dataesr/scientific-paragraphs-categorization huggingface dataset:
A preliminary analysis comparing openness fractions on a grid cell-by-grid cell basis showed very little correlation with either the KK10 or the HYDE dataset (Figure  S1 ). We therefore elected to compare openness fractions on the spatial unit of roughly country-sized population regions. These population regions represent the smallest spatial unit covered by a time series of population data  [22]  that were used to drive the ALCC models. For each time window and for grid cells where REVEALS data were available, the area of open land within each population region was summed and divided by the total area of the region. In this manner, we evaluate the quantity of modeled land use within approximate geographic regions, but not necessarily its distribution on a grid-cell by grid-cell basis. To quantify the relationship between REVEALS and the ALCC scenarios, we fitted linear regressions  [60]  between the REVEALS mean open land fractions per population region and the open land fraction for each modeled ALCC scenario.
Results In Figure  2 , we present the relationship between REVEALS and HYDE for the modern era (0-100 BP). For this period, land use in Europe in the HYDE scenario is largely based on remotely-sensed land cover and therefore reflects the "real" distribution of open land at a time close to the present. While the spatial distribution of non-forested land does not match precisely between the HYDE and the REVEALS reconstructions (Figure  2 , maps; see also Figure  S2  for results that include heather as part of the open fraction), the open fractions between the two datasets correlate very well (slope of the regression line 0. 971; r 2 = 0. 925) when aggregated into country-sized population regions (Figure  2 , lower right plot). For comparisons that include HL as part of the open fraction in REVEALS, the correlation is not as good (slope: 0.
For the preindustrial Holocene periods we examined, a distinguishing feature of the REVEALS reconstructions for northern and central Europe is that landscape openness was high in some relatively confined areas in Europe. Open areas expanded gradually with time, but landscape openness in Europe remained heterogeneous, expanding out from previously-cleared settlements, while closed forests persisted in certain areas. In contrast, forest opening in the KK10 dataset is quite diffuse, especially in areas with relatively homogenous suitability for agriculture, such as the lowlands of France and Germany. In HYDE, landscape openness is patchier, but tends not to be in the same areas as those reconstructed by REVEALS. In fact, it is very difficult to see any correspondence between the ALCC scenarios and the spatial pattern of landscape openness reconstructed by REVEALS at the level of individual 1 • grid cells (Figures  2 3 4 ), an observation that is further highlighted when directly comparing corresponding grid cells (Figure  S1 ).
Another reason for the more gradual increase in open land could be due to the broad time windows used in the REVEALS reconstructions. HYDE, on the other hand, shows an even more gradual, near linear relationship between open land and time and tends to underestimate preindustrial land use with increasing magnitude towards the present (Figure  6 ). The linear regression between REVEALS and HYDE shown in Figure  5b  has a slope of less than one, indicating that HYDE consistently underestimates land use as compared to REVEALS. Total open land in the entire study area is also low for each preindustrial time slice (Figure  6 ). A major reason for the lack of landscape openness during the preindustrial period in the HYDE database is the use of late 20th century (AD 1961) values for per capita land use that are nearly constant throughout the entire dataset.
Mechanized agriculture, high yielding crop varieties, fertilizers and pesticides all contributed to a significant decrease in per capita land use during the mid-20th century. Over the past 7 ka, technological and socio-cultural developments could have led to roughly a 10-fold decrease in per capita land use  [21, 72, 73]. We suggest this as the main reason why the HYDE scenario for the preindustrial period is smaller than those in the REVEALS reconstruction. While the magnitudes of used or open land estimates are more similar between KK10 and REVEALS, both ALCC models need improvement on the distribution of land use in the past. In fact, distributing land use based on a large-scale gridded climate and soil datasets offers only a rough estimate of places that would have been hospitable to preindustrial farmers and pastoralists.
Conclusions In this study, we evaluated the quality of the REVEALS pollen-based reconstruction of landscape openness based on comparison to remote sensing-based maps of anthropogenic land use in the present day. Establishing that the REVEALS method does a reasonable job of representing present-day patterns of land cover, we use REVEALS reconstructions for the mid-and late-preindustrial Holocene to evaluate two differing ALCC scenarios: KK10 and HYDE. Neither ALCC scenario captures the fine-scale spatial pattern of land use as inferred from the REVEALS reconstruction, but at an aggregate country-scale population region, the KK10 land use dataset shows good correspondence with pollen-based reconstructions. The nonlinear forest-population density relationship used in the KK10 model adequately hindcasts the amount of land use in the past, at least for the parts of northern and central Europe we studied.
In contrast, the HYDE database appears to consistently underestimate landscape openness during preindustrial time. While these ALCC scenarios, KK10 in particular, may be useful for studies on very coarse-scale feedbacks between land cover change and climate in the preindustrial Holocene, further developments to ALCC models must focus on improving the distribution of land use, particularly in the distant past. This should be done in part by making more use of archaeological information and also by taking into account the importance of non-agricultural activities for the spatial pattern of land use. As both land use models discussed here are global, further data-model comparisons will be possible as REVEALS compilations become available for larger parts of the world  [81]. By performing pollen-based reconstructions of land use in diverse environmental and socio-cultural settings, we will be able to further improve the accuracy of modeled land use in the past and better understand how the human transformation of the Earth's surface over the Holocene may have affected the evolution of anthromes, climate and the long-term sustainability of human-environment interactions.
FSPs are generally calculated using measurements of pollen grains. Because pollen morphological types usually include many species (e. g. , Artemisia) or even entire families (e. g. , Cyperaceae, Gramineae) and measurements of pollen grains are made either on fresh material or fossil pollen grains, FSPs may differ between studies. However, the differences are seldom very significant. The REVEALS estimates used in this paper are based on a standard dataset of FSPs and PPEs  [47]. The errors on the REVEALS estimates due to potential differences in the true FSPs and PPEs over space and time cannot be quantified. Data-and data input-related uncertainties: The pollen records and related chronologies used to generate the REVEALS reconstructions have all been controlled and filtered for reliability.
Acknowledgments:  Jed O. Kaplan  and  Kristen M. Krumhardt  were supported by the  European Research Council  (COEVOLVE,  313797 ). Marie-José Gaillard  was supported by  Linnaeus University's Faculty of Health and Life Sciences (Kalmar-Växjö, Sweden)  and the strategic Research Area ModElling the Regional and Global Earth system (MERGE). Shinya Sugita  was supported by the  Estonian Research Council  (Grant  IUT18-19 ). We are grateful to all members of the  LANDCLIM  project who provided data for the REVEALS reconstructions. LANDCLIM was supported by the  Swedish Science Research Council VR , the  Nordic Council of Ministers' NordForsk  and MERGE. This study is a contribution to the  Past Global Changes (PAGES)  project and its LandCover6k working group, which received support from the  U.
Recent functional, proteomic and ribosome profiling studies in eukaryotes have concurrently demonstrated the translation of alternative open-reading frames (altORFs) in addition to annotated protein coding sequences (CDSs). We show that a large number of small proteins could in fact be coded by these altORFs. The putative alternative proteins translated from altORFs have orthologs in many species and contain functional domains. Evolutionary analyses indicate that altORFs often show more extreme conservation patterns than their CDSs. Thousands of alternative proteins are detected in proteomic datasets by reanalysis using a database containing predicted alternative proteins. This is illustrated with specific examples, including altMiD51, a 70 amino acid mitochondrial fission-promoting protein encoded in MiD51/Mief1/SMCR7L, a gene encoding an annotated protein promoting mitochondrial fission.
Introduction Current protein databases are cornerstones of modern biology but are based on a number of assumptions. In particular, a mature mRNA is predicted to contain a single CDS; yet, ribosomes can select more than one translation initiation site (TIS)  (Ingolia et al. , 2011; Lee et al. , 2012; Mouilleron et al. , 2016)  on any single mRNA. Also, minimum size limits are imposed on the length of CDSs, resulting in many RNAs being mistakenly classified as non-coding (ncRNAs)  (Pauli et al. , 2014; Anderson et al. , 2015; Zanet et al. , 2015; Nelson et al. , 2016; Bazzini et al. , 2014; Ji et al. , 2015; Prabakaran et al. , 2014; Slavoff et al. , 2013). As a result of these assumptions, the size and complexity of most eukaryotic proteomes have probably been greatly underestimated  (Andrews and Rothnagel, 2014; Landry et al.
, 2015; Fields et al. , 2015; Saghatelian and Couso, 2015). In particular, few small proteins (defined as of 100 amino acids or less) are annotated in current databases. The absence of annotation of small proteins is a major bottleneck in the study of their function and their roles in health and disease. This is further supported by classical and recent examples of small proteins of functional importance, for instance many critical regulatory molecules such as the F0 subunit of the F0F1-ATPsynthase  (Stock et al. , 1999) , the sarcoplasmic reticulum calcium ATPase regulator phospholamban  (Schmitt et al. , 2003) , and the key regulator of iron homeostasis hepcidin  (Nemeth et al. , 2004). This limitation also impedes our understanding of the process of origin of new genes, which are thought to contribute to evolutionary innovations.
Because these genes generally code for small proteins  (Carvunis et al. , 2012; Schlo ¨tterer, 2015; McLysaght and Hurst, 2016; Sabath et al. , 2012) , they are difficult to unambiguously detect by proteomics and in fact are impossible to detect if they are not included in proteomics databases. Functional annotation of ORFs encoding small proteins is particularly challenging since an unknown fraction of small ORFs may occur by chance in the transcriptome, generating a significant level of noise  (Landry et al. , 2015). However, given that many small proteins have important functions and are ultimately one of the most important sources of functional novelty, it is time to address the challenge of their functional annotations  (Landry et al. , 2015). We systematically reanalyzed several eukaryotic transcriptomes to annotate previously unannotated ORFs which we term alternative ORFs (altORFs), and we annotated the corresponding hidden proteome.
Yet, new ORFs encoding small proteins were recently discovered in RNAs (or parts of RNA) that had previously been annotated as non-coding. Working out what these small proteins actually do will require scientists being able to find more of these overlooked ORFs. The RNAs produced by many organisms -from humans and mice to fruit flies and yeast -have been catalogued and the data stored in publicly accessible databases. Samandi, Roy et al. have now taken a fresh look at the data for nine different organisms, and identified several thousand examples of possibly overlooked ORFs, which they refer to as "alternative ORFs". This included more than 180,000 from humans. Further analysis of other datasets that captured details of the proteins actually produced in human cells uncovered thousands of small proteins encoded by the predicted alternative ORFs.
Many of the so-called alternative proteins also resembled parts of other proteins that have a known activity or function. Lastly, Samandi, Roy et al. focused on two alternative proteins and showed that they both might affect the activity of the proteins coded within the main ORF in their respective genes. These findings reveal new details about the different proteins encoded within the genes of humans and other organisms, including that many mRNAs encode more that one protein. The implications and applications of this research could be far-reaching, and may help scientists to better understand how genes work in both health and disease. DOI: https://doi. org/10. 7554/eLife. 27860. 002 different reading frames from annotated CDSs in mRNAs (Figure  1a ). For clarity, predicted proteins translated from altORFs are termed alternative proteins and proteins translated from annotated CDSs are termed reference proteins.
Prediction of altORFs and alternative proteins We predicted a total of 539,134 altORFs compared to 68,264 annotated CDSs in the human transcriptome (Figure  1b , Table  1 ). Because identical ORFs can be present in different RNA isoforms transcribed from the same genomic locus, the number of unique altORFs and CDSs becomes  183,191 and 54,498, respectively. AltORFs were also predicted in other organisms for comparison (Table  1 ). By convention, only reference proteins are annotated in current protein databases. As expected, altORFs are on average small, with a size ranging from 30 to 1480 codons. Accordingly, the median size of predicted human alternative proteins is 45 amino acids compared to 460 for reference proteins (Figure  1c ), and 92. 96% of alternative proteins have less than 100 amino acids.
Another possibility is that they evolve following the patterns of evolution of genes that evolve de novo, with a rapid birth and death rate, which accelerates their turnover over time  (Schlo ¨tterer, 2015). If altORFs play a functional role, they would be expected to be under purifying selection. The first and second positions of a codon experience stronger purifying selection than the third because of redundancy in the genetic code  (Pollard et al. , 2010). In the case of CDS regions overlapping altORFs with a shifted reading frame, the third codon positions of the CDSs are either the first or the second in the altORFs, and should thus also undergo purifying selection. We analyzed conservation of third codon positions of CDSs for 100 vertebrate species for 1088 altORFs completely nested within and conserved across vertebrates (human to zebrafish) with their 889 CDSs from 867 genes (Figure  3 ).
We observed that in regions of the CDS overlapping altORFs, third codon positions were evolving at significantly more extreme speeds (slow or quick) than third codon positions of random control sequences from the entire CDS (Figure  3 ), reaching up to 67-fold for conservation at p<0. 0001 and 124-fold for accelerated evolution at p<0. 0001. This is illustrated with three altORFs located within the CDS of NTNG1, RET and VTI1A genes (Figure  4 ). These three genes encode a protein promoting neurite outgrowth, the proto-oncogene tyrosine-protein kinase receptor Ret and a protein mediating vesicle transport to the cell surface, respectively. Two of these alternative proteins have been detected by ribosome profiling (RET, IP_182668. 1) or mass spectrometry (VTI1A, IP_188229. 1) (see Supplementary files 1 and 2).
, 2015). In agreement with the presence of functional altORFs 3' , cap-independent translational sequences were recently discovered in human 3'UTRs  (Weingarten-Gabbay et al. , 2016). Second, we re-analyzed proteomic data using our composite database containing alternative proteins in addition to annotated reference proteins (Figure  5b ; Supplementary file 2). We selected four studies representing different experimental paradigms and proteomic applications: large-scale  (Hein et al. , 2015)  and targeted  (Tong et al. , 2014)  protein/protein interactions, post-translational modifications  (Sharma et al. , 2014) , and a combination of bottom-up, shotgun and interactome proteomics  (Rosenberger et al. , 2014). In the first dataset, we detected 3957 predicted alternative proteins in the interactome of reference proteins  (Hein et al.
, 2015) , providing a framework to uncover the function of these proteins. In a second proteomic dataset containing about 10,000 reference human proteins  (Rosenberger et al. , 2014) , a total of 549 predicted alternative proteins were detected. Using a phosphoproteomic large data set  (Sharma et al. , 2014) , we detected 384 alternative  proteins. The biological function of these proteins is supported by the observation that some alternative proteins are specifically phosphorylated in cells stimulated by the epidermal growth factor, and others are specifically phosphorylated during mitosis (Figure  6 ; Supplementary file 3). We provide examples of spectra validation (Figure  6 -figure supplement 1). A fourth proteomic dataset contained 77 alternative proteins in the epidermal growth factor receptor interactome  (Tong et al.
Functional annotations of alternative proteins An important goal of this study is to associate potential functions to alternative proteins, which we can do through annotations. Because the sequence similarities and the presence of particular signatures (families, domains, motifs, sites) are a good indicator of a protein's function, we analyzed the sequence of the predicted alternative proteins in several organisms with InterProScan, an analysis and classification tool for characterizing unknown protein sequences by predicting the presence of combined protein signatures from most main domain databases  (Mitchell et al. , 2015)  (Figure  8 ; Figure  8 -figure supplement 1). We found 41,511 (23%) human alternative proteins with at least one InterPro signature (Figure  8b ). Of these, 37,739 (or 20.
13e-05, Fisher's exact test and chi-square test). Thus, predicted alternative proteins with InterPro entries are more likely to be detected, supporting their functional role. The most abundant class of predicted alternative proteins with at least one InterPro entry are C2H2 zinc finger proteins with 110 alternative proteins containing 187 C2H2-type/integrase DNA-binding domains, 91 C2H2 domains and 23 C2H2-like domains (Figure  10a ). Eighteen of these (17. 8%) were detected in public proteomic and ribosome profiling datasets (Table  2 ), a percentage that is similar to reference zinc finger proteins (20. 1%). Alternative proteins have between 1 and 23 zinc finger domains (Figure  10b ). (Iacono et al. , 2005). The total number of CDSs and altORFs with a detected TIS is indicated at the top.
(b) Alternative and reference proteins detected in three large proteomic datasets: human interactome  (Hein et al. , 2015) , 10,000 human proteins  (Rosenberger et al. , 2014) , human phosphoproteome  (Sharma et al. , 2014) , EGFR interactome  (Tong et al. , 2014)  protein-protein interactions  (Wolfe et al. , 2000). The linker sequence separating adjacent finger motifs matches or resembles the consensus TGEK sequence in nearly half the annotated zinc finger proteins  (Laity et al. , 2001). This linker confers high-affinity DNA binding and switches from a flexible to a rigid conformation to stabilize DNA binding. The consensus TGEK linker is present 46 times in 31 alternative zinc finger proteins (Supplementary file 4). These analyses show that a number of alternative proteins can be classified into families and will help deciphering their functions.
We compared the functional annotations of the 585 alternative proteins with an InterPro entry with the reference proteins expressed from the same genes. Strikingly, 89 of 110 altORFs coding for zinc finger proteins (Figure  10 ) are present in transcripts in which the CDS also codes for a zinc finger protein. Overall, 138 alternative/reference protein pairs have at least one identical InterPro entry and many pairs have more than one identical entry (Figure  11a ). The number of identical entries was much higher than expected by chance (Figure  11b , p<0. 0001). The correspondence between Inter-Pro domains of alternative proteins and their corresponding reference proteins coded by the same genes also indicates that even when entries are not identical, the InterPro terms are functionally related (Figure  11c ;  The presence of identical domains within alternative/reference protein pairs encoded in the same genes may result from alternative splicing events which connect an altORF in a different reading frame than the CDS with a coding exon in the CDS reading frame.
Such alternative proteins would likely be unannotated isoforms of the corresponding reference proteins. Thus, we examined whether there is any association between the % of overlap or identity and functional similarity within alternative/reference protein pairs. We performed blast searches of the 183,191 predicted alternative proteins against 54,498 reference proteins using BlastP. All altORFs with more than 80% identity and overlap had already been removed to generate our database (as indicated in the Materials and methods). We found 100 (0. 055%) alternative proteins with 25% to 100% identity and 10% to 100% overlap with their reference protein pairs. Among them, 20 (0. 00054%) alternative proteins have identical InterPro signatures with their respective reference proteins (Supplementary file 5).
We re-analyzed the MS data using our alternative protein sequence database to detect alternative proteins in this interactome (Supplementary file 6). Five alternative proteins (IP_168460. 1, IP_168527. 1, IP_270697. 1, IP_273983. 1, IP_279784. 1) were identified within the interactome of their reference zinc finger proteins. This number was higher than expected by chance (p<10 À6 ) based on 1 million binomial simulations of randomized interactomes. These physical interactions within zinc finger alternative/reference protein pairs suggest that there are examples of functional relationships between large and small proteins coded by the same genes.
AltMiD51 is a 70 amino acid alternative protein conserved in vertebrates  (Andreev et al. , 2015)  and conserved with its reference protein MiD51 from humans to zebrafish (Supplementary file 7). Its coding sequence is present in exon 2 of the MiD51/MIEF1/SMCR7L gene. This exon forms part of the 5'UTR for the canonical mRNA and is annotated as non-coding in current gene databases (Figure  12a ). Yet, altMiD51 is robustly detected by MS in several cell lines (Supplementary file 2: HEK293, HeLa Kyoto, HeLa S3, THP1 cells and gut tissue), and we validated some spectra using synthetic peptides (Figure  12 -figure supplement 1), and it is also detected by ribosome profiling (Supplementary file 1)  (Vanderperre et al. , 2013; Andreev et al. , 2015; Kim et al. , 2014). We confirmed co-expression of altMiD51 and MiD51 from the same transcript (Figure  12b ).