dataesr/scientific-paragraphs-categorization huggingface dataset:
Single cell quality filters Capture sites were visually inspected for the presence and viability of cells after staining with the LIVE/DEAD® Viability/Cytotoxicity Kit for mammalian cells (Thermo) at 10× magnification. Only capture sites with one live cell were retained for analysis. Additional quality control of the remaining libraries was performed using the R package 'Single-cell analysis toolkit for gene expression data in R' (scater version 1. 0. 4, https://www. bioconductor. org). Briefly, a set of cell quality indicators, such as the total number of UMIs for the cell, the total number of detected genes and the percentage of UMIs corresponding to mitochondrial genes was computed for each cell  (23). Then, all cells flagged as outlier in a principal component analysis based on these quality measures were excluded.
Statistical analysis Statistical analysis was performed using the statistical package R version 3. 3. 1. ERCC capture efficiency was estimated as the intercept of a regression line with a constrained slope of 1 fitted between the expected number of ERCC molecules and the number of ERCC molecules counted. Only ERCC for which at least 10 molecules were spiked in were used for analysis. Correlation coefficients are calculated using Pearson's method. Single cell UMI count data were normalized for sequencing depth differences using the normalize function from the scran package version 1. 0. 4.
Analysis of public single cell RNAseq data Data were downloaded from Gene Expression Omnibus, reads were matched to the reference genomes and UMIs were counted as described above. HEK293 Dropseq data are from GEO accession GSE63473  (16). 259 cells were selected for further analysis using the same quality filtering as described above. CEL-seq2 data of 96 cells processed in the Fluidigm C1 are from GEO accession GSE 78779 (samples GSM 2076519-GSM2076614)  (14). The UMIs in this dataset have just five nucleotides and not six nucleotides as stated in Hashimshony et al. (14).
Since sequencing errors affect a minority of reads, real UMIs should, on average, be covered by far more reads than UMIs generated by sequencing errors. Islam et al. (10)  exploited the anticipated low read coverage of fake UMIs to correct for UMI sequencing errors, using a 'percentile filtering' approach. For each gene, they discarded UMIs with a read coverage <1% of the median coverage for all UMIs of the respective gene. However, this approach requires a quite high UMI sequencing depth to reliably identify 100-fold differences in read coverage. In our dataset, with a mean UMI sequencing depth of 12. 6 (before filtering), only 0. 01% of the transcript molecules were eliminated (Figure  3A ), a rate far below the expected number of UMI sequencing errors. A more stringent filtering where UMIs with <10% of the mean UMI read coverage were discarded only increased the number of filtered UMIs to 0.
2% (Figure  3A ). A further increase of the cutoff to 20% had a pronounced impact on the number of retained UMIs. However, such stringent cutoffs (≥20%) capped the number of detected molecules, which barely increased when more reads were generated (Supplementary Figure  S2a ). An alternative approach that does not rely on high sequencing depths was recently introduced  (16). This approach, called 'edit distance filtering', merges UMIs of a given transcript when they differ by just one base and eliminates UMIs generated by substitution errors during PCR or sequencing. With our dataset, this filtering method eliminates 22. 6% of the UMIs (Figure  3A ) and is already effective at low sequencing depths. Edit distance filtering eliminated preferentially UMIs with low read coverage and increased the average UMI sequencing depth from 12.
77, Figure  3H ). The correlation between single cells was, as expected, somewhat lower than between pools of 100 HEK293 cells (Figure  2D ) where cell-to-cell variations (e. g. cell cycle, etc. ) are averaged out. We performed two additional HEK293 cell single cell sequencing experiments to test the reproducibility of our protocol. Mean mRNA expressions from the three experiments correlated well (Supplementary Figure  S6 ), despite the fact that the experiments were performed over a period of six months with HEK293 cells at different passage numbers. To test our protocol in a biologically relevant context, we profiled human airway epithelial cells cultured at an airliquid interface, a model that contains several distinct cell populations. The data from two independent primary cultures, which slightly differed in their cell culture conditions correlated well (R = 0.
This will be of par- Recent developments in single cell transcriptome profiling focused on an increased throughput mainly with droplet based techniques. However, ERCC capture efficiencies are apparently lower for droplet based approaches (12. 8% (16), 7. 1%  (17)  than for the Fluidigm microfluidic device (26% (this study), 22%  (7) ). The lower capture efficiency is not restricted to ERCC spike-in RNAs and was also observed for mRNAs. Macosko et al. (16)  reported a mean Dropseq mRNA capture efficiency of 10. 7%. Our observations are consistent with this. With our microfluidic approach we detected an average of 62 841 transcript molecules in a single HEK293 cell (Figure  3 ). In a published HEK293 Dropseq dataset  (16)  we identified 36 746 mRNA molecules per cell (Figure  3j ). While droplet based techniques are currently clearly the method of choice when thousand of cells are analyzed, microfluidic devices are in our opinion better suited for small to medium size projects.
FUNDING Cancérop ôle PACA and the  Commissariat aux Grands Investissements  [ ANR-10-INBS-09-03  and  ANR-10-INBS-09-02 ];  Fondation pour la Recherche Médicale  [ DEQ20130326464  to P. B. ];  Vaincre la Mucoviscidose  [ RF20140501158/1/1/70 ];  Agence Nationale pour la Recherche  [ ANR-12-BSVI-0023-02 ]; labex  Signalife  [ ANR-11-LABX-0028-01 ]; Conseil Départemental  06. Funding for open access charge:  ANR.
Replicates that included consumer-free basal species at the end of the simulation time were discarded from the data set (n ¼ 5,839, corresponding to 21% of the simulations initialized). The uncontrolled growth of such inedible basal species would outcompete other plants and reduce overall species richness drastically, leading to a fundamentally different type of ecosystem  28. In total, 21,461 valid food webs were simulated. Output parameters. The total biomass stocks of the animals, A, and the plants, P, were calculated as the average of the summed biomasses of all species over an evaluation period of 10,000 time steps after the population dynamics had reached a stationary state. Rates of biomass flow from plants to animals, F P (herbivory), and among animals (carnivory or intraguild predation), F A , were calculated as average biomass transfer per time step over the same evaluation period.
Acknowledgements We thank  Alison Feldmann-Iles  and  Amrei Binzer  for comments on the manuscript. F. D. S. has received funding by  Deutsche Bundesstiftung Umwelt  ( 20008/995 ). C. G. is supported by the  Leopoldina Fellowship Programme  under contract number  LPDS 2012-07. B. C. R. and U. B. gratefully acknowledge the support of the  German Centre for integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig  funded by the  German Research Foundation  ( FZT 118 ). This is ISEM publication  2016-161.
Deletion formation in B. subtilis Δ6 Deletions in B. subtilis Δ6 were done in a markerless way using a selection/counter-selection strategy as described previously  (Wenzel and Altenbuchner 2015). Briefly, two fragments of ∼700 bp are amplified from the upstream and downstream regions of the target sequence, fused to each other, and inserted into the nonreplicative suicide vectors pJOE6743. 1  (Wenzel and Altenbuchner 2015)  or pJOE8739. 1. The latter vector contains the ccdB gene under the E. coli rhamnose promoter. For details of construction of pJOE8739. 1, see Supplemental Methods. The sequence has been deposited in GenBank, KY200664. The PCR fragments were inserted into pJOE8739. 1 linearized by SmaI using the Gibson assembly protocol as described by the manufacturer (New England Biolabs).
Acknowledgments We thank  Julia Busse ,  Sabine Freitag ,  Adrian Kovac , and  Annette Schneck  for their expert technical assistance and help with strain construction; and  Stephan Michalik  and  Manuela Gesell Salazar  for their help with the R scripts and LC-MS/MS measurements. D. R. R. was supported by the  Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB, Deutsche Forschungsgemeinschaft  grant  GSC226/2 ). Author contributions: J. A. , U. M. , D. R. R. , F. M. C. , U. V. , and J. S. designed the study; D. R. R. , J. A. , and J. S. designed and constructed the genome-reduced strains; D. R. R. , U. M. , H. R. , T. I. , P. K. S. , A. T. , L. S. , I. F. , and R. D. performed the multi-omics analysis; C. G. , P. N. , B. Z. , and S. K. performed the bioinformatic analysis and modeling; and D.
Data access The mass spectrometry proteomics data from this study have been submitted to the ProteomeXchange Consortium via the PRIDE partner repository (https://www. ebi. ac. uk/pride/archive/)  (Vizcaíno et al. 2016)  under accession number PXD004565. The transcriptome data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO; http://www. ncbi. nlm. nih. gov/ geo/) under accession number GSE82249. The genome sequences from this study have been submitted to GenBank (https://www. ncbi. nlm. nih. gov/genbank/) under accession numbers CP016787, CP016788, and CP016789 for the strains IIG-Bs27-47-24, PG10, and PS38, respectively. The DNA sequence of plasmid pJOE8739. 1 has been submitted to GenBank (https://www. ncbi. nlm. nih. gov/genbank/) under accession number KY200664.
Subsequently, the dried sample spots were recrystallized by adding 0. 2 μL ethanol. All analyses were performed on an UltraFlextreme MALDI-TOF/TOF-MS equipped with a Smartbeam II laser, controlled by proprietary software Flexcontrol 3. 4 (Bruker Daltonics). The Ultraflex was operated in reflectron positive (RP) ion mode, calibrated with a peptide calibration standard (Bruker Daltonics). For sample measurements 10,000 laser shots were accumulated at a laser frequency of 1000 Hz, using a complete sample random walk with 200 shots per raster spot. Tandem mass spectrometry (MALDI-TOF-MS/MS) was performed on the most abundant peaks of the mouse N-glycome via laserinduced dissociation. Fragmentation spectra were annotated using GlycoWorkbench (version 2. 1)  [32]. Nano-liquid chromatography-mass spectrometry (Nano-LC-MS) analysis of IgG Fc-glycosylation IgG was isolated from 2 μL mouse plasma and 150 μL peritoneal fluid using protein G-Sepharose affinity chromatography (GE Healthcare, Uppsala, Sweden), enzymatically digested with trypsin and finally analyzed by MS as described previously  [33, 34].
Data processing and statistical analysis Automated MALDI-TOF-MS data processing was performed by using an in-house developed software named MassyTools  [35]. MassyTools is released under the Apache 2. 0 license and is freely available on GitHub (https://github. com/Tarskin/ MassyTools). Briefly, spectra were exported as. txt file and recalibrated using a defined list of calibrant masses. Only spectra showing a signal-to-noise ratio (S/N) of 9 or above (root-mean-square) for at least five calibration masses were included for further analysis. The observed masses were then assigned to N-glycan structures according to monosaccharide composition, MALDI-TOF/TOF-MS/MS data, known biosynthetic pathways of glycans and literature  [36] [37] [38] [39]. Based on this assignment, a glycan feature list including 94 N-glycan peaks was generated (Supplementary Table  S2 ) and used to proceed to the targeted data extraction of area-under-the-curve of each N-glycan profile.
Statistical analyses and charts are based on biological replicates and were performed by using GraphPad Prism 6. 0 software. For the comparison of glycosylation traits derived from total plasma N-glycome (TPNG) and total peritoneal fluid N-glycome (TPFG) of mice, paired t-test was used. A one-way analysis of variance (ANOVA) with post-hoc Tukey test was used for the multiple comparison of glycosylation traits in the different mice groups at baseline (0 h) and 24 h. Statistical comparison of the derived glycosylation traits between control and zymosan-induced peritonitis mice at the different time points was performed by two-way ANOVA with post-hoc Dunnett test. Data are presented as mean ± standard error of the mean (SEM). *p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001.
Sialic acids are predominantly NeuGc residues on which one or two OAc groups can be attached. Moreover, we have identified α2-6-sialylation of the   , 6 )-]HexNAc, across all glycan species. Data were obtained in the first experiment of zymosan-induced peritonitis. Statistical analysis was performed for all glycan features using a paired t-test (n = 3) (Supplementary Table  S3 ). *p < 0. 05 antennary GlcNAc of diantennary complex-type N-glycans that give rise to a disialylated antenna NeuGc-(α2-3)-Gal-[NeuGc-(α2-6)]-GlcNAc.
Acknowledgments This work was supported by the  Dutch Arthritis Association  (Reumafonds; project  LLP-24 ), an  Innovative Medicines Initiative Joint Undertaking (IMI JU)  funded project  BeTheCure  (project number:  115142-2 ), a  Horizon Programme Zenith project  funded by The  Netherlands Genomic Initiative  (project number: 93511033) as well as by the  European Union  ( Seventh Framework Programme HighGlycan project , grant number  278535 ). HSJ was partially financed by the  Prof. Jan. Veltkamp  fonds (LUMC). We thank  Osk Anuforo  for assistance with the peritonitis experiments. Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Considering the Rayleigh waves group-velocity dispersion curves for shallow and deep basin locations (circles in Figure  A1b ), speeds clearly correspond to the fundamental mode (R0) and first overtones (R1), respectively. It is striking that   1 ). The mesh considered for the simulations reaches 50 km depth. This figure has been created using the TetView Linux software Version 1. 0, http://wias-berlin. de/software/tetgen/tetview. html. even in the absence of attenuation, the first overtone dominates in the deep basin (i. e. , between 10 and 23 km along the array). Unlike the elastic case, attenuation rapidly dissipates the fundamental mode and makes the overtones dominant along almost the whole array in the viscoelastic simulation (Fig. 5b ). The most prominent wave train in the shallow basin regions propagates with the speed of the bedrock fundamental mode (i.
1 ) are shown with black circles and error bars (after Shapiro et al. , 2001). (b) Fourier spectral amplifications (geometric mean of both horizontal components) at 0. 5 Hz with respect to the CUIG site (Fig. 1 ) averaged for the 8 sources. The black contour corresponds to the 2 s dominant-period. (d) Duration of the strong shaking phase for f < 1 Hz averaged for the 8 sources. This figure has been created using the Matlab software Version R2016a, http://www. mathworks. com/. first overtone, respectively (dashed lines in Fig. 6c  and Figure  A6b ). Although it is difficult to identify propagating pulses in the seismic profiles at 0. 3 Hz (Figure  A7 ), eigenfunctions at shallow regions reveal that ground motion is dominated by the first and/or second overtones in both the elastic and viscoelastic cases (Figure  A9d ).
The HPC-GA grant of the Marie Curie International Research Staff Exchange Scheme has supported this work under call FP7-PEOPLE-2011-IRSES. The calculations presented in this article were performed using the supercomputer Miztli of the National Autonomous University of Mexico (UNAM) under grant number SC16-1-IR-87 and the Froggy platform from the CIMENT HPC center of Grenoble Alpes University which is supported by the Rhône-Alpes region (grant CPER07_13 CIRA), the OSUG@2020 labex (reference ANR10 LABX56) and the Equip@Meso project (reference ANR-10-EQPX-29-01). J. D. S-G. acknowledges the financial support from the Universidad Industrial de Santander of Colombia and the Seismology Department of UNAM for its hospitality during the time of this project.
Variation in codon usage among functional categories results from differences in GC-content To better understand what distinguishes codon usage between sets of genes involved in cellular proliferation and differentiation, we started by investigating the main factors that would discriminate codon usage between functional categories in general. For this purpose, we grouped genes per functional category (687 biological processes, associated to more than 40 genes in the Gene Ontology database), and computed codon frequencies for each of these gene sets. Variation in codon usage among these GO gene sets was analyzed by Principal Component Analysis (PCA). In agreement with  Gingold et al. (2014) , the first principal component of this analysis explains 41% of the total variance, and segregates "proliferation" (7 categories) from "differentiation" (6 categories) GO categories  (Fig S1A).
Thus, we computed the average GC3 of each GO gene set. The GC3 of these functional categories vary widely (from 0. 45 to 0. 73) and is perfectly correlated to their coordinates on the first PCA axis (R 2 = 0. 99; Fig  1B ). Hence, variation in SCU between functional categories is fully explained by variation in GC3. On average, in our dataset, each gene is associated to 9 GO biological processes. Many genes belong to more than one GO biological-process category, either because they have several functions (pleiotropy) or because these categories are nested from specific to broad functions. Hence, GO-terms are not independent. To avoid this redundancy, for the remainder of this study we switched from analyses at the level of GO gene sets to analyses at the level of individual genes (except when stated otherwise).
Each gene was assigned with one of three categories based on their GO annotation: 1,008 genes associated with "proliferation", 2,833 genes associated with "differentiation", and 12,129 "other" genes unrelated to these keywords (see methods). The distribution of GC3 content over the entire dataset is bimodal (Fig  1C ). For the subsets of genes associated to "proliferation" and "differentiation", the two distributions of GC3 differ significantly from each other (T-test, p-value < 2. 10 -16 ), and their peaks coincide with each of the two modes observed for the whole genome. Genes associated to "proliferation" are on average less GC-rich than genes associated to "differentiation" (mean GC3 are respectively 0. 53 and 0. 61 in the two subsets). Variation in synonymous codon usage is not driven by translational selection.
Human protein coding genes For each of the human protein coding genes in the Ensembl release 83 (  (Yates et al. 2016 ); assembly GRCh38. p5), we identified a canonical transcript as defined in http :// www. ensembl. org / Help / Glossary ? id =346 (PERL script available in supplementary material on Zenodo platform: doi. org/10. 5281/zenodo. 229167). Mitochondrial genes were excluded from this analysis. Sequences of the remaining 19,766 canonical transcripts together with exons coordinates, were downloaded through the BioMart query interface  (Smedley et al. , 2015).
Expression data Gene expression levels were collected from three publicly available human RNA-seq experiment datasets. The first one includes 27 differentiated adult tissues  (Fagerberg et al. 2014 ; Kryuchkova-Mostacci and Robinson-Rechavi 2015) EBI accession number E-MTAB-1733). We downloaded normalized expression levels, already averaged across replicates, from Kryuchkova-Mostacci and Robinson-Rechavi (2015) (see supplementary information available on Zenodo platform at 10. 5281/zenodo. 229167). The second one is based on singlecell RNA-seq analysis, and includes 20 samples, corresponding to inner cell mass (ICM) of the blastocysts, and to primordial germ cells (PGC) and somatic cells, from male and female embryos at different development stages (4, 7 or 8, 10, 11 and 17 or 19 weeks,  (Guo et al.
2015 ) GEO accession number GSE63818). We downloaded normalized expression levels from their dataset of pool-split PGCs (for more details see supplementary information at doi. org/10. 5281/zenodo. 229167). Female 17 weeks PGCs are entered in meiosis  (Guo et al. 2015). This sample was therefore taken as representative of the transcriptome of meiotic cells in female. The third dataset corresponds to human male germ cells at pachytene spermatocytes (i. e. cells entering meiosis) and at round spermatids stages (post meiotic stage) (  (Lesch et al. 2016) ; GEO accession number GSE68507, human RNA expression datasets  (A) Factorial map of the principal-component analysis of synonymous codon usage in GO functional categories in the human genome. Each dot corresponds to a GO gene set, for which the relative synonymous codon usage (RSCU) was computed.
GO categories that are associated with 20 "differentiation" or with "proliferation" are displayed respectively in blue and in red. (B) Correlation between the RSCU of GO gene sets (first PCA axis) and their average GC-content at third codon position (GC3). (C) Distribution of GC3 of human protein coding genes. The black histogram represents the distribution for whole dataset (15,970 genes). The blue curve (resp. the red curve) is a smoothed distribution of GC3 for "differentiation" genes (N=2,833) (resp. "Proliferation" genes, N=1,008) (D) Correlation between the GC3 of mono-isoacceptor amino-acids and multi-isoacceptor amino-acids. For each GO gene set, the average GC3 was computed separately for amino-acids decoded by multiple tRNA isoacceptors (N=14 multi-isoacceptor amino-acids), and for those decoded by one single tRNA isoacceptor (mono-isoacceptor amino-acids: Phe, Asp, His, Cys).
Acknowledgement This work was supported by French National Research Agency (ANR) grant DaSiRe (ANR-15-CE12-0010-01/DaSiRe). FP received a doctoral scholarship from Ecole Normale Supérieure de Lyon (http://www. ens-lyon. eu/). We thank Gaël Yvert for initiating the discussion and Adam Eyre-Walker for helpful suggestions on a first version of our manuscript.
Background In recent years, advances in high-throughput sequencing technologies have offered the opportunity to partially or completely re-sequence genomes, in a relatively cost-effective manner. The availability of whole-genome sequence (WGS) data for an increasing number of individuals offers new opportunities to study genetic variations at the genomic level with unprecedented accuracy. In the past few years, several whole-genome sequencing studies have been carried out in different dairy and beef cattle breeds and identified a huge number of single nucleotide polymorphisms (SNPs) and small insertions and deletions (InDels)  [1] [2] [3] [4] [5]. To date, the Ensembl (http://www. ensembl. org) short variation database contains over 99 million SNPs and InDels identified in several cattle breeds.
During the first phase of the 1000 bull genomes project, the genomes of 234 bulls were sequenced, which has enabled the identification of over 28 million reliable SNPs and InDels  [5]. Only 13 French bulls were included in this phase. In this work, we performed a large-scale study to investigate both SNPs and small InDels in whole-genome sequencing data for 274 animals from several major French dairy and beef breeds. The collection of genome variations reported in this study will be useful to study their potential links with the genetic variability of economically important traits. available in our laboratory and were produced as previously described  [1].