dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Identifying the molecular mechanisms underlying tolerance to abiotic stresses is important in crop breeding. A comprehensive understanding of the gene families associated with drought tolerance is therefore highly relevant. The NAC gene family is one of the largest groups of plant transcription factors (TFs), which is known to regulate biotic and abiotic stress-responses such as osmotic stress and various plant developmental processes. NAC proteins are plant-specific TFs, and the NAC family has been recently reviewed by  Puranik et al. (2012). NAC genes were originally characterized in a petunia NAM mutant  (Souer et al. 1996)  and then in Arabidopsis CUC  (Aida et al. 1997 ) and ATAF mutants (GenBank accession numbers X74755 and X74756). Two parts can be distinguished in the structure of NAC proteins: the NAC domain (InterPro IPR003441), in the N-terminal region, subdivided in five well-conserved subdomains (A-E); and the transcription regulatory regions (TRRs), in the C-terminal region, which is very variable in sequence and in length.
Sequence retrieval The sequences of NAC protein-coding genes were extracted from gene family database GreenPhylDB  (Rouard et al. 2011)  based on the presence of the NAM (No apical meristem) InterPro domain (IPR003441). Excluding splice forms, 82, 111, 146 and 172 sequences were selected for V. vinifera,  A. thaliana, O. sativa, and M. acuminata, respectively. Datasets from V. vinifera, A. thaliana, and O. sativa were compared with sequences analysed in previous studies. No additional NAC members were found for V. vinifera [74 members  (Wang et al. 2013) ], although only 69 sequences were considered in this study. Five sequences were excluded from the analyses: VvNAC35 and VvNAC38 because they are not NAC but VOZ transcription factors; VvNAC72 because it was identified as a cellulose synthaselike protein G3; and VvNAC50 and VvNAC66 because they are likely pseudogenes.
3 Expert annotation of NAC sequences Since the M. acuminata NAC sequences were the result of an automatic annotation, an expert revision of the gene structure of this family was conducted. In order to detect additional InterPro protein domain IPR003441 not included in the annotated genes, a tBlastn search was performed on the M. acuminata pseudo-chromosome sequences with a sample of NAC genes. Banana sequences were curated using the Banana Genome Hub  (Droc et al. 2013 ) and more specifically with the Artemis software  (Carver et al. 2008)  connected to the Community Annotation System  (Guignon et al. 2012). Modifications of the gene structures were based on transcripts, when available, and protein similarity with published NAC genes of other plant species. The revised annotations are publically available via the Banana Genome Hub.
Some modifications of published NAC annotations for A. thaliana, V. vinifera and O. sativa were also performed. The modified sequences are reported in Online Resources 1 and a concise description of modifications is provided in Online Resources 2. Analysis of NAC duplication in Musa genome NAC genes separated by not more than 10 other genes were considered as tandem duplications. NACs generated by segmental duplication of the Musa genome were determined by the analysis of the output file (Online Resources 3) of the CoGe SynMap program (http://genomevolution. org/CoGe/SynMap. pl) obtained using a default parameter  (Lyons et al. 2008).
Number of NAC genes in M. acuminata genome Among the 36,542 automatically predicted genes in the M. acuminata genome, 172 contained the InterPro protein domain IPR003441. To improve their structural annotation, all these sequences were manually curated, as necessary. Additional genomic regions containing the domain IPR003441 were also detected and analyzed. Globally, our analysis resulted in 167 potentially functional NAC genes (Online Resources 1) that we retained for this study. Five pseudogenes and a number of remnants were also found in the M. acuminata genome. We then refined the selection of NAC sequences for other species, taking into account previous publications dedicated to NAC transcription factors (see "Methods").
Expert orthologous grouping of NAC sequences The V. vinifera NAC sequences were chosen as a reference for two main reasons: (a) V. vinifera proteins appear to have a slower evolution rate than other dicots  (Cenci et al. 2010; 2013; Yue et al. 2010) , and (b) its genome experienced the lowest number of WGDs after the monocot/dicot lineage divergence  (Bowers et al. 2003; Jaillon et al. 2007; Tang et al. 2010; D'Hont et al. 2012; Paterson et al. 2009 ) which is the probable main reason for its low number of NAC members. Among the 103 A. thaliana NAC members, 19 displayed very low Blastp scores (lower than 170) with the best hit of the V. vinifera protein database and with the other two analysed species. Consequently, only the 84 NAC members (showing Blastp score higher than 250) were considered for the grouping.
These differences could be explained by independent evolution of the genes (gain or loss of introns), but also they could be the results of erroneous annotations. In fact, the annotation of these genes is difficult in the very variable C-terminal region, due to lack of similarity with other model genes. Moreover, the level of expression of these genes is often low or tissue/ condition specific  (Wang et al. 2013) , which reduces the representation of NAC genes in transcriptome databases. All the sequences in each of the 40 OGs are supposed to derive from an individual ancestral gene that was present in the most recent common ancestor of monocot/dicot species. Most of the group assignations look robust. However, for some more divergent sequences, the assignation remains unreliable and further analysis involving improved annotations or additional species could modify the assignation of these sequences.
Potential transfer of functional gene annotation The NAC OGs are supposed to include most of the NAC copies that have evolved from an ancestral copy existing in the most recent common ancestor of monocots and dicots. It is likely that most of these ancestral genes already had their functions, and that these functions were maintained in the derived species during the independent evolution. The framework of the NAC OGs should provide a useful tool to predict the best candidates for a given function in species for which information is available. For example, the sequence database provided in Online Resources 1, the NACs of four species representative of monocots and dicots, could be used to classify any NAC sequence of a given species by a simple Blastp analysis. In particular, NACs involved in abiotic stress-resistance or tolerance could be predicted for newly sequenced genomes such as M.
The language used to describe protocols is also considerably restricted compared with natural language used in other texts. As such, this apparent 'simplicity' would be expected to offer a consistent and reproducible means for presenting protocols. We then updated the previous version of EXACT  [7]  using the entities and relation identified to be relevant to the representation of experimental procedures. We did this using the ontology editor Protégé 4. 3, and the reasoner HermiT 1. 3. 8. to detect inconsistencies. There are a number of substantial changes in EXACT2 compared to EXACT (see the summary of changes in the results section), but the most important is the addition of descriptors to each experimental action. The reason for this is that it is not possible for biologists to safely reproduce a biomedical procedure without knowing the values of such descriptors as temperature, equipment, duration of experimental actions.
Following OBO Foundry recommendations (see  [8] ), EXACT2 imports classes and relations from external resources. We employed the principle of MIREOT (Minimum Information to Reference External Ontology Terms) for consistent reference of external terms  [9]. MIREOT requires the inclusion of the following information: (1) source ontology URI (Unique Resource Identifier); (2) source term URI; and (3) target direct superclass URI. We used the OntoFox web application to import external terms to EXACT2  [10]. In total, 25 terms were imported into EXACT2 from BFO, OBI, IAO, PATO (see the section below for more detail). EXACT2 is encoded in OWL-DL, a language widely used by the research community and recommended by OBO Foundry. We used github for versioning and depositing EXACT2. The latest version of the ontology and files with external imports are available at: https://github.
Future work: petri nets for the representation of experimental workflows Ontologies are well suited for the representation of declarative and static domain knowledge, but they generally struggle with the representation of complex sequences of events unfolding in time. However there is a need in the integration of a rigorous logical representation of key experimental steps, with the temporal sequence of those steps, so as to capture the explicit semantics of whole experimental procedures. There were several attempts to integrate EXACT with the representation of experimental workflows. In the original work on EXACT  [7]  we encoded protocols in Python. This approach to the representation of workflows was not popular with biologists. Maccagnan et al  [27]  developed the COW (Combining Ontologies with Workflows) software to formalize workflows built on ontologies.
Software such as Statistical Parametric Mapping (SPM;  Friston et al. 1995; Frackowiak 1997) , FSL  (Jenkinson et al. 2012) , BRAINS  (Pierson et al. 2011)  and FreeSurfer  (Fischl et al. 2004 ) among many other tools, were widely distributed over the internet. This made it feasible to analyze neuroimaging data and compute standardized measures from brain scans in a consistent and agreed way, albeit with methods that are continually refined.
1993 ). The Talairach atlas was among the first to compile a coordinate-based reference system, and it allowed researchers worldwide to relate their findings to existing data collections. In the mid-1990s, a group in San Antonio developed the "Talairach Daemon", allowing electronic pooling of findings from brain mapping studies based on their coordinates in Talairach space. In addition to the use of standard anatomical templates for reporting results, this coordinate system opened the door for clinically-oriented consortia to scan and analyze large-scale patient populations in a consistent way. The rapid development of nonlinear registration methods also made it possible to improve the alignment of new datasets to digital anatomical templates, for coordinate based reporting of results. The Alzheimer's Disease Neuroimaging Initiative (ADNI), for example  (Weiner et al.
But a much more adventurous goal provided the driving force behind the new and emerging fields of imaging genomics. This goal was to use neuroimaging data directly, to screen the genome for common variants that might affect the brain. In other words, rather than using the images in secondary studies of what disease risk genes do, images could be screened to discover important genetic associations. (Instead of imaging genetics, the somewhat interchangeable term "imaging genomics" is also used; genomics tends to refer to any method that directly assesses variation in the genome, as opposed to studies that may assess a single locus only, or simpler family studies that may not even analyze DNA). The growing computational power to screen very large neuroimaging datasets-for the purpose of extracting meaningful features from them-made this an interesting and achievable objective.
98), amygdala (r =. 91), and intracranial volume (r =. 99) for a cohort of data collected across investigators and matched scanners. Nugent et al. (2012)  also studied the inter-scanner reliability of the FIRST software for segmentation  (Patenaude et al. 2011) , and found it to be high. However, it is overly optimistic to always expect high reproducibility from automated segmentations of brain MRI, and the reproducibility is region specific. For example, both FSL and FreeSurfer tend to do less well in segmenting small structures relative to larger structures. There are also differences in accuracy and reliability among different methods for automated segmentation of the brain  (Shokouhi et al. 2011). Cortical thickness and other local gray matter density measures can show reduced reliability owing to sensitivity to image contrast variability, which becomes particularly challenging in multicenter studies  (Schnack et al.
40×10 -9 );  (Paus et al. 2012). Furthermore, genotype in the top hit (rs716890) interacted with prenatal exposure to maternal cigarette smoking vis-à-vis cortical area and cortical folding; in exposed girls only, this genotype explained ~21 % of variance in the cortical area  (Paus et al. 2012). The ADNI work analyzed a publicly available dataset (adni. loni. usc. edu) for which MRI scans and GWAS data could be freely downloaded from approximately 800 people. The ease of access of the ADNI data, and the principle of broad dissemination of the genomic data with fairly few restrictions led to over 100 genetic studies of the ADNI data, many of them using GWAS designs  (Saykin et al. 2010). The ADNI dataset stimulated work in imaging genetics, as evidenced by the large number of published papers using the data (all ADNI genetics studies from the period 2009-2012, are reviewed by  Shen et al.
2012). The power obviously also depends on the genetic architecture of the trait-key factors are the large number of effectively independent LD regions in the genome (regions of linkage disequilibrium, with correlated SNPs) and the number of detectable causal loci per phenotype (which is typically small). The first series of studies identifying genetic markers for MRI phenotypes that replicated across independent samples, used a Norwegian discovery sample and replicated the findings in the ADNI or PING cohorts  (Joyner et al. 2009; Rimol et al. 2010; Bakken et al. 2012). Even for high frequency variants, neuroimaging databases of 1,000 subjects would be underpowered to detect commonly observed effect sizes. We must bear in mind that, in the standard experimental design (see below for others), the significance of a genome-wide hit has to be at least 20 million to one, to account for the very large number of variants tested.
2012)  In December 2009, a group of researchers expert in large-scale neuroimaging or large-scale genetics studies formed a network called "Enhancing NeuroImaging Genetics through Meta-Analysis" or ENIGMA. The goal of the effort was to bring together researchers with genome-wide data and images, to meta-analyze evidence in neuroimaging datasets worldwide (see Fig. 2 ). In  Stein et al. (2012) , the ENIGMA consortium, in collaboration with another multi-site consortium (CHARGE;  Seshadri et al. 2010; Fornage et al. 2011; Bis et al. 2012; Ikram et al. 2012 ) reported that the mean bilateral volume of the hippocampus was significantly associated with the intergenic variant rs7294919 (see Forest plots, Fig. 3 ). The genotype at that locus was also nominally associated with expression levels of the positional candidate gene TESC in adult human temporal lobe tissue, based on the UCL database of brain tissue resected during epilepsy surgeries.
Practical considerations and opportunities The first ENIGMA project demonstrated the feasibility of discovering statistically significant effects of "single letter" genomic differences in brain data worldwide, using imaging and genetic data collected using diverse protocols. Novel, harmonized data analysis and meta-analysis protocols were vital to the success of this project. First, all genotypic data was imputed to the HapMap3 reference dataset to correct for diversity of the genotyping chips (this reference was subsequently updated to the 1,000 Genomes dataset for ENIGMA2, which began in May 2012). The imputation protocols (detailed at enigma. ini. usc. edu) and the standard reference datasets allow the consistent reporting of genotypes at the same set of genetic loci across cohorts. Imputation effectively adds prior knowledge to the data and may in this way also increase the power of a study.
Not all ENIGMA cohorts are Caucasian: the GOBS cohort consists of Mexican-Americans and the NIMH cohort contains a significant number of African-Americans. As in any GWAS, population structure is taken into account during the statistical modeling of associations, to ensure that differences in SNP frequencies with ancestry are not picked up as spurious associations. Also in the imputation step, the appropriate reference populations are used for each individual, which may in some cases be Yoruban or Hispanic, as well as the CEU population that is used to represent Caucasians. However, it is not a computationally trivial task to impute very large samples of data from so many subjects. The successive refinement of reference panels means that re-imputing the same dataset to the most current standards is likely to further boost power.
Among these tests, we demonstrated that different, widelyused programs to measure regional brain volumes produced consistent results on a broad range of cohorts worldwide, based on data from young and old samples and mixtures of both  (Stein et al. 2012 ; see supplement comparing FSL and FreeSurfer segmentations). It was not feasible to require all sites to use one specific program to measure brain regions; as might be predicted, no single algorithm performed best for quantifying brain volumes across all datasets. Fourth, ENIGMA did not use the "mega-analysis" model adopted by the Psychiatric Genomics Consortium, where all phenotypic and genotype data are sent to a centralized site for analysis. Instead, ENIGMA uses a "meta-analysis" concept: GWAS was run locally using pre-agreed covariates-often with and without adjustments that may affect interpretation (e.
This also encourages maximum participation as each site retains fiduciary responsibility for their data and its curation and integrity. Nevertheless, there is a growing perception of the community that data sharing is one fundamental building block of reproducibility in science, and a rapidly expanding number of imaging datasets are being shared thus facilitating discoveries. New ways of acknowledging data acquisition are being developed concurrently  (Poline et al. 2012). However, there are also disadvantages associated with the meta-analysis approach. For instance, it is more challenging to perform meta-analyses for more complex and potentially more informative analyses such as (1) polygenic scoring, which determines how much of the phenotypic variance can be explained by common SNPs in aggregate  (Purcell et al.
The current dataset also allows targeted studies of individual genes, enabling research groups, even if they are Fig. 3  Forest plots from the ENIGMA1 study (adapted from  Stein et al. 2012). Forest plots are a graphical display designed to illustrate the relative strength of an effect in different cohorts. In the left panel, we show the effect of the genetic variant at rs7294919 on the hippocampal volume, in a range of cohorts in ENIGMA. In ADNI, for example, the confidence interval on the effect overlaps zero, which means that there is no evidence to reject the hypothesis of no effect, if only that cohort were considered. The "ENIGMA Discovery" line combines the effects of all cohorts above it. At the bottom of the figure, the meta-analysis of all effects above the line includes data from another large consortium, CHARGE, and several replication samples.
ENIGMA-CHARGE genome-wide meta-analysis Researchers from the ENIGMA and CHARGE consortia have recently joined forces to perform a genome-wide meta-analysis of hippocampal and intracranial volume using updated versions of the data analyzed in ENIGMA1. Specifically, both CHARGE and ENIGMA will be meta-analyzing GWAS results from the much more densely sampled 1,000 Genomes reference set; rather than exchanging only the top hits, the entire genome-wide data is being meta-analyzed. In addition, the ENIGMA project will be contributing updated results from many new samples who have joined the effort since the completion of the ENIGMA1 project. This analysis is currently ongoing. ENIGMA-DTI is a Working Group developing a harmonized protocol for analyzing DTI data for GWAS meta-analysis  (Kochunov et al.
Initial studies suggest that a proportion of the variance in fiber integrity can be predicted from common variants  (Kohannim et al. 2012; Jahanshad et al. 2012 Jahanshad et al. , 2013c;; Thompson and Jahanshad 2012; Braskie et al. 2012; Sprooten et al. 2013). Genome-wide screens of connectome data have also been successful, and have shown the benefit of ranking connections by their heritability, prior to entering the genome-wide screening phase  (Jahanshad et al. 2013b; Thompson et al. 2013). ENIGMA has not yet attempted a multi-site genetic study of brain connectivity. Agreement on a protocol depends on ongoing harmonization of DTI analysis across ENIGMA sites, which is being finalized by the ENIGMA-DTI working group  (Jahanshad et al. 2013a ). The harmonized DTI analysis will also allow a multi-cohort examination of the association between white matter integrity and general cognitive ability across the life course from late adolescence to old age (see also  Penke et al.
ENIGMA Disease Working Groups An implicit goal of ENIGMA is to see whether genetic variants impact the brain in a way that affects disease risk. In fact, a number of imaging genetics papers have identified genetic variants that appear to affect the brain and behavior;  Hall et al. (2006) and McIntosh et al. (2008)  reported a variant in the neuregulin 1 gene that was associated with abnormal cortical function, altered white matter integrity, and with psychosis, and there are many other examples of variants with wide-ranging effects. Around a third of the data in ENIGMA is from patients with psychiatric illness, so once ENIGMA1 was complete, a large volume of new data could be brought to bear on the question of brain differences in a variety of disorders. To make connections between psychiatric risk genes and brain measures, it may make sense to prioritize brain measures with robust case-control differences.
ENIGMA offers a promising framework to rank brain measures in order of their effect sizes for case-control differences; these effects could also be further weighted based on their genetic correlation with the illness, to give another ranking. Nor is it expected that the disease should have identical effects on the brain in all cohorts; the variety of participating cohorts in ENIGMA makes it possible to dig deeper into medication-related, or even geographic or demographic factors to explain why brain differences vary so drastically across different studies (see Fig. 5  for the locations of ENIGMA sites in disease-related working groups). In a meta-analysis of data from 1,136 patients and 1,401 controls, the ENIGMA-Schizophrenia Working Group found that hippocampal volume gave among the highest effect sizes for any subcortical brain structural difference in schizophrenia % heritable (100*h 2 ) Fig.
4  A meta-analysis of tract-wise heritability, by the ENIGMA-DTI working group, showed most tracts in the brain are moderately to highly heritable across cohorts of different ethnicities, even though they were imaged with different parameters. The "skeleton" of the white matter, reconstructed using a widely used DTI analysis program called "tract-based spatial statistics" (TBSS;  Smith et al. 2006) , is shown in purple for reference. The corticospinal tract (in light blue) was the least heritable region of interest and therefore, will not be carried forward as a phenotype in an initial GWAS of DTI-FA measures. Other methods for phenotype selection and prioritization are summarized in Table 1  (Turner et al. 2013; van Erp et al. 2013). In a related metaanalysis of structural MRI data from 1,022 patients and 1,415 controls, the ENIGMA-Bipolar Disorder Working Group found consistent differences across the subcortical regions, but in a different pattern than that characteristic of schizophrenia  (Hibar et al.