dataesr/scientific-paragraphs-categorization huggingface dataset:
SNP and genotype validation To assess genotype accuracy between the probabilistic callers and CLC and estimate the error rate of identical genotype calls, we validated 180 genotypes by classical Sanger sequencing. We randomly picked 58 overlapping SNPs with the only requirements that a minimum of 10 individuals per population had a genotype called, and that at least one individual showed a conflicting genotype call. We validated genotypes of several individuals at those SNPs, which appeared to belong to three different classes: (i) homozygote genotype with GATK/SAMtools but heterozygote with CLC, (ii) heterozygote genotype with GATK/SAMtools but homozygote with CLC, (iii) identical genotype call. We also validated (iv) singleton sites (only one alternative allele called in the entire dataset) that were determined by only one of the callers through Sanger-sequencing of the individual that exhibited the singleton.
Additionally, to specifically investigate the genotype accuracy of SNPs present in the intersect data of all three callers, we randomly picked an additional 14 SNPs from this dataset. BEDtools v. 2. 16. 2  [71]  was used to extract the DNA sequences 400 bp downstream and upstream of the targeted SNPs from the orangutan reference genome ponAbe2. PCR primers flanking the SNPs were designed with Primer3  [72]  (Additional file 8: Table  S7 ). We verified genotypes by sequencing PCR products on a 3730 DNA Analyzer (Applied Biosystems). Details on PCR conditions, cycle sequencing and data analyses are provided in the Additional file 1.
Statistical analyses We considered all sites with mapping and base quality phred scores of ≥30, and a minimal sequence depth of 10 as high-quality base pairs (bp hiqual ). We estimated the target efficiency of our iRRL protocol by calculating which percentage of the actually sequenced bp hiqual was predicted by our in-silico digest of ponAbe2 with HaeIII (=target sites). Furthermore, for each SNP and population we calculated the observed site heterozygosity as the number of individuals carrying both alleles divided by the total number of called genotypes in this population. Kernel density plots of the minor allele frequency and site heterozygosity distributions were drawn in R with the 'sm' package  [73]. We assessed the significance of equality of the density estimates among the different datasets with the 'sm.
density. compare' function with 10,000 permutations. In addition, we performed sliding-window analyses for each dataset to detect selective sweeps in the genome based on population differentiation using custom R-scripts. For all SNPs we estimated population differentiation using allele-frequency differentials, defined as: D = ∑ [abs(pSK -pWA) + abs(qSK -qWA)]/2, where p and q denote the frequencies of the two alleles for each SNP. We scanned each chromosome ('chrXY_random' excluded) and calculated for each window (100 kb window size, 25 kb step size) the average D of all SNPs. We arbitrarily defined outlier regions as windows with an average population differentiation D > 0. 95 (covered by at least 2 SNPs).
Overview of genotype validations at overlapping SNP sitesOverlapping SNP sites but discordant genotype assignments. b Loci were exclusively counted in this category without considering them in the homo-or heterozygote categories below. c 100 of the 114 genotypes were validated from the same sites used to validate the discordant genotypes. The remaining 14 genotypes were validated from 14 SNPs chosen randomly from the GATK-CLC intersect dataset (exclusively identical genotype calls).
Acknowledgments We thank the  Functional Genomics Centre Zurich  and  Gerrit Kuhn  from  Life Technologies  for their support on sequencing and bioinformatics. We are grateful to  Giada Ferrari  for assisting in the CLC SNP analyses,  David Marques  who genotyped some of the WA individuals,  Glauco Camenisch  for help with in-silico digests, and  Erik Willems  for providing the map of Borneo and Sumatra. We thank  Christian Lexer  for general support. We are indebted to the staff at the  Sepilok Orangutan Rehabilitation Centre , the  Shangri-La's Rasa Ria Resort , the  Lok Kawi Wildlife Park  and the  Sumatran Orangutan Conservation Programme  who helped collecting samples. Furthermore, we thank the following institutions for supporting our research:  Sabah Wildlife Department (SWD) ,  Indonesian State Ministry for Research and Technology (RISTEK) ,  Indonesian Institute of Sciences (LIPI) , and  Leuser International Foundation (LIF).
91 17. 98 12. 82 9. 99 20. 53 19. 37 10. 54 23. 08 21. 60 Median site heterozygosity a 0. 250 0. 222 / 0. 286 0. 231 / 0. 266 0. 222 / Median coverage per individual b 107× (65) 81× (27) 96× (37) 55× (98) 18× (98) 20× (99) 69× (76) 19× (35) 26× (46) We required all SNPs to have a genotype call passing all stringent quality filters in a minimum of eight individuals per population (population-based filtering). The intersect datasets contain exclusively concordant genotype calls between the designated SNP callers. Pop_SK: South Kinabatangan population, Pop_WA: West Alas population. a Based on the sites being polymorphic within the population. b Coverage values of intersect datasets are taken from the first named SNP caller. The coverage values of the second named caller are given in brackets.
We characterize their modes of regulation through allele-specific expression and their relevance in nature through re-analysis of published datasets. We demonstrate that the FRI-Ler allele induces FLC expression, delays flowering time and confers sensitivity to vernalization in contrast to the true null FRI-Col allele. Nevertheless, the FRI-Ler allele revealed a weaker effect when compared to the fully functional FRI-wt allele, mainly due to reduced expression. Conclusions: The present study defines for the first time the existence of a new class of Arabidopsis accessions with an intermediate phenotype between slow and rapid cycling types. Although using available data from a common garden experiment we cannot observe fitness differences between accessions carrying the FRI-Ler or the FRI-Col allele, the phenotypic changes observed in the lab suggest that variation in these alleles could play a role in adaptation to specific natural environments.
0095, FLC p = 0. 0138, interaction p = 0. 0824). This epistatic interaction suggests the existence of a functional FRI allele in Ler, as no other gene located in this region of chromosome 4 has been shown to delay flowering time through interactions with a locus in the region of chromosome 5 containing FLC. Because strong vernalization requirements are associated with functional FRI alleles, we decided to analyze variation in vernalization sensitivity among Arabidopsis accessions  [34, 35]. We calculated the decrease in flowering time in response to vernalization in a published dataset containing 126 Arabidopsis accessions classified as FRI-Col, FRI-Ler or FRI-wt  [22]. Accessions carrying the FRI-Ler allele accelerated flowering by 10 days in response to vernalization (Figure  1b ). This acceleration was intermediate to the low response of accessions containing the FRI-Col deletion and the high response of accessions carrying the FRI-wt allele (Figure  1b ).
Structural characterization of the FRI-Ler and FRI-Col alleles We looked for further evidences of a functional FRI-Ler allele by studying sequence variation among Arabidopsis accessions. We sequenced genomic DNA from FRI including its upstream and downstream regions in Col-0, Ler-1 and Bil-7, the latter being a winter-annual accession that contains a functional FRI-wt allele identical to the published H51 allele  [2, 14]. All polymorphisms found in FRI-Col and FRI-Ler correspond exactly to the ones described before, such as the insertion/deletions that define these two allelic classes (Figure  1c ;  [2, 14] ). The H51 allele is predicted to encode a fully functional 609 aa protein [GenBank: AAG23415] while the Col allele yields a 314 aa protein [GenBank: AEE81913], truncated due to a premature stop codon resulting from a 16 bp deletion in exon 1 (Figure  1c  and d ;  [2] ).
Analysis of published datasets Leaf number from Recombinant Inbred Lines (RILs) in the Ler x Col population  [31]  was analyzed by grouping lines according to their genotypes at the closest molecular markers to FRI (m506, chromosome 4 at 0. 0 cM) and FLC (g4560, chromosome 5 at 17. 3 cM). Phenotypic and genotypic data for these individuals was kindly provided by Johan W. van Ooijen and Caroline Dean. Responses to vernalization from 126 Arabidopsis accessions were obtained from published data  [22]  and analyzed by subtracting days to flowering with vernalization from days to flowering without vernalization (plants grown in controlled environment rooms with 16 hour light). In this dataset, FRI alleles are classified as FRI-Ler, FRI-Col, nonfunctional alleles or novel alleles. The rest of the alleles were assumed to be functional (FRI-wt) as they do not contain the Ler or Col-0 indels and the accessions that carry them are late flowering.
For our analysis, we considered only those accessions containing alleles classified as FRI-wt, FRI-Ler or FRI-Col. Accessions for which the FLC allele was classified as non-functional or novel were removed. Expression data for FRI and FLC was obtained from available repositories holding northern hybridization quantification for 137 Arabidopsis accessions (greenhouse grown, 16 h light  [38] ). Accessions in this dataset were classified according to their FRI alleles by combining genotypes from the following sources:  [14, 21, 22, 27, 37]. The assignment of FRI alleles for each accession can be found in Additional file 5. Fitness data was obtained from Arabidopsis accessions grown in four different field locations  [40]. These accessions were classified according to their FRI alleles as above.
Acknowledgements We thank  Caroline Dean  and  Johan W. Ooijen  for kindly providing the phenotypic and genotypic data of the Ler x Col RIL population. Furthermore, we thank  Ute Tartler  and  Regina Gentges  for plant handling. We are grateful to  Maarten Koornneef ,  Niels Müller  and  Jinyong Hu  for discussions during the course of the work and for comments on the manuscript. IS acknowledges support from the  German Research Foundation  ( DFG  project number  SCHM2793/1-1 ) and the  Max Planck Society. LZ was funded by an  International Max Planck Research School PhD  fellowship. MR received funding from the  German Research Foundation  under the  German-Israeli Project Cooperation program  ( DFG   DIP  project number  FE552/12-1 ). Received: 10 March 2014 Accepted: 4 August 2014 Published: 13 August 2014.
The HelioClim-3 database (HC3v3) provides records of surface solar irradiation every 15 min, estimated by processing images from the geostationary meteorological Meteosat satellites using climatological data sets of the atmospheric Linke turbidity factor. This technical note proposes a method to improve a posteriori HC3v3 by combining it with data records of the irradiation under clear skies from the new McClear clear-sky model, whose inputs are the advanced global aerosol property forecasts and physically consistent total column content in water vapour and ozone produced by the MACC (Monitoring Atmosphere Composition and Climate) projects. The method is validated by comparison with a series of ground measurements for 15 min and 1 h for 6 stations and for daily irradiation for 23 stations.
, 2011). The project comprises several databases that cover Europe, Africa and the Atlantic Ocean. The HelioClim-1 (HC1) database offers daily means of the global SSI for the period 1985-2005. The HelioClim-3 (HC3) database contains 15 min values of the global SSI. It was created in 2004, and is updated daily from images taken by the Meteosat Second Generation satellites. Its recent improvements have taken place in the framework of the European MACC and MACC-II (Monitoring Atmosphere Composition and Climate) projects funded by the European Commission. The HelioClim-4 database is under creation in these MACC projects. It will contain 15 min values of the global, direct and diffuse components of the SSI with a daily update. Published by Copernicus Publications on behalf of the European Geosciences Union.
The HelioClim databases are available on the Internet through the SoDa website (http://www. soda-pro. com), and support research and business by providing data of known quality on surface solar irradiance. More than 100 000 requests were made in 2012 to HC1 by users and more than 2 million to HC3, demonstrating the large use of HelioClim databases. Lefevre et al. (2014)  performed a review of the scientific literature citing HelioClim, and found many examples of usages in various domains: oceanography, climate, energy production, life cycle analysis, agriculture, ecology, human health, and air quality. The HC1 and HC3 databases are derived from images of the Meteosat series of satellites using the Heliosat-2 method  (Rigollier et al. , 2004). The Heliosat-2 method needs a socalled clear-sky model to predict the SSI that should be observed under a clear sky.
, 2011; Kaiser et al. , 2012; Peuch et al. , 2009). Up to now, a multi-annual reanalysis data set has been provided, and is used here  (Inness et al. , 2013). Such information has not been available so far from any operational numerical weather prediction (NWP) centre. A new clearsky model called McClear has been developed to exploit this new input data source for estimating the direct and global SSI  (Lefèvre et al. , 2013). Validation of McClear outputs against beam and global irradiances measured at 1 min by BSRN stations in the world reveals satisfactory results. Good correlation is attained; bias, standard deviation and root mean square error (RMSE) are small  (Lefèvre et al. , 2013). How can such advanced data sets on aerosol properties, water vapour and ozone be exploited to bring a significant improvement to the widely used HC3 without re-factoring the Heliosat-2 method and re-processing all Meteosat images since 2004? If this is possible, the dynamics of the aerosol properties, water vapour and ozone would be taken into account in the enhanced HC3, thus possibly yielding better estimates under clear-sky conditions.
Data sets and method The method is the following. A standard request to HC3v3 (version 3 of HC3) for a given site integrated over a given period, called summarization, e. g. 1 h or 1 day, yields several data, including the global SSI I HC3v3 , that under clear-sky condition I ESRA , and I 0 the irradiance received on a horizontal surface at the top of atmosphere. The clear-sky index Kc is computed: Kc = I HC3v3 / I ESRA. (1) The McClear model may be invoked through the SoDa website. It yields the clear-sky value I McClear for the requested summarization and site, and the new version of the SSI I HC3McClear is obtained: I HC3McClear = KcI McClear. (2) A series of ground measurements of surface solar irradiation I ground was assembled and serves as a reference in the comparison of I HC3v3 and I HC3McClear.
The clearness index KT, also called the global transmissivity of the atmosphere, or atmospheric transmittance, or atmospheric transmission, is defined as KT = I ground / I 0. (5) For clear skies, KT is close to 0. 8, and is close to 0 for overcast skies. This index has the advantages of removing most of the effects due to sun position and indicating the type of sky. While irradiation for clear-sky conditions but a large solar zenith angle may be similar to that under cloudy conditions but with a low solar zenith angle, the clearness indices will be different. The clearness index is useful for analysing causes of discrepancies between the data sets. Another approach could be to compute I HC3McClear every 15 min, and then to perform the summarization for 1 h or 1 day, though this is less practical for the many users of the SoDa website.
An exception to the overall decrease in bias is Tamanrasset, where the bias is 1. 3 J cm -2 (3 % of the mean irradiation) for I HC3v3 and 1. 7 J cm -2 (4 %) for I HC3McClear. A closer examination of the data sets of irradiation and the clearness index for Tamanrasset reveals that I HC3v3 exhibits a negative bias for clear-sky conditions and a positive bias for cloudy situations. The balance between these negative and positive biases yields an overall bias of 1. 3 J cm -2. The combination of I HC3v3 with McClear yields more accurate results for clear-sky conditions, as expected. The bias in these conditions is now strongly reduced and close to 0. On the contrary, there is almost no change in the results for cloudy situations, which exhibit a positive bias. Contrary to I HC3v3 , this positive bias is not counterbalanced in I HC3McClear by an equivalent but negative bias for clear skies.
Conclusions This technical note proposes a very simple method to improve HC3v3 records by combining them with data records of the irradiation under clear skies from the new McClear clear-sky model. Inputs to McClear are the advanced global aerosol property forecasts and physically consistent total column content in water vapour and ozone produced by the MACC projects. All irradiation data sets may be retrieved on the SoDa website (http://www. soda-pro. com), and therefore the method is easily applicable. The method can be applied at any scale; it is not necessary to correct HC3v3 at 15 min resolution and then to sum up to obtain hourly or daily irradiation. Hourly and daily irradiation can be corrected using the corresponding irradiation from McClear. The method has been validated against ground measurements made at several summarizations: 15 min, 1 h, and 1 day.
Acknowledgements. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 283576  ( MACC-II  project) and no. 262892  ( ENDORSE  project). The authors thank all ground station operators of the BSRN network for their valuable measurements and the  Alfred Wegener Institute  for hosting the BSRN website, and acknowledge the help of the  Baseline Surface Radiation Network (BSRN) , the  World Radiation Data Center (WRDC)  and the meteorological networks supplying measurements of irradiation to the WRDC. Edited by:  R. Engelen.
Results: We performed kinetic transcriptome analysis of the NG 14 and RUT C30 strains of early enzyme production induced by lactose using bioreactor cultivations close to an industrial cultivation regime. RUT C30 exhibited both earlier onset of protein production (3 h) and higher steady-state productivity. A rather small number of genes compared to previous studies were regulated (568), most of them being specific to the NG 14 strain (319). Clustering analysis highlighted similar behaviour for some functional categories and allowed us to distinguish between inductionrelated genes and productivity-related genes. Cross-comparison of our transcriptome data with previously identified mutations revealed that most genes from our dataset have not been mutated. Interestingly, the few mutated genes belong to the same clusters, suggesting that these clusters contain genes playing a role in strain performance.
The cultivation data for each fermentation are presented in (Additional file 1: Figure  S1 ). Carbon source concentrations and biomass and protein production, focussed on the first 48 h after fed-batch are presented in Figure  1 , with merged cultivation duplicates. CO 2 production was monitored to achieve carbon balances, which reached more than 0. 90 gC produced /gC consumed (data not shown). We chose to determine protein concentration using the Bradford method  [31]  to avoid background protein with peptides contained in the corn steep in the culture medium when protein concentration is low. However, for cellulases, this method is known to underestimate actual protein concentrations 3. 5 to 5-fold  [32]  compared to the Lowry method  [33] , which reflects actual production. This conversion factor was used for mass balancing and to compare specific productivities with previous work (Jourdier et al.
A difference is seen at 48 h with 24 g L -1 for NG 14 and 17 g L -1 for RUT C30. However, the measure for NG 14 is Figure  1  RUT C30 and NG 14 bioreactor culture profiles. Growth curves (dry biomass concentration g. L -1 ) and protein production levels (extracellular protein concentration g. L -1 (EC Prot. Conc. ), as assayed by Bradford method (see Results), are displayed for NG 14 and RUT C30 T. reesei strains. Time 0 h marks the start of lactose feeding. Negative values represent the 24 h of batch culture and positive values the first 48 h of fed-batch culture with lactose. Each curve represents the average measure of two independent cultures; error bars show average standard deviation to give an estimate of replicates? quality (excluding technical replicates). One NG 14 replicate is lacking after 24 h lactose induction, so dispersion data is not available.
Cluster characterization using functional categories From a manual annotation of each up-or down-regulated T. reesei gene (see Materials and methods), genes were categorized according to the Functional Catalogue (Fun-Cat) (Additional file 5: Table  S2 ). The most representative categories (categories with more than ten genes in the whole dataset) and the number of genes found in each category in each cluster are shown in Table  1. Genes related to amino acid metabolism and ribosome biogenesis were found more in cluster 1, up-regulated only in NG 14. Genes linked to metabolism, lipid metabolism, energy metabolism and extracellular protein degradation were mostly found in clusters 2, 3 and 4. Cluster 2 contains the majority of lipid metabolism genes, while most genes for metabolism, energy metabolism and extracellular protein degradation were found in cluster 4.
In addition, four proteasome components, pre2, pre5, pre6 and pre9 (Trire2:121009, Trire2:121343, Trire2:76010, Trire2:124031, respectively), were found in cluster 4, and the gene phosphoenolpyruvate carboxykinase pck1 (Trire2:124115), key in gluconeogenesis, was found in cluster 3. CAZyme genes were more represented in clusters 8 and 9. These clusters group the most induced genes during induction for both strains and gather most of the genes encoding enzymes linked to cellulose degradation. We found among them the main endoglucanase genes cel7b, cel5a, cel12a, cel45a and cel74a (Trire2:122081, Trire2:120312, Trire2:123232, Trire2:49976, Trire2:49081, respectively), cellobiohydrolase cel6a (Trire2:72567) and swollenin swo1 (Trire2:123992). The main cellobiohydrolase, cel7b, is absent from our dataset as induction is so strong that a saturating signal was obtained under all conditions.
Few genes have a higher basal expression in RUT C30 As we were surprised by the large number of genes regulated in the NG 14 strain, we attempted to explain the apparent loss of up-and down-regulation in the RUT C30 strains. To achieve this, the time 0 references from RUT C30 and NG 14 were compared using RNA-seq experiments (Additional file 6: Table  S3 ). From the 568 differentially expressed genes found using microarrays, we identified Only the FunCat categories with more than ten genes in the whole dataset are considered main functional categories. The number of genes belonging to categories is provided for each cluster. Category-associated genes found more abundantly (at least a 2-gene of difference between clusters) are indicated in bold. Percentages in parentheses indicate the rounded percentage of genes from each category found in a cluster.