dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We kindly thank the anonymous reviewer of this work for useful comments. Useful discussion with  Richard Ellis ,  Rychard Bouwens , and  Larry Bradley , are much appreciated. This work is based on observations made with the  NASA/ESA Hubble Space Telescope  and was supported by award  AR-13079  from the  Space Telescope Science Institute (STScI) , which is operated by the  As-sociation of Universities for Research in Astronomy, Inc. under  NASA  contract  NAS 5-26555. It is also based on observations from the Spitzer Space Telescope which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. This work utilizes gravitational lensing models produced by P. I. s  Zitrin & Merten , and  P. I. Ebeling , funded as part of the  HST Frontier Fields program  conducted by  STScI.
Over 80% of the most significant single nucleotide polymorphisms (SNPs) associated with human phenotypes and disease occur within non-coding regions of the genome  (Hindorff et al. , 2009). Recent integrative analyses have shown that open chromatin regions obtained for a specific cell type (e. g. , DNase I hypersensitivity sites in T-cells) are enriched for reported GWAS SNPs. Importantly, this GWAS enrichment appeared most significant when the DNAse data was ascertained in a cell type relevant to the phenotype studied  (Maurano et al. , 2012; Reddy et al. , 2012; Schaub et al. , 2012). Examples of regulatory DNA mutations that explain differences in disease gene function are increasingly being discovered (e. g. ,  Musunuru et al. , 2010)  and there is tremendous interest in methods that can predict which non-coding variants are of functional consequence  (Schaub et al.
The most significant enrichments that were unique to the shared liver CRMs include: liver disease (Binomial FDR q-value = 4. 53 × 10 -130 ) from the Disease Ontology database and metabolism of lipids and lipoproteins (q = 2. 96 × 10 -73 ) from MSigDB Pathway (Figure  3A ,B, Figure  3 -source data 1A). The most significant liver-related enrichments obtained using human only CRMs were for biological oxidations (q = 1. 95 × 10 -39 ; in MSigDB Pathway). These enrichments were driven by genes involved in the metabolism of xenobiotics by the cytochrome P450 gene family (q = 2. 24 × 10 -19 ; HGNC gene family database) (Figure  3A ). Given the liver's major contribution to the detoxification of xenobiotics and the well established species-specificity of the proteins involved in the process  (Gonzalez and Nebert, 1990) , these results suggest that evolutionary filtering of CRMs has the potential to enrich for both conserved and species-specific biological pathways.
g. , 56% for singletons vs 33% for CRMs for the stringent cutoff). This trend was observed for all four TFs in this study (Figure  4 figure supplement 1, Figure  4 -source data 1). Our results in primary liver tissue are supported by an integrative analysis of ENCODE cell lines, which showed that active chromatin states are depleted of regulatory motif instances relative to all regions bound by a given TF  (Ernst and Kellis, 2013). After collapsing the four TFs into CRMs, there were still over 40,000 singleton TF binding events. We asked if shared singletons show any distinct genomic properties. Shared singleton TF binding events show high DNA constraint (Figure  3G ), and a larger fraction are found close to the transcription start site of annotated genes compared to the CRMs shared in the equivalent number of species (Figure  3H ).
Unlike the equivalently shared CRMs, shared singleton TF binding events gave with ChIP-seq data in macaque, dog, mouse, and rat. Singletons and CRMs were considered shared if they overlapped at least 10 bp with another TF bound region in the EPO multiple sequence alignment (MSA). (B) Experimentally determined combinatorial binding at the blood coagulation F7 locus. Raw sequencing reads from ChIP-seq experiments: CEBPA (red), HNF4a (green), ONECUT1 (yellow), and FOXA1 (green) are overlaid and called peaks are displayed for each species. ChIP-seq determined TF binding events were assembled into CRMs (black bars) underneath the enriched regions (peaks). Grey lines are drawn to illustrate shared CRMs using the EPO-MSA. DOI: 10. 7554/eLife. 02626. 008 The following source data is available for figure  2:  Source data 1.
4 × 10 -16 ) (Figure  5A ). This striking p-value is due to several CRMs being found close to highly expressed liver genes including albumin, fibrinogen (FGA, FGB, FGG), and several acute phase response genes (e. g. , CRP, SAA1 etc). We therefore broke down each CRM and singleton by transcription factor, which still revealed a significant difference between genes close to Deeply shared CRMs relative to singletons (p < 1 × 10 -3 ; Figure  5-figure supplement 1 ). Using a reference transcription data set that comprises RNA-seq data for liver and 15 additional human tissue types (E-MTAB-513), we confirmed the above observation and found that the gene expression association with liver CRMs, and to a lesser extent singletons, was tissue-specific (Figure  5B ). In sum, Deeply shared CRMs are associated with genes that are highly expressed in a distinctly liver-specific manner.
20 × 10 -9 ; 2. 4-fold enriched; Figure  7 -figure supplement 1, Figure  7 -source data 1B). These enrichments were not found when using two distinct null models (Figure  7 -source data 1C,D). We then asked if any specific disease traits, as written in the NHGRI GWAS catalog, were enriched for shared CRMs. We found that for the 2. 5 kb window analysis, only LDL cholesterol significantly enriched for Deeply shared CRMs (p = 0. 037; 4. 54-fold enriched; Figure  7 -source data 1E), whereas the LD window analysis revealed 11 disease traits that were enriched for Deeply shared CRMs (see Table  1 ; Figure  7 -source data 1F). For example, enrichments driven by lead SNPs for LDL cholesterol (blood lipid category, p = 4. 84 × 10 -5 ; 3. 7-fold enriched) involved several loci including TRIB1, ABCG8, APOB, SORT1, TOMM40, APOA5 and HNF1A.
Lead SNPs for fibrinogen (liver category, p = 3. 40 × 10 -3 ; 3. 4-fold enriched) occurred in LD with the fibrinogen locus. C-reactive protein (liver category, p = 1. 17 × 10 -4 ; 4. 0-fold enriched) enriched for Deeply shared CRMs near CRP itself in addition to RORA, MLXIPL, HNF1A, BAZ1B, and IRF1 loci (Table  1 ; Figure  7 source data 1G). In order to explore the functional relevance of these findings, we looked for annotated regulatory SNPs in the RegulomeDB database  (Boyle et al. , 2012)  within the 1020 CRM or single TF regions we found to be within 2. 5 kb of a lead GWAS SNP. Of these 1020 regions, 753 contained at least one variant, 90% of which showed evidence of regulatory potential in RegulomeDB (Supplementary file 2). In particular, 317 of these 753 regions had TF binding in orthologous regions in additional species, making them rational candidates for future functional exploration.
(B) Alignment of FOXA1 de novo ChIP-seq motif to the HNF4A motif. Motif comparison (alignment) was performed using compare-matrices from RSAT. The program calculates the correlation between two matrices shifting positions; the correlation is normalized based on the width of the alignment to avoid high correlation based on few flanking positions. DOI: 10. 7554/eLife. 02626. 012 The following source data and figure supplement are available for figure  4 : Source data 1. Comparison of motif matches between CRMs and singletons. Chi-square test for differences between the number of peaks associated with CRMs and singletons, for each TF, that contained at least one predicted motif using three different p-value thresholds for motif scanning: stringent (10 -4 ), moderate (10 -3 ) and lenient (10 -2 ).
Blue shadows highlight siginficnat p-values. DOI: 10. 7554/eLife. 02626. 013 To further investigate the functional role of shared CRMs and to test the hypothesis that disruption of conserved combinatorial binding can lead to human disease, we overlapped our CRMs and singletons data with manually curated regulatory mutations directly linked to human disease (Human Genome Mutation Database Professional version; HGMD)  (Stenson et al. , 2012). This database contains the most comprehensive set of curated and functionally validated mutations that have occurred in human regulatory DNA regions and have led to a change in disease gene expression. A total of 157 genes associated with regulatory mutations overlapped our human ChIP-seq data, 106 of which were in our CRMs (Table  2 ; Supplementary file 3).
We recently demonstrated that ONECUT1 binds to the -6 site of F9 in human and mouse  (Funnell et al. , 2013). Inspection of the multiple species alignment suggests the same arrangement and spacing of CEBPA, HNF4A, and ONECUT1 motifs in the human and macaque CRMs, whereas the mouse and rat ONECUT1 motifs are predicted to begin three base pairs upstream. In this analysis of known functional and disease causing regulatory mutations from the HGMD database, it is worth noting that most of the examples we found overlapping Deeply shared liver CRMs resided close to the TSS. As most TF binding of the factors we profiled occur outside of proximal promoters, it is likely that many more human mutations that regulate genes through long-range interactions remain to be found. Just as gene sequencing has uncovered a diverse array of mutations for most disease genes, we expect that disruption of conserved TF bound regions will be found to have pathological consequences.
(Fragments Per Kilobase of exon per Million reads) was recorded. In contrast to Singletons, TFs in Deeply CRMs are associated with highly expressed genes (adjusted p-values shown). The numbers of target gene associations for the singletons and CRMs in categories 1 to 5 are: 19354(S), 32706(CRM); 6325(S), 14669(CRM); 1935(S), 5755(CRM); 1005(S), 3292(CRM); and 459(S), 2530(CRM). (B) Comparison with CRMs and Singletons to a reference mRNA-seq data from 16 human tissues (E-MTAB-513) further shows that relative to singletons, liver-specific CRMs are highly expressed in liver, and that each TF contributes to this specificity. The number of gene associations for each category in the liver data is shown in white text within the heat map. DOI: 10. 7554/eLife. 02626. 015 The following figure supplement is available for figure  5 :.
The top five enrichments for all comparisons performed were collected and the enrichments, if available are plotted. Databases used for GREAT enrichment analyses are indicated by color and are ranked according to the -log10 binomial FDR q-values plotted on the x-axis. Significant enrichments are labelled with an asterisk which is sized according to fold enrichment of the given database category. DOI: 10. 7554/eLife. 02626. 017 Figure  6. Continued Figure  7. Using a window of ±2. 5 kb, lead SNPs obtained by GWAS were enriched for shared CRMs in a tissue/ disease specific manner. Heatmap representation of the -log10 of Bonferroni corrected p-values from hypergeometric testing for enrichment of CRMs or single TFs (broken down into categories related to their degree of conservation) by lead GWAS SNPs obtained from the NHGRI catalog  (Hindorff et al.
, 2009). The NHGRI catalog disease traits were summarized into 25 categories prior to enrichment. Each GWAS lead SNP was given a ±2. 5-kb window prior to identifying overlapping CRMs/singletons. DOI: 10. 7554/eLife. 02626. 018 The following source data and figure supplements are available for figure  7 : Source data 1. Full tables of 2. 5 kb and LD GWAS enrichments performed in Figure  7   locus is disrupted need to be identified. Finally, because transcriptional regulatory networks are highly tissue-specific, regulatory regions can only be accurately done within the tissue concerned. In this study, we demonstrated that genomic regions harboring shared combinatorial TF binding were enriched in a tissue-specific manner for essential biological pathways, common DNA variants associated with complex traits, and regulatory DNA mutations associated with rare diseases.
In addition to the requisite measures of ChIP-seq data quality and reproducibility, it could be argued that combinatorial binding of transcription factors, relevant to the cell type of interest, increases the likelihood that the region identified is biologically important. However, more than half of the human liver TF binding sites detected in our study are in CRMs. In many cases, this makes CRM clustering on its own an insufficient filter for prioritizing TF binding sites for further study. Given the rapid turnover of TF binding within a mammalian lineage  (Stefflova et al. , 2013) , requiring combinatorial binding to be shared in multiple mammals is a strict filter that provides evidence that these biochemical interactions are under selection, even if the percent identity in the sequence alignments themselves is not exceptional.
Just as using DNA constraint as a sole criteria for enhancer finding has its limitations, using filtering approaches that require conserved combinatorial binding will also miss important regulatory events. Shared CRMs link TF binding with disease-causing variants in coagulation and lipid regulation in the liver. Human CRMs that had TF binding in syntenic regions in at least two additional species (n = 5046) were intersected with the HGMD database. All protein coding genes associated with a regulatory mutation were analysed. Relationships among these genes were investigated and a representative analysis obtained using GeneMANIA ('Materials and methods'). Genes (large grey circles) are connected by pathways and proteinprotein interactions are shown. The smaller white circles are genes predicted by GeneMANIA to be in the network.
, 2010; E-TABM-722) ; ChIP-seq for Hnf4a, Cebpa, and Foxa1 in C57BL6/J mice (E-MTAB-1414), as well as for Brown Norway rat (E-MTAB-1415)  (Stefflova et al. , 2013) ; Human and mouse ONECUT1 ChIP-seq (E-MTAB-890)  (Funnell et al. , 2013) ; and RNAseq data for all five species  (Kutter et al. , 2011; E-MTAB-424). To facilitate analysis of this data set by others, all ChIPseq files used here have been deposited in ArrayExpress under a single accession number (E-MTAB-1509). We filled genomic gaps in the coding regions for dog FOXA1 and macaque ONECUT1 and deposited them under NCBI accessions numbers JN601139 and JQ178331 respectively. Illumina BodyMap data were obtained from E-MTAB-513.
Search for secondary motifs was done using peak-motifs from RSAT (Thomas-Chollier et al. , 2011) with default parameters. peak-motifs uses a word count approach which enables users to input large sequence sets and recover motifs in a short time, in contrast with most alignment based approaches like MEME. CRMs and Singleton binding regions were classified in accordance to their conservation status, and these separated sets were used as input. Output motifs were compared with motifs discovered using MEME (see above) and external motifs coming from JASPAR (Vertebrate core 2009 version)  (Sandelin et al. , 2004)  and UniPROBE  (Newburger and Bulyk, 2009) (2009 version)  databases, in order to assign identity to the discovered motifs. Motif comparison was performed using compare-matrices from RSAT.
Interspecies overlap of transcription factor binding and CRMs Shared binding events and CRMs were identified using the 9-way Enredo-Pecan-Ortheus (EPO;  Paten et al. , 2008)  multiple sequence alignments from the Ensembl Compara database  (Flicek et al. , 2010). We performed these comparative analyses requiring different overlaps within the MSA criteria (1 bp, 10 bp, 25 bp, and 50 bp) (Figure  1 -source data 1G). This range of parameters did not overtly affect the number of shared CRMs we retrieved. For the results presented, we define a CRM (or single TF) occupied region to be shared if it overlaps at least 10 bp of a CRM (or single TF) occupied region in a second species. See Figure  2 -source data 1 for all the CRMs and singletons along with their phylogenetic categories.
Liver RNA-seq data analysis Liver RNA-seq data from the same species were used to analyze if CRM status and conservation were related to gene expression levels. Polyadenylated mRNA levels were assessed using previously described RNA-seq data (E-MTAB-424;  Kutter et al. , 2011). Briefly, trimmed 36 bp reads were aligned using TopHat and FPKM estimates were obtained using Cufflinks  (Trapnell et al. , 2012)  for all features annotated in Ensembl 52. Individual TF binding events and CRMs were associated to the nearest gene. The number of shared species for individual TF binding events and CRMs were recorded and associated with expression data.
Illumina Body Map analysis We retrieved raw FASTQ files from ArrayExpress (accession E-MTAB-513). We first filtered these data with prinseq-lite (v0. 15) and fastx (v0. 0. 13): we trimmed both read ends with a quality threshold of 20, and we then discarded those reads that were shorter than 35 nucleotides, had more than 5% of Ns, a dust score bigger than 10 (i. e. , low complexity reads), and more than 5% of the bases with a quality inferior to 30. This subset of high quality reads was then mapped to the genome using BWA (v0. 5. 9, default options). We retrieved mapped reads and filtered them further by removing reads with a mapping quality below 20 (including multireads). In the case of paired-end data, we also removed all reads that were not properly paired. Finally, we calculated exon expression levels using htseq-count (HTSeq v0.
5. 3p3) and averaged those to produce gene expression estimates. TF binding sites not found in CRMs (Singletons) or within CRMs (Modules) were associated with the nearest gene. The FPKM (Fragments Per Kilobase of exon per Million reads) for each gene was recorded (obtained from the Human BodyMap 2. 0 dataset from Illumina) and displayed as a heatmap (Figure  5 ).
Comparison to ENCODE data We first compiled data relevant to transcriptional regulation from ENCODE, for all cell lines. For each ENCODE data set, we then calculated its observed overlap with each of our ChIP data sets (i. e. , 'Deeply shared CRMs', and 'Shared Singletons') by counting the number of regions that overlap by at least 1 bp. We then built a distribution of expected overlap values from 1000 iterations of randomizing the input regions, by shuffling each one into a random genomic region chosen from among the DNaseI hypersensitive regions in any cell type (i. e. , only shuffling them into possible regions of open chromatin, as a conservative estimate of possible regions where TFs might bind). The distribution of the overlap scores from the randomized data resembles a normal distribution, which we used to generate a Z-score and p-value for the observed number of peaks that overlap each real data set (see Supplementary file 1).
Overlap of shared CRMs with curated human regulatory mutations Human CRMs that had TF binding in syntenic regions in at least two additional species (n = 5046) were intersected with the Human Gene Mutation Database (HGMD; professional version). A representative network analysis of these genes was obtained using the GeneMANIA server using default parameters  (Warde-Farley et al. , 2010). (Figure  8 , Figure  8 -source data 1). Using this data and the Default parameters for its Pathway analysis, GeneMania drew upon information from six sources (five of which are within Pathway Commons) and the sixth being  Wu et al. (2010). For its physical interaction analysis, it utilized 190 data sources supported by five data sets from iRefIndex  (Razick et al. , 2008)  and one study in BioGrid  (Zanon et al.
Location and GERP analysis of CRMs and singletons To define the locations of each regulatory region, we sliced the genome into categories: TSS ±3 kb, intronic, intergenic, and exonic. We obtained all transcripts (exons and introns) defined by the Ensembl genebuild, keeping the longest features. Any regions falling within ±3 kb of a TSS was characterized as the TSS ±3 kb category. In the event of complex gene arrangements or regions bridging two categories, we prioritized the locations as follows: TSS ± 3 kb > intron > intergenic > exon. Conservation scores were obtained from the Ensembl Compara database  (Flicek et al. , 2010). Genomic Evolutionary Rate Profiling (GERP;  Cooper et al. , 2005)  score was used to calculate the conservation of each nucleotide in multi-species alignment The multiple alignment used to derive GERP score is the 29-way EPO alignment.
Figure8. Shared CRMs link TF binding with disease-causing variants in coagulation and lipid regulation in the liver. Human CRMs that had TF binding in syntenic regions in at least two additional species (n = 5046) were intersected with the HGMD database. All protein coding genes associated with a regulatory mutation were analysed. Relationships among these genes were investigated and a representative analysis obtained using GeneMANIA ('Materials and methods'). Genes (large grey circles) are connected by pathways and proteinprotein interactions are shown. The smaller white circles are genes predicted by GeneMANIA to be in the network. The 47 unique genes were associated into 35 clusters using DAVID (Huang da et al. , 2009). Eight Gene Ontology terms from the 35 clusters had an adjusted p-value of less than 0.
Acknowledgements We thank the  Cancer Research UK-Cambridge Institute cores: genomics (James Hadfield) and bioinformatics (Matthew Eldridge ) and the  BRU (Selina Ballantyne ) for support. We thank  Vinh Truong  for help with the GWAS null model implementation. We also acknowledge  Dani Welter ,  Fiona Cunningham ,  Sushmita Roy , and  Alan Moses  for helpful discussions.
However, the significant genetic effects show concordant (N = 6) as well as discordant effects (N = 9). Across all SNPs there was no significant excess of concordant vs discordant effects (37 vs 31, binomial p = 0. 27) or significant correlation of effect sizes (r 2 = 0. 03, p = 0. 18, Supplementary Table  10 ). Collectively, these findings suggest that while the fundamental electrophysiologic mechanisms underlying the SNP-QT relationships for some SNPs may be shared, many involve cell-type-specific effects and that a consistent general relationship between SNP effects on QT interval and QRS duration does not hold. Examination of the NHGRI GWAS database (Supplementary Note) revealed additional associations of our QT associated SNPs (or their close proxies with r 2 > 0. 8) at SCN5A-SCN10A with PR interval  15  , at MKL2 with age of menarche  16  and at FEN1-FADS2 with high-density lipoprotein cholesterol, triglycerides  17  , n-3 fatty acids  18  , fasting plasma glucose and HOMA-B  19  and alkaline phosphatase  20  (Supplementary Table  11 ), which may point to novel repolarization mechanisms or simply reflect independent (pleiotropic) effects of the same genetic variation in different tissues.
8) at the 68 SNPs at 35 loci in publicly available eQTL datasets from diverse tissues. Twelve QT interval loci are associated with variable expression of at least one gene in one or more tissues with high correlation (r 2 > 0. 8) between the top QT SNP and the top eQTL SNP (Supplementary Note, Table  1 , Supplementary Table  12 ). QT-associated SNPs are associated with expression of the nearest gene at loci including ANKRD9, ATP1B1, CNOT1, FADS1, LIG3, and TCEA3. The eQTL data help point to specific genes at these loci as a potential source of the repolarization association signal, presumably through regulatory variation. However, some loci are associated with expression of multiple genes. We did not observe a significant signature of eQTLs among the QT loci that implicate a specific tissue or cell type in an atlas of human and mouse expression (P>0.