dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. The original version of the AutoRadio database used in this study can be downloaded at: http://microautomate. cerege. fr/dat. It is currently composed of 17,065 images, corresponding to 112 classes/taxa. Code and data availability. A manual version of the AutoRadio_Segmenter. ijm plugin (automated image processing performed on ImageJ / Fiji), developed to process a root folder ("Core"), containing subfolders ("Samples") of images ("FOVs") is available online for free at https://github. com/microfossil/ImageJ-LabView-Scripts. To use it, download the. ijm file and save it into the ImageJ/plugins folder, and it will be available to use after restarting ImageJ / Fiji.
Using AI risk assessment tools in the US criminal justice system to reduce the number of pretrial detentions of suspects may inadvertently increase the disproportionate detention of racial minorities (Partnership on AI 2019;  Koepke et al. 2018). Skilled users of AI technology can create "deep fake" video, where a person can be made to say almost anything, and it is impossible to detect the manipulation and fake content  (C-SPAN 2019, 42:50). Such risks are the unintentional result of companies like Google or IBM making freely available to researchers, graduate students, and anyone with an interest in machine learning tools like Tensor Flow, an open source software library that makes it easier for any developers to design, build, and train deep learning models, and Apache System ML, a machine learning system that can scale and optimize machine learning projects using big data.
The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1 ).
Pan-cancer analysis of whole genomes The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale  [1] [2] [3]. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete.
Direct download of PCAWG data Aligned PCAWG read data in BAM format are also available at the European Genome Phenome Archive (EGA; https://www. ebi. ac. uk/ega/search/site/pcawg under accession number EGAS00001001692). In addition, all open-tier PCAWG genomics data, as well as reference datasets used for analysis, can be downloaded from the ICGC Data Portal at http://docs. icgc. org/ pcawg/data/. Controlled-tier genomic data, including SNVs and indels that originated from TCGA projects (in VCF format) and aligned reads (in BAM format) can be downloaded using the Score (https://www. overture. bio/) software package, which has accelerated and secure file transfer, as well as BAM slicing facilities to selectively download defined regions of genomic alignments.
ICGC Data Portal The ICGC Data Portal  85  (https://dcc. icgc. org) serves as the main entry point for accessing PCAWG datasets with a single uniform web interface and a high-performance data-download client. This uniform interface provides users with easy access to the myriad of PCAWG sequencing data and variant calls that reside in many repositories and compute clouds worldwide. Streaming technology  86  provides users with high-level visualizations in real time of BAM and VCF files stored remotely on the Cancer Genome Collaboratory.
Article than 95% of SNV calls made by each of the core pipelines were genuine somatic variants (Fig. 1a ). For indels-a more-challenging class of variants to identify with short-read sequencing-the 3 core algorithms had individual sensitivity estimates in the range of 40-50%, with precision of 70-95% (Fig. 1b ). For individual SV algorithms, we estimated precision to be in the range 80-95% for samples in the 63-sample pilot dataset. Next, we defined a strategy to merge results from the three pipelines into one final call-set to be used for downstream scientific analyses (Methods and Supplementary Note 2). Sensitivity and precision of consensus somatic variant calls were 95% (90% confidence interval, 88-98%) and 95% (90% confidence interval, 71-99%), respectively, for SNVs (Extended Data Fig.
11i-k ). MBD4 encodes a DNA-repair gene that removes thymidines from T:G mismatches within methylated CpG sites  71  , a functionality that would be consistent with a CpG mutational signature in cancer. Fourth, we assessed long interspersed nuclear elements (LINE-1; L1 hereafter) that mediate somatic retrotransposition events  [72] [73] [74]. We identified 114 germline source L1 elements capable of active somatic retrotransposition, including 70 that represent insertions with respect to the human reference genome (Fig. 6d  and Supplementary Table  7 ), and 53 that were tagged by single-nucleotide polymorphisms in strong linkage disequilibrium (Supplementary Table  7 ). Only 16 germline L1 elements accounted for 67% (2,440 out of 3,669) of all L1-mediated transductions  10  detected in the PCAWG dataset (Extended Data Fig.
Article As reported in a companion paper  13  , 16% of tumours in the PCAWG dataset exhibited somatic mutations in at least one of ATRX, DAXX and TERT. TERT alterations were detected in 270 samples, whereas 128 tumours had alterations in ATRX or DAXX, of which 71 were proteintruncating. In the companion paper, which focused on describing patterns of ALT and TERT-mediated telomere maintenance  13  , 12 features of telomeric sequence were measured in the PCAWG cohort. These included counts of nine variants of the core hexameric sequence, the number of ectopic telomere-like insertions within the genome, the number of genomic breakpoints and telomere length as a ratio between tumour and normal. Here we used the 12 features as an overview of telomere integrity across all tumours in the PCAWG dataset.
We consolidated histopathology descriptions of the tumour samples, using the ICD-0-3 tumour site controlled vocabulary  89. Overall, the PCAWG dataset comprises 38 distinct tumour types (Extended Data Table  1  and Supplementary Table  1 ). Although the most common tumour types are included in the dataset, their distribution does not match the relative population incidences, largely owing to differences among contributing ICGC/TCGA groups in the numbers of sequenced samples.
Uniform processing and somatic variant calling To generate a consistent set of somatic mutation calls that could be used for cross-tumour analyses, we analysed all 6,835 samples using a uniform set of algorithms for alignment, variant calling and quality control (Extended Data Fig. 1 , Supplementary Fig. 2 , Supplementary Table  3  and Supplementary Methods 2). We used the BWA-MEM algorithm  90  to align each tumour and normal sample to human reference build hs37d5 (as used in the 1000 Genomes Project  91  ). Somatic mutations were identified in the aligned data using three established pipelines, which were run independently on each tumour-normal pair. Each of the three pipelines-labelled 'Sanger'  [92] [93] [94] [95]  , 'EMBL/DKFZ'  96, 97  and 'Broad'  [98] [99] [100] [101]  after the computational biology groups that created or assembled them-consisted of multiple software packages for calling somatic SNVs, small indels, CNAs and somatic SVs (with intrachromosomal SVs defined as those >100 bp).
Two additional variant algorithms  102, 103  were included to further improve accuracy across a broad range of clonal and subclonal mutations. We tested different merging strategies using validation data, and choses the optimal method for each variant type to generate a final consensus set of mutation calls (Supplementary Methods S2. 4). Somatic retrotransposition events, including Alu and LINE-1 insertions  72  , L1-mediated transductions  73  and pseudogene formation  104  , were called using a dedicated pipeline  73. We removed these retrotransposition events from the somatic SV call-set. Mitochondrial DNA mutations were called using a published algorithm  105. RNA-sequencing data were uniformly processed to quantify normalized gene-level expression, splicing variation and allele-specific expression, and to identify fusion transcripts, alternative promoter usage and sites of RNA editing  8.
Bottom, the variant allele fractions of somatic mutations distributed along the relevant chromosomal region. Extended Data Fig. 7  | Association of chromothripsis with covariates and driver events. a, Odds ratios per cancer type of containing chromothripsis in whole-genome duplicated versus diploid samples (n = 2,583 patients). ***q < 0. 001; **q < 0. 01; *q < 0. 05. Two-sided hypothesis testing was performed using Fisher-Boschloo tests, corrected for multiple-hypothesis testing. b, Same as a for female versus male. c, Proportion of mutations explained by single-base substitution signature 1 and age at diagnosis in prostate cancer samples (n = 210 patients) with or without chromothripsis (q < 0. 05). The earlyonset prostate cancer project drives the signal and was sequenced at lower depth.
Data availability The PCAWG-generated alignments, somatic variant calls, annotations and derived datasets are available for general research use for browsing and download at http://dcc. icgc. org/pcawg/ (Box 1 and Supplementary Table  4 ). In accordance with the data access policies of the ICGC and TCGA projects, most molecular, clinical and specimen data are in an open tier which does not require access approval. To access potentially identifying information, such as germline alleles and underlying read data, researchers will need to apply to the TCGA Data Access Committee (DAC) via dbGaP (https://dbgap. ncbi. nlm. nih. gov/aa/wga. cgi?page=login) for access to the TCGA portion of the dataset, and to the ICGC Data Access Compliance Office (DACO; http://icgc. org/daco) for the ICGC portion.
c, Accuracy (precision, sensitivity and F 1 score, defined as 2 × sensitivity × precision/(sensitivity + precision)) of somatic SNV calls across variant allele fractions (VAFs) for the core algorithms. The accuracy of two methods of combining variant calls (two-plus, which was used in the final dataset, and logistic regression) is also shown. d, Accuracy of indel calls across variant allele fractions. non-coding drivers 34-37 with those newly discovered in PCAWG data; a larger contribution in colorectal adenocarcinomas (2. 4 ± 1. 4 SVs this is reported in a companion paper 4. Using this approach, only compared with 7. 4 ± 7. 0 point mutations; P = 4 × 10 -10 ) and mature 13% (785 out of 5,913) of driver point mutations were non-coding B cell lymphomas (2. 2 ± 1. 3 SVs compared with 6 ± 3. 8 point muta- in PCAWG.
Background: With the rapid increase in availability of genomic resources offered by Next-Generation Sequencing (NGS) and the availability of free online genomic databases, efficient and standardized metadata curation approaches have become increasingly critical for the post-processing stages of biological data. Especially in organelle-based studies using circular chloroplast genome datasets, the assembly of the main structural regions in random order and orientation represents a major limitation in our ability to easily generate "ready-to-align" datasets for phylogenetic reconstruction, at both small and large taxonomic scales. In addition, current practices discard the most variable regions of the genomes to facilitate the alignment of the remaining coding regions. Nevertheless, no software is currently available to perform curation to such a degree, through simple detection, organization and positioning of the main plastome regions, making it a time-consuming and error-prone process.
Here we introduce a fast and user friendly software ECuADOR, a Perl script specifically designed to automate the detection and reorganization of newly assembled plastomes obtained from any source available (NGS, sanger sequencing or assembler output). Methods: ECuADOR uses a sliding-window approach to detect long repeated sequences in draft sequences, which then identifies the inverted repeat regions (IRs), even in case of artifactual breaks or sequencing errors and automates the rearrangement of the sequence to the widely used LSC-Irb-SSC-IRa order. This facilitates rapid post-editing steps such as creation of genome alignments, detection of variable regions, SNP detection and phylogenomic analyses. Results: ECuADOR was successfully tested on plant families throughout the angiosperm phylogeny by curating 161 chloroplast datasets.
As a result, massive amounts of genome-scale data have been generated at a relatively low cost, with ever faster turnaround times  (Zhang, Ma & Li, 2011). The availability of this expanding data volume has allowed for the exploration of chloroplast organization and chloroplast features throughout the angiosperm tree at the molecular level  (Stull et al. , 2013)  and the development of novel approaches for phylogenetic studies  (De Abreu et al. , 2018). To date, for angiosperms alone, there are over 4,500 sequenced plastomes available in NCBI GenBank through the INSDC database (as of June 2019) and this number has seen an exponential increase in recent years. A consequence of the rapid development of NGS techniques is the emerging need to handle and curate increasingly large organellar genome datasets.
For example, a recent study  (Li et al. , 2019) , used nearly 3,000 plastomes to reconstruct angiosperm phylogeny and evolution. Because mitochondrial genomes and plastomes are usually circular, but are represented as linear text sequences in bioinformatic pipelines and online databases, any base position can represent the start of the string sequence (see Fig. 1 ). Plastome sequences are typically characterized by two inverted-repeated regions (hereafter called IRs) separated by a long (LSC) and a short (SSC) single copy region  (Brázda et al. , 2018; Palmer, 1985; Stadermann, Holtgräwe & Weisshaar, 2016). Due to the replication mode of organellar DNA, each part can be found in both directions (i. e. , in 5′->3′ orientation, or in the reverse complement orientation) in the cell  (Palmer, 1985).
This organization implies that orthologous regions need to be assessed for each of these parts (Fig. 1 ). By general consensus and some computational constraints, most of the assembly methods start the reconstructed sequence at the first base of the large single-copy region (LSC). However, depending on both the quality and quantity of the NGS sequences (i. e. , the "raw reads"), as well as the complexity of the plastome to reconstruct, a sequence may start randomly along the circular sequence (Fig. 1B ), occasionally making the identification of homologous regions among sequences laborious and time-consuming, especially for large datasets (100-1,000 s of sequences). Despite there being some consensus in the community on the starting position of plastome sequences, a quick assessment of sequences in GenBank shows extensive disparity in the organization and order of the different regions.
Accurate and complete molecular sequence data are essential in phylogenomic reconstruction. Therefore, accurate homology detection among sequences is a vital step. Consequently, improvement in the functional annotation of organisms will become an extremely important step to delineate evolutionary processes. Usually, three distinct steps can be identified in phylogenomic studies: (1) The reconstruction of organelles in the different species; (2) the assessment of orthology and alignment of the plastome sequences (i. e. , the dataset assembly); and (3) the actual phylogenetic reconstruction  (Gruenstaeudl, Gerschler & Borsch, 2018). Current software focuses on organelle reconstruction  (Castandet et al. , 2016; Jin et al. , 2018) , multiple sequence alignment (MSA) algorithms  (Bi et al. , 2018)  and phylogenetic approaches (Granados  Mendoza et al.
, 2013). Software currently available can aid in assembly and analyses of plastid and mitochondrial genomes. However, orthology assessment and curation of the assembled plastomes usually remains a manual task  ((McKain & Wilson, 2017)  for a combined assembly and orientation script). Indeed, there is currently no open-source software that reconstructs and assesses plastome orthology for large datasets (numbering thousands of plastomes) in a fast and accurate way from assembled draft sequences. While orthology assessment can be easily done for datasets based on individually extracted coding regions, the specific structure of the plastome (LSC, IRb, SSC, IRa) complicates the proper alignment of orthologous regions. Indeed, any differences in organization/orientation of these parts in the reconstructed draft plastomes, can result in improper assessment of orthologous positions and thus increase the risk of discarding potentially useful data at the end of alignments.
This process is either done manually (representing 3-5 min per chloroplast for a trained bioinformatician, using commercially available software, for example, Geneious;  Ripma, Simpson & Hasenstab-Lehman, 2014) , or skipped by extracting the coding regions (thus discarding the most variable and useful regions of the plastome: see above). This represents an emergent bottleneck in dataset assembly and downstream analyses. Therefore, the development of flexible and user-friendly tools that remove these labor-intensive and time-consuming components from genomic workflows has become a latent priority for the plant genomics community. We designed ECuADOR to facilitate both the rapid processing of organelle genomic data as well as providing output requirements for downstream analyses. ECuADOR is a rapid, platform-independent and user-friendly algorithm built in Perl, that automates detection and reorganization of sequence features in newly assembled plastomes.
Analysis pipeline ECuADOR is written in Perl (tested with Perl 5. 18) and uses the following modules: Bio:: SeqIO, IO::String, Set::IntSpan, IO::File, Bio::AlignIO, Bio::Factory::EMBOSS, File::Temp qw/tmpnam/ and Cwd. Input for ECuADOR is a draft plastome sequence (GenBank or fasta), the length of the sliding window, format of input file (GenBank or fasta), and output format file (fasta or GFF3). ECuADOR reads and analyzes single or multiple plastomes stored in a designated folder, containing one sequence per file. ECuADOR is based on a user-defined sliding window and dynamic suffix array approach. The algorithm partitions the sequence into fragment intervals, of sizes defined by the "window size" option. This window slides along the plastome in both 5′->3′ and 3′->5′ direction (as a reverse-complement sequence).
e. , the window size parameter). Finally, ECuADOR is executed using the following command-line perl command: ECuADOR. pl (-h) -i, <folder containing the plastomes>, -w, <sliding window length> (1,000 bp option by default), -f, <input file format (option not by default, GenBank or fasta)>, -out <output prefix>, -ext, <output format> (either fasta or GFF3), -save_regions, <save chloroplast regions (LSC, SSC, IRs separately, or in combination)>, -orient <TRUE> (reorientation of each plastome regions, providing the user with the cpDNA regions ready to use for MSA), finally the option -noIRs <3 or 4> (to generate files including either only the concatenated regions LSC-IRb-SSC or the entire one LSC-IRb-SSC-IRa) The program is divided into seven main steps (Fig. 2 ): 1. Argument check and analysis of the input file, setting format files and sliding window size (if no slide window size is provided, the default option will be set to 1,000 bp.
7. ECuADOR generates five output files. The first one is a summary of the main annotation features of the regions, including the length of the IRs. The second one is the reordered sequences (i. e. , LSC-IRb-SSC-IRa) in either fasta or GFF3 format, depending on user options (for GenBank files, it will provide a complete reorientation and extraction for all its established annotations in a new GFF3 output file). If a sequence cannot be reliably curated, ECuADOR generates a third file containing a problem description for that particular sequence. If the option "all-orient TRUE" was selected, an additional step is performed, that will homogeneize the direction of the sequences for each plastome fragment before generating the final results files (for this purpose, we integrated in the ECuADOR code a modified version of the script seqOrient.
pl, available at). Finally, we included an additional option number of inverted repeats ("-noIRs") which allows the user to get either one or two IRs. The default value is 1 (thus outputting the LSC-IRb-SSC), while "-noIRs 2" will output the complete plastome sequence (LSC-IRb-SSC-IRa, useful e. g. , before annotation for GenBank submission). The output (fasta file) can be immediately used in software for downstream phylogenetic and genomic analysis (e. g. , Geneious, CLC workbench or MAFFT, PHYML and BEAST).
Taxon selection and dataset construction To test and assess performance of ECuADOR, two main datasets and a reference case were generated. The first (dataset1: data control) was used to reevaluate sequences corresponding to 161 published plastomes from a selection of 51 major angiosperm groups  (Ruhfel et al. , 2014). Fasta sequences of the plastomes were downloaded from GenBank and analyzed using ECuADOR with default parameters and a sliding window adjusted to 1,500 bp. We reviewed the accuracy of IR identification by evaluating the similarity and position of each retrieved region compared to their original annotation, using Geneious R9 v. 9. 0. 5  (Ripma, Simpson & Hasenstab-Lehman, 2014) , with the "Find repeats" function (1,500 bp as minimum repeat length; no allowed mismatch between repeats).
To further evaluate the quality of the obtained locations (LSC, SSC, IRs), we built a phylogenetic tree based on the 161 reanalyzed plastomes and compared this with the previously published topology  (Ruhfel et al. , 2014). The sequences were aligned using MAFFT v. 7  (Katoh & Standley, 2013)  under the FFT-NS-1 option and PhyML v3. 3. 20  (Guindon et al. , 2010)  was used to build a maximum-likelihood (ML) tree with the GTR DNA substitution model and the fast likelihood based method (aLRT SH-like). Secondly, to assess the robustness of our algorithm, we simulated low quality/noisy plastomes by introducing random substitutions with different percentages of variation in the plastome of Arabidopsis thaliana (dataset2: data testing). Eleven levels of variation were chosen, ranging from 0. 01% to 5.
31% and 1,000 simulations were generated for each variation level, respectively, using an in-house script (available at https://github. com/ BiodivGenomic/ECuADOR/). These "low quality" plastomes were then evaluated through ECuADOR with default parameters. Finally, in order to evaluate the applicability of our algorithm to any kind of chloroplast data (not only for families previously analyzed) and taking advantage of its fast detection and extraction speed, a dataset corresponding to 4,541 angiosperm chloroplasts was downloaded from GenBank (database INSDC accessed on 2019/06/20-dataset3: mass data evaluation) and analyzed using default options in the same format. This further served as an additional survey to detect cases with missing or poorly uploaded regions currently available in GenBank.