dataesr/scientific-paragraphs-categorization huggingface dataset:
Indexing of ENA snapshot The fastqs from accessions listed in Supplementary Data 7 were downloaded via ENA's Globus FTP and included all WGS bacteria and viruses, but also eukaryotic parasites with larger genomes, which we did not intend to index. We removed the eukaryotic genomes implicitly, by setting thresholds to exclude datasets with too many k-mers for a 5Mb genome. De Bruijn graphs (k=31) were constructed and cleaned from the downloaded fastq files using mccortex19 v0. 0. 3-539-g22e27b7. The unique k-mers in the union of the cleaned graphs were counted using the redis (v3. 2. 6) hyperloglog (https://redis. io/commands#hyperloglog) approximate cardinality counter. In the union of all cleaned graphs there were 6. 05 × 10 10 ± 5 × 10 7 k-mers. We estimate this number would have been at least an order of magnitude higher without the denoising step where mccortex removed sequencing errors.
Species identification The proportion of species in each dataset was determined using Kraken32 and Bracken31. Kraken v0. 10. 5 was run on the k-mers from each cleaned de Bruijn graph using the minikraken 20141208 database. The resulting taxonomy labels assigned by Kraken were then analysed by Bracken vfd88a06a to estimate the proportion of k-mers originating from each species present in a dataset. Bracken failed to report species abundance for 12,889 datasets. The taxonomic data for the remaining 434,944 datasets is reported in Supplementary Data 6.
Plasmid search and exclusion of contaminated datasets 2,826 plasmid sequences were taken from the ENA plasmid pages (www. ebi. ac. uk/genomes/ plasmid. html; December 2016) (See Supplementary Data 2) and downloaded from the ENA. We then queried the all-microbial-index for these sequences with a proportion of k-mers threshold of 40% (T=40%) present and filtered for hits with T >= 90% for downstream analysis. Queries were run with 1 GB cachesize (memory) per process and parallelised across 8 vCPUs. In order to determine distribution of plasmids across taxa, while avoiding ENA/SRA metadata errors, we filtered these hits for datasets which (were bacteria and) had no secondary genus above 0. 1% frequency. This criterion was chosen to avoid multi copy plasmids from contaminating species establishing false positive hits within a non-host genus.
Conjugative system search MOB (MOB_B, MOB_C, MOB_CQ, MOB_F, MOB_H, MOB_P,MOB_T, MOB_V) and T4SS sequences (VirB4_TRaU, VirD4_TcpA) as defined in Guglielmini et al 39 and Supplementary Data 8 in the all-microbial-index with T=100%. Full search results are available in Supplementary Data 9. Results were filtered for bacteria and contamination following the same method as described in "Plasmid search". Accessions with at least one MOB and T4SS were said to contain a putative conjugative system. BIGSI does not return copy number, or location on chromosome or plasmid, so it was not possible to determine if the genes were co-located on a chromosome or on a plasmid.
Searching for ABR genes in the ENA We downloaded all 2157 sequences associated with antimicrobial resistance from the CARD database (v1. 1. 7)40. We searched for these in the all-microbial-index with thresholds of 100% and 70%, using a 1 Gb cache size, and 8 CPUs. A full table of the search results can be found in Supplementary Data 5.
Searching for M. tuberculosis variants in the ENA We searched the all-microbial-index for the variants from the catalogue described in 27 by generating "probe sets" consisting of a reference and alternate alleles of these variants from the NC_000962. 3 reference and searching for these alleles. If only a reference allele is present the genotype is returned as 0/0, if only an alternate allele 1/1, if both 0/1 and if neither -/-. From the resulting genotypes we classified each of the datasets as resistant or susceptible to 12 antibiotics following the model described in27. The date when this data was first available to the public was extracted from its ENA metadata. Datasets were classed as MDR (multi-drug resistant) if resistant to isoniazid and rifampicin, as XDR (extensively drug-resistant) if MDR and also resistant to a fluoroquinolone, and any of capreomycin, kanamycin and amikacin, and as Resistant if resistant to any antibiotic but not MDR or XDR, and susceptible otherwise.
Editors summary The global set of bacterial and viral sequences can be rapidly searched using a data structure inspired by web-search algorithms. Querying with an assembly, the fingerprint of the assembly is compared with that of RefSeq to find the closest reference. d) Sequence Bloom Tree13 was the first scalable method to search through raw unassembled readsets (unassembled readsets are shown as "piles" of reads (short lines), all in same colour to signify same species), by indexing the k-mers in the data and then compressing the index. Designed for human data, SBT can be applied to find which RNA-seq datasets contain a given transcript. e) BIGSI can search the complete set of raw sequence data for bacteria and viruses. RefSeq is shown in a dotted box amongst unassembled readsets; different colours to signify the massive range of species and phyla.
The different input data for SBT and BIGSI mean that these methods have different speed and compression trade-offs. is applied to each k-mer (η =3 in this figure), giving a tuple of positions which are all set to 1 in a bit-vector (a Bloom Filter). b) In step 2 each dataset is stored as a fixed length bloom filter, as a column in a rectangular matrix. To query the BIGSI for k-mer AAT, the η hash functions are applied to the query k-mer, returning η rows to be checked (namely 3,7,5 here). All columns (datasets) that have 1 in all of those η rows contain the query k-mer: these rows that are checked are called "bitslices". A hash, mapping from row index to corresponding bit-array is stored to allow fast, i. e. O(1), access to each row when needed. Adding a new dataset requires adding a new column.
c) Naïve encoding is shown to contrast with the BIGSI approach. A complete list of all k-mers in all datasets form the rows of a large matrix, and columns are datasets. For any given k-mer, entries are set to one for datasets containing that k-mer. When a new dataset is added, the matrix grows vertically (new k-mers added) and horizontally (new column for new dataset). Benchmarking of BIGSI against fast and small versions of each of SBT and SSBT, using a set of 2,157 antimicrobial resistance genes as a query dataset. We performed inexact search (T = 40%) and show query speed vs. peak disk size when searching databases of sizes from 10-10,000 microbial datasets. Both axes are on a log scale; the diameter of a dot represents the number of datasets indexed. To compare two methods it is necessary to compare dots of the same size.
The ideal method would produce dots towards the bottom-left. For database sizes greater than 2000, we were unable to build the SBT-fast/SSBT-fast as their uncompressed disk usage exceeded available space; triangles signify estimated values based a calculated lower bound for disk use (as k-mer content is known), and extrapolated query times (Online Methods). Plasmid sequences (37) that are found at least 5 times in more than one genus in the allmicrobial-index are shown. The heatmap shows the frequency of each plasmid in each genus. The plasmids/genera were hierarchically clustered using the UPGMA algorithm and Euclidean distance metric. The plasmid at the left (AF012911) with extremely wide phylogenetic distribution is a known cloning vector. The large amount of sharing between Escherichia, Salmonella and Shigella is consistent with known promiscuity within Enterobacteriaceae.
mccortex31 build -t 1 -m 7G -k 31 -s "DATASET_ID" -1 "FASTQ_FILES" mccortex31 clean -m 7GB -B 2 -U -T De Bruijn graph error cleaning and tip trimming were performed using mccortex. Bloom filters were built using the k-mers from the cleaned graphs with parameters (m = 2. 5 × 10 7 ;η = 3, K max = 10 7 ) with BIGSI v0. 2 as follows: cbg init -k 31 -m 25000000 -h 3 cbg bloom -c "CLEANED_GRAPH_FILE" Of the full set of datasets, 4. 6% (21,822/ 469,654) fastq files failed to produce a resulting bloom filter. Of these 21,822: 7,799 exceed the maximum number of k-mers allowed after error cleaning (namely 10 7 ) and 14,023 exceeded the maximum number of k-mers allowed in the raw dataset (namely ~7x10 9 ).
c) MASH stores a tiny fingerprint of each reference in the database (in this case RefSeq). Querying with an assembly, the fingerprint of the assembly is compared with that of RefSeq to find the closest reference. d) Sequence Bloom Tree13 was the first scalable method to search through raw unassembled readsets (unassembled readsets are shown as "piles" of reads (short lines), all in same colour to signify same species), by indexing the k-mers in the data and then compressing the index. Designed for human data, SBT can be applied to find which RNA-seq datasets contain a given transcript. e) BIGSI can search the complete set of raw sequence data for bacteria and viruses. RefSeq is shown in a dotted box amongst unassembled readsets; different colours to signify the massive range of species and phyla.
Acknowledgements We would like to thank, for critical reading and helpful suggestions:  Grace Blackwell ,  Robyn Ffrancon ,  Martin Hunt ,  Jerome Kelleher ,  Janet Thornton ,  Rob Patro ,  Kerri Malone ,  John Marioni ,  Andrew Page ,  Simon Gog ,  Timo Bellman ,  Florian Gauger. For enormous assistance with data download from EBI:  Robert Esnouf ,  Guy Cochrane. For hosting our BIGSI demonstration: CLIMB. We acknowledge funding from  Wellcome Trust   Core Award  Grant Number  203141/Z/16/Z. ZI was funded by a  Wellcome Trust/Royal Society Sir Henry Dale Fellowship , grant number  102541/A/13/Z. GM was funded by  Wellcome Trust  grant  100956/Z/13/Z. ER was funded by the  ANR MAGISBAC  grant number  ANR-14-CE10-0007-02. PB was funded by  Wellcome Trust  Studentship  H5RZCO00.
Code availability An open source implementation of BIGSI can be found at https://github. com/phelimb/ BIGSI. BIGSI v0. 3. 0 supports disk-based indexing via Berkeley-DB, or rocksDB, and distributed in-memory (via redis (https://redis. io)) key-value stores and can be extended to any key-value store. The benchmarking uses the rocksDB key-value store, and v0. 2. 0 of BIGSI, and the all-microbial index uses Berkeley-DB and BIGSI version v0. 1. 7.
These retests and confirmatory tests generate additional reagent and staff costs. For example, an immunoblot requires technician time, slows the transmission of the patient's test results to the clinician, and significantly increases the cost. Thus, the primary objective of the present study was to evaluate the Centaur® CHIV assay in a large cohort of patients from 2012 to 2018 in a region with a low prevalence of HIV infections. The study's secondary objective was to use machine learning software (Orange Data Mining (  6 )) to refine our decision model and thus provide clinicians and laboratory diagnostic staff with more accurate, cheaper, and more rapidly available HIV screening test results. We hypothesized that an artificial-intelligence (AI)-based analysis of reactive HIV serological assays in our population could (i) increase the Centaur® CHIV assay's cut-off index aiming an improved PPV without loss of sensitivity, and (ii) generate a decision tree for easy application in routine clinical practice.
at least one positive confirmatory test) or false positives (i. e. negative confirmatory tests). As recommended by the manufacturer, all the non-reactive CHIV assays (CHIV index <0. 8) were considered to be true negatives for both the Ag and the Abs. Our data were managed and visualized using Orange Data Mining, an open-source software package for visual programing based on Python  (6). To perform data mining on positive results and improve PPV, we used case and control groups of equivalent size. In order to balance our groups, an equal number of non-reactive results and reactive/indeterminate results were randomly selected through a command of Orange Data Mining. Next, we used Scikit-learn (7) (a Python AI package for machine learning with Orange Data Mining) to build a classification tree that featured a sensitivity of 100% below the newly defined CHIV cut-off index.
In our study, a cut-off of 2. 12 combined a sensitivity of 100% with greater specificity. The application of this value would make the interpretation of Centaur® CHIV results more efficient, by reducing the number of FPs by 76. 02% which leads to a PPV improvement from 61. 92% to 86. 88%. Moreover, we used our data to build a classification tree that could help the laboratory diagnostic staff in CHIV index > 2. 12 interpretation. The application of the algorithm highlighted a PPV up to 95. 86%. This would have saved both time and money (additional test reagents, etc. ). Overall, these improvements would accelerate the delivery of HIV test results for poorly reactive assays, and receiving a negative result faster could improve care. We built a large database from the HIV serological assays performed in Amiens University Medical Center over the past 6 years, and used it to develop an easy-to-visualize, AI-based classification tree with several useful applications.
RESULTS We developed our model with the open source software Cytosim  (Nedelec and Foethke, 2007). Cytosim is a very flexible agent-based cytoskeleton simulation engine following Langevin dynamics (see Materials and Methods). We first aimed at identifying the minimal necessary components of the system that would collectively generate the desired behavior in a simulation, given their identified properties. We specifically focused on explaining the tight regulation of spindle assembly by HSET  (Bennabi et al. , 2018). With this in mind, we identified from the literature (reviewed in  Marlow, 2018; Mogessie, Scheffler, and Schuh, 2018)  the components that seemed essential to our model (Figure  1 ; Supplemental Materials): 1) explicit dynamics of all MTs, 2) Ran-mediated MT nucleation around mobile chromosomes, 3) MT-nucleating aMTOCs, 4) the plus end-directed kinesin 5 (Eg5), and 5) the minus end-directed kinesin 14 (HSET).
Depending on the studies, we collected data from the literature on either spindle shape, aMTOCs position, chromosome position, spindle length, or area or microtubule density. We combined the last three measures into one single measure of "spindle size. " In the majority of cases, the literature provided us with only a qualitative idea of these features. To compare those results, we classified   1 ). Specifically, we considered that simulation behavior was in agreement (green check mark) when the measured value of spindle aspect ratio, aMTOC position, or chromosome position was correspondingly above or below our arbitrary fixed threshold of 0. 5 (50%). For spindle size, we considered the behavior as correct if the trend of change (increase, decrease, or unchanged) was similar. Overall, we used experimental data obtained for 12 different perturbations, and predicted the phenotype for eight additional perturbations.
Numerical simulations Cytosim (www. cytosim. org) is an opensource program designed to simulate cytoskeletal systems. It calculates the motion of semiflexible filaments according to Langevin dynamics  (Nedelec and Foethke, 2007)  and can simulate a large number of filaments and associated proteins. It has already been used to study the auto-organization of cytoskeletal systems composed either of actin filaments  (Letort, Politi, et al. , 2015; Ennomani, Letort, et al. , 2016)  or of microtubules  (Surrey, Nedelec, et al. , 2001;  The majority of perturbations that have been explored experimentally so far indicate that increased chromosome individualization is usually associated with chromosome segregation errors (Figure  5 , In the bottom row, the morphologies of the system were represented, based on the results from Table  1 : microtubules are in green, chromosomes in blue, and aMTOCs in red.
ACKNOWLEDGMENTS We thank the members of the Verlhac/Terret team for helpful discussions and  Renata Basto  for sharing Kinesin 5 antibody. This work was funded by the  Agence Nationale de la Recherche (ANR)  ( ANR-16-CE13  to MET) and by  Inca  ( PLBIO 2016-270-TRAN ). This work has received support from the  Fondation Bettencourt Schueller  and under the program " Investissements d'Avenir " launched by the  French Government  and implemented by the  ANR , with the references  ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02   PSL* Research University. F. N. was supported by the  Centre for Modelling in the Biosciences  (https://www. bioms. de)and the  European Molecular Biology Laboratory  (EMBL).
Expression of the androgen receptor splice variant 7 (ARV7) in circulating tumor cells (CTCs) has been associated with resistance towards novel androgen receptor (AR)-targeting therapies. While a multitude of ARV7 detection approaches have been developed, the simultaneous enumeration of CTCs and assessment of ARV7 status and the integration of validated technologies for CTC enrichment/detection into their workflow render interpretation of the results more difficult and/or require shipment to centralized labs. Here, we describe the establishment and technical validation of a novel ARV7 detection method integrating the CellSearch ® technology, the only FDA-cleared CTC-enrichment method for metastatic prostate cancer available so far. A highly sensitive and specific qPCR-based assay was developed, allowing detection of ARV7 and keratin 19 transcripts from as low as a single ARV7 + /K19 + cell, even after 24 h of sample storage.
"Mock" samples were generated to mimic clinical sample handling. Differing amounts of ARV7 - and ARV7 + cell line cells were manually spiked into healthy donor (HD) blood and directly processed by our workflow (Table  1 ). Following the qPCR run, generated products were applied to a gel electrophoresis allowing final confirmation of gene expression status (data not shown). Table  1. Validation of protocol specificity and sensitivity. Titration experiments of spiked cell lines in blood from healthy donor (HD) samples. Indicated cell counts of ARV7 + (22Rv1) and ARV7 -(PC3) cells were manually spiked into HD blood and processed by our protocol. ARV7 status is highlighted as "+" for positive and "-" for negative samples. Detection of gene expression was confirmed when at least 2/3 triplicates were positive in qPCR analysis.
Assessment of Sample Storage Parameters mRNA instability represents a common issue for RNA analysis. Sample processing time frames and optimal blood collection tubes therefore need to be carefully assessed to allow for reliable mRNA detection. As cells are not fixed in EDTA blood tubes, which is essential for subsequent RNA isolation, potential CTCs could deteriorate over time. This is especially crucial when calculating time frames for shipment of clinical samples. EDTA blood spiked with cell lines was left at room temperature (RT) for 24 h (Table  2 ) and 48 h (Table  3 ), respectively, to test processing windows. Following the qPCR run, generated products were applied to a gel electrophoresis allowing final confirmation of gene expression status (data not shown). Table  2. Validation of protocol specificity and sensitivity after 24 h.
Clinical Feasibility of the Complete ARV7 Detection Workflow The clinical feasibility of our assay was demonstrated by analyzing blood samples of 26 metastatic prostate cancer (mPCa) patients. Detailed clinical patient data is listed in Table  S1. qPCR based ARV7 analysis was performed within 24 h of sample collection from 7. 5 ml of EDTA blood for all 26 patients. Parallel blood draws to assess CTC counts via CellSearch ® were collected and processed for 23/26 patients. AR-FL staining in the fourth fluorescent channel was available for 12/23 patient samples processed via CellSearch ® (Table  4 ). Table  4. Correlation of qPCR results, AR-FL detection and CTC enumeration via CellSearch for 26 mPCa patients analyzed. Detection of a gene was confirmed when at least 2/3 triplicates were positive in qPCR analysis.
Discussion The CellSearch ® Profile technology allows a reliable, standardized, and automated enrichment of EpCAM-positive cancer cells. ARV7 expression in CTCs of prostate cancer patients has been linked to resistance toward AR-targeted therapy, in particular enzalutamide and abiraterone  [20, 25]. Our novel approach ensures specific detection of ARV7 transcripts in CTCs isolated by the CellSearch ® system down to the single cell level. The specificity of our assay was indicated as no signals for ARV7 or K19 were seen in 9 blood samples from healthy, male individuals (Tables  1 2 3 ). Our protocol ensures specific detection of tumor cell transcripts in 7. 5 mL of blood even after 24 h of sample storage (Table  2 ). Robust ARV7 and K19 detection is feasible in as low as 5 ARV7 + /K19 + cells (Table  2 ).
Whether this antibody might represent a promising novel candidate for immunocytochemical analysis and combination with CellSearch ® needs to be investigated in future studies. However, so far most sources of CTC-related ARV7 information stems from RNA measurements. The meaningful clinical impact of ARV7 expression of CTCs  [20, 46]  has led to the development of a multitude of different assays targeting ARV7 protein  [25]  or ARV7 transcripts  [30, 31, 47, 48]. Primarily the developed methods are based on the analysis of pooled lysate of an enriched CTC fraction  [26, 30, 48] , only few perform whole blood gene expression analysis  [47]. CTC are enriched by bead-based approaches  [20, 48] , or the CellSearch ® Profile kit and analyzed by subsequent qPCR or RNA-seq  [26, 30]. While these approaches effectively asses ARV7 status, they give no additional information on the abundance of CTCs in a patient at the time point of blood draw.
Apart from the AR-FL targeting antibody (by Janssen Diagnostics) used in this study, other well-established alternative antibodies have been published for AR-FL detection in the fourth channel of the CellSearch ®  [55]. To our knowledge, only two assays have been developed allowing parallel CTC enumeration and ARV7 protein  [24, 25]  or transcript detection  [31]  on the same cell so far. El-Heliebi et al. isolated CTCs via the CellSearch ® Profile kit or the size-dependent Parsortix™ platform (ANGLE plc, Guildford, UK)  [56]  and subsequently characterized them for ARV7, AR-FL, and PSA expression via in situ padlock probe technology  [31]. This approach allows for absolute transcript quantification while keeping cell morphology intact and thereby enabling tumor cell enumeration  [31]. In regards to CellSearch ® Profile kit pre-enrichment, a single patient with high CTC load was included in this study to demonstrate general feasibility of the approach  [31].
Plant and pathogen materials Darmor-bzh is a winter dwarf line of B. napus, nearly isogenic to the genotype Darmor  (Foisset et al. , 1995) , with an available genome sequence  (Chalhoub et al. , 2014). Yudal is a spring inbred line of B. napus selected from a Korean population. The segregating doubled haploid (DH) population used in this study was derived from an initial cross between the genotypes Darmor-bzh and Yudal  (Foisset et al. , 1996). The progeny, hereafter called the DY population, was first genotyped by  Delourme et al. (2006). Based on these data, a subset of 130 DH lines was selected from this population, using the MapPop software  (Brown and Vision, 2000)  to optimize recombination events over the whole genome. eH is a selection isolate of P. brassicae  (Fähling et al. , 2003).