dataesr/scientific-paragraphs-categorization huggingface dataset:
Mapping scRNA-seq data onto FISH datasets  36, 37  introduces spatial dimensions to the single-cell transcriptional analysis. However, due to limitations of the scRNAseq, it enables only a rough estimation of the tissue regions that the individual cells originate from. Our approach enables large scale quantitative gene expression analysis of a transcription factor targetome at single-cell resolution. This approach works on whole-tissue level, with very high sensitivity and dynamic range, while preserving spatial information. Thanks to whole tissue imaging, we are able to gather gene expression data from millions of cells. We have both spatial information as well as the precise measurements of expression levels for the transcription factor driving the studied developmental process. Thus, our approach enables us to identify transient states that cells progress through during differentiation.
Putative target gene predictions and construct design The list of putative Ato target genes was generated using i-cisTarget webtool  22  (https://med. kuleuven. be/ lcb/i-cisTarget). We used the same lists of up-/downregulated genes in Ato gain or loss of function, as in the original cisTargetX (predecessor of i-cisTarget) publication  17. For each putative target gene, a suitable fosmid or BAC has been found using the TransGeneOmics database  23, 42  (https://transgeneome. mpi-cbg. de). Recombineering primers were automatically designed using the same database. Fosmid clones were preferred over BACs. Clones with more upstream than downstream sequence and shorter clones were prioritized. The constructs for Ato[mCherry] knock-in were designed in CLC Main Workbench (https://www. qiagenbioinformatics.
Segmentation The first step in the applied image segmentation processes was pixel classification using the random forest machine learning approach. Nuclei and background were manually marked using 2 labels (nucleus and background) in the DAPI channel for one z-stack image (randomly selected 512 x 512 pixels window cropped from full-size image) from each imaging session using Ilastik  47. Custom Fiji 48 plugins and scripts (https: //github. com/rejsmont/rdn-wdp) utilizing WEKA  49  and 3D ImageJ Suite  50  were developed for training and application of the classifier. The classifier was trained based on several filters (Gaussian blur, Hessian, Derivatives, Laplacian, Structure, Edges, Difference of Gaussian, Mean, Median, Variance) with sigma values between 1-16 pixels applied to the training images.
Registration To enable direct comparisons between different samples, sample registration was performed using a custom python script (analyze2) available from the data analysis repository (https://github. com/rejsmont/rdn-wdppython). First, nuclei were read from the point cloud CSV file. Nuclei that differ from the sample mean by more than 50% were rejected. Coordinates of each nucleus were scaled to a unit of mean nucleus diameter in the sample. Rotation of each sample was automatically adjusted so that the visible edge of the disc is on the left and the anterior of the disc is at the top. The morphogenetic furrow (MF) was identified using the Max of Hessian eigenvalue filter applied to the xyz projection of the mCherry channel onto a 1-unit 2D grid. The intensities of each channel were normalized to the mean intensity of mCherry along the MF.
Cell-type classification To determine the identity of cells in the MF area (-8>y>8) we decided to cluster these cells based on their position along the A-P axis (y), the expression level of Atonal protein (A), and the prominence of Ato expression (P A ). Due to the number of cells in our samples (millions), simple linkage analysis is impossible due to huge memory requirements. On the other hand, we found that the clustering approaches designed to deal with large datasets (such as K-means  51  or DBscan  52  ) do not perform well on datasets with smooth transitions between the clusters. As changes of both Ato intensity and prominence are continuous is our data, these approaches failed to correctly identify different stages of R8 specification. Faced with these restrictions, we sought to divide a big problem of clustering millions of cells into multiple smaller ones.
We randomly selected sets of 20 samples and performed the linkage analysis, using the Euclidean metric and the Ward variance minimization algorithm  53  , on cells in these samples. To recover clusters from linkage analysis we used the maximum number of clusters as a criterion. We optimized this parameter until the R8 cells were resolved in most samples, and found the optimal number of clusters to be six. The random sample selection and clustering have been repeated 1000 times to maximize sample coverage and variability. To identify similar clusters from all the attempts we used a custom clustering algorithm, similar to the K-means method, but optimized to cluster centroids of a known number of clusters (see cluster_centroids method in https://github. com/rejsmont/rdn-wdp-python/blob/master/analysis/clustering.
Chromatin immunoprecipitation (ChIP) 800 eye-antennal discs were isolated from (w; ato-GFP) third-instar larvae. Chromatin was immunoprecipitated using anti-GFP (ab290, Abcam) antibody, purified and sequenced following previously published protocols  54. Raw data of eye-disc samples were deposited in the Gene Expression Omnibus (GEO) database (http://www. ncbi. nlm. nih. gov/projects/geo/) with the accession number GSE110827. blob/master/analysis/figures_paper. py). The gene expression patterns and prominence patterns (Fig. 3 , Supplementary Fig. 3  and 5 ) were plotted as xyz projection of the respective values onto a 1-unit 2D grid. The mean projection was used for the expression values and the max projection was used for the prominence. The ChIP peak area (Fig. 5c ) was computed as a product of the peak length (in bp) and the peak height.
Acknowledgements We are grateful to  Pavel Tomancak ,  Hugo Bellen  and  BACPAC Resources Center  for the genomic clones. We thank  Mihail Sarov  for the Red/ET recombineering plasmid. We are grateful to  Stein Aerts  for help with i-cisTarget analysis. We thank the  Bloomington stock centre  for fly stocks. We thank  Mark Fiers  for help with ChIP-seq analysis. We are grateful to  Ariel Lindner  and  Dusan Misevic  for discussions and critical feedback on the manuscript. We acknowledge the  Nucleomics core facility ,  Vlaams Instituut voor Biotechnologie  (VIB;  Flanders Institute for Biotechnology ) for their sequencing services, and the scientific and technical assistance of the ICM. Quant imaging core facility (" Investissements d'avenir" program  [ ANR-10-IAIHU-06 ] and [ ANR-11-INbS-0011 ]).
We thank  Best Gene ,  Inc. ;  Genetic Services, Inc. ; and GenetiVision for the transgenesis services. This work was supported by the program " Investissements d'avenir "  ANR-10-IAIHU-06 ,  ICM ,  VIB , the  WiBrain Interuniversity Attraction Pole network (Belspo) , and the  Paul G. Allen Frontiers Group. R. K. E. was supported by the  EMBO long-term fellowship  ( EMBO ALTF 1056-2011 ) and the omics@vib fellowship ( FP7-PEOPLE-2010-COFUND ). N. M. was supported by the  Fonds Wetenschappelijke Onderzoeks (FWO)  fellowship. B. A. H is an Allen Distinguished Investigator and an  Einstein Fellow  of the  Berlin Institute of Health.
The associated computational data mining thus represents a bottleneck on the path to optimal modification calling. To address this shortcoming, we here present an automated workflow implementation based on Galaxy  (Afgan et al. , 2018) , whose components are depicted in Figure  1B. The Galaxy implementation provides a first module (B1) for the automation of typical and recurrent RNA-Seq-associated operations such as trimming and mapping. While these operations can be customized to accommodate a range of data formats, it allows procedurally stable and reproducible treatment of data package of comparable content, such as RNA-Seq data obtained under variegated conditions for library preparation. This, in turn, allows a comparative evaluation of those experimental conditions, as outlined above.
Preprocessing of Raw Reads (Trimming) The raw reads from the sequencing data (stored in fastqformat) are first subjected to removal of auxiliary sequences such as adapters, barcodes, and unique molecular identifiers (UMIs). For this task, the workflow uses the Cutadapt trimming software  (Martin, 2011). Due to the necessity to remove multiple sequences from the raw reads, their respective arrangement, and the configuration of Cutadapt, the trimming is separated into The pipeline has two parts: (A) general workflow for the processing of RNA samples and (B) the implemented automated graphical workflow system with the available modules for bioinformatics data analysis. (A) consists of (A1) possible and partly necessary pretreatments for different RNA species, (A2) library preparation with the possibility of adaptations (e.
g. conditions for reverse transcription), (A3) sequencing with Illumina sequencing platforms (e. g. MiSeq/NextSeq and HiSeq), and (A4) data processing including basic data treatment like adapter trimming, alignment, and format conversion, as well as data analysis (e. g. machine learning and RT-signature analysis). The elaborate data processing (A4) was fully automated in (B) by using the open-source Galaxy platform to create and provide a quick and user-friendly feedback mechanism to optimize the experimental design, sample preparation, and data processing. The standard workflow (B1) is supplemented by various additional modules (B2) including workflows for (a) machine learning, (b) visualization, and (c) filtering. multiple steps. In a typical Illumina paired-end sequencing run, the forward and reverse reads are stored in individual fastq files; the reads show slightly different characteristics concerning the auxiliary elements; hence, the trimming for forward and reverse reads is performed separately.
Machine Learning For the prediction of m 1 A and other modifications, a machine learning model for binary classification is included in the Galaxy distribution (Workflow Workflow_Prediction). workflows for training and prediction are based on a random forest model from the "scikit-learn" Python package  (Pedregosa et al. , 2011). For the training process, the positive class (modified bases) and negative class (nonmodified bases) are given as input in a 1:1 ratio. This ratio is used in order to counter the tendency of RF models to bias toward the majority class. This RF property frequently leads to false negatives for the positive class (the modifications) when making predictions. Importantly, this bias is not necessarily reflected by the evaluation scores. The random forest performs e. g. 10 repetitions of a 5-fold crossvalidation.
Visualization A graphical representation of the position of interest within sequence context can be created using a Python-based script (Workflow Visualize_V3), extracted from the CoverageAnalyzer tool  (Hauenschild et al. , 2016). The user can plot a sequence containing up to 1000 bases where the leftmost and rightmost bases can be selected by position. In addition, various sizes can be adjusted, including the width and height of the plot, the font size, and the size of markers within the graphic (exemplary plot shown in Figure  3 ).
Filtering for Demethylation Candidates The Profile files were used for statistical analysis. Figure  2  illustrates the Galaxy Filtering module Demethylation_relative_ change, which was used to filter and extract all positions that show an absolute and relative change in the mismatch rate of a certain threshold between the untreated and AlkB-treated sample. Table  1  shows the extracted Profile file with all candidate positions after filtering. From our sample comparison, with our selected thresholds, 13 candidate positions fulfilling the requirements were filtered out, with high probability to be m 1 A sites.
Visualization of Demethylation Candidates In addition, the Profile files were used in the visualization workflow Visualize_V3 to obtain graphical plots for each sample. The visual comparison of the untreated (A) and AlkB-treated (B) yeast tRNA Lys  (CTT) , which includes an m 1 A at position 58, is shown in Influence of Mn 2+ on the RT Signature at m 1 A Sites in Yeast tRNA In a second exemplary sample processing, four samples of total tRNA from S. cerevisiae were used for sample preparation, sequencing, data processing, and analysis. The samples were used for library preparation and differed in the reverse transcription step (Figure  1  [A2]). For reverse transcription, we used SuperScript ® III Reverse Transcriptase (Thermo Fisher Scientific, Germany) in four different buffer mixtures to investigate the influence of Mn 2+ during reverse transcription  (Zhou et al.
This Galaxy distribution, developed in a Unix environment, has not been tested on Windows platforms. Detection efficiency of modified ribonucleotides is highly dependent on the dataset. tRNA samples show a high number of RT-impairing modifications, which can negatively affect the RT signals for surrounding positions, making it more difficult to detect modified positions of interest through filtering or machine learning. We also observed that detectability is highly dependent on read coverage. In some cases, modified low-coverage sites could not be detected as the RT signatures were noisy and thus not very pronounced. Moreover, the machine learning and prediction processes require an adequate number of training instances for a given modification. Modifications that are present only in low amounts are not compatible with the available machine learning process.
0 mM (D)] as Mg 2+ substitute. Sites with error rates of more than 10% are highlighted with yellow arrows, with colored bars indicating the nature of the reads. Mismatch rates are depicted as black crosses, and arrest rates as red lines. The m1A site is located in the middle of the shown sequence segment at position 59. step, which allows the user to remove this step when using other library preparation protocols. In addition, the Galaxy interface allows for user-friendly customization of many input parameters. The customization is not limited to the software packages such as Cutadapt  (Martin, 2011)  and Bowtie  (Langmead and Salzberg, 2012) , but also includes individual Python scripts for the multiple workflows. Accordingly, adapter and barcode sequences can be replaced to fit the library preparation protocol, and other tasks like quality trimming can be performed.
For the Python scripts, the range of adjustable parameters allows the user to change the modification of interest, filter criteria, features, and parameters for the machine learning model as well as several options for the visualization. Furthermore, existing workflows can be easily rearranged to suit the desired analysis. The associated Galaxy toolshed allows for the installation of additional bioinformatics programs and enables the user to create entirely new workflows. For example, other alignment tools can be implemented that may improve or accelerate data processing or allow transcriptome-wide analysis for other data packages. In the provided tutorial, the installation of new software is described. As an example, we have incorporated the CUSHAW2 tool  (Liu et al. , 2012) , which allows significant acceleration of the alignment speed, as a substitute for Bowtie 2.
DATA AVAILABILITY The graphical workflow system, an instruction manual, and a tutorial are available at: https://github. com/HelmGroup, Repository: Galaxy_modification_calling. Operating system(s): Linux, Programming language for custom scripts: Python, Other requirements: Docker (software) needs to be installed. The AlkB test datasets analyzed and generated for this study can be found in the repository: Galaxy_modification_calling (https://github. com/HelmGroup/Galaxy_modification_calling/ tree/master/TestData/AlkB). Compressed files are provided in PKZIP and ZIP format and were compressed with 7-Zip. Files: total_tRNA_yeast_untreated_R1. fastq (untreated yeast total tRNA -Read 1) total_tRNA_yeast_untreated_R2. fastq (untreated yeast total tRNA -Read 2) total_tRNA_yeast_AlkB_treated_R1. fastq (AlkB-treated yeast total tRNA -Read 1) total_tRNA_yeast_AlkB_treated_R2.
fastq (AlkB-treated yeast total tRNA -Read 2) total_tRNA_yeast_untreated. profile (untreated yeast total tRNA -Profile) total_tRNA_yeast_AlkB_treated. profile (AlkB-treated yeast total tRNA -Profile) total_tRNA_yeast_reference. fasta (Reference total tRNA yeast) Files for testing of the machine learning workflow can be found in the repository: Galaxy_modification_calling (https:// github. com/HelmGroup/Galaxy_modification_calling/tree/ master/TestData/Prediction). Files: Known_m1A_sites_yeast (list of known m 1 A sites) total_tRNA_yeast_untreated. profile (untreated yeast total tRNA -Profile) All other data are available from the corresponding authors upon reasonable request.
ACKNOWLEDGMENTS We are grateful to  Valérie Igel-Bourguignon  ( NGS Core Facility, Biopôle, University of Lorraine, Nancy, France ) for excellent technical assistance. We further thank the help of  Hannah Back, BSc  ( Biomedical Chemistry, Johannes Gutenberg-University, Mainz, Germany ), for experimental assistance, as well as  Dr. Lyudmil Tserovski  and  Dr. Ralf Hauenschild  for their contributions during the early stages of this project. The pET30a-AlkB plasmid was a gift from  Tao Pan  (Addgene plasmid #79050; http://n2t. net/addgene:79050;RRID:  Addgene_79050 ).
When G = 3, x 31 was set to 50 while x 12 and x 23 were varied between 10 and 200. When G = 4, x 41 and x 12 were set to 50 (following results from simulations with G = 3) while x 23 and x 34 were varied between 10 and 200. Arti cial experiments. We ran 4 separate simulations before the experiment using the same distribution of agents as for experiments (2 matching the conditions of Experiment I and 2 matching the conditions of Experiment II). e cognitive parametrization of the agents was: α =. 175, β = 1. 000 & γ =. 125 (these values correspond to the average value of each parameter used for the simulations). Post-hoc simulations. We ed our behavioral data on the decision-making model using Scipy's  (Jones et al. , 2001)  di erential evolution algorithm (provided by the module optimize). We optimized model parameters by minimizing the negative log-likelihood of the model for each subject individually.
, 2017; Lefebvre et al. , 2017; den Ouden et al. , 2013; Frank et al. , 2007; Van Den Bos et al. , 2012; Aberg et al. , 2016). Interestingly, our subjects also present this asymmetry in value-update and seem to preferentially learn from exchanges that result in be er-than-expected outcomes (see Fig. S18F ). Investigating how such bias a ects the coordination of agents in an experience-based money emergence paradigm could then constitute a relevant subject for further studies. Code & data availability. e so ware we used was based on a client/server architecture. e client part has been developed using the Unity game engine. e application ran on 7 Android tablets. e assets of the application are available at https://github. com/AurelienNioche/ MoneyApp. e experiment server was hosted on a local server and has been developed in Python.
e code of the server part is available at https://github. com/AurelienNioche/MoneyServer. e analysis program is available at https://github. com/AurelienNioche/MoneyAnalysis. e data are available at the same address than the analysis program: https://github. com/ AurelienNioche/MoneyAnalysis. Based on these simulation results, we deduced the optimal experimental conditions required to see money emerge with human subjects. A. The phase diagram summarizes the results of 10,400 simulations with 3 goods. The number of type (3, 1) agents is set to 50 while the number of agents of type (1, 2) and (2, 3) varies between 10 and 200 (corresponding respectively to the values on the x-axis and y-axis). The ho er the color, the higher the indirect exchange frequency involving good 1 as a medium of exchange.
This diversity decline seems to be linked to a decline in speciation rate rather than an increase in extinction rate. Our approach, implemented in the R package RPANDA, should be useful for evolutionary biologists interested in understanding how past diversity dynamics have shaped present-day diversity. It could also be useful in other contexts, such as for analyzing clade-clade competitive effects or the effect of species richness on phenotypic divergence. [phylogenetic comparative methods; birth-death models; diversity curves; diversification; extinction; anurans] Estimating species diversity through geological time is key to our understanding of what controls biological diversity. Diversity curves have been extensively explored from fossil data and are at the origin of intensive debates on the role of stochasticity, diversity-dependence, and biotic and abiotic drivers on long-term diversity dynamics (.
, 2016). These return estimates of λ(t) and µ(t) with t measured from the present to the past. In the second step, we compute the probability associated with each t and m using the formulas above. From those, we obtain for each time t: i) the expected number of species by computing E[N (t)] = m mP(N (t) = m) and ii) the confidence interval around this expected value by keeping the values of m with highest probability that collectively sum up to 0. 95 and discarding the remaining m values. Codes for these analyses are freely available on GitHub (https://github. com/hmorlon/PANDA) and included in the R package RPANDA  (Morlon et al. , 2016). The prob dtt function computes the probabilities, and the plot prob dtt function computes and plots the expected values and confidence intervals around them.
(2011) , and iii) the number of lineages on the reconstructed phylogeny (i. e. the well-known lineage-through-time plot, an estimate that ignores extinctions). For i) and ii), we first selected the model, among the 5 described above, that gave the best support (i. e. had the lowest AIC score) given the data (the model providing the best support was not necessarily the generating model) before computing the corresponding DTT curve. We used the crown age condition for both the deterministic and probabilistic approach. Finally, we measured a global error D between the observed (denoted obs) and estimated (or theoretical, denoted th) diversity curves by averaging the relative error over time: D = 150 t=0 |(N obs (t) -N th (t))|/N obs (t) 151 Fig. S1  illustrates examples of simulated diversitytrajectories and estimated DTT curves (with confidence intervals), along with associated global errors, under each of the five diversification scenarios.
Estimating extinction rates with extant clades (i. e. without fossils) is contentious  (Rabosky, 2010) , yet key studies have found that one can reasonably estimate extinction given appropriate methods  (Morlon et al. , 2011)  and conditions  (Beaulieu and O'Meara, 2015). We focused on the phylogeny of Archaeobatrachia and used the amphibian phylogeny from  Pyron (2014) , which contains 135 of the 264 species (f = 0. 51) from this group and was the most completely sampled time-calibrated anuran phylogeny available at the time of our analyses. Jetz and Pyron (2018)  recently published a tree with nearly all described anuran species. We expect that analysing this phylogeny would produce similar results, given that (1) most additional species in the fully sampled tree were semi-randomly imputed (based on taxonomy) onto a smaller molecular phylogeny; (2) the molecular datasets and phylogeny estimation methods of the two papers  (Jetz and Pyron, 2018; Pyron, 2014)  are highly overlapping, thus likely producing very similar moleculardata-only trees; and (3) Jetz and Pyron (2018)'s diversification analyses of the fully sampled tree gave similar results as their analyses based on the tree based on molecular data alone.