dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative properties can be also demonstrated in the landscape, the strongest of which is the link to mean normalised ER and its link to overall evolutionary conservation at a position. The first UMAP dimension strongly correlates with mean normalised ER (r 2 ¼ 0:9036, P < 2:2 Â 10 À16 ) and more weakly with mean log 10 SIFT4G score (r 2 ¼ 0:2169, P < 2:2 Â 10 À16 , Fig  2B ), which is a measure for evolutionary conservation. Indeed the first principal component (46. 7% of the variance, Appendix Fig S5 ) essentially is mean normalised ER (r 2 ¼ 0:9991, P < 2:2 Â 10 À16 ). We computationally derived each position's physical properties from its sequence and structure (Dataset EV2) and demonstrated strong patterns in UMAP space. Positions segregate on the mean hydrophobicity of their wild-type amino acid  (Bandyopadhyay & Mehler, 2008) (Fig 2C)  and show a strongly related pattern in their surface accessibility  (Hubbard & Thornton, 1993) (Fig 2D).
Mapping the diversity of amino acid subtypes Next, we used the mutational landscape to study the diverse roles played by each amino acid, utilising the broad range of proteins in our dataset to cover as much biochemistry as possible. We have shown that positions with similar properties, and therefore likely similar roles, cluster in this space so an approach to mapping role diversity would be to split positions of each amino acid into typical subtypes. This would give an overview of typical functions played by each amino acid, their relationships to other amino acids and how frequent they are. We clustered the mutational profiles of each amino acid's wild-type positions independently  (Fig 3A, Materials and Methods) , in order to study each amino acid's roles in an unbiased manner. Clustering mutational profiles was dominated by the position's mean ER score, which prevented separation by other properties.
To avoid this, we clustered using the principal component representation of mutational profiles, excluding PC1 because it correlates strongly with mean ER. Permissive positions have low magnitude ER scores for all substitutions and so the balance of other principal components is noisy. To account for this, we split positions where the magnitude of all ER scores is < 0. 4 into a permissive subtype for each amino acid. Finally, we use hierarchical clustering and hybrid dynamic tree cutting  (Langfelder et al, 2008)  to identify subtypes (Dataset EV3), which we label XP, XO, X1, X2,. for permissive positions, outliers and the main subtypes (in frequency order) of amino acid X. This means, for example, the subtypes labelled 1 are the most frequent for each amino acid, as opposed to sharing     functional roles.
To ask how complete is this set of 100 amino acid subtypes, we first observed that the frequency of each successive subtype  (Appendix Fig S7A)  decreases exponentially across all amino acids ( f ¼ e À0:56197AE0:01655ÂN , r 2 ¼ 0:9359, P <2:2 Â 10 À16 ), suggesting that increasing the size of the dataset would primarily identify less common subtypes. Reclustering the dataset with an increasing number of positions (Appendix Fig S7B ) suggests we are not close to identifying all of the subtypes. While the number of subtypes of most amino acids steadily increases as more positions are added that of cysteine and histidine plateaus and tryptophan only increases very slowly  (Appendix Fig S7C) , suggesting additional subtypes for those amino acids may be less common. Altogether this suggests that a larger dataset would very likely lead to discovery of several more subtypes that are probably rarer than those observed here.
Conversely, it is relevant to note that the identification of any meaningful number of subtypes would not be possible without performing the combined analysis of several proteins. To allow for the future expansion of this study, we have developed the DeepScanScape R package (github. com/allydunham/dee pscanscape) to apply the data processing, deep landscape analysis and subtype assignment to aid analysis of future deep mutational scans. This tool also allows for the mapping of positions from a new study to this reference landscape allowing for the predictions of some structural properties.
Properties and frequency of amino acid subtypes The DMS datasets combined here allow us to identify and determine the frequency of amino acid subtypes. We then clustered the average mutational profiles of the subtypes (Fig 3C ) finding groups with similar mean mutational profiles. These groups of subtypes represent cases where positions from different amino acids have similar mutational properties. For example, they may generally tolerate or select against a specific type of amino acid, for example requiring small aliphatic amino acids or tolerating everything apart from proline (referred to as the small aliphatic group and not proline group, respectively). Subtype groups occupy different regions of UMAP space, thus tending to have different physical properties as well as different mutational profiles (Fig  3D ).
The frequencies of the most selective subtype, with the highest mean ER score, and the permissive subtype, defined to be positions with all jERj < 0:4, vary widely between amino acids (Fig  3E ), indicating how often each amino acid fills either highly specific or very general roles. For example, the most selective subtype of cysteine, methionine and tryptophan all occur in at least 65% of their positions in our data, meaning these amino acids are very often used for specific functions. Conversely, a much smaller proportion of glycine positions are the most selective subtype (27%) but that subtypes' mean ER profile  (Fig 3F)  shows those positions are some of the most selective in the dataset; it is not common for a glycine to be highly selected but when they are they fulfil a very specific role.
Characterising amino acid subtypes The results so far show that broad subtype analysis can shed light on general trends in amino acid roles, but more targeted analysis is Another interesting set of subtype groups is those of the charged amino acids: negatively charged aspartate and glutamate and positive arginine and lysine. These visually split into 12 subtypes that fall into five broad categories (Fig 5A ), with the frequency of charged amino acid positions falling in each category noted: selective for each of the two charge polarities (26. 7% negative and 21. 1% positive); selective for general polarity (7. 83%); selection against negative charge (6. 18%); and selection against proline only (12. 2%), leaving 26. 1% in rarer subtypes, permissive positions and outliers. These groups are quantitatively differentiated by the average electrostatic force they contribute to the protein (Fig  5B ) not absolute but the majority of cases of each role examined in structural models fall into the appropriate subtype, which is also the case with other examples mentioned (see Dataset EV4).
Discussion We have demonstrated the utility of combining deep mutational scanning studies for performing proteome scale analyses, even when the experiments differ between studies. While the results of these studies are strongly related to evolutionary conservation, they also provide quantitative information about rare variants that would require prohibitively large sample sizes to measure through sequencing of different species. As we demonstrate here, this makes them useful for analysing mutational profiles because these frequently include rare variants. While this dataset only covers a small slice of the protein universe, and so only forms the start of a reference landscape, many findings relate to common protein features and so we expect that it may apply to a wide range of other proteins.
In this work, we identify 100 subtypes, including permissive positions of each amino acid but excluding outlier positions. The average ER scores for the subtypes were found to be typically either neutral (tolerating some mutations) or negative (not tolerating) and only rarely positive  (preferring; see Fig 3C). This relates to the fact that mutations in these datasets generally destabilise rather than stabilise function, as seen in the example distributions in Fig 1C. We think this is in line with the biological expectation that mutations away from the natural protein sequence are more likely to destabilise the function of the protein than improve it. One key aspect that depends on the joint analysis of multiple proteins is determining the differences in the frequency of these roles. As examples, we have discovered how often each amino acid is used in proteins for their most specific purposes  (Fig 3E) , including that cysteine positions seem to utilise cysteine-specific properties in 65% of positions or that charged residues only rely heavily on their charge in 21-27% of positions.
The identification of amino acid subtypes, their characterisation and quantification of how often they are used in proteins is only possible when combining data from many proteins. This allows us to quantify patterns across proteins and highlight recurring patterns that are not apparent when considering each protein individually. For example, if we had studied 1,000 protein positions, which is larger than a typical single protein, we would have only been able to identify 17 subtypes  (Appendix Fig S7). To allow for the future extension of this work, we make the DeepScanScape R package available, allowing others to apply our landscape and subtypes analysis to new DMS studies. Subtype characterisation also breaks down typical roles of amino acids and their frequencies, which helps quantify the spread of functions across the proteome.
The subtypes we identify include both amino acid-specific functions like ion binding and general roles, such as selection against proline or for hydrophobicity. This highlights another way to view some subtypes; they are shared between amino acids, and so an alternative approach would be to combine these subtypes, either after subtypes have been generated or by clustering all amino acids together. Such an analysis could help further quantify amino acid diversity by explicitly identifying positions where several amino acids share the same properties. The relationship between deep mutational scanning and evolutionary conservation suggests that a similar mutational landscape analysis could be performed using sequence alignment data. We experimented with applying a conservation-based analysis to our dataset, based on positions SIFT4G score profile.
This suggests that conservation-based subtypes are a potentially powerful future direction, allowing coverage of a much larger portion of the proteome, but lack the resolution and detail provided by deep mutational scanning approaches. A method combining both data types could also be powerful. Exploring how to combine and use this type of data will be important in future because many more deep mutational scanning experiments are expected. The most recent DepMap release  (Meyers et al, 2017; Tsherniak et al, 2017; preprint: Dempster et al, 2019)  suggests 7,293 genes are essential in at least one cell line and thus directly targetable by deep mutational scanning. This suggests data from 1,000s of genes could be available in future, ideally from more standardised experiments. While we have data from a diverse range of proteins and have amassed as large a dataset as possible, we are ultimately limited to data from 6,291 positions, a very small slice of the proteome.
Although the fact that our analysis largely relates to protein biochemistry rather than specific protein functions suggests our results may still be broadly applicable across the proteome, this is part of the reason we do not, and did not expect to, identify clear novel amino acid functions from our data; we only had access to data from a limited range of well-studied proteins. Potentially, a larger range of proteins would contain enough examples of rare, unstudied interactions to identify novel roles. A larger dataset has potential for both extending this work and exploring other areas, for example a proteome-wide analysis of different types of substitution in different contexts (i. e. focussing on the mutant amino acid rather than the wild type). For our work, a larger dataset has the potential to expand the range of roles discovered, with rarer subtypes either completely missing or too rare to identify in our data.
Overall, our analysis shows three key points. Firstly, deep mutational scanning data can be combined from disparate studies into a meaningful dataset that relates to real biology. Secondly, the mutational landscape is a powerful tool for analysing protein positions, with rich relationships to biophysical properties, enabled by combining data from many proteins. And finally, positions of each amino acid can be broken down into typical subtypes using these profiles, allowing us to quantitatively map the diverse range of functions each amino acid plays across the proteome and the frequency of their usage.
Materials and Methods Combining the data First, we selected as many deep mutational scanning studies (Dataset EV1) as we could find that fulfilled the following criteria: • Available data. • Selection criteria matching the proteins' natural function. • Scores that cannot be transformed to the standard This included searching MaveDB  (Esposito et al, 2019)  and searching the literature. Each study was then processed into a standard state, with a complete set of variant scores for single substitutions at each position. Some studies generated multiple mutations to individual sequences, without generating all single substitutions as well. In these cases, we used the average ER score of sequences including each substitution that was not directly tested, limiting the average to sequences with a maximum number of variants, depending on the deviation from known single substitution scores and resultant variant coverage  (Appendix Fig S1, Dataset EV1).
In studies with multiple comparable replicates, either direct replicates or in equivalent conditions, we averaged across conditions, and when multiple incomparable conditions were available, we chose the most representative of proteins' natural functions (Dataset EV1). Studies were then filtered if they had very poor correlation with SIFT4G scores, indicating unrealistic selection criteria or experimental oddities, or low coverage of substitutions at each position. The scores for each study were transformed onto the standard scale, with a different transform required in each case (Dataset EV1), and normalised by dividing all scores by the median score of the lowest 10%. This threshold was chosen heuristically to encompass the typical ER scores of nonsense mutations. These scores were put together into a combined dataset, from which positions with < 15 of the 20 missense and nonsense substitutions were filtered and remaining missing data were imputed using the median of that substitution type (A → C, A → D,.
) across all normalised scores. Some studies include variant codons and so measure synonymous variants' ER but where not measured synonymous substitutions were imputed to be 0 because 58% of study values were exactly 0 and 92% have absolute values less than 0. 05. We identified the best structural model for each protein in SWISS-MODEL  (Waterhouse et al, 2018) , selecting higher resolution and coverage where possible, as well as favouring experimental models over homology models. The models used are detailed in Dataset EV1.
Analysing the mutational landscape The combined mutational landscape data were annotated with additional biophysical data from a number of tools: • SIFT4G -substitution effect scores (with a custom patch to output SIFT4G scores to 4. d. p). • FoldX -substitution ΔΔG predictions. • naccess -surface accessibility measurement. • Porter5 -secondary structure predictions. The different physical components of the FoldX ΔΔG predictions were averaged across all substitutions at a position in most analyses, giving a measure of the importance of different structural effects at that position. The UMAP  (preprint: McInnes et al, 2018; Melville et al, 2020)  and principal component dimensionality reductions were calculated and cross referenced with those factors.
This is potentially partially required due to the amount of data we have, and more data could allow us to optimise this parameter across the dataset as a whole. Subtypes were characterised using the averages of the various statistics and metrics we previously annotated the mutational landscape with, as well as their average ER score profiles. The number of subtypes identified with variable numbers of positions was determined by applying the clustering algorithm to increasingly large subsets of 100 positional shuffles of the main dataset. 1,000 positions were initially included in each and then increased in increments of 200.
Data availability The datasets and computer code produced in this study are available in the following databases: • Combined landscape data: Dataset EV2. • Subtype assignments: Dataset EV3. • Dataset processing, landscape analysis and clustering code: GitHub (https://github. com/allydunham/aa_subtypes). • DeepScanScape R Package: GitHub (https://github. com/allyd unham/deepscanscape). DeepScanScape allows users to process new DMS studies using the methods presented here and analyse them based on the deep landscape dataset. This lets you identify unusual properties of studies and positions, visualise data on the deep landscape and assign amino acid subtypes to positions to predict potential properties. Expanded View for this article is available online.
E Positive subtype position interacting with the DNA backbone (GAL4, PDB ID: 3COQ). F Negative subtypes binding an ion ligand (TEM1, PDB ID: 3COQ). G "Not negative" subtype positions surrounding bound RNA (PAB1, PDB ID: 6R5K). H "Not proline" position at the end of an alpha helix (APH3II, PDB ID: 1ND4).
The EKA currently has 208 members. No approval was obtained from an institutional review board, because the survey was anonymous and no patient data were included. Data were collected using Survey-Monkey (https ://www. surve ymonk ey. com): an online data collection program. The survey consisted of 20 questions (single/multiple choice and ranked questions) and was conducted from March 30, 2020, to April 10, 2020 (see Online Appendix 1 for survey details). Four main topics were addressed: (1) origin and surgical experience of the participant (four questions); (2) potential disruption of arthroplasty surgeries (12 questions); (3) influence of the pandemic on the particular arthroplasty surgeon (four questions); (4) a matrix provided 14 different arthroplasty surgeries and the participant was asked to state whether the dedicated surgery was stopped, delayed or cancelled at her or his department.
Web-based platforms such as Galaxy (http://galaxyproject. org/) or community-based computational initiatives such as Bioconductor (http://www. bioconductor. org), Biopython (https://biopython. org/) rather focus on the standardization the sharing of processes, in particular how computational scientists can package and distribute computing toolboxes in bioinformatics  [2, 11, 12]. Some developments oriented toward clinical data integration and sharing such as tranSMART and eTriks are also concerned (http://transmartfoundation. org/, http://www. etriks. org/) [3]. More recently, one assisted to the emergence of containerization systems to ensure the reliability of the packaging and sharing of computational applications. They rely on docking systems (e. g. Docker, Virtual environments) that work like virtual machines to ensure that each docked application (a container) can be executed safely and generate reproducible results in different environments.
For instance, the Anaconda (https://www. anaconda. com) and BioContainer (https://biocontainers. pro/) projects are communitydriven projects that provide guidelines and repositories to create and distribute containers in data science and bioinformatics, respectively  [4]. All these initiatives are driven by a need of reproducibility, transparency and traceability in computational sciences. However, it remains difficult for data scientists to transparently share and integrate heterogeneous analysis codes coming from different scientific domains, projects or teams. Generally, the use of a given toolbox written in a given language (R, Matlab, Python, etc. ) requires dealing with non-standard interfaces. For example, different Galaxy instances can rapidly diverge and become incompatible although they are based on the same initial platform.
In these conditions, custom data parsers and exporter may be required to adapt the building blocks. It therefore lacks today an agnostic (i. e. non-language specific) framework to help computational scientists to standardize, annotate and share their computational processes at the code level. To address this issue, we first introduce here PRISM (Process Resource Interfacing SysteM), an agnostic open architecture that standardizes the way processes and resources can be interfaced in computational workflows, to ensure traceability, reproducibility and facilitate sharing. This framework is not only devoted to computational experiments, but can be extended to model any experimental process. It is based on a high-level abstraction to provide an agnostic and transversal framework that allows integrating processes and resources coming from heterogeneous communities while allowing computational scientists to derive custom workflows that fit to their specific requirements.
Contrary to existing frameworks, it is specifically designed to fully capture real-world processes while ensuring simplicity at the code level. We therefore present BioTracs, which is an implementation of the PRISM architecture. BioTracs is a computational framework implemented in MATLAB (The MathWorks) and it delivers a set of libraries to implement building blocks in bioinformatics. It also provides libraries to wrap and use language-specific programs without compromising the integrity of the whole system. BioTracs provides ready to used templates classes to created complex applications. BioTracs library is freely available online on GitHub (https://bioaster. github. io/biotracs/).
; https://doi. org/10. 1101/2020. 02. 16. 951624 doi: bioRxiv preprint 5 of the subtasks that are delegated. In the PRISM architecture, a process may therefore be composed of zero to several engines as presented by Figure  1. In practice, the implementation of PRISM must guarantee the safe execution of the processes, to store and visualize the resources that are used and produced at each steps of a workflow. PRISM was therefore built on the basis of the well-known MVC (model-view-controller) architecture. In the MVC architecture, a model contains information (e. g. metadata) that are displayed or stored in a database. A view is responsible for managing human-machine interfaces and interactions (e. g. the visualization of plots, tables, graphs, button clicks, …). The controller implements the actions that are execute when the user interacts with the model and the view (e.
g. an action to launch a workflow, to display a plot). This architecture therefore separates the role and responsibilities of these three components in the system (Figure  2 ). In PRISM, the Process, the Configuration and the Resource inherit from the base Model class because these objects store user and system data. For example, a Process stores system metadata associated with the state and the execution of an algorithm. A Configuration stores user parameter values. Resource stores the analysis data and result values. These objects can therefore be associated with several views. For example, a dataset can be visualized as a table, a series of plots, a heatmap or a graph depending on the context. Basically, models and views are generally sufficient to implement simple and complex workflows.