dataesr/scientific-paragraphs-categorization huggingface dataset:
RCA Basics Relational Concept Analysis (RCA) has been designed to deal with multi-relational datasets  [21]. In the present study, the different relations represent either the systems, or the different implementations of the mechanisms in the systems, or they connect the systems to their mechanism implementation. RCA input, i. e. , a relational context family (RCF), is formally a pair (K,R) with: K = {K i } i=1,. ,n a set of formal contexts K i = (O i , A i , I i ) and a set of relational contexts R = {r j } j=1,. ,p. r j ⊆ O k × O l for k, l ∈ {1,. , n}. RCA groups objects (e. g. , operating systems), not only by their intrinsic attributes, but also by the relations they share to other objects or object groups (concepts). This is highlighted through relational attributes. From a relation r j ⊆ O k × O l , relational attributes for K k are built using relations between objects of O k and concepts built upon objects of O l.
Experimental Setup and Process As presented before, we analyze 8 operating systems: Android5. 1, Android6. 0, iOS5, MacOS10. 10, Ubuntu12. 04, Ubuntu14. 10, Windows7. 0 and Windows8. 1. We study zoom, contrast/colors, text style (font size and style) and brightness. When we apply the KD process on these systems, data preparation step produces: • 32 feature models corresponding to the 4 mechanisms for the 8 systems; • then we reduced the dataset to: 3 ontology parts and 3 mapping tables, for brightness, contrast and colors, and text styles mechanisms; this reduction has been decided for time and human effort reduction purpose; it is worth to note that we included the largest mechanism (contrast and colors). • using the ontology, mapping tables and feature models, we derive the formal contexts: one central formal context lists the 8 OS; 3 formal contexts are derived for the 3 mechanisms.
We can make the following observations. FMs MacOS1010_Brightness and Ubuntu1204_Brightness (resp. Android51_Brightness and Android60_Brightness) have the highest (resp. the lowest) number of features. FM Windows81_Contrast (resp. iOS5-_Contrast) has the maximum (resp. the minimum) number of contrast features. FM Android51_TextSize (resp. Android60_TextSize) has the maximum (resp. the minimum) number of size and font type features. FM Windows70_Zoom (resp. Android51_Zoom and Android60_Zoom) has (resp. have) the maximum (resp. the minimum) number of Zoom features. Due to their size, input data and results cannot be entirely given in this paper. They are available on the gitlab repository (https://gite. lirmm. fr/AccessibiliteNumerique/ osvisualaccessibilityoptions, accessed on 1 September 2021).
e. g. , in  [42] , it is shown that Ubuntu 6. 10 provides more accessibility features than Windows XP Service Pack 2. In  [12] , authors determine the most common features in three OS (Linux, MacOS and Windows). E. g. , they show that features Display Customization and Basic Screen Magnification Software belong to all systems. Ref. [15]  analyzes the latest OS versions of Apple, Microsoft and Google and concludes that the same high-level vision accessibility features are implemented in these platforms. In  [13, 15] , the versions of the systems involved in the analysis are not explicitly specified. Their findings, restricted to the accessibility options we are studying in this paper, are respectively presented in Tables  12 13 14. These tables show that these studies are very concentrated on a few aspects, a restricted set of systems and high-level descriptions.
Acknowledgments: The authors thank  Alain Gutierrez , the author of CoGUI software for his valuable help and guidance in using it to elaborate the ontology. They also thank:  Jessie Galasso  for her great help in using the CLEF software and the discussions we had on the connections between Software Product Lines and this work;  Clément Jonquet  for fruitful discussions about ontologies; the anonymous reviewers who gave valuable feedbacks that help to considerably improve the different versions of the paper.
Data Availability Statement: Data are available at: https://gite. lirmm. fr/AccessibiliteNumerique/ osvisualaccessibilityoptions, accessed on 1 September 2021. CLEF, the Java library  [35]  is available at https://gite. lirmm. fr/jcarbonnel/CLEF, accessed on 1 September 2021. RCAexplore is available at http://dataqual. engees. unistra. fr/logiciels/rcaExplore, accessed on 1 September 2021. CoGUI is avalaible at http://www. lirmm. fr/cogui/, accessed on 1 September 2021.
The resulting MS/MS spectra are typically matched against in silico generated spectra derived from a protein sequence database to identify the analyzed peptides and infer the original proteins. The inferred proteins are then used to describe the various active taxa in the community, their functions, and the relative gene expression levels  8. Each of the aforementioned steps can potentially influence the outcomes of a metaproteomic analysis and every step brings specific benefits as well as challenges. As a result, multiple workflows have been established. While such diversity brings flexibility, it also complicates the comparison of results across different experiments. Wet-lab challenges include protein recovery due to the presence of different matrices  9  , the presence of different types of microorganisms with different optimal lysis conditions  10, 11  , and limited depth of analysis  3  and quantification  12  due to an increased sample complexity.
Moreover, metaproteomics comes with many specific bioinformatic challenges  8, 13. First, the choice of an appropriate sequence database is critical for peptide identification  14, 15. Typically, large databases can strongly impact sensitivity and false discovery rate (FDR) estimation  16  , while incomplete reference databases can lead to missing or false positive identifications  17, 18. Second, the protein inference problem  19  is more pronounced in metaproteomics due to many homologous proteins from closely related organisms  20. As a result, several dedicated bioinformatic tools have been developed or extended for metaproteomic analysis  [21] [22] [23] [24] [25] [26] [27] [28]. Despite these challenges, the added value of metaproteomics has already been demonstrated in numerous examples from both the environmental and medical fields, providing unprecedented insights into the functional activity of microbial communities  7, 20, [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41].
Participating laboratories received two microbial samples: a simplified mock community simulating the gut microbiome (SIHUMIx) and a complex, natural stool sample (fecal sample). Each group was allowed to use any preferred sample preparation, analysis, and data evaluation pipeline. Here, we describe the results of this community-driven study, referred to as the Critical Assessment of MetaProteome Investigation (CAMPI). We compare and discuss the employed workflows covering all analysis steps from sample preparation to the bioinformatic identification and quantification. Moreover, we compare the metaproteome results with sequencing read-based analyses (metagenomics and metatranscriptomics). We found that meta-omics databases performed better than public reference databases across both samples.
Results At the 3rd International Metaproteome Symposium in December 2018, individual lab outcomes of a collaborative, multi-lab effort to compare metaproteomic workflows were presented. In this study, metaproteomics data was acquired in seven labs, using a variety of well-established platforms. 1  for an overview of all methods). The two samples (SIHUMIx and FECES) were, prior to the symposium, aliquoted and distributed over the participating labs. (ii) Pre-symposium work by participants (middle panels). Every used method by the participants, going from cell disruption to mass detection, is displayed. (iii) Post-symposium work by participants (right panel). The bioinformatics analyses, i. e. database creation and database search for peptide and protein identification, were harmonized to make the results between all participating laboratories comparable.
At the Symposium, the decision was made to re-analyse the acquired data with different bioinformatics pipelines, to obtain the first multi-laboratory effort in metaproteomics to independently evaluate available methodological and computational approaches, in line with similar community-driven benchmarking studies  [45] [46] [47] [48]. In the first Results section, we analyzed 42 raw files (21 for the SIHUMIx sample and 21 for the fecal sample) from 24 different workflow combinations with X!Tandem using either public or in-house generated protein databases (see Figure  1  for a general overview, and Figure  2  for the results; see online Methods for the database construction). A more in-depth comparison of sample preparations, bioinformatic pipelines, and taxonomic and functional annotations using a sub-selection of ten data sets is available after the first Results section.
Complex wet-lab workflows and sample-specific meta-omic search databases lead to more identifications In order to study the effect of the different wet-lab and LC-MS/MS workflows on the identification outcome, we searched all submitted MS files using the widely used X!Tandem search engine  49. To investigate the influence of the chosen database, we searched each file against a publicly available reference database (SIHUMIx_REF and GUT_REF) and against a multi-omic database (SIHUMIx_MO and GUT_MO). The comparison of all CAMPI workflows is displayed in Figure  2  (raw data in Supplementary Table  2 ). Figure  2. Comparison of identification rates across all CAMPI workflows. On the left side, the bar charts show the number of identified spectra using the reference (REF) database (orange), the number of identified spectra using the multi-omic (MO) database (dark blue) and total amount of measured spectra (red).
On the right side, the light blue bars represent the identification rate calculated as the percentage of spectra that yielded a peptide identification at 1% FDR for both the REF database (orange) and the MO database (dark blue). The specific protocols can be found in Supplementary Table  1. For database searching, X!Tandem was used as a single search engine. The results greatly differed between the samples and workflows in terms of absolute numbers of acquired spectra, identified spectra, and relative amount of identified spectra (identification rates). For the SIHUMIx data set, the number of acquired spectra varied between 37k to 260k, and identification rates varied between 29. 99% and 68. 64% for SIHUMIx_REF and between 32. 52% and 73. 34% for SIHUMIx_MO. For the fecal data set, between 9k and 223k spectra were acquired, with identification rates between 11.
As expected, if an MS instrument is provided with the ability to acquire more spectra, it will do so. However, the gains in spectral acquisition do not readily translate into gains in identification. There is thus a potential for diminishing returns when going for more complex methods. There is also a somewhat consistent drop in number of acquired spectra of around 10% when comparing SIHUMIx samples with fecal samples for similar workflows (e. g. , S09-S10 with F05-F06, and S13 Reps 1-3 with F09 Reps 1-3). However, occasionally this drop is much greater, as for S11_Fract1-4 and F07_Fract1-4. The overall limited drop might be attributed to the higher complexity of the fecal sample, and corresponding ion suppression effects. The differences in identification rate are likely to be derived from the choice of the search database.
The identification rates for the publicly available databases were invariably lower, which is due to their larger and less specific search space, consistent with literature  14, 16, 18, 42, 53. Here, these public reference databases (SIHUMIx_REF and GUT_REF) contained 1. 6 and 16 times, respectively, more unique in silico digested peptides than the corresponding multi-omic databases (SIHUMIx_MO and GUT_MO) (Supplementary File 1). Overall, our results indicate that generating a sample-specific meta-omic database can be advantageous for complex metaproteomics samples, such as the human gut microbiome, and even more so for complex and poorly characterised samples such as soil microbiota. The smaller meta-omic databases require less computational resources (e. g. , CPU and RAM) and tend to be more accurate due to their tailored composition.
However, for their generation, meta-omic databases require additional experimental and computational resources, and are often not as well assembled and/or annotated as reference databases. Because the composition of SIHUMIx was known, the benefit of using a tailored meta-omic database was limited and the analysis was feasible with available reference proteomes. In contrast, the community for the fecal sample was unknown, which represents the typical scenario in metaproteomics. For known reference samples (such as SIHUMIx), it is therefore reasonable to simply use the reference database, while the largely unknown fecal sample community is best analysed using a tailored meta-omic database. In the following sections, we thus opted to use only the SIHUMIx_REF and GUT_MO search databases for SIHUMIx and fecal data sets, respectively.
Different bioinformatic pipelines resulted in highly similar peptide identifications To investigate the effect of the bioinformatic pipelines on peptide identification, we compared the two data sets with the most identified peptides (S11 and F07) (Figure  3 ). To ensure a robust and reliable comparison, we fixed the search parameters for the four different bioinformatic pipelines employed (see online Methods for details). For SIHUMIx, the majority of the identified peptides (54. 2%) were found by all four bioinformatic pipelines (Figure  3A ), while this ratio dropped to 40% for the more complex fecal F07 sample (Figure  3B ). As expected, this percentage increased to 73% and 55%, respectively, when considering the peptides identified by at least three out of four tools. Interestingly, 16% of the peptides were uniquely identified by a single bioinformatic pipeline for the S11 data set (3138, 2670, 891, and 841 peptides for SearchGUI/PeptideShaker, MaxQuant, Proteome Discoverer, and MPA, respectively), while this was 27% for the F07 data set (6024, 1264, 819, and 332 peptides for the same pipelines, respectively).
Comparison of meta-omic methods reveals differences between peptide and protein-derived analysis of taxonomic community composition To determine if differences between workflows have an effect on the overall biological conclusions, we quantitatively compared the identified taxa for each selected sample from both data sets, and this at the peptide, the protein subgroup, and the sequencing read level. We found different trends between the SIHUMIx and fecal samples (Figures  5  and 6 ). For SIHUMIx, the taxonomic distributions were relatively similar between the metagenomic read, peptide, and protein group levels based on the principal component analysis. Hierarchical clustering highlighted clusters of samples, with the peptide and protein subgroup profiles for samples S07 and S14 clustering with the read-based profile (Figure  5A ) (Supplementary Figure  8A  and B ).
Interestingly, samples with more complex wet-lab methods (S03, S05 and S08) did not show clustering between the peptide and the protein subgroups level. While species were found to be similar between methods overall, there were some notable differences (Figure  5B ). All methods agreed that Bacteroides thetaiotaomicron was the most abundant species, and found Escherichia coli at 10-13% abundance. However, differences were found for Blautia producta, which was barely found by the proteomics methods, while found at around 5% abundance by metagenomics. It is interesting to consider that this might be caused by the construction of the reference database: at the moment of construction, the UniprotKB reference proteome of Blautia producta was not available, and multiple Blautia sp. proteomes were therefore provided instead.
When looking at the Unipept results in detail, 15% of the peptides were associated with the genus Blautia (Supplementary Table  5 ), which indicates that the lower identification of Blautia producta at the peptide level is due to difficulties in resolving Blautia at the species level, rather than a lack of identified Blautia peptides during the metaproteomic search. Additionally, a false negative was observed for Clostridium butyricum, which was not found by the read-based method, while two false positives were observed for Clostridiales bacterium and Bacteroides dorei, which were found by the protein-centric method even though these are not present in the SIHUMIx sample. However, these last two were both found at very low abundance. For the fecal data set, which was grouped at the family level, relatively distinct assessments of community composition were obtained from the read-based, peptide, and protein subgroup levels (Figure  6A ).
This discrepancy is likely due to the fundamental issue of protein inference. Indeed, in metaproteomics, identification and quantification usually rely on discriminative peptides. As the data sets get more complex, higher levels of sequence homology for many proteins will be observed and will lead to a much greater level of peptide degeneracy across taxonomies  61. Direct taxon inference from peptides thus likely results in more stringent taxonomy filtering, due to the necessity to rely only on taxon-specific peptides. In fact, the proportion of unclassified peptides between the SIHUMIx and the fecal samples went up from 24. 2% to 73. 4% due to the increased taxonomic complexity of the fecal data set. In contrast, the proportion of unclassified protein subgroups went down from 69. 9% for SIHUMIx to 9.
Ultimately, due to the sequence homology issue, worse taxonomic resolution will be available for larger, more complex data sets as illustrated in the differences between the SIHUMIx and the fecal data sets. A promising approach to tackle these limitations can take advantage of shared rather than taxon-specific peptides (and thus avoiding the previously mentioned issues) to assess the biomass content of a given community  61  However, regardless of the chosen approach, it is clear that a higher level of peptide coverage will be quite helpful for higher resolution taxonomic annotation, and that metaproteomics will therefore benefit from focusing on analysis depth at the peptide level.
When looking at different proteomics approaches, differences tend to show up primarily at the finest resolution, such as the sequences of the identified peptide sequences. When considering information from the protein subgroup level up, much of this variation disappears. Different protocols tend to primarily display different levels of analytic depth, which correlates with more extensive sample fractionation and faster instruments. Moreover, differences between search engines appear somewhat complementary, giving an advantage to integrative, multi-search engine approaches using more sophisticated scoring engines. Interestingly, there appears to be an important contribution to any observed differences from the sequence database used for identification. This is particularly evident in the protein inference step, where peptide-level degeneracy in the database becomes an important factor in the outcome of protein grouping, as already shown and discussed previously  63, 64.
All MS proteomics data and The four databases were in silico digested into tryptic peptides with an in-house developed script, with two missed cleavages allowed, to compare their theoretical search spaces. Additionally, all peptides identified with each database in the explorative analysis, which was carried out using all data sets, were retrieved and compared. For metaproteomic data analysis, the number of spectra, PSMs, and identification rates (calculated by dividing the number of identified spectra by the total number of acquired MS/MS spectra) were extracted for all data sets searched against the selected databases (SIHUMIx_REF and GUT_MO) and compared. Finally, a representative subset of data sets, based on the different methods, was selected for further analysis (S03, S05, S07, S08, S11, S14 for SIHUMIx and F01, F06, F07, and F08 for the fecal sample).
Data analysis using four different bioinformatic pipelines All submitted MS/MS raw files were first analyzed with a single commonly used database search method to assess both the quality of the extraction and the MS/MS acquisition, as well as the effect of the search database composition (reference proteomes vs. multiomics). For this, X!Tandem 49 was used as search engine with the following parameters: Peptides were filtered on length (between 6 and 50 amino acids), and charge state (+2, +3, and +4), and a maximum valid expectation value (e-value) of 0. 1  78. The following database search engines were used for the pipeline comparison: (i) MaxQuant  79  (including the search engine Andromeda) (ii) Galaxy-P workflows  80, 81  consisting of SearchGUI 82,83 (using OMSSA  84  , X!Tandem 49 , MS-GF+  57  , and Comet 85 ) and PeptideShaker  86  to merge the results, (iii) MetaProteomeAnalyzer 26 (server version 3.
Protein inference To allow protein group comparison, groups were created using the combined peptide evidence of all compared samples. Two different protein grouping methods were tested: SIHUMIx species and contaminating species in the cRAP database), after which we calculated the SIHUMIx-specific LCA. This advanced taxonomic analysis using Unipept is possible since the composition of the sample is known, and resulted in a more accurate taxonomic annotation of the peptides. For more information and examples of the advanced Unipept analysis (Supplementary Note 1. 4). For the taxonomic annotation of the fecal data sets with Unipept, the desktop  87  and CLI  21, 88  versions were used. In both analyses for SIHUMIx and the fecal data sets, isoleucine (I) and leucine (L) were equated. For the functional analysis at the peptide level, we used the Unipept command line option to extract the GO terms for each identified peptide per data set (below 1% FDR).
The functional similarity of these sets of GO terms was calculated with MegaGO 62. Prophane was used for the protein-centric approach  89, 90. For both the functional and taxonomic annotations, a generic output format created by the in-house developed protein grouping script and the protein database for a given analysis were used. Within Prophane, the taxonomic annotation was performed with DIAMOND blastp against the latest NCBI non-redundant (nr) database (2019-09-30)  91  , while two functional annotation tasks where performed against the eggNOG (database version 4. 5. 1)  92 and Pfam-A (db version 32) databases  93  using eggNOG-mapper  94, 95  and hmmscan  96  , respectively. Using eggNOG-mapper, the e-value threshold was set to 0. 0005 while we applied a gathering threshold supported by PFAMs (cut_ga parameter) when searching using hmmscan.
76Tandem results have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org)via the PRIDE partner repository75. SIHUMIx consisted of the combined reference proteomes of the strains extracted from UniProt in July 201976except for Blautia producta, for which the whole genus Blautia was taken (SIHUMIx_REF). The IGC 9. 9 database 77 (available at http://meta. genomics. cn/meta/dataTools) was used as the catalog database for the fecal sample (GUT_REF). Additionally, a meta-omic database from the assembled contigs was produced for both samples using the open reading frame generated with Prodigal (SIHUMIx_MO and GUT_MO).