dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The 18-genome data package To investigate the genome inversion landscape of Asian rice from a population structure perspective, we first combined a set of 16 previously published high-quality genomes  [32] [33] [34]  that represent the K = 15 population structure of O. sativa, plus the largest Xian/indica (XI) admixed subpopulation (XI-adm: Minghui 63 (MH63)) to create a "Rice Population Reference Panel" (RPRP -https://yongzhou2019. github. io/ Rice-Population-Reference-Panel/). This panel was annotated with a uniform pipeline to minimize methodological artifacts using RNA-Seq and Iso-Seq data generated or collected from all 16 genomes as evidence (Table  1 , Supplementary Tables  1 2 3 , Supplementary Fig. 1 , Supplementary Data 1, and Supplementary Note 1). To anchor this panel within a phylogenetic context, we long-read sequenced, and de novo assembled two additional genomes from both a representative species of the progenitor of Asian rice -i.
Characterization of gene content within inversions and their breakpoints Based on the pan-genome inversion index, we identified a total of ~971 annotated genes per genome within inversions or at their breakpoints (Supplementary Data 12). To investigate the possible effects of inversions on gene expression, we interrogated a set of transcriptome datasets derived from three O. sativa subpopulations (i. e. , XI-adm: MH63, XI-1A: ZS97 and GJ-temp: Nipponbare (i. e. , dataset#2 -see Methods). Based on a comparison of transcript abundance levels between Nipponbare and MH63 and ZS97 (across four tissue typesroot, panicle, young leaf, and mature leaf) we detected that 5-12 genes from MH63 and 4-11 genes from ZS97 within inversions, 2-7 genes from MH63 and 2-4 genes from ZS97 in inverted flanking regions, and 19-42 genes from MH63 and 9-30 genes from ZS97 that were located in non-inverted randomly resampled 20 Kb regions, were differentially expressed (DEG, fold change >2, p value <0.
cB GJ-trop2 GJ-tmp GJ-subtrp GJ-trop1 cA1 cA2 XI-2B XI-2A XI-adm XI-3A XI-3B1 XI-3B2 XI-1A XI-1B1 XI-1B2 cB GJ-trop2 GJ-tmp GJ-subtrp GJ-trop1 cA1 cA2 XI-2B XI-2A XI-adm XI-3A XI-3B1 XI-3B2 XI-1A XI-1B1 XI-1B2 Genome Specific INVs, To investigate the effect of inversions on the transcription of genes located at inversion breakpoints -i. e. , about 55 genes per genome (Supplementary Data 12), we interrogated both our baseline RNA-Seq datasets (dataset#1-see Methods) and dataset#2 for changes in transcript abundance. On average, transcript evidence for 28 of the 55 genes per genome could be detected in the tissues tested (Supplementary Data 12). Of these, transcript abundance of an average of 20 genes per genome did not change due to the presence of duplicated genes at both ends of their inversion breakpoints (Supplementary Data 12).
Recombination rate and genomic inversions To evaluate the effect of inversions on recombination frequency, a previously published recombinant inbred line (RIL-10) population of 210 inbred lines  43  derived from a cross between O. sativa cv. XI-adm: MH63 and XI-1A: ZS97 was investigated. We detected 78 inversions between MH63 and ZS97, totaling 3. 58 Mb and 3. 51 Mb in size, based on the MH63 and ZS97 genome assemblies, respectively (Supplementary Data 13). The recombination rate along each chromosome was assessed by comparing genetic and physical distances between neighboring bins. The average recombination rate for each chromosome ranged from 5. 95 (chromosome 6) to 9. 92 (chromosome 12) cM/ Mb, and varied from 0 to 153. 93 cM/Mb across the genome with an average of 6. 98 cM/Mb (Supplementary Fig.
Effect of large inversions on population SNP variation The occurrence of inversions can affect DNA polymorphism at the population level in several ways, including increased divergence in the inverted region and changes in linkage disequilibrium (LD) patterns  40. The latter is particularly interesting as it can affect SNPs that are mapped to positions megabases apart, and can be a confounding factor in LD-based analyses. To determine whether large O. sativa inversions (>100 Kb) left a trace in patterns of LD along the IRGSP RefSeq, we used the 3K-RGP dataset to examine LD blocks near inverted regions (n = 88) (Supplementary Data  14 ). An inversion fixed in a population may lead to the disruption of LD blocks, in which some SNPs flanking the inversion on one side are in LD with SNPs on the distal part of the inversion, but not on the adjacent part (Fig.
The next step will be the generation of a digital genebank for Asian rice whereby resequencing data from >100,000 accessions will be mapped to our Rice Population Reference Panel. Preliminary data (unpublished) shows that we can now easily call SNPs with resequencing data from >3000 individuals in 5 days per genome or less using high-performance computational workflows optimized for GATK4  55  software. Such call rates will undoubtedly increase over the next year with a targeted rice digital genebank release date of January 1 st , 2025.
Sequence and assembly Seed and/or tissue from O. rufipogon (IRGC 106523) and O. punctata (IRGC 105690) species were obtained from the International Rice Research Institute (IRRI), Philippines. The O. rufipogon accession was selected as a true O. rufipogon representative based on its origin (Papua New Guinea, reproductively isolated from rice cultivation) and phenotype. The two genomes were sequenced to a minimum of 100× coverage using PacBio long-read technology (PacBio RSII), and were assembled and validated to a PSRefSeq quality level following the identical strategy as described in Supplementary Note 1. The Benchmarking Universal Single-Copy Orthologs (BUSCO v4. 0) software package  56  was employed to evaluate the gene space completeness of each assembly.
Genome annotation Genome annotation used PacBio Iso-Seq and Illumina RNA-Seq data derived from RNA isolated root, panicle, and young leaf tissue from Nipponbare and 13 newly sequenced O. sativa accessions 32 as baseline transcript evidence (i. e. , RNA-Seq dataset#1). In addition, we collected deep RNA-Seq data from O. sativa cvs. Nipponbare, Minghui 63 and Zhenshan 97 (RNA-Seq dataset#2  57  ) for upgrading their annotations with the same pipeline and downstream transcriptome analyses. Pac-Bio Iso-Seq data was deposited in NCBI under BioProject PRJNA760839. The RNA-Seq dataset#1 is deposited in NCBI under  BioProject PRJNA659864. RNA-Seq dataset#2 is retrieved from NCBI BioProject PRJNA597070  57. Protein coding genes for the 16 O. sativa genomes above were predicted using MAKER-P (3. 01.
03)  58  including expression evidence, homology and ab initio gene predictors FGENESH (v. 8. 0. 0)  59, 60  , SNAP (0. 15. 7)  59, 60. Repeat masking was performed with RepeatMasker (http:// www. repeatmasker. org) using an Oryza specific repeat library  31. Expression evidence included reference guided transcript assemblies generated using StringTie (v1. 3. 4a)  61 and Cufflinks (v2. 2. 1)  61, 62. To generate assembled transcripts, quality inspected RNA-Seq reads from each library were mapped to their respective genomes using STAR (v2. 5. 3a)  63  with an iterative 2-pass mapping approach in which splice junctions generated from the first round were used to refine alignments in the subsequent round. Mapped reads from each library were merged, sorted, and indexed using SAMTools (v1. 9)  64  to generate input for transcript assembly programs.
All software packages were run with default options. High quality full length transcripts were clustered using CD-HIT (v4. 6)  65  for 95% sequence identity using parameters -c 0. 95 -n 10 -d 0 -M 3000. The clustered transcripts were further filtered for intron retention events using SUPPA2  66. Additional transcript and homology evidence was used  31  to run MAKER-P with keep_preds option set to 1. The gene structure of the predicted models was further improved using PASA (v2. 4. 1) 67 using full length cDNA and EST's downloaded from GenBank with the query "EST [Keyword] AND Oryza sativa [Organism]". Functional domain identification was completed with InterProScan (v5. 38-76. 0)  68. TRaCE (https://github. com/warelab/ TRaCE)  69  was used to assign canonical transcripts based on domain coverage, protein length, and similarity to transcripts assembled by Stringtie (v1.
3. 4a)  61. Annotation quality was assessed with MAKER-P generated Annotation Edit Distance (AED) values  70  and BUSCO, respectively. Only transcripts with AED scores <1 were retained. Finally, gene annotations were imported to Ensembl core databases, verified, and validated for translation using the Ensembl API  71. All genome annotations are available at the Gramene Pan Oryza database (https:// oryza. gramene. org/) and the Rice Population Reference Panel (RPRP, https://yongzhou2019. github. io/Rice-Population-Reference-Panel/ data/).
Genome visualization For alignment, analysis, visualization, and public availability, we uploaded the 18-genome data package into the Persephone® multigenome browser (https://web. persephonesoft. com/). The types of data tracks visualized include gene models, marker locations, BLAST matches, RNA-Seq coverage and sequence tracks. As the genomes are closely related, the maps could be aligned by connecting short sequence tags (100 bp) derived from the IRGSP-1. 0 RefSeq and mapped onto the other 17 genomes using BLASTN, as shown in Supplementary Fig. 2.
Identification of genomic inversions and assessments To discover large inversions (>100 bp), we first tested four different analysis workflows (https://gitlab. kaust. edu. sa/zhouy0e/svfor-o. sativa)  74  on two genomes, i. e. , GJ-temp: IRGSP-1. 0 and XIadm: MH63 (the genome of Minghui 63 accession). Workflow 1: The MH63 genome assembly was split into overlapping reads of 50 Kb in length at 5 Kb step intervals, resulting in ~10× coverage. The reads were then mapped onto the IRGSP-1. 0 genome   75. Inversions were called with SVIM (v1. 1. 0)  76 , retaining only inversions with a depth greater > 6 that passed the caller's filtration criteria. Workflow 2: Steps were the same as in workflow 1, except that Sniffles (v1. 0. 7)  75  was used to call inversions. Workflow 3: The MH63 genome assembly was aligned to IRGSP-1.
0 reference sequence using Minimap2 (v 2. 15)  77. This step was followed by filtration for identity greater than 90% and length longer than 100 bp. Inversions were called using SyRI (i. e. , Synteny and Rearrangement Identifier, v1. 4)  78. Workflow 4: Steps were the same as in workflow3, except the alignment tool used, i. e. , Nucmer, included with the MUMmer (v4)  79  software package. To assess the accuracy of the four workflows, dot-plots of the syntenic regions including the putative inversions were generated to visually validate the inversions. Out of all workflows, workflow4 was selected based on our validation criteria. As a final validation check of workflow 4, PacBio raw reads were also applied for validation of a subset inversions. Raw reads of query genomes were mapped to the IRGSP RefSeq, and the assemblies of the query genomes themselves.
Pan-genome inversion index of 75 high-quality genomes Seventy-four inversion vcf files derived from 74 genomes compared to the IRGSP RefSeq were generated. Briefly, vcf files were sorted according to chromosome ID, coordinates and strand, and were merged using SURVIVOR (v1. 0. 7)  80  , run under default parameters (https://github. com/fritzsedlazeck/SURVIVOR/wiki), except for the maximum allowed distance of 10 bp. In this case, inversions having start and end coordinates no more different than 10 bp were collapsed and considered as single inversions.
Differential transcript abundance analysis within inversions To investigate the effect of an inversion on gene expression, we searched for differences in transcript abundance within inversions in Asian rice based on two RNA-Seq datasets (dataset#1 and dataset#2). FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) values were identified following an accurate pipeline  85  , which included HISAT2 (version 2. 2. 1)  86  for alignment, and StringTie (v1.
Genome recombination rate of inversions To study the genome recombination rate of inversions, we used genetic data based on a RIL population derived from accessions O. sativa cv. XI-adm: Minghui 63 and XI-1A: Zhenshan 97  88, 89. Briefly, re-sequencing data was obtained from 210 RILs and a Bin Map containing 1619 bins was generated. The physical positions of each bin were derived from updated versions both the MH63 and ZS97 reference genomes  89. Then, a genetic map based on the RILs panel of 1619 bins was constructed using the MSTMap algorithm  90. By comparing genetic and physical distances between neighboring Bin Map markers, we estimated the relative changes of the recombination rate of the two genomes  91. Then, we compared recombination rates in the bins that overlapped with inversions and genome-wide, respectively.
3. 4a) 61  to obtain normalized FPKM values. Taking into account different transcript abundances between the two RNA-Seq datasets (2 Gb for dataset#1 vs 6 Gb for dataset#2), we used different minimum filtration of, i. e. , FPKM > 0. 1 in RNA-Seq dataset#1 and FPKM > 1 in RNA-Seq dataset#2. Differential Transcript Abundance (DTA) of dataset#2, was carried out by edgeR (3. 1. 0) 87 with p value <0. 01 and abundance change >2 cutoffs.
Acknowledgements This research was supported by  King Abdullah University of Science & Technology's Baseline funding , and the  University of Arizona 's Bud Antle Endowed Chair for Excellent in Agriculture to R. A. W. ;  Huazhong Agricultural University's Start-up Fund, Fundamental Research Funds for the Central Universities  ( 2662020SKPY010 ), and the  Major Project of Hubei Hongshan Laboratory  ( 2022HSZD031 ) to J. Z. ;  USDA ARS 8062-21000-041-00D  to D. W. The authors acknowledge support from the  Shaheen Cray   XC40  platform at  KAUST Supercomputing Laboratory , the computing platform of the  National Key Laboratory of Crop Genetic Improvement at HZAU ,  Peter VanBuren  for systems support and the  Elzar High Performance Computing facility  ( NIH S10 OD0286321-01 ) at  Cold Spring Harbor Laboratory  for providing computational resources, and help from the Persephone team that hosts our 18-genome data package on their portal for genome visualization.
Data availability All sequencing data has been submitted to NCBI. The assembly data of IRGSP-1. 0/NIPPONBARE (GCA_001433935. 1), CHAO MEO::IRGC 80273-1 (GCA_009831315. 1), Azucena (GCA_009830595. 1), KETAN NANG-KA::IRGC 19961-2 (GCA_009831275. 1), ARC 10497::IRGC 12485-1 (GCA_ 009831255. 1), PR 106::IRGC 53418-1 (GCA_009831045. 1), Minghui 63 (GCA_001623365. 2), IR 64 (GCA_009914875. 1), Zhenshan 97 (GCA_ 001623345. 2), LIMA::IRGC 81487-1 (GCA_009829395. 1), KHAO YAI GUANG::IRGC 65972-1 (GCA_009831295. 1), GOBOL SAIL (BALA-M)::IRGC 26624-2 (GCA_009831025. 1), LIU XU::IRGC 109232-1 (GCA_ 009829375. 1), LARHA MUGAD::IRGC 52339-1 (GCA_009831355. 1), N22 (N 22::IRGC 19379-1) (GCA_001952365. 2), NATEL BORO::IRGC 34749-1 (GCA_009831335. 1), O. rufipogon PNG91-7::IRGC 106523-1 (GCA_ 023541355. 1), and O.
punctata:: IRGC 105690 (GCA_000573905. 2) were deposited in GeneBank of NCBI. The raw sequencing data of O. rufipogon and O. punctata generated in this study were deposited in NCBI under BioProject PRJNA609053 and PRJNA13770, respectively. PacBio Iso-Seq data for all accessions in this study was deposited in NCBI under BioProject PRJNA760839. The RNA-Seq dataset#1 was deposited in NCBI under BioProject PRJNA659864. RNA-Seq dataset#2 was retrieved from NCBI BioProject PRJNA597070. The details are also listed in Table 1 and the Rice Population Reference Panel [https://yongzhou2019. github. io/Rice-Population-Reference-Panel/ data/]. Source data are provided with this paper.
Fortunately, new computational algorithms can help reduce false-positive and false-negative signals, even when no 'normal' paired sample is available for comparison  [13] [14] [15]. Recent deep-learning technologies and state-of-the-art image-based artificial intelligence software such as DeepMosaic, trained on non-cancer mosaic variants have significantly improved accuracy  14. The NIH-supported Brain Somatic Mosaicism Network (BSMN) established a 'Common pipeline', incorporating a 'best practice' workflow to reliably and reproducibly identify low VAF somatic variants  16. With these advances, we assessed for mosaic variants that might point to gene networks beyond mTOR in MCD lesions. Our results offer insights into potentially druggable pathways in cases of incomplete resection or drug-resistant forms of MCD.
1b , Extended Data Fig. 1 ). In Phase 1, we performed amplicon sequencing (AmpliSeq, ~1000 X) profiling the entire open reading frame of 87 genes ('MCD panel v1', Supplementary Table  2a ) previously detected in FCD/HMEs or known PI3K-AKT3-mTOR interactors (Supplementary Table  2b ). In Phase 2, 80 cases (75 unsolved cases plus 5 solved cases) from Phase 1 and additionally collected 54 cases, we performed unbiased deep whole-exome sequencing (WES, ~300 X) on paired samples, where available, or unpaired samples (i. e. brain plus blood/saliva vs. brain only). In Phase 3, from an additional subcohort of 96 new cases (86 MCD plus 10 neurotypical), we designed the 'MCD panel v2' (Supplementary Table  2c ) including known and novel genes detected in Phases 1 and 2 (Methods). We also resequenced unsolved cases from Phase 2 (30 cases), expecting that the higher read depth afforded by panel sequencing could provide greater sensitivity to detect low VAF mutations potentially missed by WES.
2e ). GRIN2A p. T531M mutation, an analog mutation of GRIN2C p. T529M in our cohort, was previously reported in epilepsy-aphasia spectrum disorders, where it increased NMDA receptors 'open-state' probability  40. This suggests that the p. T529M mutation activates the channel, likely in an mTOR-independent fashion. RHOA encodes RHOA protein, a small GTPase, regulating cytoskeletal dynamics, cell migration, and cell cycle. RHOA p. P75S mutation is located in the interdomain region between the second GTP/GDP binding domain and Rho insert domain (Extended Data Fig. 2f ). This mutation is implicated in skin cancer multiple times in the Catalogue Of Somatic Mutations In Cancer (COSMIC) database (DB)  41. Thus, all mutations assessed here are likely gain-of-function and exert functional impact on cells in which they are expressed.
Since this case (FCD-7967, type 2B) has additional MTOR mutation, the increase in cell body size of dysplastic cells is likely to be induced by MTOR but not RRAGA mutation (Extended Data Fig. 6b ). To assess correlation with human samples, we assessed available archived neuropathological tissue sections for histology and pS6 activity. Similar to our mouse models, we found patient FCD-3560 carrying KLHL22 p. R38Q showed enlarged neurons that co-stained for excess pS6, whereas FCD-5157 carrying GRIN2C p. T529M showed only a slight increase in cell body size and no evidence of excessive pS6 (Fig. 3d ). While this analysis does not take into account the genotype of individual cells, it suggests KLHL22 but not GRIN2C mutations impact mTOR signaling.
MCD genes enriched in the excitatory neuronal lineage To infer the cell type in which MCD genes function, we mapped the net expression of the MCD genes (i. e. eigengene) onto a published single-cell transcriptome dataset from the 2 nd -trimester human telencephalon, at a time when mutations probably arose  50  (Fig. 5a, b ). This showed a strong positive correlation of the net expression levels of the MCD geneset with dividing radial glial cells (Pearson r = 0. 3655, p = 7. 915×10 -121 ) and a moderate correlation in dividing intermediate progenitor cells (IPCs; Pearson r = 0. 1527, p = 2. 448×10 -21 ) and mature excitatory neuron cells (Pearson r = 0. 1780, p = 1. 557×10  -28  ). We found a lack of positive correlation with inhibitory neuronal lineages including medial and central ganglionic eminences (MGE, CGE) and mature interneuron clusters (Fig.
We also included brains from four neurotypical cases for comparison. We sequenced a total of 33,206 nuclei (see Methods). While the FCD brain snRNAseq data showed substantial overlapping cell clusters with controls using UMAP clustering, HME and TSC brains showed distinct cell cluster distributions (Fig. 6a, b ). We found that very few HME cells matched expression patterns for typical brain cells, even after standard normalization and scaling (Fig. 6b, c , see Methods). We characterized transcriptomic profiles of each cluster based upon established marker gene expression (Fig. 6d ) as well as differentially expressed gene (DEG) analysis (Extended Data Fig. 7a , Supplementary Table  5a ) and weighted gene coexpression network analysis (WGCNA, Extended Data Fig. 7b , Supplementary Table  5b ) to assign likely cell types.
8 ). Previous experiments indicate that it is most often the dysplastic cells within HME and MCD that carry mosaic mutations  9  , suggesting an effect of these mutations on growth factor receptor expressions that correlates with dysplasia. Next, we investigated the net expression patterns of genes mutated in MCD within this snRNAseq dataset. The net expression of the MCD geneset was enriched in Ast-L1/3 and OD-L, which were labeled as dysplastic cells (Fig. 6e ). Interestingly, the individual mutated genes displayed converging expression patterns resulting in four different eigengenes (Fig. 6f , grey, turquoise, blue and brown), with gene members for each eigengene (Extended Data Fig. 9 ). These show distinct enrichment patterns across cell types, implying that members of each eigengene may be associated with the pathophysiology of the corresponding dysplastic cell type in HMEs.
8. 1) single-mode and DeepMosaic  14. A union of different pipelines was selected to get maximum sensitivity. Mosaic candidates from the combined lists were refined using the following criteria: (i) the variant had more than 3 reads for the alternative allele; (ii) the variant was not present in UCSC repeat masker or segmental duplications; (iii) the variant was at least 2 bp away from a homopolymeric tract; and (iv) the variant exhibited a gnomAD allele frequency lower than 0. 001. Variants that exist in the 1000 genome project (phase 3) also were excluded from the analysis. Variants from both exome data sources were tested and a combination of tissue-specific mosaic variants and tissue-shared mosaic variants were collected and the credible interval of VAFs was calculated using a Bayesian-based method described previously 62.
To filter for candidate MCD diseasecausing variants, we further filtered out synonymous variants in coding regions, variants with CADD Phred score < 25, and candidates that fell out of coding regions and were not predicted to affect splicing, annotated by ANNOVAR and BEDtools (version 2. 27. 1), the annotation scripts were provided on GitHub (https://github. com/shishenyxx/MCD_mosaic).
Orthogonal validation and quantification of mosaic mutations with targeted amplicon sequencing Targeted amplicon sequencing (TASeq) with Illumina TruSeq was performed with a coverage goal of >1000X for 554 candidate variants detected by computational pipelines described above for both AmpliSeq and WES, to experimentally validate the mosaic candidates before functional assessment. PCR products for sequencing were designed with a target length of 160-190 bp with primers being at least 60 bp away from the base of interest. Primers were designed using the command-line tool of Primer3  66, 67  with a Python (v3. 7. 3) wrapper  17, 18  (Supplementary Table  8 ). PCR was performed according to standard procedures using GoTaq Colorless Master Mix (Promega, M7832) on sperm, blood, and an unrelated control.