dataesr/scientific-paragraphs-categorization huggingface dataset:
QTL mapping in the multi-cross design QTL mapping in the multi-cross design was performed with the MCQTL package  [90]. The model was described as additive (no dominance effect) and connected (Col-0 centered design) and the following 3 steps process was applied. Step 1: thresholds were calculated by trait on the whole genome using 1000 resampling replicates (PRA29_WW = 3. 73; PRA29_WD = 3. 84; RER16-29_WW = 4. 22; RER16-29_WD = 3. 95; Compactness29_WW = 3. 64 Compact-ness29_WD = 3. 30). Step 2: QTL detection was performed using iQTLm method with a general and conservative threshold of 4 LOD. To perform this detection, cofactors were automatically chosen by backward selection with a threshold of 2. 8 LOD among a skeleton with a minimal inter distance of 10cM. Search for QTL was not allowed within +/-10cM window surrounding the QTL to avoid linked genetic regions.
Raw phenotypic data obtained on the Arabidopsis RILs with the Phenoscope robots [48] This dataset gathers the main raw phenotypic data obtained and exploited in Marchadier, Hanemian, Tisneet al. (2018). It contains data from 4 RIL sets across 9 Phenoscope experiments. For each Phenoscope experiment, Recombinant Inbred Line (RIL) and Condition ('WW' = Well Watered / 'WD' = Water Deficit), the data set indicates the phenotypic value for 6 traits at 21 successive time points. 'Trait. XX' = Trait at XX days after sowing, with 'XX' = 09 to 29 and 'Trait' = PRA (Projected Rosette Area; in cm2), GreenMean / RedMean / BlueMean (rosette pixels' colour components; arbitrary unit), ConvexHullArea (area of the convex hull encompassing the rosette; in cm2) and CircleRadius (radius of the smallest circle encompassing the rosette; in cm).
Acknowledgments We thank  Yann Serrand  for the supervision of the Phenoscope, we thank  Francisco Cubillos ,  Cecile Grondin ,  Isabelle Gy  and  Mathieu Canut  for the selection of recombinants from  8HV294. We also thank  Laurence Moreau  for her help in performing multi-cross analyses. We thank  JoseJimenez-Go  ´mez for discussions on statistical analyses of the 'microStairs' experiment.
This work was supported by funding from the  European Commission   Framework Programme 7 ,  ERC Starting Grant  ' DECODE'/ERC-2009-StG-243359  to OL and  Agence Nationale de la Recherche (ANR)  grant  '2Complex'/ANR-09-BLAN-0366  to OL The IJPB benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2006; Richard et al. 2006). Therefore, the data support the current paradigm of RNP biogenesis in which the assembly machinery actively engages with its substrates B A C FIGURE 5. Effect of the expression of amino-terminal fragments of Bcd1p on cell growth, on selected snoRNA steady-state expression levels and association with selected snoRNAs. (A) The GAL1::3HA-BCD1 strain was transformed with a p416 empty vector or p416 vectors expressing Flagtagged versions of Bcd1p, either full length or amino-terminal fragments. The cells were maintained in YPG medium, then shifted to YPD medium at an OD 600nm of 0. 05 for 15 h, and their growth was monitored. (B) The relative RNA content of the same cells was analyzed when maintained in YPG medium after a 16-h shift to YPD medium. The ALG9 gene was used as an endogenous control.
ACKNOWLEDGMENTS We are deeply grateful to  F. Schlotter  for experimental advice and  A. Visvikis  for the careful reading of the manuscript. We also thank  C. Allmang-Cura  and  G. Eriani  for the gift of antibodies. This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  University of Lorraine (UL) , the  European Union and Region Lorraine (Fond Européen de Développement Régional -FEDER) , and the  National Research Agency  ( ANR-11-BSV8-01503 ;  ANR-16-CE11-0032 ). A. P. is a predoctoral fellow.
Small non-coding RNAs (sRNAs) regulate numerous cellular processes in all domains of life. Several approaches have been developed to identify them from RNA-seq data, which are efficient for eukaryotic sRNAs but remain inaccurate for the longer and highly structured bacterial sRNAs. We present AP-ERO, a new algorithm to detect small transcripts from paired-end bacterial RNA-seq data. In contrast to previous approaches that start from the read coverage distribution, APERO analyzes boundaries of individual sequenced fragments to infer the 5 and 3 ends of all transcripts. Since sRNAs are about the same size as individual fragments (50-350 nucleotides), this algorithm provides a significantly higher accuracy and robustness, e. g. , with respect to spontaneous internal breaking sites. To demonstrate this improvement, we develop a comparative assessment on datasets from Escherichia coli and Salmonella enterica, based on experimentally validated sRNAs.
We also identify the small transcript repertoire of Dickeya dadantii including putative intergenic RNAs, 5 UTR or 3 UTR-derived RNA products and antisense RNAs. Comparisons to annotations as well as RACE-PCR experimental data confirm the precision of the detected transcripts. Altogether, AP-ERO outperforms all existing methods in terms of sRNA detection and boundary precision, which is crucial for comprehensive genome annotations. It is freely available as an open source R package on https://github. com/.
Consequently, algorithms searching for uniform coverages, such as sRNA-Detect  (22) , tend to cut the detected transcripts at their spontaneous cleavage sites, resulting in excessive numbers of small transcripts (see Results). Other algorithms usually use a coverage threshold to define a small transcript or find its boundaries  (23) (24) (25) (26) , but here also, spontaneous breaking/cleavage sites tend to produce coverage drops that are difficult to robustly distinguish from the actual transcript end. Recently, ANNOgesic  (27)  obtained a gain in robustness by tolerating several nucleotides below the chosen coverage threshold. Whatever the algorithm, utilization of coverage distribution certainly contributes in the heterogeneity of results reported on bacterial small transcripts. Our algorithm differs from all those previously mentioned in that it avoids the step of converting the reads into a coverage distribution where significant information is lost, resulting in the issue mentioned above.
It must be noted that one existing method (DETR'PROK) also directly deals with reads rather than with coverage  (28) , but since it only considers singleend reads, the benefit of our approach is lacking, and the results are indeed very comparable to coverage-based methods (see below). In fact, several of the latter were developed when paired-end data were still scare, and the improvement offered by APERO was therefore not yet technically relevant. Existing algorithms were also tested either on RNA-Seq data where the library preparation includes a fragmentation step (thereby adding an additional and artificial source of noise), or more recently on specific datasets of non-fragmented, size-selected RNAs. Although our method can also be applied on both types of data, it is more specifically suited to the latter case, where many small transcripts should then be directly sequenced as intact fragments with high precision, even when partial spontaneous fragmentation results in inhomogeneous coverage values.
In the following, we present the APERO algorithm and apply it on RNA-Seq datasets from Escherichia coli and Salmonella enterica. To evaluate its performance, we develop a comparative assessment between APERO and seven existing methods (Rockhopper  (24) , DETR'PROK  (28) , TLA from RNA-eXpress  (23) , sRNA-Detect  (22) , the two custom-made scripts developed by G ómez-Lozano et al. (25)  and Nuss et al. (26) , and ANNOgesic (27) using sets of known sRNAs annotated in these two species. We show that APERO outperforms all previous methods in terms of sRNA detection and boundary precision. In addition, it is able to detect different isoforms of a single sRNA. We also analyze a new set of RNA-Seq data from the phytopathogenic bacterium Dickeya dadantii, in presence or absence of Terminator EXonuclease (TEX) treatment that enriches primary transcripts.
MATERIALS AND METHODS APERO determines a set of small transcripts from pairedend RNA-seq data, preferably obtained with size-selected but un-fragmented RNAs. As input, it requires a sequence alignment file in BAM format, which is filtered using bitwise flags in order to select paired-reads that are correctly oriented and positioned with respect to each other. APERO is written in R and depends on the Rsamtools and Reshape2 packages to extract the genomic positions of the sequenced fragments, as well as on the Snowfall package for parallel computations. We also implemented the algorithm as a tool on a local Galaxy server instance (bioinfo. insa-lyon. fr), where users can upload data and run the workflows without any software installation. APERO is composed of two modules: the first one infers small transcript 5 ends from the ends of mapped fragments (i.
Determination of 5 ends The first APERO module aims at identifying 5 ends of small transcripts, i. e. transcriptional start sites or 5 processed ends (Figure  1B ). The number of read starts is computed at each position along both strands of the genome, and 5 ends (hereafter called start sites) are defined as regions exhibiting local enrichment in read starts compared to neighboring genomic positions. 5 ends are not always defined at a single position, but can extend over a few (usually up to three) nucleotides. The user can set the maximal width w max , and start sites of width 1 ≤ w ≤ w max are then iteratively analyzed by the algorithm. For a current genomic position i and a current start site width w, the number of read starts in the considered region (i ± w) is compared to that of neighboring regions of increasing sizes 1 ≤ f ≤ d, where d is the second parameter provided by the user.
With this choice, d represents the minimal distance for distinguishing two separate 5 ends, i. e. the spatial resolution of the algorithm. The local enrichment E i,w,f is defined as: E i,w, f = j [i -w-f,i -w-1] c j + j [i +w+1,i +w+ f ] c j 2 f j [i -w,i +w] c j 2w+1 where c j is the number of reads starting at position j. A region i ± w is then considered as a start site if E i,w,f is above a threshold value E min whatever the f value. If several start sites are identified at the same position i (but with different widths w), only the narrowest (and most precise) start site is retained. Start sites identified at different positions i within the same window are merged and define a start cluster. The enrichment threshold value was chosen equal to E min = 1/(2d). The output of this APERO module is a list of 5 ends containing the central position of the start region, its half-width w, the strand from which the transcription is initiated and the number of reads starting from this start region.
Determination of 3 ends The second APERO module determines the length of small transcripts (i. e. their 3 end) by extending them iteratively in the 5 → 3 direction from the 5 end, as long as sequenced fragments are present in sufficient numbers (Figure  1C ). Fragments starting at a 5 end start site are first selected and ranked according to their length. In order to rule out potentially irrelevant transcriptional readthrough, the 1% longest fragments are discarded. We call e the 3 end of the remaining longest fragment, C e the coverage value at position e, C start,e the total number of fragments between start and e, and L start,e the distance between the start site and e. The coverage ratio at position e, F start,e , is then defined as: F start,e = C e C start,e L start,e F start ,e represents the number of fragments that overlap e, normalized by the expression strength of the whole transcript.
If F start,e , < F min , e is then considered as the 3 end of the transcript. If F start,e ≥ F min , the small transcript is extended further (see Figure  1C ): the longest fragment overlapping position e is identified using the procedure described above, and its end e' is tested as a new putative 3 end. This operation is repeated until the F start,e' value is below the threshold F min. The threshold value F min controls the tendency of the algorithm to increase transcript lengths (with lower values resulting in longer transcripts). Tests showed that the optimal value depends on the dataset, maybe due to differences in library preparation, protocols, sequencing depth, etc. We determined this optimal value by analyzing the distributions of F start,e after one iteration of the algorithm on the experimentally validated sRNAs: threshold values F min = 3 (for S.
enterica) and F min = 6 (for E. coli) efficiently discriminated between the transcripts where the 3 end was already found after a single iteration, and those which required further iterations (Supplementary Figure  S1 ). These values correspond approximately to the first quartile of the F start,e distribution for both datasets; consequently, a default F min value is computed automatically in the same way when handling a new dataset. This procedure also gives a value F min = 3 for the D. dadantii dataset.
Annotation/Classification As shown in Figure  1 , if the user provides an annotation file of the reference genome to APERO (in ptt format), the detected small transcripts are classified according to their position with respect to annotated CDSs and their strand (Supplementary Figure  S2 ). Orphan RNAs are transcribed from intergenic regions and could be regulatory sRNAs. RNAs transcribed on the same strand as the CDS are classified as: Primary RNA (P) when located in the 250 nucleotides upstream of the CDS without overlapping it; 5 -UTR and 3 -UTR RNAs when overlapping the start and stop codon respectively. RNAs transcribed on the opposite strand are classified as: Ai (antisense internal) when transcribed inside CDS; Div (divergent) when starting in the 250 nucleotides upstream of the CDS; 5 Ai and 3 Ai RNAs when overlapping the start and stop codon respectively.
Size-selected RNA libraries We considered three paired-end sequencing data obtained from size-selected and non-fragmented RNAs: one from S. enterica (accession number SRX1036363)  (18)  and two new datasets from E. coli (accession number SRX4670654) and D. dadantii (accession number SRX4664132). For S. enterica, the total RNAs from the log-phase were isolated using Trizol ® (Life Sciences) standard manufacture protocol. The small RNA fractions were separated from the large fractions using the RNeasy MinElute Cleanup Kit (Qiagen) and submitted to Otogenetics (http: //www. otogenetics. com/) for commercial RNA-Seq on an Illumina MiSeq genome sequencer according to the Illumina TruSeq Small RNA Sample Prep protocol. For E. coli, the total RNAs from the early stationary phase were isolated using the hot-phenol procedure and RNAs of size ranging from 50 and 200 nucleotides were extracted from a denaturing polyacrylamide electrophoresis gel.
enterica serovar Typhimurium strain SL1344, NC 016810. 1; E. coli str. K-12 substr. MG1655, NC 000913. 3; D. dadantii 3937, NC 014500. 1) using Bowtie2  (31)  with the local alignment mode (Galaxy Version 2. 3. 2. 2). These RNA-seq datasets were chosen for benchmarking because (i) the size-selection and absence of fragmentation of RNAs should remove a noise-generating step in the library preparation and improve the statistical coverage of small transcripts; (ii) many sRNAs were experimentally validated in S. enterica and E. coli; (iii) they exhibit differences in sequencing depth and read length: 2 × 2. 6 million mapped paired-end 100-bp reads for S. enterica, 2 × 15. 5 million mapped paired-end 125-bp reads for E. coli, 2 × 40 million paired-end 75-bp reads for D. dadantii. Furthermore, D.
Performance evaluation of sRNA detection algorithms Predictions of APERO were compared to those of different programs dedicated to the identification of small transcripts in bacteria (listed in Results). All methods (except Rockhopper) were used with the same minimum height/coverage parameter, fixed to (20 × total number of reads)/genome size. This value approximately matches that suggested by authors for the sequencing depth of the S. enterica dataset  (15, 32). To evaluate their performance, we computed two alternate estimators: recall and Jaccard index. Recall indicates the proportion of annotated small transcripts detected by a given approach (true positives divided by the total number of positive). Jaccard index is defined as the number of intersecting base pairs between an annotated sRNA and a detected small transcript divided by the number of base pairs in the union of the two sRNAs.
Detection and mapping of small transcripts from paired-end reads Paired-end RNA-seq data from S. enterica  (18)  and from E. coli (this work) were used as training datasets to compare APERO with other methods and evaluate their performance in the identification of known sRNAs. In all RNA libraries presented in this paper, small transcripts were separated according to their size and sequenced without any fragmentation step. Technical details, as well as methods and parameters of the algorithm, can be found in Materials and Methods; in the following paragraphs, we only give a short description of the program before evaluating its performance on the data. APERO is written in R and can be used on all operating systems after installing several R packages (Rsamtools, Reshape2, Snowfall). A webserver version is also available on a local instance of the Galaxy bioinformatics platform (bioinfo.
insa-lyon. fr), which provides an installation-free access to all users. The workflow of the program is described in Figure  1 , together with a screenshot of the STnc 1590 sRNA from S. enterica (Figure  1D ). The required input is a BAM sequence alignment file from a RNA-Seq dataset, and the output is a text file giving the list of detected small transcripts, with different information fields (genomic coordinates, strand, value of the width parameter used, intensity of small transcript start and number of iterations of the second module). Optionally, the user can provide a genome annotation (in ptt format), which is then used to classify each small transcript with respect to neighboring or overlapping CDS. Detected small transcripts could correspond to intergenic RNAs, 3 UTR RNAs, 5 UTR-derived RNA products or antisense RNAs.
APERO is composed of two separate modules. The first one (Figure  1B ) is dedicated to the identification and mapping of the 5 ends of small transcripts, i. e. the putative transcriptional start sites (TSSs) or 5 processed ends. Inspired by previous algorithms specifically dedicated to TSS detection  (26, 33) , 5 ends are defined as regions exhibiting a local enrichment in read starts compared to neighbor regions. Two common difficulties are encountered: (i) due to wobbling of the RNA polymerase, TSSs peaks are not always defined sharply, but can extend over several nucleotides; (ii) e88 Nucleic Acids Research, 2019, Vol. 47, No. 15 PAGE 6 OF 13 adjacent peaks distant of several nucleotides are sometimes observed, reflecting alternate 5 starts of the same transcript which prevent the identification of individual peaks.
enterica dataset and 10 991 from the E. coli dataset. As a benchmark of APERO's ability to accurately detect 5 ends, we first applied its 5 module on previously published +TEX sequencing data of total (small + large) RNAs from E. coli (GSE55199)  (35)  and Salmonella transcripts (GSE49829)  (32) , where TSSs were already analyzed. For E. coli, 57% of TSSs identified by Thomason al. (35)  are also identified by APERO within 10 nucleotides resolution (among those, 95% are identified within 3 nucleotides resolution) and for Salmonella 80% are in common with those described by Kr öger et al. (32). These controls show that the first module of APERO can safely be applied to determine the 5 ends of small transcripts (from new datasets). An advantage of our method compared to previous ones is that it can be applied either on usual RNA-Seq datasets or on RNAs treated with Terminal Exonuclease (TEX) for primary transcript enrichment.
enterica, two major RaiZ species, a 160nucleotides form and a 122-nucleotides processed sRNA, were detected  (38). As illustrated in Supplementary Figure  S3 , APERO correctly identified both TSS and 5 processed end of these sRNAs from datasets obtained without TEX treatment and is the only algorithm to do so. The second module of APERO starts from the identified 5 ends, and localizes the 3 end of each small transcript, as shown in Figure  1C. Theoretically, since the small transcripts were not fragmented, the paired-end sequencing should immediately allow identifying the 3 boundary from the aligned reads. However, due to experimental bias, spontaneous fragmentation and degradation of 3 ends by polynucleotide phosphorylase (PNPase), small transcript lengths are generally underestimated by this ap-proach.
Starting from an identified 5 end, the method thus consists in iteratively extending the identified small transcript by locating the 3 end of the longest fragments (if their number is sufficient). This end position can then be either the actual 3 end of the transcript, or a mere intermediate point or spontaneous breaking site within the transcript; to distinguish between these two scenarios, the program computes the number of fragments that overlap this position and were not yet counted in the previous iteration (see Figure  1C ). If this number is significant (as compared to the number of fragments starting at the 5 end, i. e. the expression strength of this transcript), the transcript is further extended toward the 3 end of these overlapping fragments and the operation is repeated; otherwise, the iteration stops and this point is defined as the 3 end.
The tendency of the algorithm to increase the transcript size is set by a threshold parameter, the optimal value of which can be obtained automatically from the analysis of the dataset provided by the user (see Materials and Methods and Supplementary Figure  S1 ). The rationale of this method is that the typical size of s is of the same order as that of typical fragments, so that only a small number of iterations should be required to recover the full-length transcripts if spontaneous fragmentation is not too strong. This is indeed what we observe: most small transcripts are found with less than three iterations (85% in the D. dadantii dataset). We used several sources of information on transcript ends in E. coli to control the accuracy of the resulting 3 ends, detected by APERO from the small transcript dataset (SRX4670654).
We first compared the 3 end positions of the 9 riboswitches, 15 attenuators and 312 transcriptional terminators described in EcoCyc, 66% of these are accurately detected by APERO, i. e. with a median distance less than 10 nucleotides (Supplementary File S3). The 3 end positions of 49 sRNAs determined by Term-seq in E. coli  (39)  were also analyzed and 80% of these positions are detected by APERO with a distance less than 10 nucleotides (Supplementary File S3). In addition, we used data from E. coli obtained by SMRT-cappable-seq  (40). 94% of 3 ends identified by this approach were also detected by APERO with a median distance to 3 end of 10 nucleotides (Supplementary Figure  S4 ). These controls show that the module can accurately detected 3 end RNA boundaries. For the following analyses, we discarded internal small transcripts located inside CDSs, which could correspond to mRNA degradation products.