dataesr/scientific-paragraphs-categorization huggingface dataset:
The significant interaction between reports and protein regions revealed particularly higher CADD-Phred score value increases in the linker domain (healthy 23. 1 vs patients 32. 2, p < 0. 001), which hints at an evolutionary well-conserved domain (Fig. 2 ). The tail domain itself shows rather low conservation throughout species and variants in the tail domains can be observed quite often in human, thus CADD-Phred score values in the healthy dataset are rather low. The tail domain mutations in patients tend to have rather high CADD-Phred score values and are situated at residues with rather high tolerance toward missense variants. The CC regions in the tail domains are highly conserved are also connoted with high CADD-Phred score values in patient mutations. As the CADD scores also comprise protein impact tools (e.
g. , Grantham, SIFT, and PolyPhen), the amino acid exchanges in the patients mutations show higher CADD scores and more drastic effects in physicochemical properties than in the healthy dataset. Clustered around the CC regions, the MTR scores show significantly higher intolerance toward missense mutations (p < 0. 001). The CADD and MTR score values further support that the motor domain is highly conserved and the mutational effects lead to drastic changes on the protein level. MTBD domain variants in the healthy and patient datasets show rather similar MTR scores, thus a similarly high intolerance toward missense variants. When comparing Fig. 1c, d , we note that there is a much lower number of variants as well as significantly lower mean CADD score value in the MTBD (p < 0. 001), i. e. , MTBD variants are generally scarce in healthy population datasets and the protein level scores do not show drastic effects in physiochemical properties.
Based on this genomic profiling of healthy and patient variant datasets, we underline the domain-specific effects of genetic variation in DYNC1H1 and we further recommend to interpret DYNC1H1 variants based on the following model: (1) Region location/ conservation: mutations in the linker, motor, and MTBD region are in highly evolutionarily conserved domains with important functional roles in processive and powerstroke movements. (2) Gene-wide missense intolerance: as a measure of regional intolerance to missense variation, the spatial distribution of observed vs expected variants have to be evaluated concerning the healthy population. Patient mutations are situated at genomic coordinates with significantly lower MTR scores, i. e. , are more prone to intolerance from missense variation. (3) Protein level change in physicochemical property: based on multiple lines of pathogenicity prediction scoring tools, the effect of the amino acid exchange in a mutation can be evaluated for missense variations for protein structural and functional properties, including secondary structure, solvent accessibility, functional domains, methylation, phosphorylation, and glycosylation.
The fact that MTBD domain mutations do not appear in healthy datasets hints at the possibility of early onset lethal disease courses for MTBD domain mutations. Motor domain mutations, with the six AAA domains forming the core complex, were associated with microtubule gliding defects (AAA5) or inhibition of any movement (AAA1), whereas other protein areas (tail, linker, or MTBD) do not alter glinding  [8]. Moreover, mutations lead to static binding to microtubule (AAA1) or diffuse binding behavior (AAA5 and MTBD), both resulting in disturbed motor activity and possibly secondarily in severe disruption of neuronal migration and myelination  [8, 45]. We identified four AAA domain mutations with NMD, cognitive-behavioral impairment, and brain malformation (P4, P5, P7, P9, Fig. 1a-d ). These findings highlight that motor domain mutations lead to MCD due to a more severe disruption of the dynein movement.
[33]red2MutPred2 score, range from minimum 0. 00 to maximum 1. 00 (http://mutpred. mutdb. org/),CADD-PhredCombined Annotation-Dependent Depletion scores[33], Population database reports in Exome Aggregation Consortium (ExAC) database (http://exac. broadinstitute. org), reports in 1000 Genomes Project database (http://www. internationalgenome. org/); reports in genome aggregation database (https://gnomad. broadinstitute. org/), reports in Greater Middle Eastern Database (http:// igm. ucsd. edu/gme/data-browser. php), ACMG criteria variant classification according to guidelines from the American College of Medical Genetics and Genomics-Association for Molecular.
Acknowledgements We would like to thank the patients and their families of this multi-center study. We furthermore thank  Özkan Özdemir, PhD  for support in the bioinformatics analyses, Dr. med. Annegret Holm  for clinical information, and the  Regional Computing Center of the University of Cologne (RRZK)  for providing computing time for the bioinformatics analyses on the DFG-funded High-Performance Computing (HPC) system CHEOPS as well as support. This work was supported by the  German Research Foundation  (DFG;  Emmy Noether Grant   CI 218/1-1  to SC and  FO3004 ,  SFB1315  to AMK), the  Cologne Clinician Scientist Program (CCSP)  to HSD via  DFG FI 773/15-1 , and  Gerok Program/Faculty of Medicine/University of Cologne  (to HSD), and the  Charité-Universitätsmedizin Berlin  (to AMK). The  MYO - SEQ  project was funded by  Sanofi Genzyme ,  Ultragenyx ,  LGMD2I Research Fund ,  Samantha J Brazzo Foundation ,  LGMD2D Foundation ,  Kurt+Peter Foundation ,  Muscular Dystrophy UK , and Coalition to Cure Calpain 3.
[33]red2MutPred2 score, range from minimum 0. 00 to maximum 1. 00 (http://mutpred. mutdb. org/),CADD-PhredCombined Annotation-Dependent Depletion scores[33], Population database reports in Exome Aggregation Consortium (ExAC) database (http://exac. broadinstitute. org), reports in 1000 Genomes Project database (http://www. internationalgenome. org/); reports in genome aggregation database (https://gnomad. broadinstitute. org/), reports in Greater Middle Eastern Database (http:// igm. ucsd. edu/gme/data-browser. php), ACMG criteria variant classification according to guidelines from the American College of Medical Genetics and Genomics-Association for Molecular MutationTaster score, range reports in ClinVar database, MutationTaster , ClinVar from minimum 0. 00 to maximum 1. 00 (http://www. mutationtaster.
The Illumina library construction and sequencing were performed at Duke University using KAPA Hyper Prep kits (Kapa Biosystems, Wilmington, MA) and the Illumina NovaSeq 6000 platform (paired-end 150 bp reads). The raw sequencing reads for strains used in this project are available from the NCBI Sequence Read Archive (Project PRJNA549503). Isotype characterization. Raw sequencing reads from 609 wild strains were trimmed using the Trimmomatic (v0. 36)  71  to remove low-quality bases and adapter sequences. Following trimming, we called SNVs using the BCFtools (v. 1. 9)  72 and the following filters: Depth (DP) ≥ 3; Mapping Quality (MQ) > 40; Variant quality (QUAL) > 30; Allelic Depth (FORMAT/AD)/Num of high-quality bases (FORMAT/DP) ratio > 0. 5. We classified two or more strains as the same isotype if they have the same call at 99.
The DNA samples were submitted to the Duke Sequencing and Genomic Technologies Shared Resource for library construction and sequencing using the Pacific Biosciences Sequel platform. Quality control was performed with the Qubit dsDNA Broad Range Assay Kit and Agilent Tapestation. Some samples had 30-50 kb fragment sizes, and a 15 kb cutoff was used for size selection of these samples during library preparation. A 20 kb cutoff was used for the other samples. The raw sequencing reads for strains used in this project are available from the NCBI Sequence Read Archive (Project PRJNA692613).
Sequence alignments and variant calling After isotypes are assigned, we used BWA (v0. 7. 17-r1188)  73, 74  to align trimmed sequence data for distinct isotypes to the N2 reference genome (WS245)  75. Variants were called using GATK4 (v4. 1. 0)  76. First, gVCFs were generated for each isotype using the HaplotypeCaller function in GATK4 with the following parameters: --max-genotype-count =3000 and --max-alternate-alleles =100, using the WS245 N2 reference genome. Next, individual isotype gVCFs were merged using the MergeVcfs function in GATK4 and imported to a genomics database using the GenomicsDBImport function. Genotyping of the gVCFs was performed using the GenotypeGVCFs function in GATK4 with the following parameter --use-new-qual-calculator. The 328 isotype cohort VCF was annotated using SnpEff  77  and an annotation database that was built with the WS261 gene annotations  77.
Genetic relatedness Similarity analysis. Using BCFtools (v. 1. 9), with the command filter -i N_MISSING=0, we filtered the high-quality VCF file and generated a VCF file (complete-site VCF) containing 963,027 biallelic SNVs that are genotyped for all 328 C. elegans isotypes. We used the vcf2phylip. py script  78  to convert the complete-site VCF file to the PHYLIP format. The distance matrix and unrooted neighbor-joining tree were made from this PHYLIP file using dist. ml and NJ function using the phangorn (v2. 5. 5) R package  79. The tree was visualized using the ggtree (version 1. 16. 6) R package  80. Principal component analysis. The smartpca executable from the EIGENSOFT (v6. 1. 4)  35, 81 was used to perform principal component analysis. We performed analysis with the complete-site VCF with or without removing outlier isotypes to analyze the population structure with highly genetically divergent isotypes.
We only considered bi-allelic SNVs to calculate population genomic statistics. Tajima's D , Watterson's theta, and Pi were all calculated using scikit-allel  83. Each of these statistics was calculated for the same non-overlapping 1000 bp windows as hyper-divergent regions (described below). We calculated the standardized β (1) 84,85 using the BetaScan. py script by providing Watterson's theta estimates with the -thetaMap flag. Default window parameters surrounding each marker were used (+/-500 bp from the focal marker). Linkage disequilibrium (LD) decay. We filtered the complete-site VCF file using BCFtools (v1. 9) with the command view -q 0. 05:minor and generated a VCF file (MAF-05 VCF) containing 123,830 SNVs of which minor allele frequencies are greater than or equal to 5%. Then, we selected 41,368 SNVs on autosomal arms (MAF-05-autoarm VCF) and split the MAF-05-autoarm VCF by the location of SNVs; MAF-05-autoarm-div VCF with 17,419 SNVs within hyper-divergent autosomal arm and MAF-05-autoarm-nondiv VCF with 23,949 SNVs within the non-divergent autosomal arms.
Long-read genome assembly We extracted PacBio reads in FASTQ format from the subread BAM files using the PacBio bam2fastx tool (version 1. 3. 0; available at https://github. com/PacificBiosciences/bam2fastx ). For each of the 14 isotypes, we assembled the PacBio reads using three genome assemblers: wtdbg2 (version 0. 0; using the parameters -x sq -g 102m )  87  , flye (version 2. 7; using the parameters --pacbio-raw -g 102m )  88  , and Canu (version 1. 9; using the parameters genomeSize=102m -pacbio-raw)  89. For each assembly, we assessed the biological completeness using BUSCO  90  (version 4. 0. 6; using the options -l nematoda_odb10 -m genome ) and contiguity by calculating a range of numerical metrics using scaffold_stats. pl (available here: https://github. com/sujaikumar/assemblage ).
9). We searched each assembled contig against the NCBI nucleotide database ('nt') using BLASTN  95  (version 2. 9. 0+; using the parameters -max_target_seqs 1 -max_hsps 1 -evalue 1e-25 ) and against UniProt Reference Proteomes 96 using Diamond (version 0. 9. 17; using the parameters blastx --max-target-seqs 1 --sensitive --evalue 1e-25 )  97. We removed contigs that were annotated as being of bacterial origin and that had coverage and percent GC that differed from the target genome. Genome assembly metrics are shown in Supplementary Table  4.
Coordinates and identities of the aligned sequences were extracted from the alignment files using the 'show-coords' function with NUCmer. Then, we calculated the average alignment coverage (alignment coverage) and average alignment identity (alignment identity) for each non-overlapping window in the reference genome. Next, we used the long-and short-read alignment datasets to identify the optimal coverage fraction and variant count parameters to apply to the rest of the population for which we do not have long-read sequence data. We tested a wide range of parameters to define hyper-divergent regions from short-read and long-read alignments. For the short-read based approach, we classified each window as hyper-divergent if its variant count ≥ x or coverage fraction < y% or both; we also classified windows that are flanked by two hyper-divergent windows as hyper-divergent.
Then we clustered contiguous hyper-divergent windows and defined clusters that are greater than or equal to 9 kb of N2 reference genome length as hyper-divergent regions  32. For the long-read based approach, we classified each window as hyper-divergent if its alignment coverage < z% or alignment coverage < w% or both; we also classified windows that are flanked by hyper-divergent windows as hyper-divergent. Then, we clustered contiguous hyper-divergent windows and defined clusters that are greater than or equal to 9 kb of the N2 reference genome length as hyper-divergent regions. Because we lacked a "true" hyper-divergent region dataset to which we could tune our parameters, we identified the set of x, y, z, w values that maximized the overlap between hyper-divergent regions identified by short-and long-read based approaches (Extended Data Fig.
Gene-set enrichment analysis We analyzed the gene-set enrichment of hyper-divergent regions using web-based WormCat  103  that contains a near-complete annotation of genes from the C. elegans reference strain N2. We also performed a conventional gene ontology (GO) enrichment analysis using the clusterProfiler (v3. 12. 0) R package  104  and org. Ce. eg. db: Genome-wide annotation for Worm  105. Because the majority of hyper-divergent regions are found on autosomal arms (Fig. 3a , Supplementary Table  6 ), we used gene-set enrichment on the autosomal arms as a control dataset  36.
Protein-coding gene prediction To generate ab initio gene predictions for all 14 assemblies and for a previously published long-read assembly for the Hawaiian isotype CB4856  33  , we first used RepeatMasker  106  (ver 4. 0. 9; using the parameter -xsmall ) to identify and soft-mask repetitive elements in each genome assembly using a library of known Rhabditida repeats from RepBase  107. We predicted genes in the masked assemblies using AUGUSTUS (version 3. 3. 3; using the parameters --genemodel=partial --gff3=on --species=caenorhabditis --codingseq=on )  108. We extracted protein sequences and coding sequences from the GFF3 file using the getAnnoFasta. pl script from AUGUSTUS. We assessed the biological completeness of each predicted gene set using BUSCO (version 4. 0. 6; using the options -l nematoda_odb10 -m proteins ) using the longest isoforms of each gene only.
29. 2; with the parameter maskfasta ) to mask the coordinates of the exonerate-predicted genes with Ns. We then used AUGUSTUS (version 3. 3. 3; using the parameters --genemodel=partial --gff3=on --species=caenorhabditis --codingseq=on ) to predict genes in the unmasked regions. To remove spurious gene predictions or transposable element loci, we searched the protein sequence of each AUGUSTUS-predicted gene against the N2 reference genome using BLASTP (version 2. 9. 0+; using the parameters -max_target_seqs 1 -max_hsps 1 -evalue 1e-5 ) and against the Pfam database 111 using InterProScan (version 5. 35-74. 0; using the parameters -dp -t p --goterms -appl Pfam -f tsv )  112. Predicted genes that contained Pfam domains associated with transposable elements, or those genes that lacked sequence similarity to a known C.
elegans protein sequence and a conserved protein domain were discarded (Supplementary Data 4). The coordinates of all curated predicted protein-coding genes were used to generate gene content plots (Fig  5a ; Extended Data Fig. 8a, 9a ) using the ggplot R package  113. To generate amino acid identity heatmaps and gene trees, we aligned the protein sequences of all conserved genes (those predicted with exonerate) using FSA (version 1. 15. 9)  114. We inferred gene trees using IQ-TREE  115  (version 2. 0. 3), allowing the best-fitting substitution model to be automatically selected for each alignment  116. Gene trees were visualised using the ggtree R package. We calculated percentage identity matrices from the protein alignments using a custom Python script (available at https://github. com/AndersenLab/Ce-328pop-div ).
We then generated heatmaps, ordered by the strain relatedness tree inferred for the region, using the ggplot R package  113. Files containing gene coordinates, protein alignments, gene trees, and percentage identity matrices for each characterised region are available at https://github. com/AndersenLab/Ce-328pop-div. To construct dendrograms of hyper-divergent regions, we first converted the hard-filtered VCF to a gds object using the snpgdsVCF2GDS function in the SNPrelate package  117. Next, we calculated the pairwise identity-by-descent fraction for all strains using the snpgdsIBS function in the SNPrelate R package, and performing hierarchical clustering on the matrix using the snpgdsHCluster function in the SNPrelate package. We visualized the strain relatedness using ggtree  80.
Comparing divergence between C. briggsae and C. nigoni To compare the divergence between hyper-divergent haplotypes with closely related Caenorhabditis species, we downloaded the protein sequences predicted from the genomes of Caenorhabditis briggsae  118  and Caenorhabditis nigoni  119  from WormBase (WS275). We clustered the longest isoform of each protein-coding gene for both species with the longest isoform of each protein-coding gene in all 16 C. elegans isotypes using OrthoFinder (version 2. 3. 11; using the parameter -og ). We identified 8,741 orthogroups containing protein sequences that were present and single-copy in all 18 genomes and aligned their sequences using FSA (version 1. 15. 9). We calculated a percentage identity matrix for each protein alignment using a custom Python script (available at https://github.
Acknowledgments We would like to thank members of the  Andersen lab  for providing comments on this manuscript. We especially thank  Michael Ailion ,  Jean David ,  Robert Luallen ,  Nathalie Pujol , and citizen-scientists for their contributions of wild C. elegans strains to CeNDR. We also thank the  Duke University School of Medicine  for the use of the Sequencing and  Genomic Technologies Shared Resource , which provided Pacific Biosystems long-read sequencing.
Funding This work was funded by an  NSF   CAREER award  ( 1751035 ) and a  Human Frontiers Science Program Award  ( RGP0001/2019 ). NIH  grant  ES029930  to E. C. A, M. V. R. , and L. R. B. also funded this work. S. Z. received funding from The  Cellular and Molecular Basis of Disease training program  ( T32GM008061 ) and the  Rappaport Award for Research Excellence  through the  IBiS graduate program. A. K. W. is supported by the  National Science Foundation Graduate Research Fellowship. Long-read sequencing of three isolates was funded by the  National Institutes of Health   R01  ( GM117408 ) to L. R. B. and a T32 training grant for the  University Program in Genetics and Genomics  ( GM007754 ). M. V. R. is supported by  NIH  grant  GM121828. M. G. S. was supported by  NWO domain Applied and Engineering Sciences VENI  grant ( 17282 ).
Data availability The raw short-read sequencing reads for strains used in this project are available from the NCBI Sequence Read Archive (Project PRJNA549503). The raw PacBio long-read data, along with the de novo assemblies and gene predictions, are available from the NCBI Sequence Read Archive (Project PRJNA692613). Strain information and short-read genomic variation data are available at the C. elegans Natural Diversity Resource (www. elegansvariation. org)  69.
S Deficiency Strongly Upregulates 24 Genes during Grain Filling To identify transcripts and metabolites that respond strongly to N and S treatments during grain filling at multiple time points, we performed statistical analyses at each grain developmental stage from 300°Cd to 600°Cd after anthesis. We filtered the resulting datasets for transcripts and metabolites with a significant treatment effect as described above (Fig. 1 ) and with a log 2 fold change (LFC). j1j and FDR , 0. 05 for at least one treatment comparison and at least one time point. This analysis revealed that more transcripts and metabolites were differentially accumulated at 500°Cd and 600°Cd after anthesis compared to 300°Cd and 400°Cd after anthesis (Fig. 3 ). At the transcriptome level, many transcripts were differentially accumulated in the N1S2 treatment compared to the S1 treatments.
Plant Material Analyses were performed on grains collected from the same biological material used in  Bonnot et al. (2017). Briefly, seeds of Triticum monococcum ssp. monococcum (accession ERGE 35821) were germinated on filter paper in petri dishes moistened with demineralized water, then seedlings were transplanted on February 4, 2014, to 50-mL PVC columns (7. 5-cm i. d. 3 50 cm deep; two seedlings per column) filled with a 2:1 (v/v) mixture of washed perlite:river sand. Columns were placed in a greenhouse to obtain a homogenous plant stand with a density of 512 plants m 22. The four N and S treatments were arranged in a complete randomized block design with four replicates (Supplemental Fig. S6 ). Air temperature, relative humidity, and photosynthetic photon flux density were measured at the height of the ears in the center of each block using a temperature and relative humidity CS215-L sensor (Campbell Scientific) and a JYP 1000 hemispherical photosynthetic photon flux density sensor (SDEC) mounted on miniature air-forced flues connected to a CR1000 data logger (Campbell Scientific).
Size selection was done using a beadbased method targeting an average insert size of 150 to 400 bp. After PCR amplification, the resulting fragments were cleaned up, pooled, quantified and used for cluster generation. For sequencing, pooled libraries were loaded on the cBot (Illumina) and cluster generation was performed according to the manufacturer's instructions. Paired-end sequencing using a 125-bp read length was performed on a HiSeq2500 machine (HiSeq Control Software v2. 2. 38) using HiSeq Flow Cell and TruSeq SBS Kit. For processing of raw data, RTA v1. 18. 61 and CASAVA v1. 8. 4 (Illumina) were used to generate FASTQ-files.
Read Alignment and Counting Reads were mapped to the T. monococcum reference genome v1 available at the Unité de Recherche Génomique Info database (urgi. versailles. inra. fr/ download/iwgsc/), using a BWA-MEM (v0. 7. 12-r103;, http://bio-bwa. sourceforge. net/) tool. Number of reads and percentages of mapped reads per sample are given in Supplemental Fig. S7. Only reads with a unique mapping position and a mapping quality score of at least 10 were considered for read counting. Read counting was realized using the software featureCounts from the Subread package (v1. 4. 6). Paired-end reads that were mapped to the same reference with the expected insert size were counted as one read. Paired-end reads that were mapped to different references or those with an unexpected insert size were counted as two reads.