases listed in miRdSNP to GTEx tissues (manual curation, see Supplementary Table  S2 ). This left us with 24 GTEx tissues for which miRNA-target gene associations with disease were available (a total of 591 miRdSNP associations). For each of these miRNA-target gene associations, we obtained the miRNA's top predicted tissue-specific function from our Shiny app (settings Àlog 10 ðFDRÞ > 0:8, highest ES).
Discussion In this article, we describe PUMA, a new method to model gene regulation by miRNAs. PUMA integrates target gene co-expression information with initial target predictions, which can be obtained from (but are not limited to) resources such as TargetScan or miRanda. We applied PUMA to a large-scale RNA-Seq dataset from GTEx to identify tissue-specific regulatory patterns of miRNAs. We modeled two different collections of tissue networks by integrating gene expression data from GTEx with two prior datasets-target predictions from TargetScan and miRanda, two of the most widely used miRNA target prediction resources. We found that tissuespecific gene regulation by miRNAs was reproducible for most tissues, except for testis. Potentially, the aberrant gene expression pattern in testis is, at least in part, caused by differential regulation by miRNAs.
While tissue-specificity of gene regulation was reproducible for different types of edges, it was highest for edges that were predicted in both priors, indicating that compendium-like approaches using the intersection of different miRNA target prediction resources as prior data for network modeling could result in more accurate results. Further evaluation would be needed to determine whether combining target prediction resources would help the accuracy of PUMA. While this is beyond the scope of this study, it could be a future extension of the method. We performed high-throughput GSEA on the tissue-specific targeting profiles of each of the miRNAs to characterize tissue-specific regulation of biological processes. We found that tissue-specific regulation of biological processes by miRNAs was highly similar in the networks modeled on different priors.
LIONESS uses linear interpolation to extract networks for individual samples from an aggregate network model and has been shown to reconstruct reproducible networks on datasets with as few as 20 samples  (Kuijjer et al. , 2019). Using PUMA with LIONESS to extract single-sample networks would allow us to perform more robust statistical tests of differences between biological states and might allow identification of new miRNA-based disease subtypes and identify miRNAs that have a subtype-specific regulatory effect. Gene regulation is a complex process involving multiple factors, including both transcription factors and miRNAs. Understanding these regulatory processes, and how they change between phenotypes, helps elucidating the network changes that occur between health and disease. Identifying genes that are differentially regulated, but not necessarily differentially expressed, can help us to understand the likely potential that a given biological state has to respond to changes, including drug treatment or disease progression.
Funding M. L. K. was supported by a  Charles A. King Trust Postdoctoral Research Fellowship Program ;  Sara Elisabeth O'Brien Trust ;  Bank of America ; N. A. ; co-Trustees and by grants from the  Norwegian Research Council ;  Helse Sør-Øst  and the  University of Oslo  through the  Centre for Molecular Medicine Norway  (NCMM). M. F. was supported by the project  Investment for the future AMAIZING ANR-10-BTBR-01  ( ANR-PIA AMAIZING ). J. Q. was supported by a grant from the  US National Cancer Institute  [R35CA220523]. K. G. was supported by a grant from the  National Institutes of Health  [K25HL133599].