dataesr/scientific-paragraphs-categorization huggingface dataset:
Development of a DIA Acquisition Workflow For comparison purposes with the DDA method, we also implemented a DIA workflow for untargeted metabolomics applications. Such an approach has the benefit that any detected ion will trigger fragmentation (even those below the selection threshold for DDA) and thus its MS/MS mass spectrum will be collected. The method was built on a full scan MS event followed by a SWATH-like fragmentation, typically using consecutive precursor ion isolation windows with variable sizes (often 20-50 Da) to cover the mass range of interest. To guarantee sufficient data collection rate and chromatographic definition, a fixed number of 10 isolation windows was used and resulting MS/MS spectra were collected in the Orbitrap at a 15,000 resolution, while MS acquisition was performed at 120,000 (both at m/z 200, FWHM).
In order to reduce the complexity of MS/MS spectra and the width of isolation windows, the studied mass range was limited to 100-600 Da which still well covers the panel of low-mass range metabolites detected in plasma samples (Supplementary Material, Figure  S6 , lipid species were not considered). The width of the isolation window drastically influences the number of precursor ion peaks isolated and simultaneously fragmented, and thus the complexity of the generated MS/MS spectrum. Therefore, we incrementally devised three distinct DIA workflows involving various isolation windows to obtain the more valuable data. During DIA method development, manual assessment of mass spectral data quality was first performed in a targeted way by specifically looking at the presence of three fragment ions shared with the corresponding spectra from our in-house database.
This was also a first step toward the development of a targeted monitoring/quantification DIA-based approach (Section 3. 4. 2). Then, MS-DIAL software was used to evaluate the consistency of collected MS/MS spectra. The first one made use of the DIA module included in the Orbitrap Fusion TM software, which automatically defines the number of windows (of equal sizes) according to the mass range studied (10 non-overlapping windows of 50 Da in this case) using a single fixed NCE (set at 30%). When evaluated on the pool of 43 compounds, this approach returned relevant MS/MS spectra for 56. 8% of the compounds. When using the "Target MS 2 " acquisition mode of the Orbitrap Fusion software, it became possible to set a stepped NCE of 30% ± 20% and define overlapping 52-Da wide isolation windows. Under these conditions, compound selection and fragmentation were improved, especially at the borders of the isolation windows, thus raising the number of correctly detected compounds to 77.
3%. Last, we devised a third acquisition scheme using variable but overlapping precursor isolation window widths (Supplementary Material, Table  S4 )  [38] , to partition the precursor ion density observed in plasma (Supplementary Material, Figure  S6 ) almost equally across the whole mass range of interest. Under these conditions, up to 90% of the targeted metabolites were efficiently detected in the NIST plasma extract by their fragment ions (Supplementary Material, Table  S1 ), which clearly underlines the advantages of using variable isolation windows. We next performed mass spectral deconvolution as well as similarity matching within the MS-DIAL software (see below), to more objectively compare the data with those from the above described DDA workflow.
Generation of Meaningful MS/MS Spectra to Confirm the Annotation of Plasma Metabolites We next compared DDA and DIA approaches through their respective ability to detect the above-described 72 endogenous metabolites by analyzing in triplicate two independent metabolic extracts of NIST plasma. The methods were qualitatively compared first by manual evaluation and then more thoroughly through DP scores. The DIA workflow provided comprehensive fragmentation of the 72 molecules, whereas only 61% of them were efficiently selected and fragmented by DDA. On average, 2. 5 and 11. 1 MS/MS spectra per compound were obtained with DDA and DIA approaches, respectively. In both cases, ~90% of the generated spectra proved of sufficient quality to enable manual metabolite confirmation while more than 10 data points/peak also yielded well defined chromatographic peaks for further quantification purposes.
Conclusions Performance of the Orbitrap Fusion TM instrument, equipped with a compact high-field Orbitrap analyzer, was studied for the efficient MS/MS data collection in an untargeted metabolomics context. Our data indicated that both DDA and SWATH-type DIA approaches are highly valuable and proved complementary, DDA not surprisingly being more suited for acquiring high-quality and higher-purity spectra. The unprecedented versatility of the Orbitrap Fusion TM enables parallelized DDA acquisition of both high resolution and low resolution spectra as well as concomitant collection of HCD and CID data, which is particularly relevant for identification of unknowns. Interestingly, comparative quantitative evaluation of those multi-event approaches revealed that the acquisition of MS/MS spectra, either in DDA or in DIA, does not downgrade the quality of the quantification at the MS1 level.
While significantly less sensitive (~five times), fragment ion-based quantification from DIA-MS/MS data enables simultaneous metabolite structural confirmation and quantification of known metabolites. Most of the methodological developments reported here on the Orbitrap Fusion and drawn conclusions are also relevant for other Orbitrap-based platforms, such as Q-Exactive or LTQ-Orbitrap instruments. Both DDA and DIA approaches for metabolomics would greatly benefit from improvements in data acquisition speed on Orbitrap mass analyzers as well as in NCE management to better cover the wide heterogeneity of metabolites in terms of optimal fragmentation conditions. Given the results of this study, we conclude that combining metabolite quantification in the MS1 space and DIA-MS/MS for obtaining structural information of all detected compounds and querying high-quality spectral databases (generated by targeted DDA at multiple NCEs), can have great potential for future biological metabolomics applications.
The branch site is a short motif upstream of the polypyrimidine tract that includes a BP adenosine, in 92% of human BP  [3]. During the first step of the splicing reaction the 2'OH of the BP adenosine attacks the first intronic nucleotide (nt) of the upstream 5'ss to form a lariat intermediate  [4]. In the second step, the 3'OH of the 5′ exon attacks the downstream 3'ss thereby releasing the intronic lariat and joining the two exons together. The 5'ss and 3'ss sequences are well characterized, mostly having been experimentally mapped, which allowed the assembly of large datasets of aligned sequences  [5] [6] [7]. Therefore, several reliable in silico tools dedicated to splice site predictions emerged, reaching an accuracy of 95. 6%  [8]. In contrast, the branch sites are short and degenerate motifs that are still poorly known and difficult to predict  [3].
The first tools Human Splicing Finder (HSF)  [14]  and SVM-BPfinder  [15]  used only 14 and 35 experimentallyproven BPs in development. In 2015, a large but not comprehensive dataset of BPs was built from lariat RNA-seq experiments  [10]. This collection of BPs was extended by two further studies: the first used 1. 31 trillion reads from 17,164 RNA-seq data sets  [16] , and the second identified BPs by the spliceosome iCLIP method  [17]. Thus, several bioinformatics tools for BP prediction have recently emerged: Branch Point Prediction (BPP)  [18] , Branchpointer  [19] , LaBranchoR  [20]  and RNA Branch Point Selection (RNABPS)  [21]  (Table  1 ). Briefly, HSF uses a position weighted matrix approach with a 7mer motif as a reference (5 nt upstream and 1 nt downstream of the branch point A) (Fig.
1 ). SVM-BPfinder was the first to take into account, not only the branch site motif, but also the conservation of 3'ss, as well as the AG exclusion zone algorithm (AGEZ)  [11]  derived from the work of Smith and collaborators  [23]. BPP combines the BP and 3'ss sequences and the AGEZ algorithm by a mixture model, a popular motif inference method. Branchpointer uses machine learning algorithms trained from a set of experimentally proven BPs. LaBranchoR and RNABPS are based on a deep-learning approach. LaBranchoR re-used the dataset of Branchpointer and implemented a bidirectional long short-term memory network (LSTM) that was shown to be performant for modeling sequential data such as natural language. RNABPS, as LaBranchoR, used the LSTM model and also implemented a dilated convolution neural network algorithm.
Here, we present a benchmarking of these six BPdedicated bioinformatics tools on their capacity to detect a relevant BP signal and to predict a variant-induced BP alteration. The resolution of the first issue allowed highlighting the specificity of each tool, i. e. the identification of BPs among background noise. For this part, we used two sets of data: a large set of 3'ss described in Ensembl database and a series of alternative 3'ss observed in RNA-seq experiments. The detection of BP alteration by a variant represents also a challenge for molecular diagnostics. To this end, we used an unprecedented collection of human variants (within the BP area) with their in vitro RNA studies to assess the prediction of variant effect on BP function.
Bioinformatic detection of branch points among the physiological and alternative splice acceptor sites In this study, two sets of 3'ss data were used, 3'ss described in Ensembl dataset and alternative 3'ss with their expression data from RNA-seq analyses (Table  2 ). The running times showed that BPP is one of the faster tools and Branchpointer one of the slower tools (Additional file 1: Figure  S3 ). We first retrieved 264,787 Ensembl 3'ss from the Ensembl data. Adding to these 3'ss, 114,603,295 random AGs were used as control data (see the "Methods" section for details). Thus, we collected 114,868,082 3'ss. ROC curve analysis was then performed for SVM-BPfinder, BPP, LaBranchoR and RNABPS on the set of Ensembl 3'ss, as illustrated in Fig. 2a. Table  3  shows the levels of accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) derived from these ROC curve analyses.
BPP also was not trained with a collection of false BPs, and SVM-BPfinder was only trained on putative BP. Thus, on our Ensembl data, Branchpointer is more powerful to detect the BPs among the background noise, i. e. the unexpected BPs sequences with random AGs (see the "Methods" section for details). Secondly, Branchpointer takes into account the structure of transcripts unlike LaBranchoR and RNABPS. Indeed, Branchpointer considers only the prediction of BPs occurring in -44 and -18 upstream of 3'ss. The relative expression of junctions was significantly correlated to the bioinformatic scores. However, these correlations remain weak, with a maximum coefficient of determination (R 2 ) of 0. 0062 for RNABPS. Added to this, even if Branchpointer had shown the best performance, the sensitivity of Branchpointer decreased by almost 60% (95.
With this strategy the best score was obtained by BPP with an accuracy equal to 89. 17%. We observed that only 31. 7% (38/120) of variants altered the splicing in the BP area while 82. 05% (32/39) alter splicing in the BPP-predicted BP motif with a sensitivity of 84. 21%. These results demonstrate the potential of BPP for prioritizing variants occurring in this region for molecular diagnostic laboratories. From our dataset, we first determined, that the 4mer TRAY in the BP motif was the most impacted by variants. A variant occurring in this motif has a high probability to alter splicing. In our work, this probability was 82% with BPP tool while the proportion of variants affecting the splicing outside this motif was 7. 4%. This bioinformatic tool takes into account several features in and around the 4-mer motif.
From a practical view point, the combination of scores makes the predictions of BPs less accessible. The accessibility of the tools represents a technical limit to the analysis of BP. Indeed, HSF, SVM-BPfinder and RNABPS have a Graphical User Interface web page for non-bioinformatician users. However, LaBranchoR and BPP score calculation was only accessible by a python script. LaBranchoR also offers a list of potential BPs predicted by the tool and visualization via the UCSC Genome Browser  [27]. Branchpointer is only accessible by an R package and needs the installation of several other libraries. Due to machine learning calculation, this tool also has the longest run-time. The score calculation for the Ensembl data set (n = 114,868,082) with a Linux machine AMD® Ryzen 7 pro 1700 eight-core processor, 8 Gb of RAM with multiprocessing way (6 at the same time) took more than two weeks, instead of a couple of days for SVM-BPfinder.
Conclusion Our study spotlighted the requirement to distinguish two issues, the capacity to detect a real BP and the capacity to predict the splicing alteration at BP level. Branchpointer exhibited the best performance to detect a real BP from our Ensembl and RNA-seq data. For research purposes, Branchpointer facilitates the study of alternative transcripts by predicting the most likely used alternatively spliced 3'ss. However, the BPP-predicted BPs were more efficient to predict the impact of variants on BP usage. Furthermore, BPP was able to predict 4mer BP motifs, with an accuracy of 89. 17%. Using a large collection of human variants (n = 120) with associated RNA in vitro splicing data, we confirm the advantage of studying the BP area ([-44-18] intronic positions) for application to molecular diagnostics.
Sets of data The Ensembl dataset contains the coordinates of a large collection of transcripts  [28] , with more than 200,000 human transcripts (download June 28th 2018). We extracted the position of exons for each described transcript then we deduced the coordinates of splice sites. As random data, we took all AG sequences found in each transcript sequence, named hereafter random AGs. For each 3'ss, the genomic coordinates were annotated according to the hg19 genome assembly. We defined as alternative splice sites all 3'ss identified from RNA-seq data that were not described in the transcripts from the RefSeq dataset  [29]  The alternative 3'ss were obtained from our in-house RNA-seq analyses. The read count mapped on these last RefSeq 3'ss served as a reference to calculate the relative expression of the alternative 3'ss.
Whole transcriptome RNA-Seq experiment was performed on 72 RNA samples corresponding to lymphoblastoid cell lines (LCLs) from four control individuals and eight patients with pathogenic variants in TP53 or in the BRCA1/2 genes, treated and untreated with bleomycin or doxorubicin, and performed in triplicate. Ribosomal RNA was depleted using the NEBNext® rRNA Depletion Kit (Human/Mouse/Rat) (NEB, Ipswich, MA, USA) and libraries were produced using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB). 2x75b paired-end sequencing was performed on an Illumina NextSeq500 yielding an average of 50 million paired reads per sample. Reads were aligned on the Ensembl reference genome GRCh37 release 75 (ftp://ftp. ensembl. org/pub/) using STAR v2. 5. 3a tool (Spliced Transcripts Alignment to Reference)  [30]  and counting was performed using FeatureCounts tool v1.
The percentage of expression permitted the estimation of a weighted expression that allows for the versatility of splicing events. The tool SpliceLauncher v2 was used to perform the calculations and also to detect abnormal splicing junction expression  [33]. As control data for the set RNA-seq data, we took any 3'ss that had a MaxEntScan  [34]  score higher than 0 but was not identified in the RNA-seq data or in the RefSeq database. We called its control 3'ss. Among these control 3'ss, we randomly selected 3'ss so that the number of control 3'ss was equivalent to the number of alternative 3'ss. The last set of data was a collection of potential spliceogenic variants, characterized by experimental RNA studies, occurring in the BP area (from -18 to -44 relative to the 3'ss) of 36 genes. Briefly, this dataset included RT-PCR data obtained from (i) minigene-based splicing assays (by Inserm U1078 and by Inserm U1245 teams), (ii) RNA extracted from lymphoblastoid cell lines treated/untreated with puromycin, (iii) RNA extracted from blood collected into PAXgene tubes (Qiagen), (iv) RNA extracted from stimulated T lymphocytes provided by the French Splice Network of the Unicancer Genetic Group.
Assessment of bioinformatics tools Six BP-dedicated in silico tools were tested: HSF v3. 1, SVM-BPfinder, BPP, Branchpointer v3. 8, LaBranchoR and RNABPS (Table  1 ). On the other hand, we were confronted with an inaccessible tool, the BPS predictor  [36] , at the time of this work, so it was excluded from this study. HSF v3. 1 did not allow browsing of a wildtype sequence to detect potential BPs, and gave only the score change of a variant. For the other tools, we used the standalone versions that were: python scripts for SVM-BPfinder, BPP and LaBranchoR and R package for Branchpointer. For the tools SVM-BPfinder, BPP and Branchpointer, we narrowed the browsed sequence by these 3 tools to include 1 to 200 nt upstream of the 3'ss. LaBranchoR and RNABPS need a 70 nt long sequence, and the browsed sequence was the 70 last nt of the intron.
We started by using five out of the six tools (exclusion of HSF) to define a list of predicted BPs in the browsed sequences from each intron that are affected by the variants in our dataset. Next, we took only one BP with the highest score per intron, for each tool. To determine whether the variant was located in the motif of the predicted BP, we assayed different motif sizes from 1 nt (corresponding to the branch point A) up to 7-mer around the A, i. e. the 3 nt on either side of A. The 7mer motifs corresponded to the length of position weight matrices used by the majority of the tools (Fig. 1 ). We established the optimal motif size as having the best compromise of sensitivity and specificity across all tools. The second question involved the calculation of a delta score defined as follows:.
Microscopy results. Thirty-one of our 47 samples were examined using microscopy. A total of 101 genera were detected by microscopy. Of these, 36 met the criterion for major genera. Fifteen major genera, including 5 of the top 10 most frequently detected genera, were known to have been cultivated in one or more of the nursery study sites (Fig. 2 ). For full, tabulated results, see Supplemental Material. comparison of metabarcoding and microscopy. Across the 31 samples for which complementary metabarcoding and microscopy data were available, 62 genus detections (30%) were made by metabarcoding but not by microscopy, 36 genus detections (18%) were made by microscopy but not by metabarcoding, and 105 genus detections were made by by both (52%) (Fig. 3 ). For genus detections shared by both methods ("shared detections"), there was a positive but very noisy linear relationship (F = 30.
89, adjusted R 2 = 0. 14, P = 9. 8 × 10 -8 ) between read count (metabarcoding) and grain volume (microscopy) (Fig. 3 ). Six of the 36 genera detected by microscopy but not by metabarcoding (Cytisus, Equisetum, Eriogonum, Liatris, Nyssa, and Securigera) were absent from the ITS2 reference database and thus undetectable by metabarcoding. Of these, only Eriogonum qualified as a major genus in the microscopy data set. comparison of genera detected with genera in nursery inventories. Nursery B's inventory contained a total of 228 genera. Of these, 40 were detected in our pollen samples (Fig. 4C ), and 22 qualified as major genera (Fig. 4A ). Nursery C's inventory contained a total of 103 genera. Of these, 44 were detected in our pollen samples (Fig. 4D ), and 18 qualified as major genera (Fig.
Discussion taxonomic composition of pollen samples. An important caveat for this discussion is that genus-level identification cannot determine conclusively whether, in our study system, a given taxon represents a cultivated ornamental plant. Indeed, all of the putative ornamentals that occur as "major genera" in our samples have native or naturalized representatives in our study region  17  , and the kilometer-scale foraging range of honey bees  18  extends well beyond the bounds of our nursery sites. Thus, the provenance of each genus in our dataset should be interpreted cautiously. The occurrence of Acer, Artemesia, Celastrus, Eupatorium, Rhus, Salix, Solidago, and Vitis in our samples should be attributed primarily to foraging on wild plants, since wild representatives of these genera occur abundantly in our study region and most are known to attract honey bee foraging  19.
For the remainder of the putatively ornamental genera in our dataset, their occurrence can plausibly be attributed to some combination of foraging on nursery stock, cultivated or naturalized ornamental plants in the surrounding landscape, and wild congeners. In some cases, the known phenology of wild congeners can help parse the data. For example, the genus Rosa was the most frequently detected genus in our metabarcoding data set, but it occurs commonly both as ornamental cultivars and wild congeners. In our study region, wild Rosa is represented primarily by invasive Rosa multiflora, which blooms in June. Thus, Rosa detected in June, when peak Rosa abundance was observed in our samples, probably represents mostly wild R. multiflora, while the Rosa detected July-October probably represents mostly ornamental cultivars.
It is worth noting the conspicuous absence of the family Ericaceae in both metabarcoding and microscopy datasets despite being represented by abundant plantings of Rhododendron at all our nursery sites. The pollen of Rhododendron is carried on viscin threads that bind together individual pollen grains and make it difficult for  bees to release pollen from the anthers and manipulate it into pollen loads  25. Some Rhododendron species can be highly attractive to honey bees, but the recovery of Rhododendron pollen from pollen traps would be unlikely even in circumstances when bees are visiting it for nectar. Both metabarcoding and microscopy attest to the importance of Nymphaea (water lily) as major pollen source from early July to early August. Not mentioned in Ayers and Harman's  19  survey of honey and pollen plants, Nymphaea has perhaps been overlooked as a significant floral resource for honey bees, and probably other pollinators, including Lasioglossum sweat bees  26.
Metabarcoding, for example, seems to have greatly underestimated the abundance of Spiraea and completely missed Elaeagnus and Iris despite these taxa being represented in the reference database. Whether these artifacts are due to negative PCR bias or taxonomic misassignment is uncertain. On the other hand, metabarcoding seems to have done a better job resolving genera within the diverse Asteraceae family (i. e. Ambrosia, Arctium, Artemisia, Cirsium, Erechtites, Erigeron, Eurybia, Euthamia, Eutrochium, Mikania, Solidago, Symphyotrichum, Taraxacum, Verbascum) whose cryptic pollen morphology presents a greater challenge to microscopy. Considering only the subset of shared genus detections, the quantitative relationship between the two methods was statistically significant, but too noisy to be considered predictive for any particular observation, consistent with past studies that have found ITS2 to be quantitatively problematic as a pollen metabarcoding marker  22, 34.