dataesr/scientific-paragraphs-categorization huggingface dataset:
Accordingly, the proportion of unassigned reads is directly linked to both host and symbiont libraries content with respect to the studied holobiont. Overall, less unassigned reads were observed when the "correct" actors are involved (M1a: 24. 4%) compared to the poorly studied models (M2: 61. 6% and M3: 72. 5%). These results highlight the sensitivity and specificity of the SRC_c requests that relies on the completeness of the database to accurately sort the reads of the holobiont. The SRC_c assignation step could be further improved by adding more sequences (i. e. , reads, assembled genes, or transcripts) from taxonomically close species (from existing SRC strategy's impact on assembled contigs quality and calculation times of the Radiolaria-Dinophyta holobiont model (M3) compared to a direct metatranscriptome assembly strategy (i.
e. , the noSRC strategy). In gray are displayed the details for the SRC strategy holobiont categories (host, symbiont, shared, and unassigned). The "total" values for N50 and remapping rates of the SRC strategy were re-calculated on pooled contigs from host, symbiont, shared, and unassigned subsets databases or newly produced) to the host and symbiont reference libraries, but also from multiple actors such as parasites and viruses that are common in multicellular and unicellular host cells. In this way, as SRC_c is a highly scalable tool, which has been improved since its first release  [39]  (Additional file 6), it is now possible and it will be relevant for future transcriptomic symbiosis studies to involve more than two reference libraries because symbiotic associations are often more intricate  [27] [28] [29] [30] [31].
qiagenbioinformatics. com/)]. Previous studies had shown that Trinity is able to generate more assembled contigs than the CLC assembler when applied on the same dataset  [40]. It is also known that assembled contigs from Trinity are shorter than those assembled by CLC but provided similar proportion of significant hits against the nr database  [40]. With M1a, our strategy produced 1. 5 times more CDs with a functional annotation (Fig. 3 ). At that point, we are unable to tell whether this observation can be the consequence of a better suited assembly strategy (SRC_c treatment and/or assembly software), and/or the use of a different annotation pipeline, and/or the supplementation of reference annotation databases between 2015  [24]  and 2017. With M3 analyses, we could estimate how SRC_c impacts the de novo assembly step and downstream analyses compared to a more conventional protocol (i.
Contrary to BLAST-like methods, SRC_c relies uniquely on shared k-mers for its similarity computation. It means that a certain amount of error-free k-mers (i. e. , k-mers that do not contain sequencing errors) must be found in common in order to output sequences, which can make SRC_c less sensitive compared to alignment methods which authorize mismatches. However, contrary to alignment methods, SRC_c was tailored to scale to very high volume datasets, and comparisons presented in  [23]  showed that SRC_c could handle sets of orders of magnitudes higher volumes than BLAST (Additional file 6). SRC_c's efficiency relies on its particular probabilistic data structure. The lightweight indexing and query of k-mers is made at the price of rare false positives. In our case, false positives correspond to k-mers that are not contained in the original indexed library.
This can be implemented as an iterative pipeline: at each round, more reads will be assigned to the host or symbiont categories and will then be used as reference libraries. Finally, the approach proposed here has been applied to holobiont systems (between two partners), but it could be used to address larger meta-transcriptomic datasets composed of more complex assemblages. Depending on the SRC_c library content, the user can choose to target either one or more specific species among the variety that composed such meta-transcriptomic datasets. Coupled to our assembly and downstream analysis strategy, the subsets resulting of the used of SRC_c are processed de novo allowing the potential discovery of newly assembled transcripts and the exploration of the functional and metabolic role for the first time of each partner without reference genome.
Conclusions SRC_c successfully processed a variety of large-scale datasets and offered a pragmatic way to classify sequences from different holobiont partners before assembly. We showed that our strategy allows improving assembly metrics in some cases and, in any cases, ensured to reduce the number of chimeras and to produce high-quality newly de novo assembled sequences. Our approach offers an efficient strategy to assemble and study holobionts involving non-model organisms. Overall, this de novo approach, allowing a taxonomic categorization of functionalities, can reveal the link between identity and function, which is necessary to better understand the functioning and contribution of each partner in holobiont systems. Applying our strategy will thus provide new insights into future adaptative and evolutionary studies of the symbioses.
Data retrieval and sequence libraries construction For each of the three holobiont models (Fig. 2 ), we built reference sequences libraries representing host and symbiont(s) by selecting the taxonomically closest organisms available in public datasets (Additional file 7). For the Cnidaria-Dinophyta holobiont model (M1), the host library includes 20 assembled transcriptomes (466,582 contigs) of cnidarian organisms  [51]  (including data from the host species Orbicella faveolata itself ) and 2 genome-derived ESTs (201,677 ESTs) of Nematostella vectensis and Orbicella faveolata  [52]. The symbiont library is composed of 123 RNA-seq reads datasets (a total of 5,563,498,607 reads) of Dinophyta (including the presumed major symbiont Symbiodinium spp. [53] ) from the MMETSP project  [54]. We built three versions of the symbiont reference library, one composed of all Dinophyta (M1a), the second exclusively composed of Symbiodinium spp.
(15 RNA-seq datasets, a total of 123,122,726 reads) (M1b), and the third composed of all Dinophyta except Symbiodinium spp. (108 RNA-seq datasets, a total of 5,440,375,881 reads) (M1c). For the Porifera-Bacteria holobiont model (M2), four RNA-seq datasets of poriferan species were included in the host library (642,229,924 total reads): Amphimedon queenslandica  [55] , Crella elegans  [56] , and both Haliclona amboinensis and Haliclona tubifera  [57]. The M2 symbiont reference library corresponds to the Tara Oceans meta-genomic gene catalogue (OM-RGC) assembled from the pico-planktonic fractions (< 3 μm) including Eubacteria or Archaea  [58]. It is composed of the bacterial gene catalog  (40, 154 ,822 assembled gene sequences) which has been downloaded from the OM-RGC website (http://ocean-microbiome.
embl. de/companion. html). For the Radiolaria-Dinophyta holobiont model (M3), we gathered Rhizaria sequences from four de novo assembled holobionts: 7215 presumed host transcripts were extracted among a total of 15,404 de novo assembled transcripts  [15]. Host specific sequences were extracted from holobionts assemblies removing first sequences from prokaryotic origin with a BLASTn (e-value 1e-3) against the OM-RGC database, and second, removing symbionts sequences with a BLASTx (e-value 1e-3) against Dinophyta de novo assembled transcriptomes  [46]. The exhaustive Dinophyta library created for the M1a was used for the reference symbiont library. All reference libraries described above include assembled transcriptomes, genomes, or RNA-seq raw read datasets for eukaryotic or prokaryotic holobiont partners (Additional file 7).
The fingerprint table is used to verify the membership of an element to the indexed set of elements using the MPHF. This way, stranger elements to the MPHF can be filtered out. The quasi-dictionary is a probabilistic structure with a controlled false positive rate that depends on the size of the fingerprint. SRC_c needs as input two sets of sequences (that can be identical). To compare sequences from a query set Q to those from a target set T, the set indexed in the quasi-dictionary is a set of k-mers from T. Finally, for each sequence S from Q, the number of k-mers of S shared with T provides a similarity measure of S with the set T. This implies that the similarity measure given is asymmetrical: it depends on the placement of the k-mers on the reads of Q, not of those of T. SRC_c is available at https://github.
Application on data In this study, SRC_c is used to assign reads from a holobiont meta-transcriptome either to the host or to the symbionts. We divided the query of the holobiont dataset Q in two parts, one that consists in the comparison of Q reads to a bank (i. e. , reference library) of host sequences and another that performs the comparison to a bank of symbiont sequences. The sets to index are composed of k-mers from the sequences. In each comparison, two sequence sets are considered: the whole holobiont set Q and the target bank set B. First, the set B, which contains reads or assembled sequences and represents sequences close to the host (resp. symbiont), is indexed. During the indexation phase, the solid set of k-mers (i. e. , the set composed of any k-mer which occurrence is above a user-fixed threshold (the solidity threshold) in the data set) from T is computed using the DSK  [60]  method.
This set is next indexed in the quasi-dictionary previously described. Then, the reads from the holobiont data set (Q) are queried. For each read, the query phase reports the abundance of its indexed k-mers. In the meantime, reads are checked to have enough positions (i. e. , more than a given threshold which can be parameterized) for which an indexed k-mer starts over their length. This enables to add stringency to the query: a read that shares only a few k-mers with the index is considered not enough similar to the index. Finally, each read from Q (the holobiont) which was found similar to T (the host or the symbionts) during the query are returned in a binary vector and can be extracted to a FASTA format.
Parameters choice Parameters from SRC_c were carefully chosen. First, the solidity k-mer solidity threshold was adapted according to the nature of the sequences in the bank data set. For libraries for which sequences were shorter than 300 bp with a relatively high coverage (e. g. , M1 symbiont library involved only reads), the default value was kept (solidity threshold = 2). For longer sequences (e. g. , M1 host library was composed of ESTs and M2 symbiont library was composed of de novo assembled genes), the threshold was adapted and set to 1. Due to the presence of small reads (50 bp) in our holobiont datasets, we also modified the default k-mer size value of 31 to a value of 25, so that any read contains at least a few k-mers. Usually, the k-mer size is higher  [49] ; however, 25 base pairs correspond to a decent value to ensure the uniqueness of the read  [61].
During the query phase of SRC_c, a query sequence (from a dataset Q) must contain at least s% positions covered by at least one indexed k-mers (from a dataset B), to be considered similar to data from the set B  [23]. As the s default value is set to 50%, it means that a read of size l should have at least l × s positions covered by (overlapping or non-overlapping) indexed k-mers. Consequently, when a large majority of the reads could not be assigned, our strategy was to decrease the s parameter from 50 to 40 in order to increase the quantity of recalled reads. We set the similarity value s to 50% for M1 and M3 and decreased it to 40% for M2. Both query and indexation phases are parallelized in SRC_c. For this study, analyses were performed on a Linux system with 40 cores, with the option -t 0 (maximal number of available threads is used), and 250 GB of memory.
Read filtering, de novo assembly, and downstream analysis For M1, M2, and M3 datasets, SortMeRNA  [62]  has been used with default parameters with the Silva 104 SSU and LSU nr reference databases, in order to estimate the proportion of reads corresponding to rRNA sequences. For M2, in comparison to the original publication in which the CLC workbench has been used and 41% of rRNA reads has been detected  [29] , we finally chose to consider the total read set (i. e. , 16,818,599 more reads than  [29] ) in the assembly step as the rRNA detection with Sort-MeRNA detected only 8% or rRNA reads. All read subsets resulting from the SRC_c step were first filtered (sequences trimming and cleaning) with the Trimmomatic program  [63]  (v0. 36) and custom parameter SLIDINGWINDOW:10:20. Filtered reads were assembled using the de novo transcriptome assembly program Trinity  [37]  (v2.
4. 0) with default parameters. The newly assembled contigs metrics were calculated with the Transrate program  [64]  (v1. 0. 3). Additional downstream analyses include protein coding domain prediction using Transdecoder  [65]  (v3. 0. 1) and functional annotation with InterProScan 5  [66]  (v5. 24-63), both with default parameters. The pipeline used for the steps described above is publicly available on a GitHub repository https://github. com/arnaudmeng/dntap [46].
Taxonomic assignment with MEGAN6 The contigs sequences were compared to the nr database (August 2017 version) with the DIAMOND software  [67]  (v0. 28. 22. 84) using default parameters for BLASTx comparison and a e-value of 1e-3. The resulting alignments were processed with the daa2rma tool script provided with MEGAN6, and GeneInfo Identifier (GI) was mapped to alignments using the gi_taxid. bin file (version of May 2017). Finally, taxonomic assignment has been calculated with default parameters using the MEGAN LCA (Last Common Ancestor) algorithm and was visualized through the MEGAN6 software.
Chimera identification We followed the protocol described in  [68]. Fifty thousand randomly sampled de novo assembled contigs for the M3 (with the SRC strategy and without SRC strategy) were compared to the 7215 Rhizaria presumed contigs from  [15]  and 3,494,295 coding domains from de novo assembled contigs of 54 dinoflagellates transcriptomes  [46]. The comparison was made using the BLASTx program  [69]  (e-value Gaston Crémieux, 91057 Evry, France. 5 UMR8030, CNRS, Evry, France.
Acknowledgements We thank the  RCC  staff for providing the dinoflagellates cultures as well as  ABIMS  staff for the help on computational facilities. This work was supported by a 3-year Ph. D. grant from the " Interface pour le Vivant" (IPV) program  at the  University of Pierre et Marie Curie (UPMC), Paris, France. This project was supported by  Région Ile-de-France  and benefited from the support of the project IMPEKAB  ANR-15-CE02-0011.
Methods We obtained IRB approval from the New Mexico Health Enhancement and Marathon Clinics Research Foundation (NMHEMC Research Foundation) for the measurements of hands and feet in modern subjects using calipers. Informed written consent was obtained from living subjects. We based our analyses on 221 data points available from measurements of dominant hands and feet in modern people from Europe, the Americas, Australia and Africa. We supplemented our measurements of hands and feet in various media (photos, paintings, cave stencils, chiseled images and images of bas-reliefs) gleaned from the World Wide Web (www. com) of ancient images and modern images (Table  1 ). In living people (modern), our gold standard, we used calipers (General® stainless steel digital Fractional Caliper; precision ± 0.
At the end of a trial, the fish was removed from the test arena with a beaker, returned to its respective treatment water, and the test arena was rinsed with seawater. All videos were analyzed blind to treatment using Lolitrack software (v4. 1. 0 Loligo Systems, Tjele, Denmark), which tracked and quantified the movements of the fish. The first and last minute of each video were discarded to allow for the researcher to enter and exit the arena area. Before each video analysis, a circular arena was drawn within the test arena, with the same central point, but which was 13 cm in diameter, or approximately three body lengths away from the edges of the test arena. This "inner zone" was used to quantify boldness. The open field test has been commonly used in fishes to determine boldness based on the idea that a novel, open field is considered dangerous, and that venturing into the inner zone represents boldness, or the willingness to undertake risk  [44, 68].
Statistical Analyses All analyses were conducted using R version 3. 1. 3 [77]. Linear mixed-effects models (LME, "nlme" package in R) were used to determine the effect of CO 2 and temperature treatment on behavioral and metabolic traits. Total distance traveled (in body lengths) and velocity (in body lengths per second) were standardized by body length to facilitate comparisons between treatments where fish were differently sized. For these dependent variables, the CO 2 and temperature treatments were fixed effects, with time of day as a covariate, allowing for interactions between CO 2 treatment, temperature treatment, and time of day. Time of day was mean-centered to help with the interpretation of model intercepts. For time spent active and time spent in the inner zone, similar linear mixed effects models were used, with the addition of mean-centered mass as a covariate, since these measures were not standardized by fish length, but distance traveled and velocity were.
Acknowledgments: We thank  Bridie Allan ,  Michael Exton ,  Simon Griffiths ,  David McQueen ,  Alvin Setiawan , and  Carly Wilson  for assistance with fish husbandry and maintaining the experiments. We thank  Kim Currie  and the  University of Otago Research Centre for Oceanography  for water sample analysis,  Rhondda Jones  for statistical advice, and  Erin Walsh  for illustration.
6 ± 3. 0 in selected cells and 2. 5 ± 1. 4 in non-selected cells. Before transplantation, aliquots of cells were cultured on meth-ylcellulose. The mean VCN was 1. 68 ± 0. 01 for selected cells and 0. 87 ± 0. 01 for non-selected cells. As expected, the proportions and distributions of insertion sites were significantly different from those obtained for a matched random control dataset (Figures  S12  and S13 ; Table  S1 ). No significant difference between non-selected and selected in vivo samples was observed for any of the features investigated including the distance of insertion sites to oncogenes (Table  1 ). We can, therefore, conclude that drugmediated selection had not biased the distribution of insertion sites beyond the possible bias due to in vivo expansion. We evaluated the potential distribution distortions specifically due to in vitro drug selection or in vivo expansion through the following comparisons: (1) non-selected versus selected samples in vitro, to evaluate the effect of drug selection; (2) non-selected in vitro versus nonselected in vivo samples, to evaluate the role of cell expansion; and (3) in vitro selected versus in vivo selected samples, to determine whether any insertion biases observed were additive (Figure  S14 ).
For subjects with b-thalassemia, the association of G-CSF and plerixafor has been shown to display strong synergy,  64  increasing the number of mPB CD34 + cells, and is the preferred combination in ongoing clinical trials. 2  Plerixafor has also been shown to mobilize HSCs effectively without the detrimental effect of G-CSF. It is currently tested in humans with severe SCD (NCT02140554 and NCT02193191) and may enhance the number of collected hematopoietic (stem) cells prior to ex vivo culture. Additionally, one part of the drug product may be expanded ex vivo with agents such as StemRegenin-1 or UM171 before cell infusion, in order to support and accelerate neutrophil and platelet recovery. 65, 66 Myeloablative conditioning with busulfan also needs to be optimally delivered to achieve maximal hematopoietic engraftment by meticulous monitoring by pharmacokinetics analyses and adjustment of the dosing to a fully myeloablative target value, as currently conducted in ongoing clinical trials.
Flow Cytometry and SCID-Repopulating Cell Assays The antibodies used for cytometry were directed against murine CD45 (clone 30-F11; Thermo Fisher Scientific), human CD45 (clone 5B1; Miltenyi Biotec), human CD33 (clone WM53; BD Biosciences, Le Pont de Claix, France), human CD15 (clone VIMC6; Miltenyi Biotec), human CD20 (clone 2H7; BD Biosciences), human CD3 (clone BW264/56; Miltenyi Biotec), human CD133 (clone AC133; Miltenyi Biotec), human CD34 (clone 581; BD Biosciences), and human CD243 (clone UIC2; Thermo Fisher Scientific). Isotype control antibodies were used to set the negative gates for the in vitro identification of human cell subsets. Non-viable cells were excluded by SYTOX Blue nucleic acid staining (Thermo Fisher Scientific) or with 7AAD (Thermo Fisher Scientific). Data were acquired with a MACSQUANT instrument (Miltenyi Biotec) and analyzed with FlowJo software (Tree Star, Ashland, OR, USA).
The percentages of human cells in NSG bone marrow were calculated as follows: the number of human CD45 cells was divided by the number of human plus mouse CD45 cells, and the result was multiplied by 100. For limiting dilution assays and the assessment of human stem cell frequency, mice were arbitrarily considered to be positive for human SRCs if the level of human chimerism 3 months after transplantation was >1%, with the presence of both myeloid and lymphoid cells. Mice that had not undergone the transplantation procedure were used as a negative control. Background levels of human cell detection in these negative control mice were z0. 01% (Figure  S6 ). The frequency of SRCs was calculated by using Poisson statistics and L-Calc software (STEMCELL Technologies).
Statistical Tests Comparisons between two or more groups were performed using Student's t test, Mann-Whitney U tests (if the data were shown to be non-normal in Shapiro-Wilk tests and/or if equal variance test failed), and one-way ANOVA (possibly based on ranks). Degrees of significance were calculated with SigmaPlot 10. 0 software. Bestfit curves rising to a maximum were constructed with SigmaPlot 10. 0. We assessed the significance of differences between proportions of cells bearing various numbers of LV copies in experimental groups and expectations, by creating 2 Â 2 contingency tables assuming equivalent numbers of cells in the two groups. Nonrandom associations between variables were assessed by Fisher's exact test. Odds ratio and 95% confidence interval were calculated with GraphPad Prism 6.
ACKNOWLEDGMENTS This work was supported by an  Industrial Chair (HemGenTher) from France's "Agence Nationale pour la Recherche" (ANR)  awarded to P. L. We thank  Manfred Schmidt  and  Cynthia Bartholomae  ( National Center for Tumor Diseases, Heidelberg, Germany ) for teaching the linear amplification-mediated (LAM)-PCR technique to E. D. ,  Inpyo Hwang  for technical help,  Che Serguera  and  Noëlle Dufour  ( MIRCen, CEA de Fontenay aux Roses, France ) for their help in the implementation of the LAM-PCR protocol, and  Benjamin Versier  and  Didier Thenadey  ( MIRCen, CEA de Fontenay aux Roses, France ) for implementing the local Galaxy platform. Next-generation sequencing was performed at the high-throughput sequencing core facility of  I2BC (Centre de Recherche de Gif-sur-Yvette, France ; http://www.
041 a 0. 772 0. 475 0. 758 0. 565 p value d 0. 906 0. 375 0. 642 0. 472 0. 362 0. 378 0. 369 MRC, matched random control; NS, non-selected cells; onco, oncogene; Sel, selected cells. *p < 0. 05, **p < 0. 005, and ***p < 0. 0005, respectively, by Fisher's exact test (two-tailed) comparing test samples with MRC dataset. a p < 0. 05. b Fisher's exact test (two-tailed) comparing samples from non-selected and selected groups. c Fisher's exact test (two-tailed) comparing in vitro and in vivo dataset of non-selected samples. d Fisher's exact test (two-tailed) comparing in vitro and in vivo dataset of selected samples.