dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010; Rosauer et al. , 2015) , which account for how chimpanzees may have tracked suitable climate conditions across time. To calculate the CV stability in each pixel across all of the temporal snapshots of habitat suitability, we created a rasterstack of model outputs for each temporal snapshot and used the cv function of the "raster" package version 3. 1-5  (Hijmans et al. , 2011). For the static stability estimate, we summed the negative log of suitability through time for each pixel and took the exponent of this value to give a value between 0 and 1 to represent the degree to which the pixel has continuously provided suitable habitat. Similarly, dynamic stability reads the suitability at each time period but permits dispersal across time periods (i. e. paleoclimate time snapshots) using a cost distance (a function of habitat suitability), based on a user-defined maximum dispersal distance.
We then calculated the number of pixels present in each of Maley's refugia and expressed the proportion of each of then known refugia that were captured by our analyses as a percentage. Moreover, we report additional areas inferred as refugia which are not inferred by  Maley (1996)  in terms of their size and location. As an additional validation of our putative refugia, we tested our obtained habitat stability estimates for chimpanzees against species richness data for two keystone tropical plant families, figs (Ficus, Moraceae) and palms (Arecaceae). Based on the premise that there should be greater species richness in refugia than surrounding non refugial areas, and a high diversity of food plants should support higher densities of chimpanzees in refugial areas, our habitat stability estimates should show some correlation with the fig and palm data.
To this end we utilized data from  Onstein et al. (2020) , which incorporated distribution data for figs from  Kissling et al. (2007) , and for palms from  Blach-Overgaard et al. (2013). Fig species  richness  was calculated from digitized range map GIS shapefiles which were converted to binary presence-absence raster maps and then summed in R to give species richness in each pixel. Palm species richness was calculated in a similar way, but directly from the species distribution models, so instead of binary presence-absence per pixel the values represent probabilistic values of a species occurring in that pixel. Both the fig and palm data were clipped to the exact same size as our study region so that direct comparisons could be made with our stability estimates. To assess correlations between the data sets, we stacked the raster layers (dynamic stability, static stability, CV, fig species richness, palm species richness in R using the "raster" package version 3.
| Contemporary chimpanzee habitat suitability Our contemporary habitat suitability model performed generally well at the species level (i. e. , all four subspecies combined), with AUC > F I G U R E 2 Current chimpanzee distribution and presence points from our own fieldwork, from the IUCN SSC A. P. E. S. database (http://apes. eva. mpg. de/) and from the Pan African Programme: The Cultured Chimpanzee (http://panafrican. eva. mpg. de/). Currently recognized subspecies ranges are depicted by colored polygons  (Humle et al. , 2016) , with black dots representing sampling localities (n = 139,902 georeferenced presence points) used for species distribution modeling, historical distribution range (ca. 500kya, McBrearty & Jablonski, 2005) represented by diagonal shading. Country borders, topography (lowlands = green, highlands = brown), and major hydrological features are also shown 0.
, 2010; Rosauer et al. , 2015) , their accuracy may be improved by combining and testing different predictions in the ensemble model to capture variation between GCMs. Underlying assumptions regarding the HyDE human density data should also be acknowledged, mainly regarding the uncertainty and reliability of historical data which often had no other independent data to validate it, and also the extrapolation techniques used to complete gaps in data collection. We extrapolated the human density data from 12,000 years to 120,000 years before present, which is likely not an accurate reflection of human population density changes over this period for two reasons; the HyDE data is based on agriculturalist populations, which do not necessarily represent the same population density and trends as hunterer-gatherer communities.
DATA AVAILABILITY STATEMENT We provide our scripts and data for reproducibility as a DRYAD package (https://doi. org/10. 5061/dryad. bk3j9kdcp). The package contains the raw data along with scripts to perform analyses, build and run the models, and plot the results. The full climate data set is available from Jeremy Vanderwal (see  Bell et al. , 2017). The full distribution data for chimpanzees used in the analyses are available on request from the IUCN SSC A. P. E. S. database manage (http:// apes. eva. mpg. de/).
Existing CNV callers are optimized for the detection of either large deletions (usually spanning more than three exons), or deletions of full single exons  (14, 32). Other established callers, such as GATK, are not designed to detect CNVs and can therefore identify deletions of only a few dozen base pairs (typically up to 50 bp, https://gatkforums. broadinstitute. org/gatk/discussion/5938/usinggatk-tool-how-long-insertion-deletion-could-be-detected and (  33 )). The human genome contains ~235,000 exons, about 20% of which are larger than 200 bp  (34). HMZDelFinder_opt therefore makes possible the systematic discovery of currently unknown HMZ deletions in ~47,000 exons that are not detectable with other tools. In sum, we describe HMZDelFinder_opt, a method for improving the detection of HMZ deletions in heterogeneous exome data that can be used to identify partial exon deletions that would otherwise be missed, through an extension of the scope of HMZDelFinder.
ACKNOWLEDGEMENT We thank the members of the  Human Genetics of Infectious Diseases Laboratory  for helpful discussions. We also thank  Yelena Nemiroskaya ,  Dominick Papandrea ,  Mark Woollett ,  Dana Liu (St. Giles Laboratory of Human Genetics of Infectious Diseases ,  Rockefeller Branch , The  Rockefeller University, New York, New York, USA) , and  Cécile Patissier ,  Lazaro Lorenzo-Diaz ,  Christine Rivalain (Laboratory of Human Genetics of Infectious Diseases ,  Necker Branch ,  INSERM U1163 ,  Necker Hospital for Sick Children, Paris, France)  for their assistance.
FUNDING This research was supported in part by the  National Institutes of Health (NIH)  (grants  R01AI088364 ,  R37AI095983 ,  U19AI111143 ,  R01AI127564 ,  P01AI061093  to J. -L. C. ), the  National Center for Research Resources  and the  National Center for Advancing Sciences of the NIH  (grant  8UL1TR001866 ), the  Yale Center for Mendelian Genomics  and the  GSP Coordinating Center  funded by the  National Human Genome Research Institute (NHGRI)  ( UM1HG006504  and  U24HG008956 ), the  Rockefeller University , the  St. Giles Foundation ,  Howard Hughes Medical Institute ,  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  University of Paris , the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research (FRM)  ( EQU201903007798 ), the  SCOR Corporate Foundation for Science , and the  French National Research Agency (ANR)  under the "Investments for the future" (grand number  ANR-10-IAHU-01 ),  GENMSMD  ( ANR-16-CE17.
0005-01 ,to JB),  ANR - LTh-MSMD-CMCD  ( ANR-18-CE93-0008-01  to A. P),  Fonds de Recherche en Santé Respiratoire  ( SRC2017  to J. B. ),  ProgLegio  project ( ANR-15-CE17-0014 ). and  ECOS Nord  ( C19S01-63407  to J. B. ).
Introduction To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids. Objectives To help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver. Methods Our matching method is more flexible than an exact identifier-based correspondence since it allows establishing a link between molecules even if a different level of precision is provided in the dataset and in the metabolic network.
For instance, it can associate a generic class of lipids present in the network with the molecular species detailed in the lipidomics dataset. This mapping is based on the computation of a distance between molecules in ChEBI ontology. Results We applied our method to a chemical library (968 lipids) and an experimental dataset (32 modulated lipids) and showed that using ontology-based mapping improves and facilitates the link with genome scale metabolic networks. Beyond network mapping, the results provide ways for improvements in terms of network curation and lipidomics data annotation. Conclusion This new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.
2016; Thiele et al. 2013). Unfortunately, the task of mapping metabolites onto GSMN is still a challenge  (Pham et al. 2019). The crucial step in network-based analysis of metabolic fingerprints is to draw correspondences between identified molecules and nodes in the network. This question is far from being trivial since it requires harmonizing identifiers both in networks and datasets. Yet, metabolite names used in networks and in experimental datasets do not usually comply with any specific naming convention and come with different synonyms (e. g. , α-Ketoglutaric acid and 2-oxoglutaric acid), spellings, formatting (lower or upper-case letters),… etc. Therefore, it is often unsuccessful to perform a direct and automatic matching between metabolite names or identifiers present in the network and metabolite names provided in datasets.
Identifiers collected from metabolite databases such as KEGG  (Kanehisa et al. 2014) , ChEBI  (Hastings et al. 2016) , HMDB  (Wishart et al. 2009 (Wishart et al. , 2017) )  or Lipid Maps  (Fahy et al. 2009 ) may be referenced in GSMN and therefore used to perform mapping between networks and datasets. Efficient methods and tools are available to convert identifiers  [e. g. , BrigeDB (van Iersel et al. 2010) , Chemical Translation Service (CTS)  (Wohlgemuth et al. 2010) , UniChem  (Chambers et al. 2013) ] and thus allow adding identifiers in metabolic networks. For lipids however, this synonym-based matching often fails due to discrepancies between network and lipidomics datasets regarding the level of annotation provided for these compounds. In fact, both in lipidomics datasets and networks, the level of annotation ranges from fine grained definition (species) to general definition (classes)  (Liebisch et al.
Among the ontologies describing molecules some are focused on lipids like Lipid Maps while others are more generic like Chemical Entities of Biological Interest (ChEBI). In this article, we propose a matching method using ChEBI ontology to bridge the gap between lipidomics data and GSMN. We will show how such ontology-based method can be used to better assess how the library built within the MetaboHUB consortium (French national Metabolomics and Fluxomics infrastructure) covers the human GSMN. Beyond this library mapping, the method had been applied to a lipidomics dataset produced on liver biopsies from healthy patients and from patients with non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH)  (Chiappini et al. 2017). The implementation of this approach is freely available in a Python package and can be used online in MetExplore web server  (Cottret et al.
ChEBI ontology ChEBI is a freely available repository of molecular entities focused on 'small' chemical compounds. The term 'molecular entity' refers to any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc. , identifiable as a separately distinguishable entity. ChEBI incorporates an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. This is particularly well suited for the description of lipids. This publicly available database of chemical entities contains 56,090 annotated entities (release 179, September 2019).
MetaboHUB Lipid Library MetaboHUB gathers the complementary means (equipment, analytical techniques, software), expertise and competences of four French metabolomic facilities. Within MetaboHUB, lipidomics experts compiled a human lipidome database, which aims to reference a maximum number of identifiers per lipid analyzed in the consortium on plasma NIST  (Bowden et al. 2017 ) (Supplementary Table  1 ). For each analyzed lipid we indicated the "Family", "Class" and "Subclass" (as defined by Lipid Maps (LM) consortium), as well as a "Common Name" (usual name used for reporting), the molecular formula and different database identifiers  (ChEBI, LM, PubChem (Kim et al. 2019) , Swiss Lipids (SLM)  (Aimo et al. 2015) , InChI  (Heller et al. 2015)  and InChIKey). MetaboHUB database is built upon lipids analyzed by the consortium: fatty acids (saturated and unsaturated) are usually analyzed by gas chromatography coupled to flame ionization detector (GC-FID) or gas chromatography coupled to mass spectrometer (GC-MS), complex lipids such as glycerophospholipids, sphingolipids and bile acids are analyzed by liquid chromatography coupled to mass spectrometry (LC-MS), and Glycerolipids and Sterols can be analyzed with both techniques.
Liver lipidomics dataset To highlight the benefit of the approach for lipidomics experimental datasets, we used a lipidomics dataset from the article by  Chiappini et al. (2017). In this study, lipidomics analyses were performed on biopsies from normal liver, NAFL and NASH using classical liquid-liquid extraction and lipid profiling using, GC-MS and LC-MS. A random forest-based machine learning approach was used and allowed to identify a fingerprint of 32 lipids (Supplementary Table  2 ), discriminating NASH from healthy and NAFL livers. We manually retrieved the ChEBI identifiers for these 32 lipids and mapped them onto the metabolic network Recon2. 2 in order to identify potential metabolic pathways and reactions related to NASH.
Matching and mapping data The whole process required to analyze lipidomics data in the context of GSMN can be divided into two steps: (1) a matching step aiming at finding the metabolite identifiers in the metabolic network corresponding to each of the lipids in the dataset, (2) a mapping step consisting in positioning the lipid data onto the metabolic network (see Fig. 1 ). The matching step requires to find a correspondence between names or identifiers used to annotate lipidomics data and the ones informed in the network. The chemical names used on both sides are often different, preventing from doing a direct and automatic exact matching on names. Some public database identifiers can be informed for compounds in networks and datasets but, again, their type often differs. Hence, to perform matching it is necessary to use the same controlled vocabulary to annotate lipids in datasets and GSMN.
Finding relevant database identifiers This step, Fig. 1 , has to be done on both network and dataset molecules (or class of molecules). The aim is to select an identifier (e. g. , ChEBI) which will be used to bridge the gap between the two lists of molecules. We chose to focus on the ChEBI identifiers since these are particularly well annotated in Recon2. 2. These identifiers, like ChEBI, can be retrieved using services like CTS or BridgeDB.
Exact matching (name and ChEBI) The matching step (Fig. 2 ) is called "exact" when, for a given lipid, there is the exact same identifier or name in the Fig. 1  Steps required to perform metabolite/lipid mapping onto Genome Scale Metabolic Networks network and in the lipidomics dataset. In our application, exact matching was performed, first based on the names provided in the network and in the dataset, and then based on ChEBI identifiers.
Ontology based matching As it will be shown in both examples, the exact matching is not sufficient to establish a correspondence for all molecules. Hence, we propose to complement the mapping pipeline by using an ontology-based matching (Fig. 2 ). This approach will allow to establish a link between molecules when the level of precision is different in GSMN and dataset (e. g. , when only a class of lipids is present in the network, whereas the molecular species are detailed in the lipidomics dataset). For instance, regarding the glycerophospholipids, only the generic subclass phosphatidylcholine is present in the Recon2. 2 network whereas more specific species (PC(32:0), PC(36:2), …) can be analytically measured and are therefore detailed in the dataset. Hence, we propose to link lipids in the network and in the dataset by using ChEBI ontology.
Starting from the ChEBI identifier of a molecule in the dataset, the ChEBI ontology is scanned to see if downstream (more precise species) or upstream (more generic) ChEBI of metabolites present in the network can be found. To do so, the ontology is modelled by a graph (a directed acyclic graph more precisely) allowing to search paths between elements of the ontology and to measure lengths of these paths (number of edges between the source and target elements). Hence, we compute a distance representing, in the ontology, the distance between the metabolite in the dataset and the one finally retrieved in the network. An exact match is characterized by a matching distance of 0 between a lipid in the network and in the dataset. The distance is positive when the experimentally measured metabolite matches a more generic class in the network.
On the contrary, the distance is negative when a more precise subclass of the observed metabolite is found in the network (see Fig. 2 ). The distance is slightly increased (by 0. 1) when the anionic form (base) of the lipid is present in the network whereas the protonated form is present in the dataset (or vice and versa). Similarly, distances between tautomer molecules are increased by 0. 01. For instance, in Fig. 2 , the "C18:1 n-9" fatty acid from the MetaboHUB dataset (octadec-9-enoic acid, ChEBI:36,021) matches with the network metabolite "M_ocdcea" (Oleate) with a distance of -1. 1 ("0. 1" to account for the fact that oleate is a conjugate base of oleic acid, and "-1" because oleic acid (octadec-9Z-enoic acid) is a specific octadec-9-enoic acid). The "C18:1 n-9" fatty acid also matches with the generic metabolite "M_Rtotal" (fatty acid anion) present in Recon2.
Matching method implementation The matching method is implemented in a Python library (Metabolomics2Network) provided under CeCILL open source license. The library retrieves information from ChEBI ontology using libChEBIpy python library (Swainston ). The Directed Acyclic Graph structure of the ontology (made of ChEBI nodes and "is a" edges) is traversed to find shortest paths between lipids in the dataset and metabolites in the network. The library takes as input a json or text file with the lipids to be matched and a json file containing all the lipids from the network. The library returns, in a json or text file, the ontological closest metabolites in the network and the metabolites belonging to this shortest path in the ontology. If there are several nodes at equal distance, all of them will be retrieved (e.
Subnetwork extraction Once lipids are matched, it is possible to map them onto GSMN (Fig. 3 ). These lipids usually correspond to a few nodes among the thousand ones constituting the metabolic network (e. g. , 16 nodes out 2652 for the liver lipidomics dataset) and the challenge is then to reduce the complexity of this large network to a more relevant sub-part (sub-network). Hence, we implemented in MetExplore a sub-network extraction method aiming at reducing the size of the network to ease the visual exploration of reactions connecting lipids in a lipidomics dataset. To extract a sub-network (subset of reactions) from the entire human metabolic network, we compute the union of lightest paths between each pair of mapped lipids. The lightest path between two nodes is the path with the lowest cumulated weight (defined as the sum of the squared degree of the nodes in the path  (Croes et al.
Annotation of lipids in the MetaboHUB library As lipids are complex molecules, we cannot get the same level of identification for all the studied classes. For instance, regarding unsaturated fatty acids and their metabolites, all the main molecular species can be analyzed and we were able to retrieve the most complete identifiers such as InChI, InChIKey, or even LM identifiers in the database. On the contrary, for complex lipids such as PC, the MetaboHUB consortium is currently not able to analyze all the single molecular species, especially to identify each specific fatty acid they contain (e. g. , PC(16:0/18:1(11E)); PC(16:0/18:1(11Z)); PC(16:0/18:1(6E)); PC(16:0/18:1(6Z)); PC(16:0/18:1(9E)), and therefore only the different molecular entities of each subclass family (e. g. , PC(34:1) with just the mention of the number of carbon atoms and unsaturations) have been referenced in the library.
These molecular subclasses cannot currently be assigned to an InChI or InChIKey while ontology-based identifiers like ChEBI allow this abstraction. To summarize, for each entity, we adjusted the level of the identifier used for annotation to the accuracy of the analysis. The database compiles 968 analyzed lipid entities from 5 LM families and 21 LM subclasses. We retrieved a ChEBI identifier for 73% of them and a SLM for 61%, but only 32% have an InChI/InChIKey identifier, 28% a PubChem and 29% a LM (Table  1 ). Supplementary Table  1  contains the entire MetaboHUB database.
Exact matching on Recon2. 2 with names and with ChEBI identifiers A first matching of the 968 molecules of MetaboHUB Lipid Library on Recon 2. 2 using lipid names gave only 3 exact matches for 14,15-DiHETE, 9(10)-EPoMe and   :6n-3,6,9,12,15,18 acid). Using ChEBI, we were able to match only 4. 5% of the identified lipids from the Library to Recon2. 2 network. These 31 exact matches concern oxylipins and few fatty acids which are simple molecules for which the studied molecular species are exactly the same in Recon2. 2 and in the Library. For other molecules, this absence of matching is mainly due to the different level of characterization of lipids between network and lipidomic dataset.
Class mapping of the NASH dataset A NASH lipidomic signature, consisting in 32 lipids discriminating NASH patients from healthy subjects and NAFLD patients, was mapped onto to the metabolic network to identify which metabolic pathways are mainly involved in the development of the disease. Using our exact and class matching approach, we were able to match all the 32 lipids from the signature with network lipids. An exact matching was obtained for most fatty acids, whereas all the phosphatidylcholine, phosphatidylserine, phosphatidylinositol and triglyceride compounds were each matched with their general class compound (Fig. 5 ). Mapping the metabolites onto the metabolic network and computationally extracting a subnetwork allows to identify which metabolic reactions connect the metabolites from the signature and therefore are more likely to be modulated in the development of the disease (Fig.