dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental features vector data (ESRI shapefile format): input polylines layer (including cadastral data, roads and streams) and polygon layer (land cover), output polylines layer (the result obtained from the new GIS toolbox); raster data (Geo Tagged Image File Format): the digital elevation model (DEM); toolbox structure (tbx file format): Python scripts and Model Builder structure; All these data are defined in the polish spatial reference system PL 1992. Value of the data • The input dataset could be used to assess the degree of potential tree belt function availability in the north-west part of the Gen. Dezydery Chłapowski Landscape Park in Poland. • The data can be used for testing the TBM toolbox by a potential user and serve as a reference for the structure of any other dataset to be used in the toolbox.
Data This article presents a dataset (spatially referenced) over the Gen. Dezydery Chłapowski Landscape Park in Poland. It includes input data acquired from Head Office of Geodesy and Cartography in Warsaw and output data received from the Tree Belt Modeling (TBM) toolbox processing. The first part of the dataset contains vector layers (format: ESRI shapefile): polylines layer (including cadastral data, roads and streams) and polygon layer (land cover). The digital elevation model (Geo Tagged Image File Format) is also added to the first part. The second part contains the output vector layers obtained from all the five analysis modules described in the related article. The result of the TBM final analysis (elementary, semi-integrated and integrated level of the tree belt functions availability  [1] ) is also added to the dataset.
All these data are defined in the polish spatial reference system PL 1992. The input dataset was used for developing and testing the TBM toolbox incorporated to ArcGIS software. This toolset (tbx file for three versions of ArcGIS -10. 2, 10. 3, 10. 5) is available to download with this article. The toolbox was developed based on the editable Model Builder (MB; ArcGIS, ESRI) structure and partly on Python scripts.
Experimental design, materials and methods The TBM toolbox was developed using spatial data from the north-west part of the Gen. Dezydery Chłapowski Landscape Park (protected area) situated in western Poland. This part of the park was selected based on the assessment of the density of linear structures (watercourses, drainage ditches, roads, and plot boundaries) over the entire park. To reliably test the TBM tools, the area with the highest density of linear structures was selected. It was assumed that the dense network of linear objects concentrated within a small area is appropriate for checking the efficiency of the TBM tools because of higher probability of detection of possibly untypically distributed linear structures. The selected part of the park covers 5460. 95 ha of agricultural area (90.
93%) and 495. 19 ha of forested area (9. 07%). The total length of linear objects as potential places for linear woody features is 1023. 12 km. The presented toolset was based on the Model Builder (MB; ArcGIS, ESRI). MB facilitates the development of spatial models without the necessity of combining the typical developing software programs and advanced programming skills of a user  [2]. Moreover, MB enables a full automation of a complex data processing using existing ArcGIS tools. The MB formula gives the opportunity for editing geoprocessing schemes. In addition, the TBM toolbox contains Python scripts. This open source programming language allows to develop new software, tools and combine existing components in a computing environment, including the GIS software  [3]. It should be emphasized that the ArcGIS structure makes it more possible to compile many Python scripts and MB tools into one toolset that can be easily distributed and included in other software.
This aspect makes Python scripts available to both programmers and GIS users. The use of scripts and MB enables their integration with other GIS tools  [4] , improves computer processing efficiency and facilitates their implementation  [5]. The input data preparation tool from the first part of TBM toolbox (Fig. 1 ) was used for creating the input dataset (vector layer: all_lines). This layer was performed on the basis of the digital topographic  database (accuracy: 1:10 000), the digital cadastral database (accuracy: 1:5 000) and the digital hydrographic database (accuracy: 1:10 000). The second vector layer (land_cover. shp) was obtained from digital topographic database using the Select by attributes tool and the Field calculator tool in ArcMap. To prepare the Digital Elevation Model (DEM) on the basis of Airborne Laser Scanning data (density of point cloud: 4-6 points/m²), the LAS dataset toolbar and the LAS dataset to raster tool (Conversion tools in ArcMap) were used.
In the second part of the TBM toolbox, Python scripts were developed for the solar conditions analysis tool (Fig. 2 ) and the slope conditions analysis tool (Fig. 3 ). In these two cases, there was a need to develop new geoprocessing parameters. The three other tools in this part of the toolbox present MB structures solely based on existing ArcGIS tools: the prevailing  wind direction analysis (Fig. 4 ), the plot size analysis tool (Fig. 5 ) and the landscape diversity analysis tool (Fig. 6 ). The third part of the TBM toolbox is the tree belt functions geodatabase tool (Fig. 7 ) used for performing the semi-integrated and the integrated level of function availability. The TBM toolbox.
Acknowledgments This work was conducted with funding support from the  Uniwersytet Młodych Wynalazców  ( MNiSW/2014/DIR/609/UMW ),  research program  of the  Ministry of Science and Higher Education of Poland  and the  Leading National Research Centre (KNOW) -Poznan RNA Research Centre. The authors thank  Paulina Konieczna  and  Michał Antkowiak  for their advice and support for the project.
Our generative model is based on a forward model to compute average contact matrices, which are idealized, noise-free data. As shown in Figure  1a , each structure x k that is part of X gives rise to a smoothed contact matrix based on a threshold distance. The n contact matrices are summed together and multiplied with a scale factor α that is estimated from the data. We model deviations from these idealized data due to experimental noise, forward model inaccuracy and, in case of Hi-C data, data normalization with Poisson distributions whose rates are given by the idealized count data. Under reasonable independence assumptions, the joint prior distribution Pr(X, α|n, I) factorizes and we can formulate separate prior distributions for all states (x 1 ,. , x n ) and for the scale factor α. We model the physical interactions within a single structure x k by a coarse-grained beads-on-a-string model similar to our model for single-cell data  (23).
A unique feature of Bayesian data analysis is that it offers an objective, albeit computationally often challenging framework for model comparison  (25). Our belief in a particular choice of n is encoded by the posterior probability Pr(n|D, I) = Pr(D|n, I) Pr(n|I) Pr(D|I). Assuming that a priori all n are equally likely, the ratio of two choices for the number of states n and n can be written as  We implemented our method in a freely available Python package (http://bitbucket. org/simeon_ carstens/ensemble_hic). As a proof of concept, we first apply our inference approach to contact data simulated from two protein structures. We consider beads-on-a-string models of two protein and P (D|n = 2, I)/P (D|n = 3, I) > 10 160 (Figure  2c ). It is reassuring that while the evidence decreases for n > 2, models with an even number of states are preferred over models with a similar, but uneven number of states.
The reason for this is that for even n, half of the states adopt the conformation of the B1 domain and the other half adopts the conformation of the SH3 domain, thus having few superfluous parameters (Supplementary Figure  S1 ). For uneven n, on the other hand, there is at least one superfluous state (Supplementary Figures  S2  and S3 ), contributing uninformative parameters which are penalized in the evidence. We find that, for n > 2, the average scale factor α decreases monotonically with an increasing number of states (Figure  2f ), in agreement with our intuition that α compensates for the smaller number of simulated states as compared to typical numbers of counts in the data. Having established the validity of our approach on simulated data, we now apply our extended ISD approach to a 5C dataset covering the X-inactivation center in mouse embryonic stem cells (mESCs)  (5).
O We restrict our analysis to contact frequencies between primer sites within the two consecutive TADs by the data (Figure  3a ). This result establishes that Bayesian model comparison is indeed able to determine a unique, optimal number of states for 5C data. The low resolution of these simulations lets us expect limited biological meaning of the resulting structures. To prove that a larger number of beads and thus higher resolution yields biologically meaningful structures, we increased the number of beads to M = 308. Our model for the chromatin polymer then coincides with the model chosen in previous work  (20)  on the same dataset. In the following, we thus analyze high-resolution chromatin structure ensembles obtained from a posterior simulation for n = 20. Out of three simulations with identical parameters (but different, random initial state), we chose the one with the highest mean posterior probability.
, M }. In our structure calculations, we ignore sequential contacts (|i -j| = 1), because they do not contribute any new information which is not already encoded in the structural prior distribution described later. We also neglect data points with zero counts. A script implementing these steps starting from the raw 5C data (  5 ) is provided with the Python code implementing our approach. In Bayesian data analysis, the likelihood quantifies how well a model matches the data. It is convenient to decompose the likelihood into a forward model f , which back-calculates data D = f (X, θ) from the structure population X and the nuisance parameters θ, and an error model g, which maps the mock data D to the probability Pr(D|X, θ, I) = g( D; D) = g(f (X, θ); D). The error model accounts for both experimental noise and the error inherent in the approximate forward model.
domains, the B1 immunoglobin-binding domain of streptococcal protein G (PDB identifier 1PGA) and the SH3 domain of human Fyn (PDB identifier 1SHF). Only positions of C α atoms were taken into account, and the SH3 domain was truncated by three C-terminal residues to match the length of 1PGA. This results in two very different conformations of a chain of 56 beads (Figure2a,b). We generated simulated contact frequencies and used ISD to infer representative multi-state models. We simulated the posterior distributions for n = 1, 2, 3, 4, 5, 10 states. Model comparison shows that the model with n = 2 states is strongly preferred, as indicated by P (D|n = 2, I)/P (D|n = 1, I) > 10 307.
Acknowledgements We thank  Luca Giorgetti  for sharing DNA-FISH data. SC and MN acknowledge funding from the  Agence National de la Recherche  ( ANR-11-MONU-0020 ). SC thanks  Christian Griesinger  for funding and support. MH acknowledges funding by the  German Research Foundation (DFG)  under grant SFB  860 ,  TP B09.
Via LC-MSMS multiple allele-specific products (tryptic specific peptides) are quantified without required prior knowledge  27. Peptide identification is greatly enhanced with the availability of a reference genome. In reality, in non-model crops the database of the nearest related taxon can also be used. In essence LC-MSMS is suitable for the study of specific allelic contributions towards a preferential phenotype in polyploid non-model crops, eg. Hu et al. 28  discovered 558 allele-specific protein isoforms in Gossypium using iTRAQ. This study aims to select alleles that are correlated to drought stress in banana (Musa spp. ), an allopolyploid, non-model crop. Therefore we rely on a lab model mimicking drought stress using an osmotic stress agent (Poly-Ethylene Glycol, PEG-8000). Since different protein isoform(s)/transcript(s)/gene copy(s) are controlling the phenotype, it is crucial to identify genes specific to the favorable phenotype.
The root proteome. To unravel the molecular mechanisms behind the observed phenotypes, 234,739 features were aligned and quantified, 70,899 were targeted for MS2 (Supplementary Fig. S2 ). 51,765 spectrum sequence matches were found by integration of three search engines (Mascot, x!Tandem and MS-GF+) searching cultivar specific databases. In total 5,261 peptide sequences were successfully identified in 2,749 unique proteins. The majority of successful, peptide sequence identifications was based on results from all three search engines (4,143), 423 identifications were unique for the Mascot search engine, and 243 and 46 for x!Tandem and MS-GF+, respectively (Supplementary Fig. S3 ). The identified proteins correspond to transcripts (mRNA) with relatively high abundance (Fig. 2 ). The peptides taken up in MS2, are generally found more in higher abundant spectra.
Integration of proteomics with transcriptomics calls cultivar specific alleles. A locus (plural loci) in genetics is the position of a gene on a chromosome. The 5,261 validated feature sequences are spread over 2,749 loci (Supplementary Tables  S1  and S2 ). A variant of the similar DNA sequence located at a given locus is called an allele. To call cultivar specific allelic sequences, all identified peptides were BLASTp searched across the 3 cultivar specific databases and the best match for each database was retained (Supplementary Table  S2 ). The specificity of alignment is compared over the three cultivar specific databases. The workflow is exemplified for a query peptide (IGDSLSSQPNELVALFSR) identified in Ma02_t23730 (Sucrose Synthase 2; Fig. 3 ). A match on the same locus with less than 100% identity in another database identifies a cultivar specific peptide (Fig.
ProteoMAss LTQ/FT-Hybrid ESI Pos. Mode Cal Mix (MSCAL5-1EA SUPELCO, Sigma Aldrich) was used as an external calibrant and the lock mass 445. 12003 (polysiloxane) as an internal calibrant. The instrument was operated in data-dependent acquisition (DDA) mode with a survey MS scan at a resolution of 70,000 (fw hm at m/z 200) for the mass range of m/z 400-1,600 for precursor ions, followed by MS/MS scans of the top ten most intense peaks with +2, +3, and +4 charged ions above a threshold ion count of 1e + 6 at 17,500 resolution using normalized collision energy (NCE) of 25 eV with an isolation window of 3. 0 m/z, a dynamic exclusion of 30 s, and an Apex Trigger of 5-15 s. All data were acquired with Xcalibur 2. 2 software (Thermo Scientific, USA). For identification and alignment, all raw data were converted into mgf-files using Progenesis (v 4.
1, Nonlinear Dynamics, UK). Database construction and protein identification. Three cultivar specific databases were constructed based on mRNA seq reads from our plants (available at https://www. ncbi. nlm. nih. gov/bioproject/ PRJNA305241,30). This transcriptomic dataset was realigned and recounted to the updated reference genome  14. RNA-seq reads, contained in FASTQ files, were first trimmed based on low-quality ends and adapters were removed with cutadapt, v 2. 7. 9  64. Quality was checked with FastQC (v0. 11. 3). Reads were then mapped against the Musa acuminata genome of reference (DH Pahang v2; http://banana-genome-hub. southgreen. fr/)  14, 65 ; using the splice junction mapper for RNA-seq STAR, v 2. 5. 0 66 with default parameters. Read groups were added for each alignment, reads were split using SplitNCigarReads and locally realigned with InderlRealigner, v 3.
4 (Genome Analysis ToolKit 67 ) resulting in sample specific Binary Alignment/Map (BAM) files. Finally cultivar specific databases were constructed by concatenating BAM files per cultivar (mpileup, SAMTools 68,69 ), followed by variant calling, in reference to the Musa reference genome v2 (45,855 sequences) (bcftools, SAMTools)  68, 69. Each database was complemented by 116 common repository of adventitious proteins (CRAP, http://www. thegpm. org/ crap/) and combined into a target/decoy database  70  enabling False Discovery Rate (FDR) estimation. The decoy sequences were created by reversing the target sequences in SearchGUI v 3. 1. 4  71, 72. For every sample, peak lists (. mgf format) containing all MSMS spectra were extracted from Progenesis and were identified against its proper database integrating 3 database search algorithms: Mascot, v 2.
Results were exported and processed further in R software (v 3. 3. 0) where PSMs were filtered, keeping the most confident peptide sequence matches. Only peptides identified in genes with average summed cultivar RNA seq read count above 100 were kept as trustworthy identifications. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD006375 and 10. 6019/PXD006375  76. Cross database allele-specific SAAP detection, validation, and quantification. To further validate the identified peptides, all peptides were searched across the 3 cultivar specific databases using the BLASTp algorithm  77  and the best match for each database was retained. This approach is performed at the locus level, implying that peptides matching multiple loci are evaluated separately.
Allele-specific variants are identified as peptides matching exactly (100%) in one database, while not in another database. In case of ambiguous proteins, containing amino acids depicted as X in the database, and caused by SNPs, inherent to the triploid banana nature, the underlying, possible mRNA variants were evaluated at this genome location. In these cases the uniqueness of sequence matching was evaluated by assessing the variant specific mRNA reads. ABB specific sequences, supposed to be the B alleles, are thus identified by their absence in AAA cultivars. Quantifying homeoallele-specific reads was performed using Variant Call Format (VCF) files. These allowed to attribute the allele-specific read count to the identified allele-specific amino acid sequences in the control (0% PEG) samples of the allopolyploid Cachaco (ABB).
This was tested by binomial testing using the number of ABB specific reads as number of successes, compared to the total number of reads for the specific locus, this with an expected ratio of 0. 66. P-value adjustment was according to Benjamini-Hochberg. Multi-and univariate peptide statistics. Quantitative LC-MSMS was based on the peptide intensity using Progenesis. All samples were evaluated by Sparse Partial Least Squares Discriminant Analysis (sPLS-DA, mixOmics, R-package  78  ), using genomic constitution groups (ABB, AAAh, AAA) as response variables. This multivariate statistical technique was complemented by the univariate, non-parametric Kruskal Wallis test (α < 0. 01, p-value adjustment according to Benjamini-Hochberg; Agricolae, R-package). Functional Gene Ontology enrichment. Musa gene annotations were taken over from the Universal Protein Resource (UniProt).
Our gene identifiers were converted to UniProt standard format (http:// banana-genome-hub. southgreen. fr/convert; http://www. uniprot. org/uploadlists/) before Gene Ontology (GO) enrichment. We developed an in-house script to perform GO enrichment based on a user defined subset of genes in the UniProt format (https://labtrop. shinyapps. io/eRgo/). The tool is based on TopGO, R-package. Each molecular function (MF), biological process (BP) or cellular component (CC) is covered by a unique identifier, the GO ID. Data visualization as well as export of enriched GO terms are enabled in the tool. GO enrichment of all allele-specific proteins was performed against all 2,749 identified protein in the root proteome. We extracted all GO terms of biological process (BP) with significance level p < 0.
Acknowledgements The authors would like to thank  Kusay Arat  for the technical support at  Sybioma ,  Edwige André  and  Saskia Windelinckx  for the plant growth,  Annick De Troyer ,  Els Thiry  and  Hien Do  for isolating total RNA for RNA sequencing and protein extraction. This work was supported by the  Bioversity International  project ' Adding value to the ITC collection through molecular and phenotypic characterization' , financed by the  Belgian Development Cooperation , the  CGIAR Research Program on Roots, Tubers and Bananas (RTB)  and the  USAID  project  AID-BFS-G-II-00002-11  ' Reviving the plantain breeding program  at  IITA-International Institute for Tropical Agriculture'. Mathieu Rouard  is gratefully acknowledge for the critical reading of the manuscript. COST Action Phenomenall  FA 1306  is gratefully acknowledged for financing the presence at numerous interesting meetings about phenotyping and omics integration.
Background: Transcription regulation is a major controller of gene expression dynamics during development and disease, where transcription factors (TFs) modulate expression of genes through direct or indirect DNA interaction. ChIP sequencing has become the most widely used technique to get a genome wide view of TF occupancy in a cell type of interest, mainly due to established standard protocols and a rapid decrease in the cost of sequencing. The number of available ChIP sequencing data sets in public domain is therefore ever increasing, including data generated by individual labs together with consortia such as the ENCODE project. Results: A total of 1735 ChIP-sequencing datasets in mouse and human cell types and tissues were used to perform bioinformatic analyses to unravel diverse features of transcription control.
The ENCODE consortium has provided in-depth analyses of the TF ChIP-seq generated  [5] [6] [7] [8] [9] [10]. Despite this, the data remains globally under-exploited and new analyses are both necessary and feasible. Furthermore, other available ChIP-seq datasets have not been investigated as thoroughly as the ENCODE dataset. We therefore collected ChIP sequencing data from diverse compendia including  [11]  and consortia  [12, 13] , resulting in 928 ChIP sequencing samples for transcription related factors in around 100 cell lines and tissues in human and 807 samples in around 50 cell lines and tissues in mouse. We performed a systematic analysis of this data to understand diverse aspects of transcription control across mammalian cell types (Fig. 1 ). This work is built onto and expands analyses and tools we previously published  [14, 15].
HeatChIPSeq for identification of global relationships across experiments We analysed four large resources of ChIP-seq datasets, with between 156 and 690 transcription Factor (TF) ChIPseq experiments from mainly the ENCODE datasets in human (hg19)  [12]  and mouse (mm10)  [13]  as well as the CODEX datasets  [11]  for human (hg19) or mouse (mm10). The number of peaks varied greatly across experiments. For example, a ZNF274 ChIP-seq in HeLa-S3 cell line had only 74 peaks while a CFOS ChIP-seq in MCF10A cell line sample had over 91,000 peaks in the ENCODE human data. We used the HeatChIPseq web tool (Fig. 2 ), a part of Heat*seq  [14]  to explore the global relationships across samples by clustering correlation heatmaps from peak overlap data (see methods). In each datasets we noted similar groupings (Fig.