dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic map construction. SNPs called from the GBS data on three populations (MAPPOP1, MAPPOP2 and MAPPOP3) were first filtered for quality based on minor allele frequency, missingness and heterozygosity (Supplementary Code 2). Linkage groups were defined based on hierarchical clustering of SNPs and ordered with MSTMap. For each population, we created three maps: one from stringently filtered SNPs, one from moderately fileted SNPs, and one mapping GBS sequencing tags back to the stringently filtered map (Supplementary Code 2). The framework map generated in the largest RIL population (Tift 99B × Tift 454) formed the basis of an initial colinearity study between pearl millet and foxtail millet, and the resulting comparative knowledge was used to incorporate additional scaffolds for which orthology to the foxtail millet genome had been established using BLASTP (to identify putative orthologous pearl millet and foxtail millet genes at an E-value threshold of 1e-5) and MCScanX  59  (to identify colinear segments of at least five syntenic genes between pearl millet and foxtail millet) analyses into the framework map.
The genetic maps generated for each of the crosses, and the map that we built based on collinearity information between pearl millet and foxtail millet, were merged using ALLMAPS  60  with the most weight assigned to the synteny map followed by the stringent SNP maps, the moderately filtered SNP maps, and finally the GBS sequencing tags (Supplementary Code 3). Linkage group numbering was adopted as per an existing consensus map  17  based on mapping SSR sequences to the assembled genome (Supplementary Code 3). Repeat annotation, gene prediction and genome annotation. We searched the genome for tandem repeats with Tandem Repeats Finder 61 (Version 4. 04) (parameters: 2 7 7 80 10 50  2000 -d -h ). Transposable elements (TEs) were identified in the genome by a combination of homology-based and de novo approaches  62.
For homology-based predictions, we used the repeat database Repbase16. 10 63 to identify known repeats in the genome assembly with the program RepeatMasker 64 (Version 3. 3. 0) (parameter: -nolow -no_is -norna -parallel 1 -lib RepeatMaskerLib. embl. lib). At the protein level, RepeatProteinMask, a software in the RepeatMasker package, was used to perform RMBlast against the TE protein database (parameter: -noLowSimple -pvalue 0. 0001). For de novo prediction, the programs RepeatModeler 65 (Version 1. 0. 5) and LTR_FINDER 66 (Version 1. 0. 5) were used on the entire genome to generate a pearl millet repeat database, which was subsequently used as input library with RepeatMasker (Version 3. 3. 0) to identify TEs. For predicting genes, we applied several approaches: (i) Homology-based prediction: Proteins previously annotated in other species (Supplementary Table  9 ) were mapped to the genome using BLAT  56  (Version 34) with default parameters.
Alignments in which the coverage of the query protein was less than 0. 3 were removed. In addition, if there were multiple BLAT hits (BLAT output was set to the five best hits), secondary hits were removed if their aligned length was less than 0. 3 of the aligned length of the top BLAT hit to filter paralogs with lower sequence identity. GeneWise 67 (with parameter -trev -sum -genesf) was used to predict spliced alignments. (ii) De novo gene prediction: AUGUSTUS 68 (Version 2. 5. 5,-species = maize-uniqueGeneId = true-noInFrameStop = true-gff3 = on-strand = both) and Fgenesh 69 (Version 1. 3) were used to detect gene models in the repeat masked genome. (iii) Prediction based on transcript sequences: The assembled transcriptome sequences were aligned to the genome assembly using BLAT (Version 34) using the parameters identity ≥ 0.
Contigs sharing identity over ≥95% of the length of the shortest sequence in a set of putative homologous sequences were clustered. The final transcript assembly contained 50,313 contigs, with a total of 36,479,993 nucleotides. Three transcriptomes (Zeng et al. 16  , Rajaram et al. 17  , and the transcriptome data generated at IRD, France, available under BioProject ID PRJNA391885) were combined and clustered using CDHIT-EST  79  with default parameters to eliminate redundancy at the sequence level. Then, CAP3  80  was used to assemble the contigs. Ns on either end of the resultant contigs were trimmed. Finally, contigs of at least 200 bp in length were used in gene annotation. Gene family and phylogenetic analysis. For gene family analysis, BLASTP with an E-value cutoff of ≤ 1. 0e-05 was used to compare all annotated pearl millet protein sequences against a protein data set of 10 sequenced plant species (Arabidopsis 20 , Brachypodium 21 , banana  22  , barley  23  , foxtail millet  11  , maize  24  , rice  15  , sorghum  14  , soybean  25  and T.
urartu  26  ). The proteins were clustered using OrthoMCL 27 (-mode 3) to define gene families which included both paralogs and orthologs. The number of gene families in each species and genus was calculated based on the composition of the OrthoMCL clusters. Genes that were single copy in an OrthoMCL cluster for all species analyzed were selected to construct a phylogenetic tree using the PhyML (parameters: -d nt -b -4 -m HKY85 -a e -c 4 -t e) program 81 (Version 3. 0). Divergence times between pearl millet and other species were estimated using MCMCTREE  82  with default parameter. First, the gene family size for each species was calculated based on the output of OrthoMCL, and rooted tree in newick format. CAFE  83  (-p 0. 05 -t 4 -r 10000 -filter) was used to predict the expansion and contraction of gene family numbers based on the phylogenetic tree and gene family statistics.
Population analysis. Population genetic analyses of the PMiGAP lines, including PCA and diversity detection were conducted essentially as described for rice by Xu and colleagues  84. We used a subset of 450,000 SNPs, with a missing rate <10% across PMiGAP lines and wild accessions. Briefly, for PCA, eigenvector decomposition of the SNP genotype data was calculated using the R function eigen  85. A Tracey-Wisdom test with default parameter settings was performed to determine the significance of axes using the twstats program. To build a phylogenetic tree, the percentage of pairwise nucleotide differences between individuals (p-distance) was calculated  85. The program fneighbor (PHYLIPNEW v3. 69. 650 within the package EMBOSS v6. 6. 0. 0; parameter: -matrixtype s -treetype n) was used to construct a neighbor joining tree.
The resulting tree was edited and visualized using MEGA5  86  by choosing Radiation style. Population structure was assessed using the program Snmf (-k K -c)  87. Five runs were performed and the values with the smallest Cross-Entropy for K from 2 to 7 were selected to generate the structure graphs. To better assess the structure, we performed the analysis in a geographical context, using TESS3  88  that takes geographical coordinates of the sample into account. Furthermore, parameters of population genetic diversity π, θ ω and differentiation (F ST ) were calculated based on the SNP data as described earlier  85. To analyze diversity across the genome, we used a window of 100 kb and calculated the diversity π, θ ω and differentiation F ST for each window for PMiGAP lines and wild accessions using BioPerl modules (Bio::PopGen::Statistics and Bio::PopGen:: PopStats) on a sliding window of 100 kb using genotype data.
All pearl millet proteins were assessed for the presence of NBS domains (PF00931, NB-ARC) using the Hidden Markov Model based method implemented in hmmsearch (version 3. 0)  89  with an e-value cutoff = 1. To filter false positive hits, all identified NBS containing proteins were screened against the Pfam-A database. NBS domains that overlapped with other domains identified at lower e-values were filtered out. Likewise, the TIR domain (PF01582) was used as query against all pearl millet proteins with hmmsearch and further checked by looking at the overlapping domains. To detect LRR motifs, predicted NBS encoding proteins were searched against 10 LRR families in LRR clan (CL0022) with an e-value cutoff = 1. All regions predicted as LRR motifs and not overlapping with other domains identified with lower e-values were considered real LRR motifs.
SNP calling, structural variation and linkage disequilibrium (LD) decay. Sequence reads generated for the B-and R-lines, PMiGAP lines, and parental lines and wild lines were mapped separately to the pearl millet genome assembly using BWA (v0. 6) (parameter: aln -n 0. 04 -o 1 -e 30 -i 15 -d 10 -l 35 -k 2 -m 2000000 -t 4 -M 3 -O 11 -E 4 -R 30 -q 0 -I; sampe -a 500 -o 100000 -n 3 -N 10 -c 1. 0e-05). The BAM files generated by BWA were sorted and provided as input to the GATK software package 90 (Version 3. 1-1). The UnifiedGenotyper module within GATK was used to detect SNP variants. The variants were filtered using VariantFiltration, a module from GATK (parameters: QD < 2. 0 || FS > 60. 0 || MQ < 40. 0 || HaplotypeScore > 13. 0; parameters for indel: QD < 2. 0 || FS > 200. 0), and the number of variants distribution in intergenic/coding regions were calculated.
The data used in the downstream analysis were controlled with MAF 0. 05 and missing rate 0. 5. SNPs with a mean depth > 100 and missing rate > 0. 5 were removed. The remaining SNPs were used in further analyses. Variants for wild lines that used in population structure and domestication analysis were detected together with PMiGAP accessions and processed with the same strategy (BAM and VCF files available at http://ceg. icrisat. org/ipmgsc/). The BAM files from each resequenced accession was analyzed by Breakdancer (version 1. 1. 2)  91 with default parameters to detect structural variation namely, deletions, insertions, inversions, and intra-chromosomal translocations. Breakdancer results of accessions that come from a same line (see Supplementary Table  32 ) were combined to remove redundancy and to calculate the number and length of the rearrangements.
Statistical analysis. Phenotyping data and GWAS analysis. For establishing marker trait associations, 288 test cross hybrids were generated by crossing of PMiGAP lines as pollen parents with a common seed parent ICMA 843-22. These hybrids were grouped by maturity (early, medium early, medium and late) and phenotyped for 20 morphological traits under two drought stress conditions (early and late stress) along with controls (or no stress) for two years (2011, 2012). Experiments were conducted in an alpha-lattice designs with two replications in three test environments during Summer 2011 and 2012 (January to May) in the red precision (RP) experimental fields at the ICRISAT, Patancheru, Telengana, India (545 m above mean sea level, 17. 53° N latitude and 78. 27° E longitude). The early maturity group consisted of lines which had days to 50% flowering (DFF) from 42-52 days; the medium-early maturity group consisted of lines with DFF from 53-57 days; the medium maturity group consisted of entries with DFF from 58-62 days; the late maturity group consisted of lines which recorded more than 62 days for DFF.
Early drought stress is a more severe stress imposed by withholding irrigation from about one week before flowering until maturity. Late stress is a less severe drought stress initiated during early grain-filling by withholding irrigation from 50% flowering time till maturity. The three test environments consisted of early-onset of stress, late-onset stress, and a common, fully-irrigated non-stress treatment. Drought stress was imposed by withholding irrigation from about one week before flowering in early-onset treatment, while drought stress in the late-onset treatment was imposed by withholding irrigation from 50% flowering. Data were recorded for a total of 20 traits namely, grain yield (GYHA), panicle yield (HYHA), panicle harvest index (PHI), time to 75% flowering (TB), plant height (PH), panicle length (EL), panicle diameter (ED), panicle number (HCHA), number of tillers per plant (Till), biomass yield (BM), grain harvest index (HI), thousand grain weight (TGW), grain number per panicle (GNP), grain number per m 2 (GNM2), agronomic score (AgS), stover dry matter fraction (DMF) and vegetative growth index (GI).
PH, EL, and ED were measured on the main stems of five representative plants of each entry in a plot at maturity. At harvest, data were recorded from the harvested area on plant population (PCHA), panicle numbers (HCHA) and fresh stover yield (FSWTHA). Effective tiller number (Till) was calculated as the ratio HCHA/PCHA. HYHA, GYHA and TGW were recorded after oven drying for about 24 h. Stover dry matter yield (DMY) was estimated from plot FSWTHA using the fresh and dry weights of a chopped subsample of stover from each plot. BM was calculated as HYHA + DMY on a plot basis. Grain number per panicle (GNP) was derived from primary data as [(GYHA/HCHA)/ (TGW/1000)]. Grain harvest index was calculated as the ratio between grain yield and biomass yield at harvest, and panicle harvest index as the ratio between grain weight and panicle weight.
Flowering time was recorded as days from seedling emergence to stigma emergence for 75% of the main shoots in a plot. The traits measured include grain yield (kg/ha), panicle yield (kg/ha), panicle harvest index (%), time to 50% flowering (number of days), plant height (cm), panicle length (cm), panicle diameter (cm), panicle number, tillers per plant, biomass yield (kg/ha), vegetative growth index (kg/ha/day), grain harvest index (%), fresh stover yield (t/ha), stover dry matter yield (kg/ha), stover dry matter fraction, 1000-grain mass (g), grain number per panicle, and grain number per m 2 (Supplementary Data set 2). Analysis of variance for all traits was performed using the PROC MIXED procedure in SAS 9. 3 (SAS Institute Inc 2013) with Kenward-Roger degree of freedom approximation method considering replicates and accessions as fixed effects, whereas incomplete blocks within each replication were considered as random effects for combined intra and inter block analysis.
Genomic prediction analysis for testcross performance. Grain yield performance of 259 PMiGAP lines was used for hybrid prediction analysis. In our analysis, flowering time was considered as a cofactor. For genomic prediction analysis, we performed a one-stage phenotypic data analysis on 259 PMiGAP lines as test cross hybrid trials using a linear mixed model that included genotype, flowering time, year, stress, interaction among genotype, stress and year, replication, incomplete block and residual effects. The effect of flowering time was always assumed to be fixed. When estimating variance components, all other effects were assumed to be random. To get the BLUE of each line, we set the genotype effect as fixed. The heritability on the line mean basis was estimated as h y s ys ysr G G G Y G S G Y S E 2 2 2 2 2 2 2 = + + + + × × × × s s s s s s where s G 2 , s G Y × 2 , s G S × 2 , s G Y S × × 2 and s E 2 are variance components arising from genotype, genotype × year interaction, genotype × stress interaction, the three-way interaction and the residual, respectively.
y, s and r are the number of different years, stresses, and replications. In addition, we calculated the BLUE for each genotype in each environment (stress versus control) across years. That is, for each environment we fitted a linear mixed model including genotype, flowering time, year, genotype × year interaction, replication, incomplete block and residual effects. The assumptions of the parameters were similar to above. The heritability in this case was estimated as All phenotypic data analyses were done using the ASreml-R 3 software  93. A total of 2,235,060 SNPs with <20% missing rates were used with above mentioned phenotyping data for genomic prediction analysis. We used the genomic best linear unbiased prediction (G-BLUP) model for genomic selection: y Zg e n = + + 1 m , where y refers to n-dimensional vector of phenotypic records, 1 n is an n-dimensional vector of ones, m is the mean, g is an n-dimensional vector of additive genotypic values and e is an n-dimensional vector of residual terms.
The GBLUP model was implemented using the R software  94. Hybrid prediction analysis. Grain yield of 64 pearl millet hybrids grown at five locations in India (Jamnagar, Anand, SK Nagar, Mahuva, Kothara) during the time period 2004-2013 was measured. Trials were conducted during 2004, 2005, 2006, 2008, 2011 and 2012 in Kharif, Summer and pre rabi season. However, during 2007, 2009, 2010 and 2013 trials were conducted in only Kharif and Summer. We adopted randomized block design with a spacing of 60 cm between the rows and 10-15 cm between the plants and adopted standard agronomic practices. The 64 hybrids were generated by crossing 20 male and 23 female lines. By using the grain yield phenotyping data for 64 hybrids as mentioned above, we used the following linear mixed model to estimate the variance components as well as BLUEs: Yield ~Genotype + Replication.
ACKNOWLEDGMENTS We are thankful to several colleagues and collaborators especially  X. Tan  from  The University of Georgia  and  C. T. Satyavathi  from  ICAR-All India Coordinated Research Project on Pearl Millet , for their help in analysis and interpretation of some data. This study was supported in part by the  Bill and Melinda Gates Foundation, USA  (Grant ID#  OPP1052922 ),  Agence Nationale de la Recherche, France  ( Grant ID :  ANR-13-BSV7-0017 ),  and Basic Research Program  from the  Shenzhen Municipal Government, China  (NO. JCYJ20150529150505656 ). This work has been undertaken as part of the  CGIAR Research Program on Dryland Cereals, ICRISAT, India. ICRISAT is a member of the CGIAR.
Data availability. Genome  sequence assembly and annotation data: BioProject ID PRJNA294988; BioSample ID SAMN04124419. Resequencing data: SRA SRP063925. Transcriptome data: BioProject ID PRJNA391885. BAM and SNP files are available at http://ceg. icrisat. org/ipmgsc. GigaScience Database record: http://dx. doi. org/10. 5524/100192 Scripts used in the MS are available at https://github. com/ICRISAT-CEG/PM-Scripts. git A Life Sciences Reporting Summary is available.
However, despite extensive experimental analyses of these processes in this bacterium  [30] [31] [32]  , we still do not fully The protein copy numbers of four natural promoters (p517, p674, p036, and p665; see Methods) and strong and weak promoters are depicted as dashed lines. Dam protein copy numbers were estimated with regression models generated by published proteome-wide copy number data  30  and mass spectrometry data. The squares outlined in black represent clones that were not detected by mass spectrometry. Their protein copy numbers were estimated from the DAMRatio using linear interpolation and were not used in the correlation analysis. The overall Pearson's correlation coefficient (r) between protein copy number and the DAMRatio is 0. 96  The sequence similarity between the solved structure and sigA (MPN352) of M.
No other downstream preferences affecting these TSS shifts were found after the theoretical +2 position (Figs. 4b, c ). On the other hand, as the weak promoter contains an A at the expected TSS, we found that the majority of transcripts began at the N7 position (Fig. 4a ). With respect to activity, we observed that the DAMRatio is systematically higher for the 5′-UTRs having an A or G as the first nucleotide in the sequence (Fig. 4d ). We confirmed this first nucleotide bias in four individual 5′-UTR constructs (UTR1, 2, 7, and 8; Supplementary Table  12a  and Supplementary Fig. 7 ). Upon mutating the first base, we found that having either an A or a G consistently resulted in greater protein yields. This phenomenon also held true for a leaderless construct (Supplementary Table  12b ). Interestingly, we observed that the mRNA levels determined by qPCR were highly correlated with protein amount and activity (r > 0.
2 + R_170908. 2, see Supplementary Data 1). rRNA 16S oligos (F_q16S + R_q16S) were used as a reference (amplicon without any GATC), and each digestion was normalized independently with the 16S qPCR. Western blot. For protein detection, cells grown for 3 days in a 25 cm 2 flask were washed with PBS and extracted with 150 µl 1% SDS in TE prior to protein quantification by bicinchoninic acid (BCA) (Pierce). Fifty micrograms of total protein were loaded per gel, and a western blot was performed using M2 anti-Flag mouse monoclonal antibody (Sigma, Cat No. F3165, 1:2000 dilution) or anti-GFP monoclonal antibody (Roche, Cat No. 11814460001, 1:2000 dilution) and detected with anti-mouse IgG conjugated to horseradish peroxidase (Jackson, Cat No. 515035003, 1:10,000 dilution). Blots were developed using high-sensitivity electrochemiluminescence (ECL) reagent (Thermo Fisher Scientific) and visualized using the Fujifilm LAS-3000 developer.
Intensities of immunoreactive bands on western blots were quantified using Quantity One (Bio-Rad) software. Protein levels were normalized using a ribosomal protein (RL7) antibody as a loading control  30  (1:500 dilution). mRNA quantification by RT-qPCR. RNA was extracted from cells growing in exponential phase (24 h post inoculum) with Qiazol and purified according to the manufacturer's instructions (miRNeasy kit, Qiagen), including an in-column DNase digestion. RNA concentration was measured in a Nanodrop UV-Vis Spectrophotometer (Thermo Fisher Scientific). A standard RT (retrotranscription) reaction was carried out, in which random hexamers (37. 5 ng µl -1 final, Invitrogen) were hybridized with 1 µg of total RNA, during 2 min at 95 °C, and then at 65 °C during 5 min before placing it on ice.
Fragment ion spectra produced via collision-induced dissociation were acquired in the Ion Trap, AGC was set to 5e 4 , isolation window was 2 m/z, activation time was 0. 1 ms, and maximum injection time of 100 ms was used. All data were acquired with Xcalibur software v2. 2. Growth curves. In order to obtain equal amounts of each sample, initial inocula for the growth curves were quantified. Briefly, cells were grown for 3 days in a 25-cm 2 flask, collected in 1 ml medium, and 100 µl was used for quantification with a BCA protein assay kit (Pierce). Same amounts of total protein (1 µg) were aliquoted per well in a 96-multiwell plate in duplicates. Two hundred microliters of Hayflick medium were added per well, and the cells were incubated in a Tecan Infinite plate reader at 37 °C. Growth index (absorbance 430/560 nm, settle time at 300 ms, and number of flashes equal to 25) was taken every hour for 5 days as published  46.
Screen-sequencing library prep. Genomic DNA was prepared and digested as above (see "Dam activity detection by qPCR"). For the library prep, 50 µl reactions were prepared with 0. 5 µM oligos and 50 ng of digested input DNA. Using Phusion polymerase (NEB) DpnI-treated DNA was amplified during 12 cycles, whereas MboI-treated DNA had to be amplified during 15 cycles with the oligos F_SE_i1_dam to F_SE_i9_dam (nine indexes) and R_SE_Tn (Supplementary Data 1 and Supplementary Note 1). PCR products were purified with 50 μl AMPure XP beads according to the manufacturer's instructions (Agentcourt), and DNA resuspended in 22 µl of Elution Buffer (10 mM Tris•HCl, pH 7. 5, EB). Libraries were quantified using the Illumina KAPA quantification kit according to the manufacturer's instructions (Kapa Biosystems).