dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of high-throughput sequencing reads All the scripts used for this analysis are available at the following repository https://gitbio. ens-lyon. fr/LBMC/RMI2/ Sequencing reads were split with respect to their 5′ in-line barcode sequence. After this, 5′barcode and 3′-adaptor sequences were removed from reads using FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/index. html). Reads were mapped to a custom set of sequences including mouse 18S, 28S, 45S, 5S, and 5. 8S rRNA, tRNAs and the ERCC Spike-In sequences using Bowtie/2. 2. 4  45  with the following parameters "bowtie2 -t --fast". For RNA sequencing samples, reads that failed to map to this custom set of sequences were next aligned to the mouse mm10 assembly and the gencode vM7 annotation using TopHat 2 (v2. 0. 13)  46  with the following parameters "tophat2 --bowtie1 (bowtie version 1.
1. 1. 0) --library-type fr-secondstrand --b2-sensitive -i 30 -m 1 -g 10 --max-coverage-intron 1000000". Read counts on all transcripts of interest were obtained using the HTSeq count package  47  with the following parameters "htseq-count -f sam -r pos -s yes -a 10 -m intersection-nonempty". For ribosome profiling samples, reads that failed to map to this custom set of sequences were next aligned to the mouse mm10 assembly and the gencode vM7 annotation using TopHat 2 (v2. 0. 13)  46  with the following parameters "tophat2 --library-type fr-secondstrand --b2-sensitive -i 30 -m 1 -g 10 --max-coverage-intron 1000000". Read counts on gencode vM7 appris principal transcripts 1 were obtained using the HTSeq count package  47  with the following parameters "htseq-count -f sam -r pos -s yes -a 10 -m intersection-nonempty".
Transcript database creation The mouse transcript database was generated using the genome assembly and sequences files gencode. vM23. annotation. gff3 and GRCm38. p6. genome. fa obtained from https://www. gencodegenes. org/. A SQLite database was first generated using the python package GffUtils (https://github. com/daler/gffutils). Then a csv file was generated for each transcript with an associated coding sequence in order to collect all available components or attributes (UTRs, CDS, introns, exons, associated gene information) and their basic properties (genomic start/end points, sequence, length, GC percentage). When available, the APPRIS level of each transcript was also recovered. To avoid retaining transcripts with aberrant features, an initial quality control was performed. The quality control primary filters included the presence of a canonical initiation (ATG, or CTG) and termination (TAA, TAG, or TGA) codons in the 5' and 3' extremities of the CDS sequence respectively.
Functional analysis upon cell activation Functional enrichment analysis was performed with David  48, 49  (version 6. 8, https://david. ncifcrf. gov/summary. jsp) selecting genes with an adjusted p-value < 0. 05 in the differential gene expression and differential translation analysis. Plots were generated with Revigo 50 (http://revigo. irb. hr/) using as inputs GO Terms with an adjusted p-value < 0. 01 as calculated by the David software.
Random Forest models First, the gene-set of interest was reduced to the expressed genes based on the gene normalized read counts of 3h Triptolide libraries (~5000 genes in resting and ~6000 genes in activated T-cells). Of these, only genes with completed observations in all tested parameters were kept. Dataset was then divided in a train and validation dataset, composed of respectively 70 % and 30 % genes randomly chose. Regression Random Forest models were built using R (version) with the caret package (version) using the command train(predict ~. , data = train_set, method = "ranger", importance = 'impurity'), where "predict" refers to the predicted parameters (TDDindex or TIDindex). Briefly, the train function re-runs the model over 25 bootstrap samples and across 3 options of the number of randomly selected predictors at each cut in the tree (mtry parameter).
Contributions B. C. M, M. J. M and E. P. R conceived the study and designed all experiments. B. C. M, E. P. R and A. B performed experiments, with technical assistance from L. G. E. L performed most bioinformatics analyses with help from A. C for sequencing processing of sequencing reads and codon usage calculation, D. C for the binning plot strategy and transcript database creation, F. A for G-quadruplex analysis and L. M for statistical analysis of generated data. E. P. R wrote the manuscript with contributions from all authors.
Acknowledgments We thank  Wendy Gilbert  for helpful comments during the 2019 RNA meeting. We also thank  Vincent Vanoosthuyse ,  Mélanie Wencker ,  Christelle Morris  and  RMI2  lab members for manuscript proofreading. We gratefully acknowledge support from the  PSMN (Pôle Scientifique de Modélisation Numérique)  of the  ENS de Lyon  for the computing resources. We thank the members of the LBMC biocomputing center for their involvement in this project. This work was funded by the  European Research Council  ( ERC-StG-LS6-805500  to E. P. R) under the European Union's  Horizon 2020 research  and innovation programs, by  Fondation FINOVI  (to E. P. R) and by the  Howard Hughes Medical Institute  (to M. J. M).
TEs Display Strong Sex-Biased Patterns of Expression To understand the relationship between the TE load and their activity in the germline, we sequenced testis and ovary transcriptomes for two populations of D. melanogaster (Gotheron and Zimbabwe) and two populations of D. simulans (Fukuoka and Nairobi). We mapped these population transcriptomes on the Repbase Drosophila TE data set and first computed intraspecific variation of TE transcription. Between populations of the same species, we detected only a limited number of differentially expressed families (fig. 2A  and B ). By contrast, comparing TEs between species revealed that 62% of the TE families present in both species (n ¼ 232) were differentially expressed (fig. 2C ). A principal component analysis (PCA) on all data revealed two main axes explaining 61% of the total variance between transcriptomes (supplementary fig.
We computed the length of overlap in our data set (Antoniewski 2014) and detected global significant ping-pong signatures in both gonads of both species. However, comparing the overlap signals for a set of TE consensus shared by the two sibling species, we noticed that ping-pong signals were predominantly higher in ovaries than in testes (fig. 3D ). TE overlap signatures in D. simulans testes are stronger compared with D. melanogaster testes (fig. 3C ), suggesting that the pingpong mechanism is more efficient in D. simulans testes. Consistent with this observation, TE families with higher ping-pong signal in testes than in ovaries (fig. 3D ) are more important in D. simulans (11 out of 40) compared with D. melanogaster (2 out of 40). Using our RNAseq data, we further compared male and female gonad patterns of expression (fig.
Sequencing Libraries were prepared and sequenced by the IMAGIF sequencing platform (Gif-sur-Yvette-France) on an Illumina Hiseq 1000 instrument, with a TruSeq SR Cluster Kit v3-cBot-HS (Illumina) and a TruSeq SBS v3-HS-50 cycles Kit (Illumina), using a single read 50-bp recipe. Libraries were pooled in equimolar proportions and diluted libraries to a final concentration of 12 pM, according to Illumina recommendations. The data were demultiplexed using the distribution of CASAVA software (CASAVA-1. 8. 2)  (Mortazavi et al. 2008). The quality of the data was checked with the software FastQC 0. 10. 1 (available online at: http://www. bioinformatics. babraham. ac. uk/projects/fastqc).
Read Mapping and Differential Expression Analysis We filtered single-end reads from each library with UrQt software  (Modolo and Lerat 2015) ; we retained only high quality reads (phred score > 33) for the analyses. We remove Illumina adapters using scythe software (https://github. com/najoshi/) and kept reads with a minimal length of 15 nucleotides. Quality-control data are presented in supplementary table  S1 , Supplementary Material online. We performed RNAseq mappings using the STAR software  (Dobin et al. 2013 ) on the Drosophila TEs and reference genomes. We identified mRNA transcripts from TEs by mapping reads against a custom TE library derived from Repbase  (Jurka et al. 2005). This database contains consensus sequences of all known TE in the Drosophila genomes. We kept reads mapping to a single consensus sequence (i.
e. , one TE family) and we generated count tables of TE transcripts with HTSeq. We performed differential expression analysis from these count matrices using the R bioconductor package DESeq2  (Love et al. 2014). The package DESeq2 implements a generalized linear model in which counts for each gene, in each sample, are modeled using a negative binomial distribution. We used one factor generalized linear model formulas depending on the tested conditions (i. e. , species, population, or gonad). We selected differentially expressed TEs according to their adjusted P value (corrected P value < 0. 1 and 10% false discovery rate). We compared DESeq2 results with another normalization method: TE expression was normalized by a pool of 100 housekeeping genes with stable expression between ovaries and testes samples (not shown).
g. , The Journal of Basic and Applied Social Psychology). 14 round the turn of the century, the rapidly increasing availability of whole-genome sequencing and high-resolution imaging ushered biomedical research into the era of ''big data. ''  11, 15, 16 here is growing momentum for the creation and curation of massive datasets. For instance, the UK Biobank has gathered The term has been used by several quantitative fields with varying, sometimes conflicting definitions. 10 Here we adopt the technical meaning common in the statistical null-hypothesis testing context. 29 Statistical inference, typically involving regression models, point estimation, and hypothesis testing, is aimed at scientific discovery by trying to reveal ''true'' properties of the studied phenomenon. Quantifying whether an effect exists in the world is especially suited to ask scientific questions such as ''Is a genetic polymorphism associated with or has an effect on a disease?'' Providing such insight as a service to science is typically achieved by making probabilistic assumptions about how the observed data arose (e.
Often combined with careful experimental control and formally backed up by mathematical theory, the inference agenda is how traditional academic statistics has routinely dealt with small to medium datasets from planned data acquisition. 10 enetic, behavioral, environmental, and lifestyle data for extensive phenotyping of 500,000 volunteers-currently the largest biomedical data resource of its kind (www. ukbiobank. org). Due to the parallel improvements in data availability, computing power, and data storage,  17, 18  the realm of data analysis has probably expanded faster in the last two decades than ever before. 11, 15 Flexible prediction algorithms are particularly well suited for sieving through rich data to extract subtle patterns. 10 uch modeling approaches aimed at prediction can be less transparent but promise improved clinical translation of singlepatient prediction in a fast, cost-effective, and pragmatic manner.
However, such empirical success may not fully satisfy the scientific curiosity to understand the primary biology of diseases such as diabetes. Carefully planned and expensive experiments to confirm or reject a priori verbalized research hypotheses in animals and humans will surely remain a cornerstone for generating biomedical knowledge. In our systematic empirical investigation, we therefore try to bring analytical tools aimed at classical inference and pattern prediction to the same table, which are usually separately employed, to illuminate their characteristic commonalities and differences. Our data-simulation approach allows precise control over and, thus, careful comparisons between important dataset dimensions ranging from (1) available sample size, to (2) number of informative input variables and (3) redundancy of information carried in the input variables about the outcome, as well as to (4) random noise variation and (5) mis-specification of the quantitative model.
Simulated Datasets: Synoptic Summary Across 113,400 constructed datasets (Figure  2 ), we observed several characteristic differences between seeking statistical inference and maximizing model prediction. Fitting linear models to series of datasets generated with increasing non-linear effects easily reached significance but distinctly varied in the predictability of the outcomes (Figures  3F  and 4 ). It was expected that even, as opposed to odd, polynomial data transformation (e. g. , x 2 or x 4 ) incur larger violations to model validity because the direction of effects in the input variables is lost. As such, fourth-order polynomial expansion deteriorated model fit more than fifth-order expansion, entailing both worse p values and worse R 2 prediction performance (out-of-sample). To emulate random variation such as from measurement error, we added gradually increased noise to the data.
3 While out-of-sample prediction can be performed with a variety of regression analysis tools, this modeling goal has been an important focus of activity in machine-learning and other communities,  1  and corresponds to how data analysis is often practiced to solve problems in data-intensive industries. and the response of interest were generated in this high-collinearity setting. To capture key implications of the ongoing trend to data aggregation in biomedicine, we gradually increased the available data points per generated dataset (Figure  3A ). At the highest sample size of n = 100,000, low significance tended to more systematically agree with low predictability, and extremely high significance also mostly concurred with perfect out-of-sample performance. That is, in datasets bigger than is currently the norm, we observed more consistent correspondence between significance and prediction.
Exploring different proportions of relevant measurements in the ground-truth model (Figure  3B ), we noted that fewer truly relevant inputs gave rise to strongly significant p values in the presence of poor predictive performance. Finally, applying linear models that deviate from the data-generating process of the input and output variables (Figure  3E ) led to results with high significance and predictability in many cases. However, using the valid (linear) model to fit the randomly generated (linear) data allowed for many of the best prediction performances (Figure  4A ). Importantly, after correcting p values for multiple comparisons using Bonferroni's method, we made the same observations and the ensuing patterns of results remained identical. Across the 113,400 synthetic datasets, our results show a variety of incongruencies in identifying important variables among a set of candidate variables.
Using linear models, statistical significance and accurate predictions showed diverging patterns of success at detecting those variables that we knew were relevant for the outcome. For each dataset simulation, we have therefore computed the recovery based on the subset of ordinary leastsquares (OLS) coefficients correctly detected to be significant and the active (i. e. , non-zero) coefficients of the Lasso model indeed predictively relevant for the outcome (Figure  5 ). This metric allowed us to compare the number of correctly identified variables, analogously for OLS and Lasso. Even considering the simulation scenarios with consistent model specification (Figure  5A ), systematic disagreements between inference and prediction emerged in a number of simulation cases, depending on available sample size and the number of relevant variables.
Datasets in which OLS performed worse than Lasso had in common that the number of data points was larger and the number of relevant variables smaller (horizontal row in Figure  5A ). Note that input variables irrelevant for the outcome constitute noise, even if no explicit noise is added to the predictors or the outcome. This can explain why OLS can commit errors if noise is not intentionally added (Figure  5A ). Instead, datasets in which OLS performed better than Lasso were characterized by a small to moderate number of data points available for model building (vertical column in Figure  5A ). In data-simulation scenarios including misspecified models (Figure  5B ), the patterns of disagreements in recovery performance were more diverse. Lasso performed better with smaller sample sizes and larger number of truly relevant variables (upper left in Figure  5B ).
OLS tended to be more successful at recovering important variables with larger sample sizes and smaller numbers of relevant variables (lower right in Figure  5B ). As such, carefully comparing Lasso and OLS in a variety of synthetic datasets, we observed the largest disagreements in variable identification with small to moderate sample sizes, which is still a very common situation in day-to-day data analysis in biomedicine. The collection of findings shows that more consistent agreement between using linear models for prediction versus inference was observed when the sample size was >1,000 data points (fuchsia to red on diagonal in Figure  5B ). Yet even with R10,000 available data points we report certain disagreements (orange to yellow in Figure  5B ).
Simulated Datasets: True Negatives, False Positives, False Negatives, and True Positives Well-Specified Models Encouraged by the revealed divergences, we explored deeper explanations by dissecting the variable picks into proportions of true negatives, false positives, false negatives, and true positives (Figures  5, 6,  and 7 ). Overall, in scenarios where the model specification was coherent with the simulated dataset, we Biomedical research has long relied on regressiontype analysis tools. However, the same regression analysis tools are often used with non-identical objectives within different scientific communities. In carefully designed experiments in animals and humans, studies have focused on making progress toward inferring the role of preselected variables in explaining the observed outcome or experimental conditions, such as isolating cancer-related genetic variants in mouse models.
Propelled by larger datasets and recent advances in machine-learning algorithms, applied clinical research has shifted toward combining heterogeneous and rich measurements from different sources to ''brute-force'' forecasting of practically useful endpoints, such as predicting the duration of hospitalization of new patients based on previous electronic health records. For a long time, these disparate uses of identical analytical tools have been carried out in parallel with little crosstalk between communities. As a result, the conceptual and empirical relationship between the established agenda of statistical inference and the now expanding agenda of raw prediction performance remains largely obscure. Motivated by this increasing need, our study carried out a careful comparison of modeling for inference (left) and prediction (right) on identical datasets, based on comprehensive empirical simulations and revisiting common medical studies.
Coherently, the proportion of true negatives depended on the number of relevant variables. That is, with more truly relevant variables in a given dataset fewer true-negative hits can occur. Lasso tended to perform better with a decreasing number of relevant variables. In such cases, OLS tended to perform more poorly. Finally, for false-negative errors and true-positive hits, we observed agreement between Lasso and OLS in our simulations across weak-and strongsample regimes.
Mis-specified Models In scenarios where the employed quantitative models could be at odds with the process that has generated the dataset, distinct patterns of disagreement emerged between prediction and inference (Figure  7 ). Here, the strongest disagreements between Lasso and OLS surfaced for false-positive errors, characterized by negligible Spearman correlation and 8% more false positives for Lasso than for OLS on average (Figure  7B ). In a large subset of simulations, Lasso incurred up to about 90% of false positives, where OLS rarely committed more than 10% of these errors. Such observations also occurred in datasets with larger sample sizes. In the other three scenarios (Figures  7A, 7C,  and 7D ) we noticed consistent disagreements, which were relatively less pronounced. OLS generally detected more true negatives, scored fewer true-positive hits, and committed more false-negative errors.
For true-negative hits (Figure  7A ), OLS successfully scored fewer hits than Lasso across different numbers of relevant variables. This disagreement increased with smaller sample sizes (left-to-right horizontal gradient above the diagonal of Figure  7A , upward-pointing triangles). At the same time, Lasso also performed worse than OLS in various simulated datasets (rightpointing triangles in lower right). For false-negative errors (Figure  7C ), OLS committed more false-negative errors than Lasso in various simulations (rightward-pointing triangles in lower right). This tendency was more pronounced when the number of relevant variables was higher, and escalated in the weaksample regime. Finally, for true-positive hits (Figure  7D ), Lasso scored better than OLS by 10% on average. Here, the advantage of Lasso over OLS was most apparent in the low-and mediumsample-size regimes.
Note that the smallest p values fall within the range below the smallest 64-bit floating point number around 2. 2 3 10 À308 that can be represented in Python. Such p values are conceptually plausible and can occur in large simulated datasets such as when many input variables explain the outcome, and noise is absent. For a detailed breakdown of the relationship between inference and prediction according to experimental factors of the simulation, see Figures  3  and 4. Random Forest (RF) regression to summarize performance variation in OLS and Lasso as a function of the experimental properties of the generated datasets (Figure  8 ). With the possibility to accommodate more complicated relationships, the variable importance metric of the RF allowed us to identify which experimental factors had the largest impact on differences in recovery performance.
First and foremost, the number of truly relevant variables in the datasets had a strong impact on explaining variation in model behavior (Figure  8C ). Especially for false-negative errors, the number of relevant variables was considerably more explanatory for OLS than Lasso. Second, model violations turned out to be important to account for differences between analyses using OLS or Lasso (Figures  8B-8D ). As our third most influential experimental factor, the number of data points stood out as an aspect of the simulated datasets that differentially explained model performance comparing OLS and Lasso. This principled aggregation from 113,400 synthetic datasets provides a synoptic perspective that strengthens our core observation: Frequently, variable identification in OLS and Lasso was affected in distinct ways by the properties of the data-generating mechanisms.
Real Medical Datasets To complement the simulated datasets, we carried out the same direct comparison between explanatory modeling and predictive modeling in common real-world datasets (Figure  9 ). The quantitative re-evaluation is presented here for four medical datasets that are frequently used as examples in data-analysis teaching and textbooks. 25, 26 irthweight Dataset Standard linear regression was used to evaluate the relation of eight candidate measures to the body weight of 189 newborn babies (Figure  9A ). In this approach, the three effects that reached statistical significance at p < 0. 05 comprised the mother's weight at the last menstrual period (p = 0. 018, lwt), existing history of hypertension (p = 0. 012, ht), and presence of uterine irritability (p = 0. 002, ui). The in-sample model fit amounted to R 2 = 0.