dataesr/scientific-paragraphs-categorization huggingface dataset:
Also, no tissue specific information is provided, apart from liver. To the best of our knowledge, we are providing the first combined quantitative analysis of label-free DDA datasets. Here, we report the reanalysis and integration of 14 public label-free pig baseline tissue datasets including 14 organs and a total of 20 healthy tissues from 128 samples. The results were incorporated into Expression Atlas as baseline studies. Additionally, we report a comparative analysis of protein expression across pig and human tissues.
Datasets The PRIDE database hosted 165 publicly available MS proteomics datasets of Sus scrofa as of October 2022. For this study, we manually selected datasets based on several predefined criteria, which included: (i) label-free DDA studies from tissues without enrichment for posttranslational modifications; (ii) datasets generated using Thermo Fisher Scientific instruments to avoid the heterogeneity introduced by data generated by other platforms; and (iii) datasets with sufficient sample metadata, manually curated from the original publication. This resulted in the identification of 14 pig datasets for further analysis. Sample and experimental metadata were manually curated using Annotare  [13] , and adhering to the Investigation Description Format (IDF) and Sample-Data Relationship Format (SDRF) files  [14] , which are needed for integration of the data into Expression Atlas.
Proteomics raw data processing All datasets underwent analysis using MaxQuant version 2. 0. 3. 0  [15]  in multi-threaded mode on a Linux high-performance computing cluster for peptide/protein identification and protein quantification. Input parameters for each dataset, including MS1 and MS2 tolerances, digesting enzymes, fixed and variable modifications, were set according to the specifications provided in their respective publications and accounting for two missed cleavage sites. The false discovery rate (FDR) at both the peptide spectrum match (PSM) and protein levels was set to 1%. The remaining parameters of MaxQuant were set to the default values: a maximum of 5 modifications per peptide, a minimum peptide length of 7 amino acids, and a maximum peptide mass of 4,600 Da. For the "match between runs" option, a minimum match time window of 0.
7 seconds and a minimum retention time alignment window of 20 seconds were applied. MaxQuant parameter files can be downloaded from Expression Atlas. The Sus scrofa UniProt  [16]  Reference proteome release-2021_04 (including isoforms, 49,865 sequences) was used as the target sequence database for the pig datasets. MaxQuant uses a built-in database of contaminants, and a decoy database was generated by reversing the input database sequences following the respective enzymatic digestion.
Post-processing The post-processing of MaxQuant results followed the methodology detailed in previous publications  [6]. In short, after removing the protein groups labelled as potential contaminants, decoys, and those with less than 2 PSMs, the protein intensities in each sample were normalised by scaling the iBAQ intensity values with the total signal in each MS run and converting to parts per billion (ppb). ‫ܳܣܤ݅_ܾ‬ ൌ ‫ܳܣܤ݅‪ሺ‬‬ /∑݊ ୀଵ ‫ܳܣܤ݅‬ ሻ ൈ 1,000,000,000 UniProt protein accessions, from the MaxQuant output-proteinGroups. txt file, were mapped to their Ensembl gene identifiers (ENSSSCG) using the ID mapping dataset (Release 2022/03) at UniProt website (https://www. uniprot. org/id-mapping)  [17]. The resulting id mapping data file (idmapping_selected. tab), by default maps UniProt protein accessions to Ensembl gene identifiers of all pig breeds (i.
Protein abundance comparison across datasets The normalised protein abundances (in ppb values) within each dataset were transformed into ranked bins as described in  [7]. Briefly, normalised protein abundance (ppb) of each MS run was sorted from lowest to highest and binned into 5 equal length bins. Proteins ranked in the lowest bin (bin 1) represent the lowest abundance, and correspondingly proteins ranked in bin 5 have the highest abundance. To analyse and compare the data effectively, protein abundances from 'tissues' were grouped into 'organs'. For example, the Ileum and jejunum 'tissues' were grouped as 'small intestine'. Similarly, triceps, biceps femoris, diaphragm and skeletal muscle were grouped as 'muscle'. When combining tissues into organs, median bin values were used. Proteins detected in at least 50% of the samples were selected for UMAP (Uniform Manifold Approximation and Projection)  [18]  representation and analysed for binned abundance values using the R programming language (https://www.
Correlation between gene (RNA-seq) and protein expression One pig RNA-seq experimental baseline dataset (the only one available) was obtained from Expression Atlas (dataset E-MTAB-5895). The dataset was composed of pig samples from a Duroc breed  [21]. Transcriptomics data had been previously collated in Expression Atlas, and FPKMS (Fragments Per Kilobase of transcript per Million mapped reads) data were computed by iRAP (https://github. com/nunofonseca/irap) based on the raw data, which were first averaged based on technical replicates, and then quantile normalised within each set of biological replicates using limma  [22]  and finally averaged again over all biological replicates. Biological metadata were collected in the SDRF format, consistent with the proteomics data.
Comparison of protein abundance with spectral counting values from PaxDB We compared the protein abundances generated in our study with the protein abundance data from PaxDB version 5. 0 (https://www. pax-db. org/)  [23]  available for Sus scrofa. Normalised iBAQ abundances were compared with the spectral counting abundances for liver, the only matching organ available. This comparison was not possible for other pig organs as other data in PaxDB are labelled as 'whole organism'. Ensembl gene ids (ENSSSCG) were mapped to protein ids (ENSSSCP) in PaxDB using the Ensembl BioMart, as described in this tutorial  [19].
Pig proteomics datasets In summary, we obtained protein expression data from 20 healthy tissues in 14 organs, coming from 14 public datasets. The analyses covered a total of 599 MS runs from 128 samples that were annotated as healthy/control/non-treated samples, thus representing baseline protein expression. Non-control/disease samples associated with these datasets were also analysed but are not discussed here. Normalised protein abundances values (as ppb) from both control/healthy/non-treated and disease/treated tissue samples are available to view as heatmaps in Expression Atlas. The protein abundances along with sample annotations, the sample quality assessment summary and experimental parameter inputs for MaxQuant can be downloaded from Expression Atlas as text files. The total number of proteins and peptides identified in these datasets are shown in Table  1.
Protein coverage across organs and datasets A total of 7,767 protein groups were identified from the reanalysis of the 14 pig datasets, among which 2,164 protein groups (27. 9%) were uniquely present in only one organ and 523 protein groups (6. 7%) were ubiquitously observed (Table  S1  in Supporting File 2). However, it should be emphasised that a specific list of typical proteins detected in only one organ should be treated with caution, as the FDR of this list will be amplified due to the accumulation of false positives when the datasets were analysed individually. For proteins detected in four or more datasets, this should not be a problem, as from the common number of decoy protein hits across datasets, a protein FDR of less than 1% could be inferred for those proteins (Figure  S1  in Supporting File 1).
Protein groups were mapped to 7,780 genes (which are equivalent to canonical proteins, the term that we will be using from now on in the manuscript). The largest number of canonical proteins was detected in samples from heart (6,264, 80. 5% of the total) and the lowest number in samples from adipose tissue (1,913, 24. 6%) and from the biliary system (1,983, 25. 5%) (Figure  1A ). The lower number of proteins identified in the biliary system could be attributed to the smallest sample size (only one sample out of 128, 0. 08%). Dataset PXD012636, a dataset containing pig heart samples, which was fractionated, provided the highest number of detected canonical proteins (6,062, 77. 9%), whereas the smallest number of proteins were detected in dataset PXD002918 (biceps femoris, 789, 10. 1%, nonfractionated dataset) (Figure  1B ).
We studied the normalised protein abundance distribution in organs (Figure  1C ) and found that all organs had similar median abundances. However, one cannot attribute biological meaning to these observations, since the method of normalisation by definition fixes each sample to have the same "total abundance", which then gets shared out amongst all proteins. The normalised protein abundance distribution in datasets indicated a lower than median abundance detected in the dataset PXD012636 (heart), as a direct result of more proteins being detected overall in this dataset (Figure  1D ). In terms of distribution of proteins detected per organ, most proteins were found in just one organ (Figure  1E ).
Protein abundance comparison across organs and datasets Next, we studied how protein abundances compared across different datasets and organs. To make protein abundance values more comparable between datasets, we transformed the normalised iBAQ intensities into ranked bins as explained in 'Methods': i. e. , proteins included in bin 5 are highly abundant whereas proteins included in bin 1 are expressed in the lowest abundances (among the detected proteins). We found that 494 (6. 3%) proteins were expressed in at least 3 organs, with a median bin value greater than 4 (not including 4). At the other end of the scale, 337 (4. 3%) canonical proteins were expressed in at least 3 organs at a median bin value less than 2 (not including 2). The bin transformed abundances in all organs and in all datasets are provided in the Table  S2  and S3 in Supporting File 2.
To compare protein expression across all pig organs, we calculated pairwise Pearson correlation coefficients (rp) for the 128 samples (Figure  2A ). We observed a good correlation of protein expression within the liver (median rp = 0. 77) and muscle (median rp = 0. 65) samples. We then performed a cluster analysis using UMAP  [18]  on all samples to test the effectiveness of the bin-transformed method in reducing batch effects (Figure  2B ). We observed that samples from different datasets belonging to the same organ were generally clustered together. For example, liver samples from datasets PXD003204, PXD011536 and PXD017671 clustered together (colour blue in Figure  2B ). Additionally, muscle samples from PXD002918 (tissue biceps femoris), PXD003204 (tissue diaphragm), PXD014893 (tissue biceps femoris and triceps muscle) and PXD016003 (tissue biceps femoris, triceps muscle) clustered together too (colour purple).
Comparative analysis of pig and human protein expression We also performed a comparative analysis of protein abundances (in bins) between the pig baseline tissue datasets with the results obtained in our previous analogous study involving human datasets coming from baseline tissues, which was performed using the same overall methodology  [7]  (Table  S4  in Supporting File 2). Pig is often used as a model organism for human biomedical research and then it is interesting to compare protein expression in both organisms. We calculated metrics to study the differences between the protein abundances for all organs found in the two studies (see 'Material and Methods' for full details), as shown in Figure  3  (also see Table  S5  in Supporting File 2). Three groups of proteins were found according to their protein expression levels: (i) "Group A": protein expression is similar between human and pig tissues; (ii) "Group B": protein expression is higher in human tissues; and (iii) "Group C ": protein expression is higher in pig tissues.
and human. The canonical proteins detected both in pig and human samples, were classified into three groups: "Group A" (similar protein expression levels between human and pig), "Group B" (higher protein expression in human tissues) and "Group C" (higher protein expression in pig tissues). We found that for pig, protein expression levels were higher in pancreas, brain, and heart than in the corresponding human tissues, whereas protein expression levels were higher in the human's small intestine and adipose tissue, when compared to the corresponding pig tissues. For other organs, the number of proteins in "Group B" and "Group C" were quite similar. Since different sizes and counts of datasets have been used for the different organs in both species, the organ-level trends reflect the results found in our studies.
Comparison of protein abundances across orthologs between pig and human datasets In a previous study we compared the expression of canonical proteins found in three different species: human, mouse and rat  [6]. Here, we used the same approach to compare canonical protein expression between humans  [7]  and pig organs. Overall, 13,248 detected human canonical proteins were compared with 7,800 detected pig canonical proteins (Table  S7  in Supporting File 2). When comparing protein abundance (in ppb), we only considered the corresponding orthologous genes with unambiguous (one-to-one) mappings, which resulted in 6,811 common protein orthologs. When comparing the protein expression of orthologs in human and pig, we observed a relatively overall high correlation in protein abundance in heart (R 2 = 0.
62) and liver (R 2 = 0. 53), medium correlation for brain (R 2 = 0. 44), colon (R 2 = 0. 42), kidney (R 2 = 0. 40) and spleen (R 2 = 0. 39), and low correlation in small intestine (R 2 = 0. 18), lung (R 2 = 0. 21), pancreas (R 2 = 0. 26), and adipose tissue (R 2 = 0. 27) (Figure  4 ). We also investigated the correlation of protein expression across different organs in pigs across the datasets used in this study (Figure  5 ). The data sometimes follow expected distributions whereby tissues predicted to be more similar to each other in terms of biological function have higher pairwise correlations e. g. , small intestine and colon (R 2 = 0. 87) and spleen and pancreas (R 2 = 0. 78). However, other high correlations were found between, between lung and spleen (R 2 = 0. 83) and lung and pancreas (R 2 = 0.
Comparison between gene (RNA-seq) and protein expression, and comparison with protein expression data in PaxDB We also investigated the correlation between gene (RNA-seq based) and protein expression in baseline tissue pig datasets. For that we used the only suitable dataset available in Expression Atlas (dataset E-MTAB-5895)  [21]. We compared the normalised iBAQ protein abundances (ppb) with the baseline RNA-seq expression (FPKM). To compare expression across different organs we grouped the RNA-seq expression from various tissues into their respective organs by using their median values. We did not observe a strong correlation between protein and RNA expression across various organs (Figure  S2  in Supporting File 1). The lowest correlation between protein and RNA expression was observed in lung (R 2 = 0.
06) and the highest was observed in heart (R 2 = 0. 20). In our view, these low correlations could be due to the inherent limitations in this comparison i. e. (i) samples are not paired; and (ii) the different pig breeds used (Duroc for the RNA-Seq study and several others for the protein expression studies, see details in Table  1 ). In addition, we compared the protein abundance of organ liver generated in this study with data from the PaxDB resource generated using the spectral counting method. We observed that protein abundance values (fraction of total (FOT) normalised ppb values) calculated using iBAQ in this study correlated till a limited extent (R 2 = 0. 47) with the semi-quantitative values available in PaxDB for (Figure  S3  in Supporting File 1). Unfortunately, it was not possible to perform this correlation for other organs (see details in 'Material and Methods').
Discussion We have previously performed three meta-analysis studies involving the reanalysis and integration in Expression Atlas of public quantitative proteomics datasets coming from cell lines and human tumour samples  [8] , from human baseline tissues  [7] , and from mouse and rat baseline tissues  [6]. Here we have reanalysed 14 public proteomics datasets coming from pig tissues in baseline conditions. Our overall aim was to provide a system-wide baseline protein expression catalogue across various pig organs. We have used the same methodology as in the study involving baseline human tissues (and in the mouse/rat study), which enabled a comparison of protein expression levels across human and pig organs. To the best of our knowledge this is the first metanalysis study for pig at a protein expression level, in this case using label-free DDA data.
The resource PaxDB version 5. 0  [12]  includes pig data generated using spectral counting, but their granularity is limited, providing only organ-specific expression for liver. As done before, we reanalysed each dataset separately using MaxQuant and the same protein sequence database. The disadvantage of this approach is that the FDR statistical thresholds are applied at a dataset level and not to all datasets together as a whole. However, as also explained before  [6, 7] , using a dataset per dataset analysis approach is in our view the only sustainable manner to reanalyse and integrate quantitative proteomics datasets in resources such as Expression Atlas, where gene expression datasets are stored following the same dataset per dataset approach. It is also important to highlight that the number of commonly detected protein false positives is reduced in parallel with the increase in the number of common datasets where a given protein is detected.
In this case, for proteins detected in four or more datasets, a protein FDR of less than 1% can be inferred (Figure  S1  in Supporting File 1). This overall study of protein expression is relevant in different contexts. First of all, systems biology research of the domestic pig is of immediate relevance for food production and animal welfare. Additionally, the domestic pig is a model organism for human biomedical research. Furthermore, the diversity of available pig models is rapidly expanding. Some possible applications of these models are research in nutrition, inflammation, and hostmicrobial crosstalk. In this context, pig models present great opportunities, because the pig, like humans, is an omnivore, with very similar nutritional requirements, digestive and immune systems, and gut microbial components  [10].
It is also important to highlight that mini-pig's models are being increasingly used in drug development. Animals are still requested in the safety testing of new drug candidates, and minipigs are a potential nonrodent alternative to the use of non-human-primates (NHP), due to ethical considerations  [39]. The use of alternative in vitro models is still challenging due to complex biological responses in various organ systems following drug-treatment. Future directions in analogous studies will involve: (i) the inclusion of additional species, e. g. other model organisms or other species of economic importance; (ii) studies focused on particular diseases or physiological states; (iii) the inclusion of differential proteomics datasets in addition to baseline studies; and (iv) reanalysis of DIA datasets (e.
Acknowledgements We would like to thank all data submitters who made their datasets available via PRIDE and ProteomeXchange. This work has been funded by  Open Targets ,  BBSRC  [ BB/T019670/1  and  BB/T019557/1 ] and  EMBL  core funding. We would also like to thank  Andrew Leach  and the rest of the team involved in the Open Targets "Target Safety" project, for helpful discussions.
Atlas accession number Proteomics dataset identifier* Breed Tissues Organs Mass spectrometer Number of MS runs Number of samples cecum, colon, PXD011360 Landrace Ileum, E-PROT-117 [28] × Piétrain Jejunum intestine Q Exactive Plus 16 16 no 4,716 55,392 41184 3,981 PXD011536 Q Exactive HF- E-PROT-118 [29] NA liver liver X 5 5 no 2,356 18,816 15,078 1,773 PXD011755 House LTQ Orbitrap E-PROT-119 [30] swine eye eye XL 204 12 yes 1,337 7,039 5,660 888 PXD012636 E-PROT-120 [31] NA heart heart Q Exactive HF 120 4 yes 7,747 151,177 94,341 6,982 biceps femoris, E-PROT-122 PXD014893 triceps muscle Q Exactive HF- [32] NA muscles X 8 4 yes 1,287 13,065 8,527 1,004 biceps femoris, E-PROT-124 PXD016003 triceps muscle Q Exactive HF- [33] NA muscles X 8 4 yes 2,379 22,409 15,650 1,885 PXD017671 Q Exactive HF- E-PROT-125 [34] NA liver liver X 8 8 no 3,128 32,215 24,585 2,576 PXD019852 Q Exactive HF- E-PROT-126 [35] NA heart heart X 8 8 no 1,527 13,511 10,085 1,143 PXD026910 NA subcutaneous adipose E-PROT-130 [36] adipose tissue, tissue mesenteric adipose tissue Q Exactive HF 10 10 no 2,586 23,718 17,676 2,107 Landrace E-PROT-131 PXD027772 ×Swabia skeletal skeletal [37] n-Hall muscle, heart muscle, heart Q Exactive HF 20 20 no 3,242 39,140 25,993 2,797 599 MS 128 TOTAL 14 datasets 20 tissues 14 organs runs samples Fraction Number Number Number Number of of of of unique genes protein peptides unique (canonical groups † † peptides proteins) † mapped † PXD001800 E-PROT-111 [24] Landrace retina eye Q Exactive Plus 48 16 yes 4,579 36,992 28,055 3,643 PXD002918 LTQ Orbitrap E-PROT-113 [25] NA biceps femoris muscle XL 6 6 no 1,272 9,148 6,163 841 E-PROT-114 PXD003204 Landrace biliary LTQ Orbitrap 108 9 Yes 5,969 85,655 57,338 5,065 [26] bile duct, system, Elite heart, spleen, heart, spleen, lung, brain, lung, brain, liver, liver, diaphragm, diaphragm, pancreas, pancreas, kidney kidney PXD009577 E-PROT-115 [27] NA heart heart Q Exactive HF 30 6 yes 4,258 47,975 32,956 3,582.
Molecular adsorption in nanoporous materials has many large-scale industrial applications ranging from separation to storage. To design the best materials, computational simulations are key to guiding the experimentation and engineering processes. Because nanoporous materials exist in a plethora of forms, we need to speed up the existing simulation tools to be able to screen databases of hundreds of thousands of structures. Here, we describe a new algorithm that quickly calculates adsorption enthalpies by sampling the surface of the material instead of the whole porous space. This surface sampling has been tested on the CoRE MOF 2019 database and has been proven to be more than 2 orders of magnitude faster than the gold standard method (Widom insertion), with an acceptable level of error on an enthalpy value of 0.
Although the nal steps of this design process can only be achieved by experimentation and engineering, computational simulations can play a key role in guiding the design process and speeding up the discovery of materials for targeted applications. Thanks to initiatives such as the Materials Genome project,  5, 6  we now have databases of hundreds of thousands of structures at our disposal, including both experimentally known and theoretically predicted structures, along with their associated physical and chemical properties. These databases can be computationally screened to retrieve key structureproperty relationships, potential theoretical limitations and promising new structures. 7, 8 This is particularly true in the case of nanoporous materials, which have been extensively studied in high-throughput screening methodologies  9  for the identication of top-performing materials for applications such as electrocatalysis,  10  photocatalysis,  11  heterogeneous catalysis,  12  membrane separation,  13  adsorptive separation,  14  adsorptive storage,  15  mechanical behaviors,  16  etc.