dataesr/scientific-paragraphs-categorization huggingface dataset:
The MCC was calculated for each of the 21 residues for each of the defined position between P n to P -n. The result of the MCC provided an overview of the "abundance" for each residue at a specific position (P i ) based on the training dataset. In accordance with our scoring scheme (1 for the neutral value), the MCC results were translated by 1 unit (tMCC). This tMCC matrix (defined for each of the 20 amino acids on a "±n" residues around the starting position of the peptides present in the training dataset) was used to determine the "Spec" score for each experimental candidate (with a starting position higher than 2) as a product of the tMCCs values for each position between P -n to P n (Eq. 1). The size of the screening window (±n) was also optimized automatically to determine the optimum MCC value.
Alternative start positions (Prox score) Despite proteins processing sites are usually considered to be unique, experimentally-based results tend to display a different reality involving multiple vicinal cleavage positions  [7, 8, 17]. This provides clear and highly valuable distinctive criteria compared to internal protein fragments. The number of potential cleavage sites was determined in a defined window around the investigated position. Both the window size and the coefficient-weight could be defined in the parameters file (Additional file 4), and the "prox score" for this coefficient was obtained using Eq. 2. Initial parameters should be defined in the configuration file and optimized using the reference dataset but usually these multiple and vicinal cleavages  [3, 17]  are observed in a windows of ± 5-10 residues (defined at will in the parameter file).
Protein N-terminal scoring at position 1-2 Since a negative dataset could not be defined for the Ntermini at position 1 and 2, automated optimization of the score is not possible. The "Spec" score for these peptides is set at the optimized "Spec-score-threshold (automatically defined during parameters optimization) to favor the final NTA quantification of these peptides. To note, the other scoring coefficients (i. e. N-terminal modification characterized and peptide redundancy) were applied for these positions. Then, the final En-COUNTer score for these peptides (Position 1-2) could not be compared with the DNT associated scores.
Scoring parameters optimization and calculation Since few parameters such as sample preparation or species influencing the type and number of downstream Ntermini (True or False hits), a test sample dataset should be used to optimize the parameters. Alternatively, default parameters are provided for the A. thaliana samples. The EnCOUNTer tool is able to optimize few scoring coefficients (i. e. the optimum downstream N-termini range and the "vicinal cleavage site" scoring matrix) using a reference files. Some other parameters are not optimized automatically and could be subject to modification in the parameter file. Each of them (Prox, Rep and Loc scores) should be defined before the EnCOUNTer optimization to determine automatically the EnCOUNTer threshold. This threshold is optimized using an MCC approach at the end of the optimization step.
NTA quantification function Mascot Distiller is an interesting tool to determine d0/ d3-Acetylation yield for each characterized peptide. Although, final quantitative values are provided per protein and not per protein starting position, the quantitative data remain available in the MD xml exported file. The EnCOUNTer script re-organizes them to provide Nterminal acetylation yield for each distinct proteoform. Since MD processing was performed using relaxed parameters (see Mascot Distiller data processing section), EnCOUNTer filters those data to retain only the most relevant ones for the NTA quantitation based on MS signal quality coefficients defined by MD. EnCOUNTer tool uses the "Correlation Coefficient" (related to the fitting between the theoretical and experimental isotopic distribution; higher than 0.
8), the "Fraction Coefficient" (defining the fraction of the signal of the peak of interest over all signal; usually higher than 0. 5), the SigQual coefficient is associated to the H/L standard deviation (defined by the least squares fit to the heavy vs. light component intensities from the scans in the XIC peak; lower than 0. 05 Da), the E-value (lower than 0. 05), the Mascot score associated to the matched query (higher than 30; highly dependent of the database used) and finally the EnCOUNTer score threshold (automatically defined as previously described). These coefficients could not be defined by default and are strongly related to the raw MS signal quality. They should be adapted accordingly to the instrument (MS and LC) used for sample separation and analysis. The characterised peptides passing those criteria were used to determine the final H/L ratio for each distinct protein positions based on a logarithmic means.
EnCOUNTer data export The final results were exported in a *. csv file providing protein AC's, the proteotypicity, the starting position, the N-terminal modifications characterised, the mature N-terminal sequence (first 10 residues after the starting position), the EnCOUNTer score, the < H/L > ratio (and deviation), the N-terminus acetylation yield (Average, Min and Max values). An additional file was also exported containing all collected and processed data (EnCOUNTer Intermediary file).
Training and testing dataset An experimental dataset collected during a large scale A. thaliana N-terminome characterization was used for the optimizing and the testing steps. Out of the 16 acquired files, data associated to fraction 5 and 6 were used as training and testing datasets, respectively. First, the acquired data were processed for protein/peptide identification as described in "Distiller/Mascot data processing" section. For the peptides associated to a unique gene-ID but few different translation versions, the lowest TAIR extension number was retained in the final list. Non-proteotypic peptides (i. e. , peptide matching several distinct gene-IDs) were removed from the final list. Each characterized peptide was manually checked to identify mature N-termini. Information from specialized databases such as AT_Chloro  [22, 23] , PPDB  [24] , SUBA  [25] , TopFind  [26] , MASCP-Gator  [27]  and various prediction tools such as TargetP / ChloroP / SignalP  [5, 6] ) or Mitofate  [28]  were used to assess N-termini relevancy and protein sub-cellular localization for each candidate (Additional file 1: Table  S1 ).
EnCOUNTer Launch The EnCOUNTer script should be launched in a prompt windows associated with the required files (fully described in the help support and the user manual). First, EnCOUNTer determined the optimized scoring parameters using the training dataset (MD and Mascot exported files) and the reference N-terminal list. A few files are exported at the end of the optimization including the optimized "scoring parameter" (*. json file) and the detailed results of the optimization and "K fold cross validation" (*. bound, *. param, *. score and *. spec). Second, the experimental datasets (MD and Mascot exported files) were scored using the previously optimized parameters to discriminate and quantify the mature N-termini and associated NTA yield. At the end of the process, the EnCOUNTer script provided two distinct files, i.
Training and testing datasets Two experimental samples were defined as training and testing dataset i. e. fraction 5 and fraction 6 respectively. The peptides characterized after the Mascot identification step are filtered using few different Mascot-associated values using the peptide E-value and the minimum Mascot score defined in the configuration files. These thresholds should be adapted to reach 1% of False Discovery Rate (FDR) at the peptide level. Applying these thresholds, false positive identifications for the expected N-terminal peptide (position 1 and 2) were infrequent (Table  1 ). As an example, no false candidate was characterized in Fraction 5 and only one probable false hit was listed in Fraction 6 (Additional file 1: Table  S1 ). For these starting positions, the associated localizations were mainly the cytosol (49 hits), the membrane/vacuole (17 hits), the peroxisome (6 hits) or the mitochondria (without mTP, 5 hits).
Only one plastidic protein (AT2G44640. 1) was characterized with a mature Ntermini at position 2. This infrequent but not unusual chloroplastic N-terminus  [29]  was confirmed experimentally and reported in PPDB  [21]. The characterized N-termini at position 1-2 corresponded well to the expected cytoplasmic localizations. Additionally, 595 peptides were characterized with downstream starting position (Start position > 2). These hits were sorted between True N-termini (mature protein N-termini; 203 hits), False N-termini (erroneous mature N-termini; 329 hits) and ambiguous N-termini (mainly poor MS/MS spectra quality or inconsistencies with previous biological and experimental facts; 63 hits). Only the True/False candidates were used during the EnCOUNTer training step. The main subcellular localization is the chloroplast with 73% of the candidates (149 hits) for the "True" dataset.
Other locations such as cytosol, membrane or mitochondria were also found (21, 7 and 5%, respectively). At the contrary, the "False" dataset exhibits random location and similar distributions were also observed in Fraction 6 dataset (Table  1  and Additional file 1: Table  S1 ). These two manually curated datasets (Fraction 5 and 6) were used during the EnCOUNTer training and testing steps, respectively.
2  and Additional file 7: Figure  S3 ) used in the "Spec" score. The "Spec" coefficient is determined using a weight matrix of based on the distribution of specific residues around the cleavage site using the MCCs. MCC reflects the presence/lack of specific residues around the cleavage position. This determination can be performed for both the "True" and the "False" reference dataset (Additional file 7: Figure  S3A  and B ) compared to a random distribution of transit peptide cleavage position (Additional file 7: Figure  S3C ). "Spec" score is the main basis of EnCOUNTer scoring scheme and could be used alone to determine the final EnCOUNTer score. The Specassociated matrix was determined for both the "True" and the "False" subsets from Fraction 5 dataset. Based on the "True" hits, EnCOUNTer allows a discrimination at 94.
0% accuracy and 97. 6% specificity with 4. 3% FDR whereas the optimization based on the "False" dataset reached only 88. 5% accuracy and 91. 6% specificity with 5. 6% FDR (Table  2  and Additional file 8: Table  S3 ). Only the optimization using the true hits is retained for the final scoring scheme. th=tlb= Finally, a K fold cross validation (k = 10) was performed to determine the robustness of this approach. The accuracy reach 88. 5 ± 4. 1% and 94. 9 ± 4. 2% sensitivity with 9. 3 ± 7. 7% FDR (Table  2  and Additional file 8: Table  S3 ). Although additional features should be used to prevent the loss of "True" hits, the results obtained using only the "Spec" score are extremely promising. Peptide "starting position" score (Bound Score) For most proteins, the mature N-term position is located on the first two residues of the protein sequence (position 1-2).
Nevertheless, some proteins N-termini could be located further downstream (Position > 2). For example, the mTP cleavage position is expected between positions 20-70 whereas for the position for the cTP of A. thaliana nuclear encoded proteins is expected between positions 40-70  [16, 33]. In our training datasets (Additional file 1: Table  S1 ), the validated downstream starting positions were distributed from position 3 to  Interestingly, few distinct TP regions (Additional file 5: Figure  S1 ) could be highlighted and are associated with proteins carrying a signal peptide (positions between 20 to 35  [34, 35] ), mitochondrial TP (between positions 25 to 65  [16, 28] ) and plastidic TP (between positions 30 to 95  [18] ). Comparatively, the starting positions of the "False" candidates were evenly distributed.
Then, it is interesting to favor/penalize selected regions depending of the protein training set. This allows the EnCOUNTer tool to define the optimum range where mature Nterminal positions are characterized from the training dataset. The optimum range for Fraction 5 dataset is in between positions 14-78 with 84. 4% accuracy and 80. 9% specificity with 25. 6%. FDR. The associated "K fold cross correlation" (K = 10) highlights the robustness of this determination (Additional file 8: Table  S3 ). This parameter cannot be used alone but always in combination with the Spec score (at least). When combining "Spec" and "Bound" score, 95. 1% sensitivity and 99. 1% accuracy with 1. 6% FDR are reached on Fraction 5 training dataset. Such combination clearly improved the EnCOUNTer discrimination power compared to the "Spec" score alone.
Influence of the other scoring coefficients By default, reliable predictions are reached using the Spec score and the Bound score together (95. 1% accuracy at 1. 6% FDR on the training dataset). Nevertheless, it could be possible to improve the prediction specificity or sensitivity using the additional coefficients Acetyl, Prox, Rep and/or Loc. Depending on the coefficient applied, it was possible to improve the sensitivity or the specificity of the EnCOUNTer tool (data not shown). As an example, the combination of Spec, Bound and Prox coefficients provides a final 96. 1% accuracy and 99. 1% sensitivity with 1. 6% FDR. The associated K fold cross validation (k = 10) was performed and provided 95. 9 ± 2. 9% accuracy and 98. 7 ± 2. 3% sensitivity at 1. 9 ± 3. 2% FDR (Table  2  and Additional file 8: Table  S3 ).
Although, the overall accuracy could be improved using different scoring combinations this was usually detrimental to the sensitivity. Depending on the goal (sensitivity, accuracy, specificity), scoring coefficient combinations could be adapted to reach better result than those provided in Table  2  i. e. better accuracy or better sensitivity… In hour hands, the combination of the scoring coefficients Spec, Bound and Prox provides a good starting compromise (Table  2 ) that could be optimized at will. These optimized parameters were applied to the Fraction 6 training dataset and provided the discrimination of the N-termini at 91. 3% of sensitivity and 98. 2% of specificity (4. 3% of FDR).
Example of application As an example of application, our whole experimental dataset (N-terminus enriched fraction from A. thaliana leave lysate  [2] ) was processed using the optimized EnCOUNTer parameters. The parameters used where based on the results obtained during the optimization phase (Table  2 ), i. e. the combination of the Spec, Bound and Prox coefficients. 3964 potential N-termini were listed of which 1554 have an EnCOUNTer score higher than the threshold (EnCOUNTer Threshold = 130. 1). After the removal of the non-proteotypic peptides, 1257 probable mature N-termini were listed of which 649 were located at position 1-2 and 608 at positions downstream of the protein N-terminus (Position >2). The NTA yield was determined for 594 N-termini (excluded none proteotypic N-termini) of which 275 were located at position 1-2 and 319 were associated to DNT (Additional file 2: Table  S2 ).
Considering the other 18 nucleus-encoded proteins which were annotated in SUBA (The SUBcellular localisation database for A. thaliana  [25] ) as being translocated to the chloroplast, some of them are known to be chloroplastic such as At2g44640. 1 or At4g28440. 1  [21]  and the absence of cTP could be explained by some previously described alternative import mechanisms  [29]. Other candidates are erroneously associated to plastidic localisation such as At5g24650. 1 (TIM17) which is clearly a mitochondrial protein. In addition to these expected N-termini, 608 downstream N-termini could be characterized with an EnCOUNTer score higher than the threshold of which 319 were quantified for NTA. The pattern of the DNT-NTA yield with 8% of the downstream N-termini fully acetylated (>95%), 25% partially NTAed and 67% not acetylated (<5%) was clearly different (Fig.
Conclusions Throughout few thousands experimentally characterised N-termini, the EnCOUNTer tool is able to parse the   [25] ) for the N-termini position 1-2 and downstream most relevant mature protein N-termini with 96. 1% accuracy and 99. 1% specificity on the training dataset (91. 3% sensitivity and 98. 2% specificity using Fraction 6 dataset). Furthermore, the EnCOUNTer tool is able to provide reliable NTA yield for each distinct proteoform at the expected protein N-terminus (Pos 1-2) but also downstream. Applied to a large experimental dataset, the EnCOUNTer tool was able to characterize more than 1200 N-termini of which almost 600 were quantified for NTA yield. Those characterised DNT could be associated to different maturation processes including nuclear encoded proteins targeting to various organelles (e.
Results of the automated optimisation of the Bound and Spec parameters using the K fold cross validation result (n = 10) and the final scoring scheme using the same *) for the prediction based on the False dataset, the EnCOUNTer score must be below the determined Threshold for the True hits 106 (defined as ExpMin and ExpMax) for the validated candidates ("True" dataset) vs. 4 to 1104 for the irrelevant candidates (False dataset).
Funding This work was supported by the  "French National Research Agency  (grant No. ANR-13-BSV6-0004 ) and is directly associated to the development of the EnCOUNTer tool. JD has benefited from the support of the " LabEX Saclay Plant Sciences-SPS " ( ANR-10-LABX-0040-SPS ) for its gratification. The Lidex BIG (https://www. universite-paris-saclay. fr/fr/recherche/projet/lidex-big) finance experimental analyses to provide raw material used forthe development and the optimisation of the EnCOUNTer tool.
Availability of data and materials The EnCOUNTer script implemented in Python 2. 7. 11 is freely available at https://mycore. core-cloud. net/public. php?service=files&t=4de3e3e100c6c3ba 94114947c9ff929f. The mass spectrometry proteomics data and supplementary files supporting the conclusions of this article have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository with the dataset identifier PXD005720.
9 ± 2. 0 162 ± 3 --N. R. 16 ± 2 17 ± 4 80 ± 6 -164 ± 4 N. R. N. R. -18 ± 3 14 78 63. 2 183 17 ± 4 80 ± 6 > 129. 9 ± 0. 6 167 ± 4 N. R. N. R. N. R. 19 ± 4 86 ± 9 300 ± 1 < 69. 3 ± 6. 1 (*) 272 ± 5 N. R. N. R. N. R. 30 ± 3 14 78 65. 1 179 14 78 112. 8 180 14 78 130. 1 185 79 300 72. 2 285 79 300 66. 7 304 14 78 133. 8 186 14 78 130. 1 179 validation approach Investigated Dataset or Scoring parameters Scheme Spec (True dataset) Training Validation Bound (True Training dataset) Validation All data together Spec / Bound / Prox Training (True dataset) Validation Spec / Bound / Prox Training (False dataset) Validation Fraction 5 dataset True dataset (Spec only) True dataset (Spec, Bound) True dataset (Spec, Bound, Prox) False dataset (Spec Only) False dataset (Spec, Bound, Prox) False dataset (Stringent params) Fraction 6 dataset True dataset (Spec, Bound, Prox) (.
For example, of the 4,400 viral isolate genomes deposited into National Center for Biotechnology information (NCBI) viral RefSeq, only 43% had been ICTV-classified by 2015. This is because the lengthy 'proposal' processes lags deposition of new viral genomes, in some cases for years  (Fauquet & Fargette, 2005). Concurrently, new computational approaches are providing access to viral genomes and large genome fragments at unprecedented rates. One approach mines microbial genomic datasets to provide virus sequences where the host is known-already adding 12,498 new prophages from publicly available bacterial and archaeal microbial genomes  (Roux et al. , 2015a)  and 89 (69 and 20, respectively) new virus sequences from single cell amplified genome sequencing projects  (Roux et al. , 2014; Labonté et al.
, 2015). A second approach assembles viral genomes and large genome fragments from metagenomics datasets. The largest of such studies added 264,413 new putative (partial) viral genomes from fmicrobial and viral metagenomes across a broad range of ecosystems  (Paez-Espino et al. , 2017). Other studies include human stool samples  (Norman et al. , 2015; Manrique et al. , 2016). Such new virus genomes and large genome fragments will keep coming for the foreseeable future and represent an incredible resource for viral ecology. While this opportunity is now clearly recognized in a recent Consensus Statement from the ICTV  (Simmonds et al. , 2017) , it also represents a daunting challenge for taxonomy. Currently such rapidly expanding genomic databases of the virosphere remain challenging to integrate into a systematic framework for three reasons.
A second approach that has emerged for relatively well-studied virus groups, is to use pairwise distances between aligned sequences to identify discontinuities that can indicate classification thresholds. However, such approaches suffer from several issues: (i) they are not generalizable to the coming deluge of environmental viral genome sequences as they require a priori expert knowledge to impose similarity thresholds at each level, (ii) ICTV subcommittees have established varied sequence similarity thresholds across viral groups  (Simmonds, 2015) , which would require a sliding threshold, and (iii) the methods can only classify sequences that are similar to database references  (Zanotto et al. , 1996) , which for the oceans at least represents <1% of the predicted viral genomes thought to exist  (Brum et al.
, 2015). Complementarily, two network-based approaches have been utilized to organize virus genome sequence space in a manner that enables classification without a priori knowledge. The first, a gene sharing network  (Lima-Mendez et al. , 2008) , predicts viral genes in all the genomes, translates them into proteins, organizes these proteins into Markov cluster (MCL)-based protein families (protein clusters, ''PCs''), evaluates the number of shared protein clusters pairwise throughout the dataset to establish a protein profile, and then represents this information as a weighted graph, with nodes representing viral genomes and edges the similarity score of their shared protein content. Given the 306 bacterial viruses (phages) known at the time, this method was precise as it correctly placed 92% and 95% of these phages into their correct ICTV genus or family, respectively  (Lima-Mendez et al.
Here we re-evaluated monopartite gene sharing networks and their efficacy for recapitulating ICTV-based classifications using an expanded dataset of 2,010 bacterial and archaeal virus genomes (available as of RefSeq v75), while also deeply exploring where network-based methods have lower resolution and/or yield discontinuities with currently established taxonomies. Further, we make these approaches accessible to researchers by developing a tool, vConTACT (Viral CONTigs Automatic Clustering and Taxonomy), and deploy it as part of the iVirus ecosystem of apps that leverages the CyVerse cyberinfrastructure  (Bolduc et al. , 2016).
Reference datasets To test this methodology, we downloaded the entire NCBI viral reference dataset (''ViralRefSeq'', version 75, containing 5539 viruses) and removed eukaryotic viruses by filtering against tables downloaded on NCBI's ViralRefSeq viral genome page (http://www. ncbi. nlm. nih. gov/genomes/GenomesGroup. cgi?taxid=10239). The resulting file (''Bacterial and Archaeal viruses''; BAV) contained 2,010 total viruses; 1,905 dsDNA, 88 ssDNA, 5 dsRNA and 12 ssRNA. All viruses contained taxonomic affiliation information, though not all viruses had affiliations associated with each level of the taxonomy (e. g. , not all viruses have a ''sub-family'' designation). To improve taxonomic assignments, the ICTV taxonomy was also retrieved (https://talk. ictvonline. org/files/master-species-lists/) and the ICTV affiliations were used to supplement the NCBI data.