dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset-LEUCEGENE TET2, KRAS, CEBPA 10 The samples included in each dataset and some metadata are detailed in Supplementary Table  S1. lection of ENCODE samples that metadata indicates either polyA or ribo-depletion protocol (Supplementary Table  S1 ). The results clearly demonstrate differences between libraries prepared by ribo-depletion versus polyA selection for most of the chosen histone genes. We observe histone gene expression variability between the samples demonstrating again the disparity of public data. To help users categorize their RNA-seq data, we defined in the KmerExploR tool a threshold of 200 k-mer counts per billion for this category, above which we expect to have only the ribo-depleted samples and not the polyA ones. Strand-specific and unstranded library preparation are two commonly used preparation protocols that differ by their ability to retain or not RNA strand information.
To detect this characteristic from RNA-seq data, we designed k-mers, specific for a set of ubiquitous genes (Table  1 ) and their reverse complement counterparts. K-mers on the forward strand are counted as positive and their reverse complement as negative, permitting to determine the orientation of the library. If forward and reverse tags are found in equivalent proportions in the same fastq file, data are considered as 'unstranded'. This leads graphically to a balanced distribution between positive and negative counts. As shown in Figure  3 , using this property we are able to clearly separate unstranded and stranded libraries. 5 to 3end bias is a difference of reads' repartition along the tran-scripts, classically linked to library preparation: incomplete retrotranscription or specific protocols.
A comparison between polyA selection and ribo-depletion protocols has previously shown coverage differences across transcripts with a poor 5 -end coverage with the polyA selection method  (28). Knowing whether an RNA-seq sample possesses a read repartition bias is critical for isoform detection, or simply to give an indication on the library construction protocol used in large-scale analysis of public data. Using previously described housekeeping genes (Table  1 ), we have selected different sets of specific k-mers depending on their position in the regions defined as 5 UTR, 3 UTR and CDS. Figure  3C  shows the repartition in percent of these k-mers across the Dataset-FEATURES samples. Representing the mean k-mer counts as a percentage allows us to evaluate the distribution homogeneity across 5 UTR, 3 UTR and CDS regions between the 103 ENCODE samples.
Detection of potential contamination Different microorganisms like mycoplasma and virus can contaminate samples and cell cultures, modifying the metabolism of the cell and therefore biasing the results of ensuing analysis. Moreover, cancer research has shown that viruses are responsible for ∼20% of human cancers  (29). To detect contaminants in RNA-seq data, tools relying on alignment like DecontaMiner  (30)  or viGEN  (31)  have been widely used, but the alignment step is time and memory consuming. Exact alignment of k-mer-based approaches like Kraken  (32)  and Taxonomer (  33 ) is an alternative for taxonomic classification. However, these tools are complex and involve data cleaning from adaptors (trimming), the use of internal and external databases and/or probabilistic models for contaminant classification.
Using a specific and reduced set of k-mers, we have seen an advantage to quickly detect principal contaminants of human cells in RNA-seq datasets, free from alignment methods. Because mycoplasma is a common source of cell culture sample contamination and could affect host gene expression  (25) , we choose to control its presence in RNA-seq data. Mycoplasma contamination is evaluated through the detection of specific k-mers corresponding to 16S rRNA sequences according to the literature. In fact, Olarerin-George and Hogenesch showed that 90% of the specific mycoplasma-mapped reads from human RNA-seq samples mapped to mycoplasma rRNA. We selected six species that have the highest record rate of detection in cell culture samples (i. e. Acholeplasma laidlawii, Mycoplasma fermentans, Mycoplasma hominis, Mycoplasma hyorhinis, Mycoplasma orale and Mycoplasma arginini)  (18)  to design our k-mers.
We used part of an RNA-seq data series previously described as highly contaminated (25) (PRJNA153913 study) to test the relevance of our approach. As shown in Figure  4 , we can easily detect the six selected mycoplasma species in some samples, with a prevalence for the M. hyorhinis species. Comparing our results with the Olarerin-George and Hogenesch study that used Bowtie 1 alignment and BLAST+ to filter non-specific reads, we were able to confirm mycoplasma rRNA presence for the same samples (see Supplementary Figure  S5A ). Moreover, we observe a high proportionality between our k-mer counts and their read counts on the 33 single-read samples (Dataset-MYCO described in Supplementary Table  S1 ), for each of the six common Mycoplasma species. Viruses are a significant cause of human cancers.
Several studies interrogate for the presence of major viruses known to infect human and other mammalian cells  (22, 34, 35). Recently, Uphoff et al. screened >300 CCLE RNA-seq data using the Taxonomer interactive tool and compared the results to virus-specific polymerase chain reaction (PCR) analysis, revealing 20 infected cell lines with different viruses  (22). To rapidly explore the potential presence of viruses in RNA-seq datasets with our k-mer-based approach, we used the same virus reference genomes as described in the Uphoff et al. study. Using Kmerator at the chimera level (absent from human annotations), we designed specific k-mers for each virus and searched them in a subset of contaminated CCLE data according to Uphoff et al. (19 CCLE pairedend samples) and in negative controls (3 CCLE paired-end samples), to validate our protocol ability to detect viruses.
Her cancer was triggered by an infection with human papillomavirus type 18 (HPV-18). Nowadays, this cell line is largely used in medical research. Looking for several viruses in public RNAseq cancer-related databases revealed the presence of HPV-18 sequences in many cancers  (36)  that closely resemble the HPV-18 viral sequence that is integrated into HeLa cells, suggesting a contamination. Three segments of HPV-18 are integrated into the HeLa genome on chromosome 8 and include the long control region, the E6, E7 and E1 genes, and partial coding regions for the E2 and L1 genes  (20). These genes are expressed in HeLa cells, and mutations have been found specifically in HeLa cells. Thus, selecting these mutated HeLa HPV-18 gene-specific k-mers and counting them into three CCLE RNA-seq datasets (one positive sample and two negative controls), we validated the accuracy of our selection as we are able to find our k-mer selection specifically in HeLa cells.
We also checked the results in other HeLa samples from the PRJNA639358 study (see Supplementary Figure  S5E ). S1. For each bar plot, the legend lists the set of predictors for which k-mer mean counts are computed (details in Table  1 ). Samples are on the X-axis. Panels As for HeLa cells, cross-species contamination remains a documented 'danger' for the interpretation of results in molecular biology  (37). The probability of mixed cell lines in sample preparation, usage of PCR that can accidentally amplify the wrong piece of DNA, and an unknown probability of error in metadata assignation motivated us to create a quality check to determine the species of an RNAseq sample. In  (38) , the usage of mitochondrial DNA for phylogenetic and taxonomic inference was discussed and two extreme viewpoints emerged: using exclusively the mitochondrial DNA or fully excluding it.
It appears that mitochondrial DNA does not fully answer or impairs the perspectives of advanced phylogenetics. However, the 'mitochondrial barcode' approach does show an interesting gene marker, MT-CO1  (39) , that could be sufficient for a quick check of the species of RNA-seq data. Indeed, this gene is highly expressed and reference sequences from many distinct species of animals are available. Thus, we selected specific k-mers with Kmerator, at the gene level, for MT-CO1. We repeated the procedure for MT-CO1 orthologs in different species, principally found in the SRA database, using the appropriate species reference genome and transcriptome. These k-mers have been then quantified in three public data by species to check the efficiency of their usage. As shown in Figure  4C , the research of MT-CO1 k-mers alone can discriminate most of the common Ensembl species and can be usable for a quick quality check.
However, without proper experiments we cannot support its usage with phylogenetically close species. To conclude, we developed KmerExploR to rapidly control RNA-seq raw data quality and filter samples on unusual profiles or presence of contaminations. KmerExploR is a tool that provides a modular set of analyses like fastQC (https://qubeshub. org/resources/fastqc). It can be used in a complementary way to fastQC analysis to complete missing metadata in public datasets or to give a quick profile of the RNA-seq contents. The modular analysis is based on a k-mer selection from predictor genes, included in KmerEx-ploR. The tool can be used to control any human RNA-seq dataset, and it can also be easily modified adding any other modular function.
KmerExploR, an advanced usage for the detection of genomic or transcriptomic events The above 'checking application' of KmerExploR demonstrated all its potential in the rapid exploration of large public RNA-seq datasets before performing any biological query. However, the KmerExploR tool can also be used in a more advanced way such as biomarker search or discovery in human health. This application is a powerful one as it can compensate for the lack of completeness in genomic or transcriptomic references and we currently know that much important information may be missed by ignoring the unreferenced RNA diversity  (12). As a proof of concept, we used a set of k-mers designed with Kmerator to identify events outside reference annotations including fusion or chimeric RNA, oncogene mutations and new lncRNA expression.
We then applied k-mer quantification in a tumoral and a non-tumoral dataset to evaluate the specificity and perfor-mance of the approach. The results obtained with a part of the Leucegene cohort are presented in Figure  5. The selection includes different AML subtypes and normal CD34 + cells as control (Dataset-LEUCEGENE described in Supplementary Table  S1 ). The results obtained with two well-known fusion RNAs associated with chromosomal translocation, RUNX1-RUNXT1 t(x,21) and PML-RARA t  (15, 17) , and their reciprocal counterparts RUNXT1-RUNX1 and RARA-PML are presented in Fig-  ure 5A. In this case, the k-mers, once designed by Kmerator, are restricted to those spanning the fusion junction with at least 10 nucleotides in gene 1 or gene 2 of the fusion. All the normal CD34 + cells are negative and we only observe an expression in corresponding positive AML subtypes.
DISCUSSION Considering the growing number of RNA-seq data, the use of raw data sequences is an important step to check with RNA-seq protocols or bioinformatic pipelines bias. Here, we demonstrated that the Kmerator Suite is an efficient and useful set of tools to verify RNA-seq quality and control intrinsic method and biological characteristics that often failed in technical description. We also showed that the Kmerator Suite can be used to quantify gene/transcriptspecific expression as well as to explore sequence variations at the transcriptional level. In this first version, the tool is adapted to human data Ensembl entry, as main public data are available for this species (164 000 RNA-seq with >30 million reads for Homo sapiens in the SRA database). A new implementation with adapted predictors is necessary for other species.
The meta-analyses performed in the present study with KmerExploR are a proof of concept of the procedure potential and could be extended to other biological RNA-seq questioning: (i) to extend the application to an enlarged set of microorganisms including new ones like SARS-Cov2   S1  (131 paired-end samples). This dataset includes normal CD34 + cells as control (in green on the X-axis) and different AML subtypes (in black on the X-axis). For each bar plot, the legend lists the set of predictors for which k-mer mean counts normalized per billion (Y-axis) are computed. (A) Chimera detection. Two well-known fusion RNAs associated with chromosomal translocation and their reciprocal counterparts are presented: RUNX1-RUNXT1 t(x,21) and PML-RARA t  (15, 17). (B) Mutation detection. TET2, KRAS and CEBPA genes are used in AML diagnosis.
The bar plot shows four different mutations for these genes, detected specifically in some AML samples. The reference allele for each of these mutations is detected in almost all samples. (C) New lncRNA detection: NONE 'chr2-p21' lncRNA described in  (23). This transcript is expressed in the whole dataset but shows different levels of expression depending on AML subtype. detection and (ii) to search for immunophenotyping profile in cancer datasets as already published by Mangul et al. (41, 42). Considering advanced applications, we also demonstrated the potential of k-mers to explore gene expression in RNA-seq to reinforce biological questions or biomarker usage and discovery. Moreover, many other requests could be easily considered for annotated gene exploration like gene co-expression, or to compensate the lack of completeness in genomic or transcriptomic references to cover unreferenced RNA diversity and search for new spliced events, intron retention or new transcript categories including circular RNAs.
In order to increase the potential of the k-mer approach, access to very large-scale datasets like SRA level (164 000 human samples) could be considered with efficient indexing structure development  (43). Finally, we showed that the Kmerator Suite can be used to quantify gene/transcript expression as well as to explore sequence variations at the transcriptional level. The simplicity of specific k-mer extraction principle and quantification provide flexibility of usage. Indeed, Kmerator Suite quantification does not use probabilistic methods or expectation-maximization algorithms like in Kallisto  (7) , Sailfish  (44)  or RNA-Skim  (45). Therefore, the sets of specific k-mers for quantification can be created, merged and updated at will, without consequence on the quantification itself. The principle of user-owned collection of signatures of interest that can be searched broadly among datasets is the core of KmerExploR application.
Users can also visualize their own signatures with the advanced usage. Details are given in the text and Supplementary FigureS1. (B) Kmerator extracts gene/transcript k-mer signatures with three possible levels of stringency. This figure describes how the different levels are defined (transcript, gene or chimera) for two example genes A and B. Example gene A has three isoforms: A1, A2 and A3. A1 is the only one with a free interval, i. e. a region not covered by other isoforms, and is defined as the principal transcript (APPRIS database). Therefore, at the transcript level, each transcript has its own specific k-mer set, depending on its coverage with other isoforms. At the gene level, the principal transcript defined with the APPRIS database is used, and specific k-mers can be common to several isoforms.
ACKNOWLEDGEMENTS The authors are grateful to  Henrietta Lacks , now deceased, and to her surviving family members for their contributions to biomedical research. The HeLa cell line that was established from her tumor cells without her knowledge or consent in 1951 has made significant contributions to scientific progress and advances in human health. The authors are also grateful to  Rayan Chikhi  for his comments and corrections. Author contributions: S. R. and T. C. designed the study. S. R. , C. B. and T. C. wrote the manuscript. A. -L. B. , N. G. and D. G. were contributors in the design of the study and manuscript corrections. S. R. developed the code of Kmerator, selected and downloaded public datasets, and analyzed data. B. G. and C. B. participated in Kmerator code improvements. C. B.
DATA AVAILABILITY RNA-seq libraries were downloaded from the European Nucleotide Archive of the European Bioinformatics Institute  (46). The reference GRCh38 genome and Ensembl v91 transcripts were downloaded from Ensembl. Kmerator is distributed under the MIT license. The Kmerator, KmerExploR and countTags software, documentation and supplementary material presented herein are available from https://github. com/Transipedia/kmerator, https: //github. com/Transipedia/kmerexplor and https://github. com/Transipedia/countTags, respectively.
Terrestrial biosphere models typically use the biochemical model of Farquhar, von Caemmerer and Berry (1980)  to simulate photosynthesis, which requires accurate values of photosynthetic capacity of different biomes. However, data on tropical forests are sparse and highly variable due to the high species diversity, and it is still highly uncertain how these tropical forests respond to nutrient limitation in terms of C uptake. Tropical forests often grow on phosphorus (P)-poor soils and are, in general, assumed to be P-rather than nitrogen (N)-limited. However, the relevance of P as a control of photosynthetic capacity is still debated. Here, we provide a comprehensive dataset of vertical profiles of photosynthetic capacity and important leaf traits, including leaf N and P concentrations, from two three-year, large-scale nutrient addition experiments conducted in two tropical rainforests in French Guiana.
These data present a unique source of information to further improve model representations of the roles of N, P, and other leaf nutrients, in photosynthesis in tropical forests. To further facilitate the use of our data in syntheses and model studies, we provide an elaborate list of ancillary data, including important soil properties and nutrients, along with the leaf data. As environmental drivers are key to improve our understanding of carbon (C)-nutrient cycle interactions, this comprehensive dataset will aid to further enhance our understanding of how nutrient availability interacts with C uptake in tropical forests.
Photosynthesis in C3 species is typically represented in nearly every major large-scale TBM by the Farquhar, von Caemmerer and Berry (FvCB) model of photosynthesis  (Farquhar et al. , 1980; von Caemmerer and Farquhar, 1981). The FvCB model determines photosynthesis (A) by the most limiting of two processes, Rubisco activity and electron transport. Empirical studies to determine the key parameters of these two processes (i. e. the maximum rate of carboxylation Vcmax, the maximum electron transport rate, Jmax) and to test empirically their limitations on the leaf-and canopy-scale are necessary for obtaining the data required for parameterizing the FvCB model  (Medlyn et al. , 2015). Plant trait databases, now widely available, offer an excellent opportunity for parameterizing models. However, data on tropical forests are sparse and highly variable due to the huge species diversity  (Rogers, 2014).
Environmental drivers are key to improve our understanding of C-nutrient cycle interactions and our ability to model them. Climate data are often directly available at high spatial resolution and at the global scale from databases such as Worldclim  (Ruiz-Benito et al. , 2020) , while observations of soil properties and soil nutrient availability are often missing  (Vicca et al. , 2018). Soil variables have been shown to be strong predictors of leaf traits in higher plants  (Maire et al. , 2015). Comprehensive soil data, including soil properties such as texture and pH as well as important nutrients, are needed to further enhance our understanding of how and why nutrient availability interacts with C uptake in tropical ecosystems and their responses to global environmental change  (Vicca et al.
Bulk density We sampled soil bulk density in the wet season of 2015. In each plot, we took five cores at two depths and these samples were sieved through a 2-mm sieve. We collected the soil fraction, the roots and the stones, which were dried and weighed separately at 105°C for 24 h. In our database, we report two measures of bulk density: inclusive bulk density is the weight of the dried soil core divided by its volume (i. e. the volume of the auger, 754 cm³), whereas exclusive bulk density is calculated by dividing the total weight of the soil fraction (excluding roots and stones) by the volume of the entire core (i. e. 754 cm³).
Soil particle size distribution The particle size distribution at plot level was analysed only in the wet season of 2015, assuming it would not change with seasonality. Therefore, we mixed by hand the five samples per plot that were sieved (< 2 mm) after extraction using a gouge auger, and analyzed these mixed samples as one composite sample per depth and per plot. We determined the soil particle size distribution using sedimentation with the hydrometer method  (Gee and Bauder, 1986)  after SOM oxidation with H2O2, as described in protocol 1. 3. 5 Soil texture in the Supporting information S1 Site characteristics and data management in  Halbritter et al. (2020). Soil particles were dispersed with sodium hexametaphosphate and the quantity of sand, silt and clay were determined using a hydrometer.
Ancillary data The vertical structure of a tropical rainforest is complex and multi-layered, resulting in great variation in light availability within the canopy  (Yoda, 1974). We assessed the light environment of each studied tree by visually estimating the canopy light exposure or Dawkins' crown illumination index  (Dawkins, 1958). This index describes a tree's light environment based on a five-point scale ranging from (1) no direct light for suppressed trees to (5) crown fully illuminated for emergent trees (Figure  5 ). Sampling height of the mature trees was measured with a Forestry Pro rangefinder (Nikon, Tokyo, Japan) by tree climbers situated at sampling height pointing towards the soil. For saplings, we measured the total height of the tree using a measuring tape, and additionally we measured the diameter at 10 cm and at 50 cm above surface level.
Data availability This Photosynthesis-Soil database is provided as an excel workbook and is freely available at DOI 10. 5281/zenodo. 4719242. Photosynthetic CO2-response curves are presented as raw An-Ci files which can be analyzed by any user of the database, but Vcmax and Jmax values, fitted using the "Plantecophys" package  (Duursma, 2015)  in R, can also be found in the database in addition to the leaf traits. The soil database of 2015, including bulk density, soil particle size distribution, soil moisture and nutrients, and the fertilizer composition are shown in separate sheets.
Summary Publicly accessible and usable datasets from experimental sites are needed to greatly enhance the power of data synthesis as well as model development and evaluation  (Vicca et al. , 2018; Halbritter et al. , 2020). We provide vertical profiles of photosynthetic capacity data and important leaf traits from two three-year large-scale nutrient addition experiments conducted in two tropical rainforests in French Guiana. Our dataset is extremely valuable to the modelling and tropical ecology.