dataesr/scientific-paragraphs-categorization huggingface dataset:
Motif analysis For de novo motif discovery we ran the findMotifs. pl tool from Homer software  (33)  on the peak sequences identified by the 4 peak detection programs on each dataset, with the following parameters: -norevopp -len 7 -bits -noknownmcheck 'motif file'. The last parameter is needed to compare the motifs found enriched in the peak sequences with the miRNA sequences. As background we used 3 times the scrambled sequences of the peak sequences (homer default option). To build the 'motif file' we used the program 'seq2profile. pl' from Homer software with the reverse complement sequences of the most expressed miRNAs in each dataset. To select the most confident motifs, we set up a combined score (CS), similar to the one used in  (36) , with the following formula: CS = (-log 10 ( p) -12)/12 + (%seq -5)/100+ (ms -0.
RESULTS Before testing our hypothesis method, we needed to make sure that we have access to good quality Ago2 peaks. Thus, we performed a benchmarking of different publicly available peak detection programs on four illustrative published Ago2 HITS-CLIP datasets  (10, (19) (20)  and one in-house unpublished dataset. In the Supplementary Table  S3  we provide the main features of the datasets used in this study. To perform the analysis, we used the following peak detection programs: CIMS, PIPE-CLIP, Piranha and Pyicoclip (Table 1). Briefly, each replicate from each dataset was processed with the same pipeline (see 'Materials and Methods' section for details). After mapping and collapsing identical reads, replicates belonging to the same dataset were merged. Since each program has its own statistical model to calculate the significance of the identified peaks, for the comparative analysis we arbitrarily decided to select peaks with FDR < 0.
Comparative analysis of the main characteristics of the identified peaks For each dataset, the number of peaks identified was different (Figure  1A ). CIMS always detected much less peaks compared to the other programs, since it identifies peaks by looking for reads having cross-linked-dependent mutations. Indeed, these mutations are rare and reads containing them represent a small portion of the totality of reads  (39, 40). On the other hand, the number of peaks identified by Piranha and Pyicoclip varied to the different datasets: from few hundreds to several thousands, reflecting a higher capability to adapt to the data than the other two programs. These differences between programs and datasets are likely caused by different statistical and mathematical models that lead to different results depending on the level of noise/background of the experiments.
As shown in Figure  1C , many Ago2-peaks mapped in genomic regions other than the 3 UTR and the CDS, as it was already reported in several publications  (12, 36, (41) (42) (43). The variability of the percentage of the peaks mapped in different regions is higher across the datasets than across the programs. For example, although the replicates D and E and the replicates A, B and C of both Chi et al. (10)  datasets originate from the same biological material but processed with two different antibodies, in the replicates D and E all four programs found about 40% of peaks mapped in the 3 UTR, while in the replicates A, B and C only about 25% of peaks. Surprisingly, virtually no peaks in 3 UTR were detected by all programs for the Leung et al. (20)  dataset, while around 20% of peaks mapped in 3 UTR for the in-  house dataset, which was performed in the same cell type and same antibody of Leung et al.
(20). These results indicate how different experimental conditions can have a dramatic impact on the data analysis output. In addition, we performed a pairwise comparison of the genomic positions of the identified peaks by the different programs for each dataset (Figure  2A ). Overall, the set of shared peaks by all the programs was rather poor, which was assessed around 50% in average. As expected, CIMS, which detected much less peaks, found more shared peaks with the other programs, indicating that they represent a subset of peaks identified by others with less stringent default parameters. In a few number of cases, CIMS has even reached an identity higher than 80% with other programs,    19)  analyzed by Piranha. Similarly, Pyicoclip and Piranha reached a good agreement in many datasets.
Conversely, PIPE-CLIP showed the least degree of shared peaks with other programs. Moreover, we calculated the intersection of peaks found by the four programs for each dataset (Figure  2B ). The dataset D and E of Chi et al. (10)  showed the highest number of shared peaks (46 peaks with length >20 nt), while the Leung et al. (20)  dataset showed the lowest one. Therefore, these findings suggest that the detection of different peaks is likely due to different mathematical models employed by the programs. Altogether, this analysis indicated that both experimental and bioinformatics conditions need optimization to obtain more consistent data output across both programs and dataset. These results prompted us to perform a benchmark analysis of the peak detection programs.
Sensitivity and rank accuracy toward canonical miRNA binding sites Next, we asked whether the programs that detected more peaks gained in sensitivity to identify more peaks containing miRNA-binding sites. To address this specific task, we calculated the number of canonical miRNA seed-match occurrences for the top expressed Ago2-loaded miRNAs in each dataset (Supplementary Table  S2 ). As shown in Figure  3A , the additive number of perfect miRNA seed-matches was calculated within intervals of 10 peaks sorted by reads count. The performances of the programs are remarkably different: PIPE-CLIP overall outperformed the other methods showing a higher rate of canonical seed-match discovery in the top ranked peaks. Pyicoclip performances are close to PIPE-CLIP and often the two curves of discovery rate are superimposed especially for the top ranked peaks, while Piranha and CIMS performed worse.
Spatial resolution A further important feature to take into account is the degree of precision to facilitate the location of the binding site. In fact, the length of identified peaks can dramatically determine the accuracy of de novo motif identification and, in case of miRNAs, the identification of noncanonical binding sites, since wider peaks imply the addition of noisy sequences that can overshadow the miRNAbinding signature. To evaluate the spatial resolution of the different peak detection programs, we calculated the following two parameters, (i) the distance between the canonical seed-matches for the top expressed miRNAs in each dataset and the peak centers, and (ii) the distance between the canonical seed-matches and the cross-linked-dependent mutations. Programs with a higher of spatial resolution are expected to have peaks centered on both seedmatches and the cross-linked-dependent mutations.
In Fig-  4A  and Supplementary Figure  S2A  the distance distributions between the seed-matches and the peak centers are shown. Piranha outperforms the other programs for all datasets, except for the A, B and C replicates dataset from Chi et al. (10). Pyicoclip also showed rather narrow distribu-tions, despite the wide distribution of peak lengths (Figure  1B ). CIMS worked very well just for two datasets, even if the peak length is set up at 40 nt, while PIPE-CLIP performed the worse. Similar data were obtained by measuring the distance distributions between the seed-matches and cross-linkeddependent mutations (Figure  4B  and Supplementary Figure  S2B ), showing good and consistent performances only for Piranha and Pyicoclip. Interestingly, we found that the cross-linked-dependent mutation was shifted to the left side compared to the peak center, suggesting that Ago2 crosslinks to the target mRNA immediately upstream the seedmatch sequence.
Overall, for all programs, the positional accuracy of the canonical miRNA binding site was upper for the D and E replicates dataset from Chi et al. (10)  than for the other datasets. On the other hand, for almost all datasets the positional accuracy of the peaks identified by Piranha and Pyicoclip was upper that for the other programs. Therefore, this analysis indicates that the spatial resolution of Ago2 binding is not influenced by the peak length.
Agreement toward high confidence miRNA-binding sites predicted by Targetscan Next, we assessed the specificity of the considered peak detection programs by calculating the level of agreement with TargetScan  (35). TargetScan predicts miRNA-binding sites by searching for the presence of perfect match between 3'UTR sequences and the seed region of miRNAs  (44). We calculated the agreement with TargetScan in each dataset by calculating the percentage of peaks containing TargetScanpredicted binding sites for the top expressed Ago2-loaded miRNAs over the total number of identified peaks by each programs (Figure  5 ). Importantly, because TargetScan only predicts miRNA binding sites in the 3'UTR, this analysis was restricted to peaks located in this region. Overall, Pyicoclip showed the highest percentage of binding sites in common with the TargetScan prediction for three out of five datasets, while PIPE-CLIP for the other two.
The agreement between TargetScan and Piranha peaks is very low, under 10%, except for the dataset of Chi et al. replicates A, B and C. Agreement between CIMS and Tar-getScan is also very poor, except for the dataset of Chi et al. replicates D and E. Finally,  Leung et al. dataset (20)  showed a very poor percentage of common binding sites with Targetscan, which never overcame 2% considering the four programs. A possible source of disagreement can be that TargetScan only predicts perfect seed-pairing located in 3 UTR, while the majority of miRNA-binding sites can be expected to be non-canonicals  (12, 36, 41). We calculated the degree of overlap of the peaks containing the miRNA-binding sites predicted by TargetScan found by the different programs for the same dataset. As shown in Supplementary Figure  S3A , we found that Pyicoclip and PIPECLIP overall share the highest degree of overlap, while Piranha showed a modest overlap with Pyicoclip, but very poor with PIPE-CLIP.
PAGE 10 OF 15 In addition, to evaluate the data reproducibility between two datasets originated from the same biological material, we compared the number of binding sites predicted by Tar-getScan in peaks shared by the two datasets of Chi et al. (Supplementary Figure  S3B ). These two datasets only differ by the anti-Ago2 antibody used to perform the immunoprecipitation. We found that 75% of peaks identified in the 3 UTR by Pyicoclip containing miRNA-binding sites predicted by TargetScan for the replicates A, B and C were also identified in the replicates D and E. However, because in the replicates D and E we found much more peaks containing miRNA-binding sites predicted by Tar-getScan, this dataset only shows about 15% of overlap with the replicates A, B and C. As shown in Supplementary Table  S5 , similar percentages were found for each miRNA, except for miR-410/344de/344b-1-3p, miR-125a-5p/125b-5p/351/670/4319 and miR-193/193b/193a-3p.
Identification of miRNA binding sites through de novo motif finding and RNA duplex prediction Since canonical binding sites do not represent the totality of miRNA-binding sites  (12, 36, 41) , we propose a novel strategy to find and characterize potential binding sites. We reasoned that Ago2 peak sequences would potentially show enriched motifs reverse-complementary to any portion of the sequence of the most expressed miR-NAs. Based on this assumption, we have developed a computational pipeline, called 'miRBShunter' (Supplementary Figure  S4 ). miRBShunter (freely downloadable from https: //github. com/TrabucchiLab/miRBShunter) is fed with the coordinates of the Ago2 peaks and the sequence of the most expressed Ago2-loaded miRNAs. miRBShunter extracts peak sequences, annotates on the genome and converts miRNA sequences in Homer format by using Homer software  (33).
Homer software is also used to identify 7 ntde novo enriched motifs in Ago2 peaks  (33)  and to map them onto the reverse-complement sequence of miRNAs (for computational parameters, please see the 'Materials and Methods' section). To select only the most confident RNA motifs, we used a CS based on the following parameters calculated by Homer software: (i) -log 10 (P-value) assigned to the motif; (ii) percentage of target sequences in which the motif matches; and iii) the score of the matching of the motif on the miRNA sequence. Then, we classified as 'significant' all the motifs with CS > 0 and as 'best candidates' the ones with CS ≥ 1. After selecting the most confident motifs, miRBShunter maps them onto the peak sequences using the tool FIMO  (37). We tested de novo motif finding for the identified Ago2 peaks by the four programs in the five datasets here considered.
As showed in Figure  6 , Pyicoclip found the highest number of motifs with CS ≥ 1 in two datasets and with CS > 0 in three datasets, indicating that this program finds the highest number of peaks containing potential binding sites for miRNAs. Among the motifs with CS ≥ 1, we have found several canonical miRNA-binding sites, however the majority of the motifs were non-canonicals. Noteworthy, peaks of the dataset of Chi et al. replicates D and E showed the strongest enrichment for canonical miRNA-binding sites, especially for the peaks found by Pyicoclip (Supplementary Table  S6 ). In addition, we tested de novo motif finding by MEME software for the identified Ago2 peaks by the four programs in the five datasets. MEME software found just a small subset of the motifs found by Homer software (CS ≥ 1) and only for the D and E replicates of Chi et al.
(10)  and Karginov et al. (19)  datasets (data not shown), indicating that for our analysis Homer software is more appropriate. Thereafter, the motifs have been identified, miRBShunter uses RNAduplex program from the Vienna suite  (38)  to predict RNA heteroduplexes formed by the association between Ago2 peaks and the most expressed Ago2-loaded miRNAs. miRBShunter filters out the miRNA::RNA duplexes that do not bear any previously identified motifs by Homer software or with CS < 1. Finally, the resultant miRNA::RNA duplexes are ranked by a DS based on the following parameters: (i) the free energy of the miRNA::RNA heteroduplex given by RNAduplex tool  (38) ; (ii) the pairing degree between the full length miRNA and the target RNA sequence; (iii) the pairing degree between the miRNA motif and the target RNA sequence; (iv) the pairing degree between the miRNA seed and the target RNA sequence; and (v) the number of bulges in the seed region.
Experimental validation of miRBShunter To validate the miRNA-binding sites found by miRB-Shunter, we focused on the in-house dataset. Because Pyicoclip outperformed the other programs, the validation was carried out on the results obtained with this program. Briefly, we feed miRBShunter with the peak coordinates in CDS and 3 UTR as primary substrate of miRNAs and the sequence of the 22 most expressed Ago2-loaded miRNAs in the dataset. We found 115 potential miRNA::mRNA duplexes, including 40 canonicals and 75 non-canonicals (46 showing a seed-like motif whereas 29 outside the miRNA seed) (Figure  7A  and Supplementary Table  S7 ). To assess experimentally the validity of the identified miRNA-binding sites, we performed a gene reporter assay. Briefly, we sorted the list of 115 miRNA-target RNA heteroduplexes by decreasing value of DS and randomly selected 9 miRNA-target RNA heteroduplexes (Supplemen- tary Table  S7 ).
To prove that both method and the associated tool are adaptable to all CLIP-seq variants, we tested it on an Ago2 PAR-CLIP dataset from human embryonic stem cells  (21). We ran the pipeline on the peaks that map in the 3 UTR and the CDS downloaded from StarBase  (22)  and considered the 20 most expressed Ago2-loaded miRNAs  (21). Briefly, we found 826 miRNA:mRNA target duplexes, 215 canonical and 611 non-canonical miRNA binding sites, which include 357 seed-like and 254 outside the seed binding sites (Supplementary Figure  S5A  and Table  S6 ). Sixty-six percent of the identified binding sites contained either the canonical or the seed-like binding sites for stem cell specific miR-302 family. Importantly, 22% of the miR-302-binding sites identified by miRBShunter are in common with those identified in the original publication  (21) , supporting the validity of our approach.
By gene reporter assay, we validated five binding sites out of seven (Supplementary Figure  S5B  and Table  S6 ). As expected, both canonical and noncanonical miRNA binding sites were experimentally validated. Therefore, these data confirm the adaptability of our method to any Ago2 CLIP-seq dataset. Noteworthy, all the validated miRNA-binding sites in both datasets are top ranked in the DS list of potential heteroduplexes, confirming the validity of our method/approach. In conclusion, we have demonstrated that miRNAbinding sites containing either seed-like or outside the seed region motifs are bona-fide miRNA-binding sites actively silenced. These data confirm the validity of our method to find in an unbiased fashion both canonical and noncanonical binding sites for miRNAs. Altogether, we can conclude that higher CS and DS are associated with increased chances to identify bona-fide miRNA-binding sites.
Since a key step in the CLIP-seq data analysis workflow is peak detection, we have performed a quantitative and qualitative analysis of the performances of four publicly available peak detection programs on four published Ago2 HITS-CLIP datasets  (10, (19) (20)  and one in-house unpublished dataset. To our knowledge, this is the first time that a benchmarking of Ago2 CLIP-seq peak detection programs has been performed. The peak detection programs we considered include PIPE-CLIP, Pyicoclip, Piranha and CIMS. Our analysis shows that these programs found a different number of peaks on the same dataset with a poor overlap of peaks demonstrating that different strategies and mathematical models used by these programs lead to quantitative and qualitative differences of the output. The benchmarking was performed on the identification of seedmatch occurrences and agreement with TargetScan predicted miRNA-binding sites for the top expressed Ago2loaded miRNAs in each dataset.
To test the reliability of the miRNA-binding sites found by the four programs, we checked whether they are highconfidence binding sites predicted by TargetScan. In agreement with other analyses, Pyicoclip outperformed others showing around 15% of agreement for single miRNAs and around 40-50% considering all miRNAs by datasets. Importantly, significant differences were observed in datasets originating from the same biological material but treated with different experimental conditions. In particular, the two datasets of Chi et al. (10)  originated from the mouse neocortex were obtained with two different antibodies to immunoprecipitate Ago2. In fact, we detected many more peaks bearing high-confidence miRNA-binding sites predicted by the TargetScan in the replicates D and E compared to the replicates A, B and C.
To further validate our hypothesis, we applied our method to the 3 UTR-or CDS-located peak set obtained by Pyicoclip from the in-house dataset and obtained a subset of 115 miRNA canonical and non-canonical heteroduplexes having a 7 nt-de novo motifs with CS ≥ 1, corresponding to seven motifs matching with eleven miRNAs among the 22 most expressed in P19 cells. In order to rank them, we set up a DS based on different parameters, including biophysical, architectural (presence or absence of mismatches or bulges) and, finally, position of the pairing, based on the assumption that the miRNA seed sequence has much more weight in defining miRNA-binding sites and silencing activity. In concordance with earlier findings  (3, 4) , we found a diversity on miRNA targetome comprising both canonical and non-canonical mRNA binding activity.
Moreover, we found that many of the endogenous highly expressed miRNAs exhibited consistent seed-like or outside the seed non-canonical targeting preferences, such as miR-181b and miR-99a, respectively. However, miR-302b mainly acts through canonical binding sites. To prove the adaptability of miRBShunter to any Ago2 CLIP-seq PAGE 14 OF 15 dataset, we successfully ran our pipeline on a publicly available Ago2 PAR-CLIP dataset from human stem cell  (21). Importantly, experimental validation of our method was carried out with gene reporter assay for both datasets. In total, we showed that 11 new miRNA::mRNA target interactions are bona-fide miRNA binding sites showing silencing activity. Among them, for the in-house dataset, we validated three canonical binding sites for miR-302b-3p and three non-canonical binding sites, including two seed-like or one outside the seed motif for miR-302b-3p, miR-181b and miR-99a, respectively.
Whereas, for the public PAR-CLIP dataset from human stem cells  (21) , we validated three canonical binding sites for miR-302b-3p and two noncanonical binding sites outside the seed position for miR-106b-5p. These data demonstrate the validity of our method to consider the entire miRNA sequence as potentially active for binding and silencing target mRNAs. Presently, miRBShunter works for mouse and human miRNA-mRNA pairs, but it can be further extended to other species. In conclusion, our method illustrates the advantage of considering the de novo identification of enriched motifs from Ago2 CLIP-seq peaks to find miRNA matches for highly expressed Ago2-loaded miRNAs. Alternatively to other methods  (15) (16) (17) (18) , we did not restrict the search of motifs to the miRNA seed sequence, but we considered the unbiased possibility for miRNAs to bind to any portion of the sequence.
We believe this method provides a more global aspect of the role played by miRNAs in a determined cell type or tissue system, providing a comprehensive list of canonical and non-canonical miRNA targetome and defining the gene expression program under the control of the miRNA pathway. Furthermore, this method can be usefully adopted when differential CLIP-seq cannot be applied, such as in human biopsies in which knockout or knockdown of specific miRNAs or RBPs is not doable. Finally, we have developed a new tool, named miRB-Shunter, that implements the pipeline herein described. It takes as input the coordinates of the enriched peaks and the Ago2-loaded miRNAs expressed in CLIP-seq experiments and finds exact binding sites for these miRNAs in the peak sequences. We believe that miRBShunter is a valuable resource for researchers working on miRNA biology due to its easy applicability and reliability of the results.
FUNDING FRM [ DEQ20140329551  to M. T. ];  ANR  through the ' Investments for the Future ' [ ANR-11-LABX-0028-01  ( LABEX SIGNALIFE ) to M. T. ]; FRM [ ING20140129224  to E. R. ]. France Genomique  to the  UCA Genomix platform. Funding for open access charge: Inserm.
e71 9 PAGE 8 OF 15 ABFigure 4. 98154 Figure 4. Spatial resolution of canonical binding sites identified in peaks by the indicated programs for the indicated datasets. (A) Boxplot (upper panel) or frequency (lower panel) of the distance distribution between seed-matches and peak centers. Distances in frequency plots are represented from -40 to 40 nt for a better visualization. (B) Boxplot (upper panel) or frequency (lower panel) of the distance distribution between seed-matches and the most frequent mutations in the peaks. Distances in frequency plots are represented from -40 to 40 nt for a better visualization.