dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work, we have re-analysed phospho-enriched rice datasets and integrated results into PeptideAtlas, PRIDE and UniProtKB, for visualisation of the confident phosphosites alongside other data on rice proteins. Downstream analysis is also performed on the confident sites to identify PTMs which may be of biological interest. These analyses include investigations on common motifs seen around the phosphosites, pathway enrichment analysis for these motifs and analysis of single amino acid variations (SAAVs) identified close by to the confident phosphosites. From these analyses, we aim to identify rice phosphoproteins which may be of biological interest.
Phosphosite identification The ProteomeXchange Consortium  [25]  was used to identify suitable rice phosphoproteomics datasets, via the PRIDE repository  [26]. From this, 111 proteomics datasets were identified for the Oryza sativa species. Of these, 13 were identified as being enriched in phosphopeptides and then potentially suitable for reanalysis: PXD000923  [27] , PXD001168  [28] , PXD000857  [28] , PXD002222  [29] , PXD001774  [30] , PXD004939  [31] , PXD005241  [32] , PXD004705  [33] , PXD002756  [34] , PXD012764  [35] , PXD007979  [36] , PXD010565  [37]  and PXD019291  [38]  (Supplementary table  1 ). These 13 datasets were investigated further to evaluate their quality for use within the phosphoproteomics reanalysis. It was found that PXD001168, PXD001774 and PXD010565 contained very few phosphopeptides for the size of the dataset, these datasets were therefore excluded from the analysis.
PXD007979 was identified to be a meta-analysis of the PXD002222 and PXD000923 datasets and was therefore also excluded. Finally, PXD000857 was excluded as this is a relatively old dataset containing only one raw file. This resulted in 8 high quality datasets being carried forward for the phosphopeptide re-analysis. Sample and experimental metadata were manually curated and adhering to the Sample-Data Relationship Format (SDRF)-Proteomics file format  [39]. The search database was created consisting of protein sequences derived from the MSU Rice Genome Annotation Project, the Rice Annotation Project Database (RAP-DB), including both translated CDS and predicted sequences, and UniProtKB, including both reviewed and unreviewed sequences. A fasta file was generated from the combination of these databases, if a protein sequence occurs in more than one resource, RAP-DB was used as the primary identifier, this was then followed by any other IDs for that protein.
cRAP contaminant sequences were also added to the database (https://www. thegpm. org/crap/, accessed April 2022) and decoys across all protein and contaminant sequences were generated for each entry using the de Brujin method (with k=2)  [40]. The database was deposited in PRIDE along with the reprocessed data files (PRIDE ID: PXD046188). The analysis was conducted using the pipeline as previously described  [16]. Using the Trans-Proteomic Pipeline (TPP)  [41, 42] , the dataset files were first searched using Comet  [43]. The resulting files were then combined and processed using PeptideProphet  [44] , iProphet  [45]  , and PTMProphet  [46] , for each dataset. The files were searched with the variable modifications: Oxidation (MW), N-terminal acetylation, ammonia loss (QC), pyro-glu (E), deamination (NQ) and phosphorylation (STYA).
Phosphorylation on alanine was included as a decoy to estimate false localisation rate (FLR), using the count of pAla identified, following the methods previously described by our group  [16]. Carbamidomethylation (C) was used as a fixed modification and the iTRAX8plex label was included for the search on the PXD012764 dataset. Maximum missed cleavage used was 2, with a maximum number of modifications per peptide of 5. Table  1  outlines the datasets used and the tolerance parameters used for each dataset. The data files obtained from searching with TPP were processed by custom Python scripts (https://github. com/PGB-LIV/mzidFLR). The data was analysed in the same way as in a previous study  [16, 17]. The global FDR was calculated from the decoy counts and the peptide-spectrum matches (PSMs) were filtered for 1% PSM FDR.
This adjustment considered the number of times a specific site has been seen and the number of times this same site has been seen as a phosphosite, allowing us to give weight to those sites that are supported by multiple PSMs. The global FLRs for all the datasets were estimated using the identification of phosphorylated Alanine (pAla) as a decoy. These are known to be false localisations and can therefore be used to estimate the FLR, following the method previously established  [16] , alongside the binomial adjustment. Global FLR was estimated for every ranked site, across all PSMs and in the collapsed protein position site-based format, from which we can later apply a threshold at the lowest scoring site that delivers a desired global FLR (e. g. 1%, 5% or 10%), similar to the q-value approach for standard proteomic database searching.
When aggregating data across multiple studies, we must control the inflation of FLR. FLR inflation is observed when the same correct sites are seen identified across multiple studies and tend to accumulate slowly, whereas each study reports different and random false positives which then accumulate rapidly as more datasets are added. PTM localisation has been shown to be incomparable between independent studies  [17]. As a result, we developed an empirical approach to categorise sites based on the observations of sites across datasets at different thresholds; Gold, Silver and Bronze. Gold represents sites seen in n dataset with <1% FLR, Silver represents sites seen in m datasets with <1% FLR and Bronze represents any other sites passing <5% FLR. The values for n and m can be set empirically in a PTM "build" based on the number of datasets and the counts of decoys following the aggregation of multiple datasets, and application of possible values of n and m.
Dataset deposition and visualisation The reprocessed data has been deposited in PRIDE in mzIdentML format  [47] , as well as SDRF-Proteomics files, tab-separated text formatted files (one per dataset) containing sites detected per PSM, and sites detected for each peptidoform, following the collapse processed described above (PRIDE ID: PXD046188). To load all phosphosites into UniProtKB, the identified peptides were mapped to the canonical protein sequences within the proteome (UP000059680) using an exact peptide sequence match, following theoretical tryptic digest. The phophosites can be viewed in the Protein APIs, in the Feature Viewer under Proteomics track and in the entry page. Decoy sites were not loaded to avoid misinterpretation. All results have also been loaded as a PeptideAtlas build, available at https://peptideatlas.
org/builds/rice/phospho/. The PeptideAtlas interface allows browsing of all modified peptides from these datasets, including those passing and not passing the above thresholds. All localization probabilities for all PSMs are displayed, along with links to the original spectra that may be visualized in the PeptideAtlas interface. The corresponding mass spectra in PeptideAtlas and PRIDE (https://www. ebi. ac. uk/pride/archive/usi) can be referenced and accessed via their Universal Spectrum Identifiers  [48].
Motif and pathway enrichment analysis All eight datasets were further investigated using motif and pathway enrichment analysis. Once the confident phosphosites (5% pAla FLR) from each of the datasets had been combined and given an FLR ranking (Gold, Silver, Bronze) the enriched motifs surrounding phosphosites were identified using the R  [49]  package rmotifx  [50]. 15mer peptides were generated surrounding each of the identified phosphosites. These phosphopeptides were compared against a background of 15mer peptides with STY at the central position of the 15mer, and matched to the central residue of the phosphosite motif, to identify the enriched motifs seen around the confident phosphosites. The proteins containing these enriched motifs were then carried forward for pathway enrichment analysis using ClusterProfiler  [51].
SAAV Analysis We also explored the phosphosites across all datasets we have re-analysed and how SAAVs may potentially affect these sites. We compiled a list of unique phosphosites (by sites on unique peptides) from the confident phosphosites (5% pAla FLR) across all eight searches and created a matrix showing which sites were seen in each dataset. These were then also mapped to the relevant protein sites in the three search databases: MSU, RAP-DB and UniProtKB. We mapped this list of unique phosphosites to known SAAV positions for the 3,000 rice varieties using the Rice SNP-Seek Database API (Application Programming Interface), for those sites that mapped to the MSU database. We categorised the phosphosites with relation to the SAAV sites; where "Category 1" = SAAV at the same position as a phosphosite, "Category 2" = SAAV at the +1 position to a phosphosite, "Category 3" = SAAV at the -1 position to a phosphosite and "Category 4" = SAAV at +/-5 amino acids from a phosphosite (and not in Category 1, 2 or 3).
All other sites were assigned "Category 0". For each phosphosite in the unique list across all datasets, the nearest SAAV to each phosphosite was identified and categorised. For those protein phosphosites with SAAV data available, we then investigated which alleles carried the SAAV and the minor allele frequencies for each site. We also added in annotation to show the genes involved, obtained using the Oryzabase database  [52]. From this analysis, we could identify candidate sites of potential biological importance, which may be disrupted due to SAAVs. A protein multiple sequence alignment was created using Clustalx 2. 1, for the example protein Os09g0135400, versus the same locus in 15 other Oryza sativa genomes, which have been annotated and deposited in Ensembl Plants and Gramene  [53, 54].
Phosphosite identification First, we re-analysed each of the rice datasets identified as suitable for phosphosite identification using TPP as explained in Methods (Figure  1 ). Our scripts were used to calculate the FLR across confident PSMs identified (filtered for 1% FDR). We next collapsed the sites by protein position and remove duplicated hits. The FLR estimation was then recalculated on these collapsed sites, ordering by the calculated probability that a site had been observed, considering evidence across multiple PSMs (see Methods). The counts of phosphosites passing at different FLR thresholds are shown in Table  2  and Figure  1a  (counts are derived from the unique combinations of a peptidoform and phosphosites within those peptidoforms, i. e. not accounting for some peptidoforms mapping to more than one genomic locus (gene)).
Different datasets contributed between ~700 and ~4,000 sites at the strictest FLR 1% threshold, and between 1,700 and ~14,000 sites at 10% FLR. When performing PTM site localisation, there is a steep drop off in sensitivity when applying strict FLR thresholding (at say 1%), compared to weak thresholding (10% FLR) or say performing no explicit FLR thresholding -indicative of the challenge of confident site localization. Table  1  displays the counts of sites observed in the original studies and the statistical controls performed. Site counts ranged from ~2,000 up to ~9,000. In no original analysis (as published originally) was global FLR estimated, although this is not surprising since methods for accurate FLR estimation have not been well described until recently. While some ad hoc score thresholds were applied for local (i.
e. per PSM) site scoring e. g. PhosphoRS > 0. 9, this does not easily translate to a global (i. e. across the entire dataset) FLR, and thus it is reasonable to assume that there were variable (sometimes high) rates of FLR in different studies. The tissues used for each dataset are shown in Table  1. These include flower, leaf, anther, shoot, panicles (young and mature), root and pollen samples. Although we can make no quantitative claims about site occupancy in a given tissue, by showing the tissues present in each dataset, a reader can infer if a given site has been seen in specific tissues. We next performed a simple meta-analysis by combining all datasets, and assigned sites labels based on their scores and occurrences in datasets: Gold-Silver-Bronze (Table  3 , Figure  1b ). Decoy identifications of pAla were carried forward, enabling validation of the false reporting within these subsets.
There are only two pAla hits within the Gold set, indicating that the overall FLR is very low in this subset. The meta-analysis also demonstrates that within each set, the reported counts for pTyr are relatively similar to pAla (taking into account that Ala is more abundant than Tyr in the proteome), indicating that pTyr hits reported for these datasets are likely to be mostly/entirely false positives, and should be treated with caution when interpreting any reported observations of pTyr from these datasets in rice. We recorded five unique Gold category pTyr sites. When looking at the scores of the spectra supporting these sites (Supplementary Figure  1 ), it was seen that most of these had only weak evidence supporting them and may be false positives. In Figure  1c , we display the counts of sites depending in the source database -13,425 sites were identified on peptides within proteins from all three databases (MSU, RAP-DB and UniProtKB).
Data visualisation The gold-silver-bronze classified data has been loaded into UniProtKB for visualisation alongside other datasets and information available for rice. As one example, the phosphopeptides can be viewed in the context of AlphaFold2 (AF2)  [56]  predicted protein structures (Figure  2 ). The protein visualised in this case is OSCA1. 2 (hyperosmolality-gated calcium-permeable channel 1. 2, UniProtKB: Q5TKG1, MSU: LOC_Os05g51630, RAP-DB: Os05t0594700) and has a pSer at position 50. The AlphaFold prediction suggests that the serine forms a hydrogen bond with Arg36. It has been shown that phosphorylation can strengthen hydrogen bonds with Arg residues  [57] , and thus the pSer may have a structural role. With the widespread availability (now) of both phosphosite and structural data from AF2 models in UniProtKB, this provides a significant resource for rice cell signalling research.
The build has also been loaded into PeptideAtlas, enabling browsing or searching the evidence for individual sites, peptides and proteins. An example of evidence supporting a phosphosite identified on a peptide is shown in the Supplementary materials (Supplementary Figure  2 ). We also demonstrate how Universal Spectrum Identifiers (USIs) can be used to visualise spectra supporting modification positions and can be a valuable tool to investigate the evidence supporting identified modifications (Supplementary Figure  3 ). The USI with the highest site probability for identified sites can be located in Supp Data File 3. Loading one of these USIs via https://proteomecentral. proteomexchange. org/usi/ imports the spectra and the claimed identification. By altering the position of the modification, it is possible to test which ions support an alternative hypothesis (site position in the peptide).
SAAV Analysis We next assigned all phosphosites into five categories, determined in relationship to known non-synonymous SNPs (i. e. single amino acid variants -SAAVs) from the rice 3,000 genome set  [59] , whereby a category 1 site has an amino acid polymorphism in the reference genome (Nipponbare), causing a loss of this phosphosite in some other varieties (Figure  4 ). In the whole dataset, excluding pA decoy sites, there are 388 category 1 sites (Figure  4a ), which are further explored in Figure  4b  showing the most commonly substituted amino acid. Over-represented substitutions included S->L. Under-represented substitutions were D/E/H/K/Q/V. S/T->D mutations are potentially of great interest as Asp can mimic pSer/pThr as a constitutively active phosphorylation site, which could be a dominant allele for breeding.
1 has high homology to NOT family transcriptional regulators, and has characteristic domains of this family, and should be renamed in OryzaBase. The SAAV analysis presented above generated "pseudo-protein" sequences by substituting amino acids, based on short DNA read data from the 3K rice genome set, which have been mapped against the Nipponbare reference genome. There is thus potential for assumed SAAVs to be incorrect, due to sequencing errors (as the 3K set does not always have high depth of coverage), if gene structure genuinely differs across different varietal groups, or if the RAP-DB or MSU gene model for Nipponbare is not correct. To validate the SAAV data, we also mapped the phosphosites to recently released gene models for 16 new rice varieties, called the "MAGIC-16"  [63]. In Figure  6 , we display the protein sequence alignment across orthologs for cluster 1 protein LOC_Os09g04990.
Conclusions In this work, we have performed a meta-analysis of phosphoproteomics datasets for rice, mapped onto the reference Nipponbare proteome. The pipeline includes conservative statistics to avoid reporting false positives, and a simple Gold-Silver-Bronze metric allowing users of the data to focus understand the likelihood of a site being correct. The dataset has been deposited into UniProtKB enabling sites to be analysed alongside any other data held there about protein structure/function, including AlphaFold2 predictions for all rice proteins. The data is also available in PeptideAtlas.
993 0. 829 0. 854 0. 234 gene. :PF06862. 5;DUF1253;Family_53 LOC_Os01g12530. 1 Silver 581 S S->G 0. 857 0. 925 0. 806 0. 055 PF08590. 3;DUF1771;Domain_581 Zinc finger, LIM-type domain containing LOC_Os03g15940. 1 Bronze 196 S S->F 0. 961 0. 978 0. 854 0. 224 protein. :PF00412. 15;LIM;Domain_196 Similar to ENTH1 protein LOC_Os04g51120. 1 Silver 220 S S->G 0. 975 0. 736 0. 738 0. 179 (Fragment). :PF01417. 13;ENTH;Domain_220 Tho complex subunit 7 domain containing LOC_Os08g06360. 1 Bronze 69 S S->G 0. 905 0. 941 0. 816 0. 055 protein. :PF05615. 6;THOC7;Family_69 IQ motif, EF-hand binding site domain containing LOC_Os05g43670. 1 Bronze 365 S S->G 0. 997 0. 959 0. 874 0. 060 protein. :PF02179. 9;BAG;Family_365.
1 Bronze 53 S S->G 0. 859 0. 090 0. 476 0. 060 Nucleotide-binding, alpha-beta plait domain containing protein. :PF00076. 15; RRM_1; Domain_53 LOC_Os03g44900. 1 Silver 387 S S->P 0. 652 0. 497 0. 612 0. 055 CUL1; CULLIN 1Not CCR4-Not complex component, N-terminal domain containing protein. PF04153. 11; NOT2_3_5; Family_387 LOC_Os03g15580. 1 Silver 71 T T->A 0. 732 0. 051 0. 359 0. 025 Hypothetical conserved gene. :PF03215. 8; Rad17; Family_71 LOC_Os10g37340. 1 Bronze 86 S S->A 0. 710 0. 236 0. 417 0. 104 RRJ1; Cystathionine &gamma; -lyase: PF01053. 13; Cys_Met_Meta_PP; Domain_86.
Then, the equivalent models of these cells are interconnected according to the overall topology. The main variables of concern are the currents/voltages of the transistors. The dynam of each one are represented by coefficient-varying transfer functions (TFs). These coefficie vary, on the one hand, in order to gather both turn-ON and turn-OFF dynamics in a uniq TF and, on the other hand, in order to approximate the actual behavior of each power swit Then, the experimental voltage/current data of each transistor are periodically collected a provided to a system identification algorithm that generates the best fitting TF (in this wo the Matlab System Identification toolbox is used). As can be seen in Figure  1b , for each of the extracted commutation cells, four unif transfer functions (HiT1(z), HvT1(z), HiT2(z), and HvT2(z)) are then developed and execu simultaneously, providing a full parallel structure.
The dynamics of each one are represented by coefficient-varying transfer functions (TFs). These coefficients vary, on the one hand, in order to gather both turn-ON and turn-OFF dynamics in a unique TF and, on the other hand, in order to approximate the actual behavior of each power switch. Then, the experimental voltage/current data of each transistor are periodically collected and provided to a system identification algorithm that generates the best fitting TF (in this work, the Matlab System Identification toolbox is used). As can be seen in Figure  1b , for each of the extracted commutation cells, four unified transfer functions (H iT1 (z), H vT1 (z), H iT2 (z), and H vT2 (z)) are then developed and executed simultaneously, providing a full parallel structure. The varying coefficients are previously stored in memories.
System Identification Process The values of the coefficients and the N-order of each TF depend on the desired fitting level during the system identification process. To this aim, Matlab/Simulink System Identification toolbox is deployed to generate discrete-time linear models from the measured voltage/current. The choice of the TF order depends on the desired approximation accuracy and fitting level (trial-and-error process with a threshold set to 90% in this work). Figure  3  shows the approximation results obtained for a MOSFET SiC power switch (CMF20120D) that operates with a 500 kHz switching frequency. The measured per-unit voltage and current are sampled and averaged at a 50 ns time resolution (equal to the adopted simulation time step). For both commutations, the identification process is given 3rd-order transfer functions, with fitting levels satisfying the 90% constraint.
Application to a Three-Phase Voltage Source Inverter This section presents the eRTS of a three-phase VSI supplying a three-phase RLE load. Figure  5  highlights the corresponding architecture. Each leg corresponding to a commutation cell is represented by a dedicated eRTS module (Figure  5 ). Each one generates the voltages/currents of its inherent transistors/diodes. The voltages vT2, vT4, and vT6 are used to generate the three-phase load voltages according to the following equation. As for the FPGA implementation, the whole designs that are presented in the following sections have been developed manually in VHDL and through the Xilinx ISE design platform, following a dedicated design methodology  [28]. All the RT validations are performed using the Artix-7 AC701 Evaluation Kit provided by Xilinx-AMD  [29].
= 1 3 2 1 1 1 2 1 1 1 2 𝑣 𝑣 𝑣 (2) The supplied load consists of a three-phase balanced RLE series (R = 5 Ω; L = 15 mH). Its corresponding eRTS is based on the forward Euler discrete-time model that generates the load current according to the following equation: 𝑖 = 𝑏. (𝑣 𝑘 1 𝑒 𝑘 1 ) + 𝑎. 𝑖 𝑘 1 (3) where  = 1  ;  = ;  = (, , ); and  is the RT simulation time step (set to 50 ns). The controller is not deepened in this paper but consists of generating three-phase sinusoidal voltage references assuming that the back-EMF terms are compensated. The amplitudes of these references are equally set to 0. 25 pu (over VDC = 600 V) and their frequencies to 200 Hz. The switching signals are generated through a 100 kHz PWM module. The overall architecture is implemented in the Xilinx Artix-7 XC7A200T-2 FPGA device, and a 200 MHz system clock is used for synchronization.
The controller is not deepened in this paper but consists of generating three-phase sinusoidal voltage references assuming that the back-EMF terms are compensated. The amplitudes of these references are equally set to 0. 25 pu (over V DC = 600 V) and their frequencies to 200 Hz. The switching signals are generated through a 100 kHz PWM module. The overall architecture is implemented in the Xilinx Artix-7 XC7A200T-2 FPGA device, and a 200 MHz system clock is used for synchronization. The eRTS modules of the VSI legs and the load are both executed fully in parallel. With such organization, the total execution time is still equal to 45 ns, which allows operation with a 50 ns simulation time step. In terms of hardware resources and with the adopted fixed-point format (25Q18 for the leg eRTS and 32Q28 for the load), the overall design uses 6088 flip-flops, 3790 lookup tables (2.