dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the EUREYE study participants for providing samples for DNA analysis and eye images. This work was funded in part by the  Erasmus MC University Medical Center Rotterdam , by a previous grant from the  Netherlands Genomics Initiative (NGI) / Netherlands Organization for Scientific Research (NWO)  within the framework of the  Forensic Genomics Consortium Netherlands (FGCN)  and  Netherlands Forensic Institute (NFI). EUREYE was supported by the  European Commission 5th Framework  ( QLK6-CT-1999-02094 ). Additional funding for cameras was provided by the  Macular Disease Society UK  and by the  UK Medical Research Council  for extraction of DNA. The work of MR was supported by the  Estonian Ministry of Education and Science  (target funding  SF0940026s07 ) and the  Estonian Research Council  ( IUT5-1 ).
Finally, we analyzed tissue-specific edges in the context of the input data we used to seed PANDA and reconstruct our networks. We found that tissue-specific edges are distinct from those identified using only co-expression information (Figure  S2D ). In addition, tissuespecific edges were depleted for canonical transcription factor interactions (Figure  3C ). Thus, many of the tissue-specific regulatory interactions we identified using PANDA would have been missed if we had relied solely upon co-expression or transcription factor motif targeting information to define a regulatory network.
Discussion We used gene expression data from GTEx, together with other sources of regulatory information, to reconstruct and characterize regulatory networks for 38 tissues and to assess tissue-specific gene regulation. We used these networks to identify tissue-specific edges, and we used the gene expression data to identify tissue-specific nodes (transcription factors and genes). We found that, although tissue-specific edges are enriched for connections to tissuespecific transcription factors and genes, they are also depleted for canonical interactions (defined based on a transcription factor-binding site in the target gene's promoter). In addition, edges are often uniquely called as specific in only one tissue while tissue-specific genes often have a high multiplicity, meaning that they were identified as specific in more than one tissue.
GTEx Data We downloaded the GTEx version 6. 0 RNA-seq and genotype data (phs000424. v6. p1, 2015-10-05 released) from database of Genotypes and Phenotypes (dbGaP; approved protocol 9112). GTEx release version 6. 0 sampled 551 donors with phenotypic information and included 9,590 RNA-seq assays  (Consortium, 2015).
RNA-Seq Normalization We used YARN (https://bioconductor. org/packages/release/bioc/html/yarn. html) to perform quality control, gene filtering, and normalization preprocessing on the GTEx RNA-seq data, as described in  (Paulson et al. , 2017). Briefly, this pipeline tested for sample sexmisidentification, merged related sub-tissues, performed tissue-aware normalization using qsmooth  (Hicks et al. , 2017) , and resulted in a dataset of 9,435 gene expression profiles assaying 30,333 genes in 38 tissues from 549 individuals. eQTL Identification 19 of the 38 tissues contained gene expression samples from at least 150 distinct individuals with imputed genetic data. We identified tissue-specific, cis-acting eQTLs for these tissues, as described in  Fagny et al. (2017). Briefly, for each tissue, we identified SNPs that had a minor allele frequency greater than 0.
Transcription Factor Motif Information We downloaded Homo sapiens transcription factor motifs with direct/inferred evidence from the Catalog of Inferred Sequence Binding Preferences (http://cisbp. ccbr. utoronto. ca/, accessed July 7, 2015). For each transcription factor, we selected the motif with the highest information content, and we mapped its position weight matrix to the human genome (hg19) using the Find Individual Motif Occurrences program  (Grant et al. , 2011). We retained significant hits (p < 10 -5 ) that occurred within the promoter ([-750, +250] around the transcriptional start site) of Ensembl genes (GRCh37. p13; annotations from https:// genome. ucsc. edu/cgi-bin/hgTables, accessed September 3, 2015). We intersected this map with the expression data, resulting in a set of canonical regulatory interactions from 644 transcription factors to 30,243 genes, which we used to construct our regulatory network models.
Network Centralities We used the igraph v. 1. 0. 0 package in R to calculate the degree (using the graph. strength() function) and betweenness centrality (using the betweenness() function) of genes in the 38 complete, weighted PANDA tissue networks (based on transformed edge weights, see Equation  1 ). For the betweenness centrality, we used as the cost and treated edges as undirected.
Acknowledgments This work was supported by grants from the  National Institutes of Health , including grants from the  National Heart, Lung, and Blood Institute  ( 5P01HL105339 ,  5R01HL111759 ,  5P01HL114501 , and  K25HL133599 ), the  National Cancer Institute  ( 5P50CA127003 ,  1R35CA197449 ,  1U01CA190234 ,  5P30CA006516 , and  P50CA165962 ), and the  National Institute of Allergy and Infectious Diseases  ( 5R01AI099204 ) and from the  Charles A. King Trust Postdoctoral Research Fellowship Program ,  Sara Elizabeth O'Brien Trust ,  Bank of America , N. A. , Co-Trustees. Additional funding was provided by grant No. 2014-133322  (3953) from the  NVIDIA Foundation. This work was conducted under dbGaP-approved protocol 9112 (accession  phs000424. v6. p1).
To confirm the mtDNA result that NWSL equids fall outside of crown group equid diversity, we sequenced and compared partial nuclear genomes from 17 NWSL equids to a caballine (horse) and a non-caballine (donkey) reference genome. After controlling for reference genome and ancient DNA fragment length artifacts (Appendices 1-2), we examined differences in relative private transversion frequency between these genomes (Appendix 1-figure  1 ). We found that the relative private transversion frequency for NWSL equids was ~1. 4-1. 5 times greater than that for horse or donkey (Appendix 2, Figure  1 -source data 3, Figure  1 -figure supplement 2, and Figure  1 source data 2). This result supports the placement of NWSL equids as sister to the horse-donkey clade (Figure  1 -figure supplement 3), the latter of which is representative of living Equus diversity (e.
Divergence times of Hippidion, NWSL equids, and Equus We estimated the divergence times between the lineages leading to Hippidion, the NWSL equids, and Equus. We first applied a Bayesian time-tree approach to the whole mitochondrial genome data. This gave divergence estimates for the Hippidion-NWSL/Equus split (node 1) at 5. 15-7. 66 Ma, consistent with  (Der Sarkissian et al. , 2015) , the NWSL-Equus split (node 2) at 4. 09-5. 13 Ma, and the caballine/non-caballine Equus split (node 3) at 3. 77-4. 40 Ma (Figure  1  and Figure  1 -source data 1). These estimates suggest that the NWSL-Equus mitochondrial split occurred only ~500 thousand years (ka) prior to the caballine/non-caballine Equus split. We then estimated the NWSL-Equus divergence time using relative private transversion frequency ratios between the nuclear genomes, assuming a caballine/non-caballine Equus divergence estimate of 4-4.
The mitochondrial tree topology within Haringtonhippus does not exhibit phylogeographic structure (Figure  1 -figure supplement 1b), which is consistent with sampled Haringtonhippus mitochondrial genomes belonging to the same species. Using Bayesian time-tree analysis, we estimated a tMRCA for the sampled Haringtonhippus mitochondrial genomes of ~200-470 ka BP (Figure  1  and Figure 1-source data 1;  Heintzman et al. , 2017). The MRCA of Haringtonhippus is therefore more recent than that of other extant equid species (such as E. asinus and E. quagga, which have a combined 95% HPD range: 410-1030 ka BP; Figure  1  and Figure 1-source data 1;  Heintzman et al. , 2017). Although the middle Pleistocene holotype TMM 34-2518 (~125-780 ka BP) may predate our Haringtonhippus mitochondrial tMRCA, this sample has no direct date and the range of possible ages falls within the tMRCA range of other extant equid species.
Mitochondrial genome reconstruction and analysis We prepared raw sequence data for alignment and mapped the filtered reads to the horse reference mitochondrial genome (Genbank: NC_001640. 1) and a H. francisci reference mtDNA genome (Genbank: KT168321), resulting in mitogenomic coverage ranging from 5. 8Â to 110. 7Â (Supplementary file 1). We were unable to recover equid mtDNA from TMM 34-2518 (the francisci holotype) using this approach (Appendix 2). We supplemented our mtDNA genome sequences with 38 previously published complete equid mtDNA genomes. We constructed six alignment data sets and selected models of molecular evolution for the analyses described below (Appendix 1-table 1, and Supplementary file 1;  Heintzman et al. , 2017). We tested the phylogenetic position of the NWSL equids (=H.
, 2012)  using mtDNA data set five. We varied these analyses by including or excluding fast-evolving partitions, constrained the root height or not, and including or excluding the E. ovodovi sequence. To facilitate future identification of equid mtDNA sequences, we constructed, using data set six, a list of putative synapomorphic base states, including indels and substitutions, that define the genera Hippidion, Haringtonhippus, and Equus at sites across the mtDNA genome.
Mitochondrial genome reconstruction We initially reconstructed the mitochondrial genome for H. francisci specimen YG 404. 663 (PH047). For sequence data enriched for the mitochondrial genome, we trimmed adapter sequences, merged paired-end reads (with a minimum overlap of 15 base pairs (bp) required), and removed merged reads shorter than 25 bp, using SeqPrep  (St. John, 2013 ; https:// github. com/jstjohn/SeqPrep). We then mapped the merged and remaining unmerged reads to the horse reference mitochondrial genome sequence using the Burrows-Wheeler Aligner aln (BWA-aln v0. 7. 5;  [Li and Durbin, 2010] ), with ancient parameters (-l 1024;  [Schubert et al. , 2012] ). We removed reads with a mapping quality less than 20 and collapsed duplicated reads to a single sequence using SAMtools v0. 1.
19 rmdup  (Li et al. , 2009). We called consensus sequences using Geneious v8. 1. 7 (Biomatters, http://www. geneious. com; [Kearse et al. , 2012] ). We then re-mapped the reads to the same reference mitochondrial genome using the iterative assembler, MIA  (Briggs et al. , 2009). Consensus sequences from both alignment methods required each base position to be covered a minimum of three times, with a minimum base agreement of 67%. The two consensus sequences were then combined to produce a final consensus sequence for YG 404. 663 (Genbank: KT168321), which we used as the H. francisci reference mitochondrial genome sequence. For the remaining newly analyzed 21 H. francisci, two E. cf. scotti, and two E. lambei samples, we merged and removed reads as described above. We then separately mapped the retained reads to the horse and H.
Phylogenetic analysis of mitochondrial genomes To test the phylogenetic position of the NWSL equids, we conducted Bayesian and maximum likelihood (ML) phylogenetic analyses of data sets one, two, and three, under the partitioning scheme and selected models of molecular evolution described above. For outgroup, we selected: White rhinoceros (data set one), Malayan tapir (data set two), or dog (data set three). For each of the data sets, we varied the analyses based on (a) inclusion or exclusion of the fast-evolving partitions (third codon positions and control region, where appropriate) and (b) inclusion or exclusion of the Hippidion sequences. We ran Bayesian analyses in MrBayes (v3. 2. 6,  [Ronquist et al. , 2012] ) for two parallel runs of 10 million generations, sampling every 1,000, with the first 25% discarded as burn-in.
Divergence date estimation from mitochondrial genomes To further investigate the topology of the four major equid groups, and to infer divergence times between them, we ran Bayesian timetree analyses in  BEAST (v1. 8. 4 ;  [Drummond et al. , 2012] ). Unlike the previous analyses, BEAST can resolve branching order in the absence of an outgroup, by using branch length and molecular clock methods. For BEAST analyses, we used data set five. We did not enforce monophyly. Where available, we used radiocarbon dates to tip date ancient samples. For two samples without available radiocarbon dates, we sampled the ages of tips. For the E. ovodovi sample (mtDNA genome: NC_018783), which was found in a cave that has been stratigraphically dated as late Pleistocene and includes other E. ovodovi remains have been dated to ~45-50 ka BP  (Eisenmann and Sergej, 2011; Orlando et al.
, 2015) ; https://github. com/Paleogenomics/Chrom-Compare) (Appendix 1-figure 1, step 8). Finally, as a fourth approach and for both genome coordinate frameworks, we repeated approach three with the exception that we divided the NWSL alignments by mapped read length. We split the alignments into 10 bp read bins ranging from 30-39 to 120-129 bp, and discarded longer reads and paired-end reads that were unmerged by SeqPrep. We called consensus sequences and calculated relative private transversion frequencies for each sequence as described above. We only used relative private transversion frequencies from the 90-99 to 120-129 bp bins for divergence date estimates (Appendix 2).
DNA damage analysis For a subset of nine samples, we realigned the filtered sequence data from the libraries enriched for equid mitochondrial DNA to either the H. francisci (for H. francisci samples) or horse (for E. lambei and E. cf. scotti samples) reference mitochondrial genome sequences using BWA-aln as described above. We also realigned the filtered unenriched sequence data to the horse reference genome (EquCab2) for a subset of six samples using the same approach. We then analyzed patterns of DNA damage in mapDamage v2. 0. 5  (Jonsson et al. , 2013). Ortiz et al. , 2017] ) within the NWSL equid clade albeit with lower support (cumulative likelihood of 0. 703-0. 854). We note that in the case of EQ4 from Edmonton, this may be due to very limited available sequence data (117 bp). For EQ1, EQ16, and EQ30, the placement with the second greatest support is the branch leading to NWSL equids (cumulative likelihood of 0.
We infer from this analysis that the published MS272 sequence is therefore ~76% Haringtonhippus and that ~23% originates from Equus. The presence of Equus synapomorphies could be explained by the fact that the enriched library for MS272 was sequenced on the same run as ancient caballine horses (Equus), thereby potentially introducing contaminating reads from barcode bleeding  (Kircher et al. , 2012) , which may have been exacerbated by alignment to the modern horse reference mitochondrial genome with BWA-aln and consensus calling using SAMtools  (Vilstrup et al. , 2013). The presence of caballine horse sequence in the published MS272 mtDNA genome explains why previous phylogenetic analyses of mitochondrial genomes have recovered NWSL equids as sister to caballine Equus with strong statistical support  (Der Sarkissian et al.
We further generated ~800,000 reads from the unenriched library for TMM 34-2518, and followed a modified metagenomic approach, outlined in  (Graham et al. , 2016) , to assess if any endogenous DNA was present. We mapped the reads to the horse reference genome (EquCab2), using the BWA-aln settings of  (Graham et al. , 2016) , of which 538 reads aligned. We then compared these aligned reads to the BLASTn database. None of the reads uniquely hit Equidae or had a higher score to Equidae than non-Equidae, whereas 492 of the reads either uniquely hit non-Equidae or had a higher score to non-Equidae than Equidae. These results are consistent with either a complete lack, or an ultra-low occurrence, of endogenous DNA in TMM 34-2518.
Acknowledgements We thank the Klondike placer gold mining community of  Yukon  for their support and providing access to their mines from which many of our Haringtonhippus fossils were recovered. We thank  Matt Brown  and  Chris Sagebiel  of the  Texas Vertebrate Palaeontology Collections at the University of Texas, Austin  for access to a portion of TMM 34-2518, and also thank  Sam McLeod ,  Vanessa Rhue , and  Aimee Montenegro  at the  Los Angeles County Museum  for access to the  Gypsum Cave material  for consumptive sampling. Thanks to  Brent Breithaupt  ( Bureau of Land Management ) for.
Data availability Repository details and associated metadata for curated samples can be found in Supplementary file 1. MTIII and other element measurement data are in Figure  2 -source data 1, and the Rscript used for morphometric analysis is in the DRYAD database  (Heintzman et al. , 2017). MtDNA genome sequences have been deposited in Genbank under accessions KT168317-KT168336, MF134655-MF134663, and an updated version of JX312727. All mtDNA genome alignments (in NEXUS format) and associated XML and TREE files are in the DRYAD database  (Heintzman et al. , 2017). Raw shotgun sequence data used for the nuclear genomic analyses and raw shotgun and target enrichment sequence data for TMM 34-2518 (francisci holotype) have been deposited in the Short Read Archive (BioProject: PRJNA384940).
Target enrichment and sequencing We enriched libraries for equid mitochondrial DNA following the MyBaits v2 protocol (Arbor Biosciences, Ann Arbor, MI), with RNA bait molecules constructed from the horse reference mitochondrial genome sequence (NC_001640. 1). We then sequenced the enriched libraries for 2 Â 150 cycles on the Illumina HiSeq-2000 platform at UC Berkeley or 2 Â 75 cycles on the MiSeq platform at UC Santa Cruz, following the manufacturer's instructions. We Appendix 1-table 1. Selected models of molecular evolution for partitions of the first five mtDNA genome alignment data sets. All lengths are in base pairs. Reduced length excludes the Coding3 and CR partitions. For all RAxML analyses the GTR model was implemented. *The TrN model was selected, but this cannot be implemented in MrBayes and so the HKY model was used.
Resolving the phylogenetic placement of NWSL equids using mitochondrial genomes We ran Bayesian and ML phylogenetic analyses on mtDNA genome alignment data sets 1-3, whilst varying the outgroup, including (all) or excluding (reduced) the fast-evolving partitions (see Appendix 1), and including or excluding the Hippidion sequences. In all analyses, we recover four major equid groups (Hippidion, NWSL equids(=H. francisci), caballine Equus, and non-caballine Equus) with strong statistical support (Bayesian posterior probability (BPP): 1. 000; ML bootstrap: 96-100%; Appendix 2-table 1), consistent with previous studies (e. g. [Der Sarkissian et al. , 2015; Orlando et al. , 2009] ). We recover conflicting phylogenetic topologies between these four groups, however, which is dependent on the variables described above and the choice of phylogenetic algorithm (Appendix 2-figure  3 ; Appendix 2-table  1 ).
Across all analyses, strong statistical support (BPP:!0. 99; ML bootstrap:!95%) is only associated with topology 1 (Appendix 2-figure  3 ; Appendix 2table 1), in which NWSL equids are placed outside of Equus, and Hippidion is placed outside Appendix 2-table 1. Topological shape and support values for the best supported trees. These results are from the Bayesian and maximum likelihood (ML) analyses of mtDNA data sets 1-3, including either the all or reduced partition sets, and with Hippidion sequences either included or excluded. Topology Appendix 2-table 2. The a posteriori phylogenetic placement likelihood for eight ceratomorph (rhino and tapir) outgroups. These analyses used a ML evolutionary placement algorithm, whilst varying the partition set used (all or reduced), and either including or excluding Hippidion sequences.
produced data for the nuclear genomic analyses by shotgun sequencing 17 of the unenriched libraries for 2 Â 75 cycles on the MiSeq to produce ~1. 1-6. 4 million reads per library (Figure1-source data 2). Heintzman PD, Stil-Heintzman PD, 2017 Mitochondrial genome sequence 2017 Mitochondrial genome sequence https://www. ncbi. nlm. https://www. ncbi. nlm. Publicly available at Publicly available at ler M, Shapiro B Kapp JD, Shapiro B from YG 328. 54 from LACM(CIT) 109 / 149291 nih. gov/nuccore/ nih. gov/nuccore/ NCBI GenBank NCBI GenBank KT168322 MF134656 (accession no: (accession no: KT168322) MF134656) Heintzman PD, Heintzman PD, 2017 Mitochondrial genome sequence 2017 Mitochondrial genome sequence https://www. ncbi. nlm. https://www. ncbi. nlm. Publicly available at Publicly available at Shapiro B Kapp JD, Shapiro B from YG 378.
5 from LACM(CIT) 109 / 156450 nih. gov/nuccore/ nih. gov/nuccore/ NCBI GenBank NCBI GenBank KT168323 MF134657 (accession no: (accession no: KT168323) MF134657) Heintzman PD, Heintzman PD, 2017 Mitochondrial genome sequence 2017 Mitochondrial genome sequence https://www. ncbi. nlm. https://www. ncbi. nlm. Publicly available at Publicly available at Shapiro B Kapp JD, Shapiro B from YG 404. 478 from KU 47800 nih. gov/nuccore/ nih. gov/nuccore/ NCBI GenBank NCBI GenBank KT168324 MF134658 (accession no: (accession no: KT168324) MF134658) Heintzman PD, Heintzman PD, 2017 Mitochondrial genome sequence 2017 Mitochondrial genome sequence https://www. ncbi. nlm. https://www. ncbi. nlm. Publicly available at Publicly available at Shapiro B Kapp JD, Shapiro B from YG 402. 235 from KU 62055 nih. gov/nuccore/ nih.
gov/nuccore/ NCBI GenBank NCBI GenBank KT168325 MF134659 (accession no: (accession no: KT168325) MF134659) Heintzman PD, Stil-Heintzman PD, 2017 Mitochondrial genome sequence 2017 Mitochondrial genome sequence https://www. ncbi. nlm. https://www. ncbi. nlm. Publicly available at Publicly available at ler M, Shapiro B Kapp JD, Shapiro B from YG 130. 55 from KU 33418 nih. gov/nuccore/ nih. gov/nuccore/ NCBI GenBank NCBI GenBank KT168326 MF134660 (accession no: (accession no: KT168326) MF134660) Heintzman PD, Heintzman PD, 2017 Mitochondrial genome sequence 2017 Mitochondrial genome sequence https://www. ncbi. nlm. https://www. ncbi. nlm. Publicly available at Publicly available at Shapiro B Kapp JD, Shapiro B from YG 198. 1 from KU 53678 nih. gov/nuccore/ nih. gov/nuccore/ NCBI GenBank NCBI GenBank KT168327 MF134661 (accession no: (accession no: KT168327) MF134661) Heintzman PD, Stil-Heintzman PD, 2017 Mitochondrial genome sequence 2017 Mitochondrial genome sequence https://www.
ncbi. nlm. https://www. ncbi. nlm. Publicly available at Publicly available at ler M, Shapiro B Kapp JD, Shapiro B from YG 303. 1085 from KU 50817 nih. gov/nuccore/ nih. gov/nuccore/ NCBI GenBank NCBI GenBank KT168328 MF134662 (accession no: (accession no: KT168328) MF134662) Heintzman PD, Heintzman PD, 2017 Mitochondrial genome sequence 2017 Mitochondrial genome sequence https://www. ncbi. nlm. https://www. ncbi. nlm. Publicly available at Publicly available at Shapiro B Kapp JD, Shapiro B from YG 130. 6 from KU 62158 nih. gov/nuccore/ nih. gov/nuccore/ NCBI GenBank NCBI GenBank KT168329 MF134663 (accession no: (accession no: KT168329) MF134663) Heintzman PD, Heintzman PD, 2017 Mitochondrial genome sequence 2017 Data from: A new genus of horse https://www. ncbi. nlm. http://dx. doi. org/10.
Publicly available at Available at Dryad Shapiro B McHorse BK, Sha- from YG 417. 13 from Pleistocene North America nih. gov/nuccore/ 5061/dryad. 8153g NCBI GenBank Digital Repository piro B KT168330 (accession no: under a CC0 Public KT168330) Domain Dedication Heintzman PD, 2017 Mitochondrial genome sequence https://www. ncbi. nlm. Publicly available at Shapiro B from YG 76. 2 nih. gov/nuccore/ NCBI GenBank KT168331 (accession no: Database, license, and accessibility KT168331) Author(s) Heintzman PD, Year Dataset title 2017 Mitochondrial genome sequence Dataset URL https://www. ncbi. nlm. information Publicly available at Heintzman PD, Ca-hill JA, Kapp JD, Stiller M, Shapiro B Shapiro B 2017 Nuclear DNA sequences from 17 Haringtonhippus francisci fossils from YG 160. 8 https://www. ncbi. nlm.