dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatic and phylogenetic analysis Sequences were assembled, edited and processed with BioEdit software, BLAST and The Sequence Manipulation Suite  (Stothard 2000). ChloroP 1. 1 was used to predict chloroplast transit peptide in protein sequences  (Emanuelsson et al. 1999 ) and CELLO2GO was used for protein subcellular localization  (Yu et al. 2014). The CCoAOMT amino acid sequences were aligned using ClustalW. A phylogenetic tree was constructed using MEGA 6. 0 software  (Tamura et al. 2013)  with the maximum likelihood method based on the JTT matrix-based model  (Jones et al. 1992). Bootstrap-supported consensus trees were inferred from 1000 replicates  (Felsenstein 1985)  and branches with less than 40% bootstrap support were collapsed. UCSF Chimera, a visualization system for exploratory research and analysis, was used for structure conservation analysis.
Conservation mapping of the corresponding sequence alignment onto the structure of SbCCoAOMT-1 (PBD:5KVA) was performed using Chimera with the default setting. For conservation style, AL2CO was chosen with sum of pairs as the type of equation. The 3D model of the SbCCoAOMT-7-trimmed protein was predicted to examine structural differences between the CCoAOMT-like protein from clade 2 and SbCCoAOMT-1 (PDB: 5KVA) from clade 1a. Structure modeling was performed using the I-TASSER server with default structural template selection. I-TASSER (Iterative Threading ASSEmbly Refinement)-http://zhang lab. ccmb. med. umich. edu/I-TASSE R. Databases: Phytozome (https ://phyto zome. jgi. doe. gov), GenBank (https ://www. ncbi. nlm. nih. gov/genba nk/), PLACE  (Higo et al. 1997 ) (http://bioin forma tics.
Accession numbers of sequences used in this article Sequence data from this article can be found in the Phytozome and GenBank/EMBL data libraries, ID of genomic sequences used for gene structure analysis, which are not linked to protein sequences are given in parenthesis: Arabidopsis thaliana-AT4G34050. 1, AT1G24735. 1, AT1G67980, AT1G67990, AT4G26220. 1, AT3G62000. 1, AT 3 G 6 1 9 9 0.
S. bicolor genome harbors seven gene coding for CCoAOMT and CCoAOMT-like proteins The Sorghum bicolor genome was queried with sequences of well-characterized caffeoyl-coenzyme A O-methyltransferases (CCoAOMT) and CCoAOMT-like proteins. This yielded seven genes: Sobic. 010G052200, S o b i c. 0 0 2 G 2 4 2 3 0 0. 1 , S o b i c. 0 0 7 G 2 1 8 7 0 0 , S o b i c. 0 0 7 G 2 1 8 8 0 0. 1 , S o b i c. 0 0 7 G 2 1 8 5 0 0 , Sobic. 007G217200 and Sobic. 007G043200, Table  2. In this study, the genes and their peptides were renamed SbCCoAOMT-1, SbCCoAOMT-2, SbCCoAOMT-3, SbC-CoAOMT-4, SbCCoAOMT-5, SbCCoAOMT-6 and SbC-CoAOMT-7, Table  2. Screening the sorghum genome and sequence databases with these sequences identified genes or cDNAs coding for the same seven proteins or more distantly related sequences, such as Sb07g00386, of higher similarity to COMT than CCoAOMT or CCoAOMT-like proteins.
S-1 and CCoAOMT-CCoAOMT-like alignment in supplementary files. These amino acids are conserved in the sorghum CCoAOMT and CCoAOMT-like enzymes except SbC-CoAOMT-6, which is missing six conserved amino acids due to two large deletions. To investigate structural differences between the substrate-specific enzyme SbCCoAOMT-1 from superclade 1 and CCoAOMT-like protein from clade 2, we compared the crystallographic structure of SbCCoAOMT-1 (PDB For structure modeling, the SbCCoAOMT-7 protein was trimmed at Ser-67 to cut off the chloroplast transit peptide absent in the mature enzyme. The structure of the trimmed peptide was predicted using the I-TASSER server, which employs PDB datasets in the structure-threading method. The template structures applied for structure modeling involved two bacterial O-methyltransferases structures, PDB ID: 3R3H and 2HNKA, and human catechol-O-methyltransferase structure PDB ID: 2AVD, all with normalized Z score of above 1 (2.
93, 3. 97 and 2. 81, respectively). Subsequently, the resulting model of the SbCCoAOMT-7 protein with the highest confidence score (C score = -0. 08) and the SbCCoAOMT-1 structure (PDB ID: 5KVA) was aligned using MatchMaker in Chimera (Fig. 2 ). The most divergent region is Asp-195 to Asp-200 of the insertion loop in trimmed SbCCoAOMT-7 protein. The variability of this region is associated with discrimination between different methyl group acceptors. The differences between the SbC-CoAOMT-1 and SbCCoAOMT-7 structures also involve the shortening of two α-helices: region Pro-27 to Asn-36 of trimmed SbCCoAOMT-7 (Asp-42 to Thr-50 of SbC-CoAOMT-1) and region Ala-137 to Cys-147 of trimmed SbCCoAOMT-7 (Ala-154 to Asp-164 of SbCCoAOMT-1). The first of the α-helices corresponds to the region of reference structure 5KVA involved in dimerization.
Expression of S. bicolor CCoAOMT and CCoAOMT-like genes in field-grown plants The expression of the sorghum CCoAOMT and CCoAOMTlike genes in the inner section of the stem, mature leaves and immature seeds of field-grown plants was analyzed by RT and digital PCR, Fig. 4. The Sb09g023560 gene encoding a ubiquitin-conjugating enzyme expressed at a constant and high level was used for data normalization between different samples  (Johnson et al. 2014). Two genes, SbCCoAOMT-1 and SbCCoAOMT-4, were expressed at similar, high levels in the stems of mature sorghum plants. The transcripts of these genes were approximately five times less abundant than transcripts of the reference gene Sb09g023560. The expression of the other sorghum CCoAOMT-like genes in the stem was 5-100 times lower than that of SbCCoAOMT-1 and SbC-CoAOMT-4.
S-1. The insertion loop, which is highly variable between clades, shows high conservation within clade 1a and 2, Fig. S-2. The CCoAOMT and CCoAOMT-like proteins phylogeny together with the specificity of gene structures within the clades, suggest that cyanobacterial genes from clade 2 are ancestors of all CCoAOMT and CCoAOMT-like genes. The presence of the genes from clades 1a and 2 in the Physcomitrella patens genome and the identification of clade 1a gene in liverwort P. appendiculatum suggest that superclade 1 arose at the very early stage of land plant evolution  (Xu et al. 2015). During manuscript preparation, the Marchantia polymorpha genome sequence was released  (Bowman et al. 2017). This allowed us to identify two liverwort CCoAOMT genes from clade 1a and one CCoAOMT-like gene from clade 2 (not shown).
Acknowledgements This work was carried out with the support of  National Centre for Research and Development (NCBiR)  under Grant no. PBS1/A8/9/2012  (Project Sormisol). The authors would like to thank  Dr. Katarzyna Kluzek  and  Professor Joanna Wesoly  from  Laboratory of High Throughput Technologies in Institute of Molecular Biology and Biotechnology (Adam Mickiewicz University ) for their help in ddPCR experiments. The authors thank  Dr. Joanna Sarzynska  ( IBCH PAS ) for her advices and help in protein structure modeling.
(2015a). For CMIP6, configuration 5B of LMDz has further evolved from  Hourdin et al. (2013) : it has a different formulation of the triggering assumptions, a different radiative transfer code and it accounts for the thermodynamical effect of ice. The convective triggering is now based on evolving statistic properties on the thermal plumes by considering a thermal size distribution instead of a bulk thermal  (Rochetin et al. , 2013). The motivation behind was to depart from the QE hypothesis and to allow a more gradual transition between shallow and deep convection through three step processes (appearance of clouds, crossing of the inhibition layer, and deep convection triggering). In the shortwave, the code is an extension to 6 bands of the initial 2-band code that is used in LMDz5A  (Fouquart and Bonnel, 1980) , as implemented in a previous version of the ECMWF numerical weather prediction model.
Description of the simulations We have run the two versions of the physics described above, 5A and 6A, at several resolutions for years from 1988 to 2014. A summary of the simulations used is given on Table  1. The identification number of the LMDz code used here (that contains both physics versions) is 2791. We discard the first 10 years  (1988) (1989)  to allow enough spin-up for the tracer simulations, considering the interhemispheric exchange time of about 1 year for passive tracers  (Law Rachel M. et al. , 2003). The dynamics is nudged towards the 6-hourly horizontal winds from the ECMWF reanalysis  (Dee et al. , 2011)  with a relaxation time of 3 hours  (Hourdin and Issartel, 2000). CO 2 , SF 6 and 222 Rn initial values are set uniformly for all model grid boxes respectively at a value of 350 µmol/mol (abbreviated as ppm), 1.
Of interest here is the use of fossil fuel emissions from the Emission Database for Global Atmospheric Research version 4. 2 (EDGAR, http://edgar. jrc. ec. europa. eu/) scaled to the annual global values of the Global Carbon Budget (Le  Quéré et al. , 2015). Details of the prescribed fluxes are given in  (Chevallier, 2017). For use at resolution 2. 50 • ×1. 30 • , the natural component of the optimized fluxes has been interpolated from its native 3. 75 • ×1. 90 • resolution, and has been completed by a fossil fuel component directly interpolated from the EDGAR native 0. 1 • ×0. 1 • resolution in order to avoid artificial smoothing. All grid changes here conserve mass. Monthly averages of SF 6 emission fluxes at 1 • ×1 • are taken from the EDGAR 4. 0 inventory for the period 1988-2008 as corrected by  Levin et al.
Point samples from surface sites The surface measurements of SF 6 are taken at background stations SPO, PSA, CGO, SMO, RPB, MLO, MHD, BRW, SUM, ALT from the NOAA/ESRL network (http://www. esrl. noaa. gov/gmd). The simulated mole fractions of CO 2 were compared with some of the atmospheric surface measurements that were assimilated when optimizing the surface CO 2 fluxes prescribed here. The location of these assimilated surface stations is shown in Figure  1. As in the CAMS inverse modelling framework, we retain only early afternoon data (12:00-15:00 LST) for continuous stations under 1000 m. a. s. l and night-time data (00:00-3:00 LST) for continuous station above 1000 m. a. s. l. All measurements from flasks below 1000 m. a. s. l have been kept. The reasons behind this hour selection are the failure of transport models in general to accurately represent the accumulation of tracers near the surface at night  (Geels et al.
2 ppm  (Machida et al. , 2008). We selected from the CONTRAIL dataset all the CO 2 vertical profiles during the ascending and descending flights for the period 2006-2011 over the regions portraited in Figure  1. The regions are similar to  Niwa et al. (2011)  and have been chosen according to the number and location of the vertical profile samples. The number of hourly-mean measurements at 5. 5 km per model grid box are shown in Figure  2   The NOAA/ESRL Global Greenhouse Gas Reference Network Aircraft Program consists here of measurements of air samples collected every few days or months at 22 aircraft profiling sites over continental North America (shown in blue in Figure  1 ) between altitudes 300 and 8000 m. a. s. l. In the lowest altitudes, compared to the CONTRAIL measurements that have been sampled nearby commercial airports, these measurements are not affected by local emissions.
, 2017) , and by continuous technical changes (including bug introduction and bug fixes) that have diverse impacts. Within this flow of modifications from a large developer group, our evaluation of the two versions is based on a snapshot of the LMDz code in its release 2791, a few months before the start of the CMIP6 simulations. We performed a set of CO 2 , SF 6 and 222 Rn simulations using those two versions of LMDz at two horizontal resolutions and guided by the ECMWF wind reanalysis for nearly two decades  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). In addition, we compared two simulations with two different land surface schemes, one using the ORCHIDEE terrestrial surface model and the second using a simplified bulk scheme.
In this case, the land surface scheme only controls the heat and latent fluxes at the land-atmosphere interface. The SF 6 and 222 Rn emissions were prescribed following the TransCom 3 protocol. The CO 2 surface fluxes have been optimized beforehand by the assimilation of surface observations in a version of LMDz close to the older model version studied here. We have compared the resulting ensemble of simulations with both assimilated and unassimilated CO 2 observations from a large dataset in different parts of the globe. This study enabled us to benchmark the effects of the resolution, land-surface scheme and sub-grid scale parameterizations on CO 2 simulated values, which is a fundamental step before implementing the recent developments in our inverse modelling system. At the surface, the comparison with the assimilated CO 2 measurements showed that the land-surface scheme and the vertical resolution have a limited impact compared to the horizontal resolution and sub-grid scale parameterizations.
, 2018). From that perspective, we quantified the impact of the model setups on the simulated xCO 2 convolved with the OCO-2 space-time coverage for a given year. The model-ensemble spread is mainly due to the physics and exceeds 0. 5 ppm in the boreal summer high latitudes, in Northern Africa and in Brazil, or locally around emission hot-spots. In boreal summer, the new physics decreases the latitudinal gradient by decreasing the xCO 2 values in the high latitudes further to a less efficient vertical mixing. This may decrease the northern sink inferred by inverse modelling with the satellite data and LMDz. In austral summer, the mean xCO 2 shows large discrepancies (up to 1 ppm) over the Amazonian basin between the simulations, this region being particularly sensitive to the parameterization assumptions.
Acknowledgements. The authors thank the LMDz developers for maintaining the dynamism and creativity of this climate model. The present work was granted access to the HPC resources of TGCC under the allocation  A0030102201. It was funded by the  Copernicus Atmosphere Monitoring Service , implemented by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission. We also gratefully acknowledge the following people who provided atmospheric observations: aircraft and surface air sample measurements. OCO-2 retrievals were produced by the  OCO-2  project at the  Jet Propulsion Laboratory, California Institute of Technology , and obtained from the  OCO -2 data archive maintained at the  NASA Goddard Earth Science Data and Information Services Center. We also thank you  Martin Steinbacher  and  Luciana Gatti  for their comments on an earlier version of the text.
In scRNA-seq, individual cells are first captured, their RNA is then reverse-transcribed into cDNA, which is greatly amplified from the minute amount of starting material, and the resulting library is finally sequenced  [12]. Transcript abundances are typically estimated by counts that represent the number of sequencing reads mapping to an exon, transcript, or gene. Many scRNAseq protocols have been published for such core steps  [13] [14] [15] [16] [17] [18] , but despite these advances, scRNA-seq data remain inherently noisy. Dropout events cause many transcripts to go undetected for technical reasons, such as inefficient cDNA polymerization, amplification bias, or low sequencing depth, leading to an excess of zero read counts compared to bulk RNA-seq data  [18, 19]. In addition, excess zeros can also occur for biological reasons, such as transcriptional bursting  [20].
Typical scRNA-seq data analysis workflows often involve identifying cell types in silico using tailored clustering algorithms  [22, 23]  or ordering cells along developmental trajectories, where cell types are defined as terminal states of the developmental process  [6, [24] [25] [26]. A natural subsequent step is the discovery of marker genes for the defined cell types by assessing differential gene expression between these groups. Another common setting is the identification of marker genes for a priori known cell types. DE analysis between homogeneous cell populations, as in the aforementioned scRNA-seq applications, is the use case for our method. Popular bulk RNA-seq DE tools, such as those implemented in the Bioconductor R packages EDGER  [2]  and DESEQ2  [1] , assume a negative binomial (NB) count distribution across biological replicates, while limmavoom  [3]  uses linear models for log-transformed counts and observation-level weights to account for the meanvariance relationship of the transformed count data.
Note that the BCV plots of scRNA-seq data exhibit striped patterns (Fig. 1a, b  and Additional file 1: Figure  S2  for scRNA-seq datasets subsampled to ten cells), which are indicative of genes with few positive counts (Additional file 1: Figure  S3 ) and very high dispersion estimates. Randomly adding zeros to bulk RNA-seq data, likewise consisting of ten samples, also results in similar striped patterns (Fig. 1c, d ). NB models, as implemented in DESEQ2 and EDGER, will, thus, accommodate excess zeros by overestimating the dispersion parameter, which jeopardizes the power to infer DE. However, by correctly identifying the excess zeros and downweighting them in the dispersion estimation and model fitting, one can reconstruct the original mean-variance relationship (Fig. 1e ), thus recovering the power to detect DE (Fig.
As most bulk RNA-seq DE methods are based on generalized linear models (GLMs), which readily accommodate observationlevel weights, our approach seamlessly integrates with standard pipelines (e. g. , EDGER, DESEQ2, and LIMMA). Our method is shown to outperform competing methods on simulated bulk and single-cell RNA-seq datasets. We also illustrate our method on two publicly available real datasets. As detailed in "Software implementation, " our approach is implemented in open-source Bioconductor R packages and the code for reproducing the analyses presented in this manuscript is provided in a GitHub repository.
ZINB-WaVE extends bulk RNA-seq tools to handle zero-inflated data We argue that standard bulk RNA-seq methods for inferring differential gene expression suffer from zero inflation with respect to the assumed NB distribution when applied to scRNA-seq data. We propose instead modeling scRNA-seq data using a zero-inflated model and perform inference on the count component of the model, which is equivalent to standard NB regression where excess zeros are downweighted based on posterior probabilities (weights) inferred from a ZINB model. Such weights play a central role in many estimation approaches for ZINB models (e. g. ,  [32] ). In this contribution, we show that the weights can effectively unlock bulk RNA-seq methods for zero-inflated data, allowing us, in particular, to borrow strength across genes to estimate dispersion parameters.
[7]  scRNA-seq dataset subsampled to n = 10 cells. b BCV plot for the real Deng et al. [66]  scRNA-seq dataset subsampled to n = 10 cells. Both panels (a) and (b) show striped patterns in the BCV plot, which significantly distort the mean-variance relationship, as represented by the red curve. c BCV plot for a simulated bulk RNA-seq dataset (n = 10), obtained from the Bottomly et al. [67]  dataset using the simulation framework of Zhou et al. [57]. Dispersion estimates generally decrease smoothly as gene expression increases. d BCV plot for a simulated zero-inflated bulk RNA-seq dataset, obtained by randomly introducing 5% excess zero counts in the dataset from (c). Zero inflation leads to overestimated dispersion for the genes with excess zeros, resulting in striped patterns, as observed also for the real scRNA-seq data in panels (a) and (b).
e BCV plot for simulated zero-inflated bulk RNA-seq dataset from (d), where excess zeros are downweighted in dispersion estimation (i. e. , weights of 0 for excess zeros and 1 otherwise). Downweighting recovers the original mean-variance trend. f True positive rate vs. false discovery proportion for the simulated zero-inflated dataset of (d). The performance of EDGER (red curve) deteriorates in a zero-inflated setting due to overestimation of the dispersion parameter. However, assigning the excess zeros a weight of zero in the dispersion estimation and model fitting result in a dramatic performance boost (orange curve). Hence, downweighting excess zero counts is the key to unlocking bulk RNA-seq tools for zero inflation. BCV biological coefficient of variation, CPM counts per million, ZI zero inflated A ZINB distribution is a two-component mixture between a point mass at zero and a NB distribution.
Impact of zero inflation on the mean-variance relationship We have already noted that adding zeros to bulk RNAseq data results in an overestimation of the dispersion parameter. This leads to striped patterns in the BCV plot (Fig. 2a ), which are indicative of genes with many zeros (Additional file 1: Figure  S3 ) and very high dispersion estimates. Our ZINB-WaVE method, however, identifies many of the introduced excess zeros as such (Fig. 2a, b ), Fig. 2  Impact of zero inflation on mean-variance relationship for simulated bulk RNA-seq and Islam scRNA-seq datasets. Zero inflation distorts the mean-variance trend in (single-cell) RNA-seq data, but is correctly identified by the ZINB-WaVE method. The top panels represent simulated data based on the Bottomly et al. [67]  bulk RNA-seq dataset (as in Fig.
1 ), for a two-group comparison with five samples in each group, where 5% of the counts were randomly replaced by zeros. The bottom panels represent the scRNA-seq dataset  [35]  from Islam et al. [16]. a The BCV plot shows that randomly replacing 5% of the read counts with zeros induces zero inflation and distorts the mean-variance trend through overestimating the dispersion parameters. Points are color-coded according to the average ZINB-WaVE posterior probability for all zeros for a given gene and the blue line represents the mean-variance trend estimated with EDGER. b Receiver operating characteristic (ROC) curve for identifying excess zeros by the ZINB-WaVE method. A very good classification precision is obtained. c Downweighting excess zeros using the ZINB-WaVE posterior probabilities recovers the original mean-variance trend (as indicated with the red line) and inference on the NB count component will now no longer be biased because of zero inflation.
The light blue line represents the estimated mean-variance trend for ZINB-WaVE-weighted EDGER. The blue line is the trend estimated by unweighted EDGER on zero-inflated data as in panel (a). d The BCV plot for the Islam et al. [16]  dataset illustrates the higher variability of scRNA-seq data compared to bulk RNA-seq data. Note the difference in y-axis scales between (a) and (d). As in (a), zero inflation induces striped patterns leading to an overestimation of the NB dispersion parameter. e ROC curve for the identification of excess zeros by the ZINB-WaVE method for scRNA-seq data simulated from the Islam dataset using the simulation framework described in "Methods. " A good classification precision is obtained, but note the difference with bulk RNA-seq data. The noisier scRNA-seq dataset makes identification of excess zeros harder.
f Using the ZINB-WaVE posterior probabilities as observation weights results in lower estimates of the dispersion parameter, unlocking powerful differential expression analysis with standard bulk RNA-seq differential expression methods. Note that since many zeros are identified as excess, the scale of the BCV plot is now similar to that of a standard bulk RNA-seq dataset. The red line is the mean-variance trend for unweighted EDGER, as in panel (d), and the light blue line is the mean-variance trend for ZINB-WaVE-weighted EDGER. A similar pattern is observed for the simulated Islam dataset (Additional file 1: Figure  S26 ). BCV biological coefficient of variation, CPM counts per million, NB negative binomial, ROC, receiver operating characteristic, ZINB zero-inflated negative binomial by classifying them in the zero-inflation component of the ZINB mixture distribution.