dataesr/scientific-paragraphs-categorization huggingface dataset:
f Flow cytometry quantification of Ly6C high and Ly6C low monocyte subsets in the peripheral blood of young (<6 months, WT = 28, KO = 17) or older (6-26 months, WT = 26; KO = 21 KO) animals expressed as fraction of total monocytes (upper panels) or absolute number per mouse (lower panels). Mean ± SEM, two-tailed unpaired t test: *P < 0. 05; **P < 0. 01; ***P < 0. 001; ****P < 0. 0001. g Flow cytometry measurement of the fraction of Annexin V + cells among Ly6C high and Ly6C low monocytes in the peripheral blood and bone marrow of 6 WT (gray bars) and 10 Mir150 -/-(red bars) animals (mean ± SEM). NS nonsignificant was a promoter with ongoing transcription  40, 41. GRO-cap analyses (GSE60456 accession) identified a distinct transcriptional start site (TSS) on the minus DNA strand in K562 myeloid cells and GM12878 B cells, the TSS being located on R3 in K562 cells (Supplementary Figure  7A ).
TET3 is a miR-150 target that regulates monocyte subsets. In order to identify hsa-miR-150 targets in classical monocytes, microRNA pull-down was performed in sorted cells, 20 h after their transfection with biotinylated hsa-miR-150. Among the RNA sequences collected from four independent experiments (Supplementary Table  2 ) and predicted to be hsa-miR-150 targets using TargetScan algorithm, we selected TET3 as it was also confirmed to be a potential miR-150 target using RNA-hybrid software, providing a score of -29. 2 and -30. 8 kcal/mol in human and mouse, respectively (Fig. 8a ). TET3 had been recently identified as a hsa-miR-150 target in acute myeloid leukemia cells  43. These results were confirmed in U937 cells (Supplementary Figure  10A ). Real-time quantitative PCR (RT-qPCR) experiments detected a decreased expression of TET3 in monocytes generated by liquid culture of CD34 + cells transduced with a hsa-miR-150-expressing lentivirus, using cells transduced with a scrambled RNA as a control (Fig.
0 (Agilent, Les Ulis, France) for 20 h at 55 °C under rotation. After washes, microarrays were scanned with an Agilent microarray scanner using high dynamic range settings before extracting data using the Agilent Feature Extraction Software (10. 7. 3. 1). Quantile normalization was made using the normal-izeBetweenArray function from R package LIMMA. Data were log 2 transformed. When several probes were spotted for a same miRNA, the median of all probes was averaged. Data were filtered according to the maximum number of missing value allowed for each miRNA (15%). Unsupervised computational analysis was performed using the dist function from R and the euclidian method as distance measure. Hierarchical clustering was obtained with the hclust function from R, using the distance matrix previously computed and the ward method.
To detect apoptosis, cells were additionally stained with Annexin V-FITC antibody (BD Biosciences) and 7-amino-actinomycin D (Invitrogen) according to the manufacturer's protocols. Cell fluorescence was determined using a Fortessa (BD Biosciences) and analyzed with the Kaluza Software (Beckman Coulter). Fluorescence intensity was expressed as staining index, using the median and slope distributions of labeled and background cells. The absolute number of cells was calculated by multiplying the percentage of cells in a subset by the leukocyte cell count before staining. For cell analysis after transfer, SYTOX™ Blue Dead Cell Stain was used to exclude dead cells (Thermo Fisher). Human mononuclear cells were isolated from peripheral blood samples using density centrifugation Pancoll (Pan-Biotech, Dutscher, Brumath, France).
The final libraries were sequenced on an Hiseq 2000 for human samples and on NovaSeq 6000 for mice samples (Illumina) in paired-end 100 bp mode in order to reach at least 30 millions reads per sample at Gustave Roussy. For human samples analysis, raw reads were mapped to hg19 genome with Tophat2 (v2. 0. 14)/Bowtie2 (v2. 1. 0) with the following parameters: --read-realignedit-dist 0 --library-type fr-firststrand --mate-inner-dist 50. Expression analysis was performed by counting the number of reads per gene using HTSeq (0. 5. 4p5) with the following parameters: -m union -s reverse -a 10 and GENECODE gene annotation (v24lift37). DESeq2 (v1. 10. 1) package was used for differential gene expression analysis. For mouse sample analysis, Fastq files quality have been analyzed with FastQC (v0. 11.
7) and aggregated with MultiQC (v1. 5). The quantification was performed on Gencode mouse M18 (GRCm38p6) transcriptome and comprehensive gene annotation, with Salmon (v0. 10. 2). The index was build with the default k-mer length of 31, with the genecode flag on, the perfect hash option, and all 1569 sequence duplicates within the genome were kept. The quantification was done with the default expected maximization algorithm, verified through 100 boostrap rounds, sequence-specific bias correction, fragment GC-bias correction, and automatic library detection parameter. Clustered heatmaps were performs from normalized counts with pheatmap, an R package, using Pearson's coefficient as distance metric for rows and column and the Ward. D2 method for the clustering. Volcano plot were built using ggplot package.
Acknowledgements Our research was supported by the  Ligue Nationale Contre le Cancer  (équipes labellisées LIGUE 2017: E. S. and O. A. B. ) and grants from  Institut National du Cancer (INCa)  to E. S. ( TRANSLA13-157 ;  PRT-K16-057 ;  PRT-K16-067 ) and to O. A. B. ( PLBIO16-151 ),  Association Laurette Fugain  ( ALF 2017/04 ),  Gustave Roussy  taxe d'apprentissage 2009 and 2016 (to R. I. and L. B. , respectively), and  ANR-granted Molecular Medicine in Oncology and PACRI programs. The authors are indebted to  Jeffie Lafosse  for collecting clinical and biological annotations on CMML patients, and physicians and patients of the  Groupe Francophone des Myélodysplasies  for providing samples. We are also grateful to  Enguerran Mouly  and  Veronique Della Valle  for the establishment and maintenance of the Tet3 -/-mice and to  Françoise Porteu ,  Muriel Gaudry ,  Jean-Luc Villeval , and  Fawzia Louache  for scientific discussions.
Data availability Datasets are available in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/ arrayexpress). H3K4me3 CHIP-Seq comparing CMML and healthy donor monocytes profiles are referenced as E-MTAB-6689 and those comparing human classical, intermediate, or nonclassical monocytes profiles are referenced as E-MTAB-7290. RNA-sequencing comparing CMML and healthy donor monocytes expression profiles are referenced as E-MTAB-6712 and those RNA-sequencing comparing classical or nonclassical monocytes expression profiles between wildtype and MiR-150 -/-or wild-type and Tet3 -/-mice are referenced as E-MTAB-7291. GRO-cap analyses obtained in a human myeloid (K562) and a human lymphoid (GM12878) cell line (Supplementary Figure  7A ) were collected from www. ncbi. nlm. nih. gov/geo/ (GSE60456 accession).
Using adequate DNA barcodes is essential to unambiguously identify each DNA library within a multiplexed set of libraries sequenced using next-generation sequencers. We introduce DNABarcodeCompatibility, an R-package that allows one to design single or dual-barcoding multiplex experiments by imposing desired constraints on the barcodes (including sequencer chemistry, barcode pairwise minimal distance and nucleotide content), while optimizing barcode frequency usage, thereby allowing one to both facilitate the demultiplexing step and spare expensive librarypreparation kits. The package comes with a user-friendly interface and a web app developed in Java and Shiny (https://dnabarcodecompatibility. pasteur. fr), respectively, with the aim to help bridge the expertise of core facilities with the experimental needs of non-experienced users.
Yet, a rational and general approach allowing to optimize the design of multiplexed NGS experiments under various constraints is highly desirable. To this end, we developed DNABarcodeCompatibility, a generic R-package allowing one to generate potentially large libraries of barcode combinations compatible with most sets of multiplexing constraints and optimized to achieve least-heterogeneity in terms of barcode usage. It is accompanied by a user-friendly interface designed with Illumina barcodecombination rules in mind, while allowing the user to apply constraints specific to other NGS platforms on the distance between barcodes and on the nucleotide composition.
Graphical user interface The DNABarcodeCompatibility user interface harbours two panels for input and output, respectively (Fig. 1 ) Once barcodes are loaded into the interface, the user can easily manipulate the list of barcodes by clicking on them. The interface provides tools to visualize and filter barcodes according to their chemical properties (GC content, i. e. the number of G or C nucleotides divided by the barcode length, and homopolymer length at most 2) and pairwise separation properties according to either the Hamming, SeqLev or phaseshift distance  (Bystrykh, 2012; Buschmann, 2017). Next, the application seeks an optimized set of compatible barcodes given the number of libraries and multiplex level. Finally, the interface also allows one to visualize how barcoded libraries are distributed among lanes of flow cells.
Conclusion DNABarcodeCompatibility provides a rational optimized design of multiplex sequencing experiments integrating barcode constraints from various NGS platforms including (i) barcode chemical properties, (ii) barcode pairwise distance, (iii) Illumina barcode-combination rules and (iv) barcode frequency usage. Controlling these barcode constraints is not only critical to ensure proper demultiplexing, but also to optimize consumable usage and reduce costs. The major strength of the package lies on its generic features that are applicable to wide range of multiplex pooling design on most NGS platforms. This should be useful notably for spatially and temporally resolved transcriptomics (see Supplementary File S3 for a comparison of DNABarcodeCompatibility with other existing software).
This analysis was done using the crude culture medium harvested from several bioreactors cultured from the same hairy root clone (clone 84) showing a remarkable batch-to-batch reproducibility of the production of rIDUA_RLT protein by the selected clone (Figure  1c ). Finally, the identity of the protein of interest was further validated by a proteomic approach combined to MS 2 from crude culture medium. This analysis displayed a 68. 5% peptide recovery as compared to the theoretical sequence of the expressed IDUA (Figure  1d ). The rIDUA_RLT protein (Figure  2a ) was then purified using a two chromatographic step protocol. A protein purity degree of over 96% (Figure  2b ) without any soluble aggregate as estimated by SE-HPLC and RP-HPLC was obtained (Figure  2c ; see Data S1). The N-terminal sequence of rIDUA_RLT corresponds to the expected sequence, demonstrating that the signal peptide was efficiently removed For the recombinant protein to be secreted within the culture medium, a gene sequence encoding for a specific signal peptide already used in  (Ele Ekouna et al.
, 2017; Huet et al. , 2014)  was added to the 5 0 end of the nucleotide sequence. This signal peptide has to be removed during the secretion step, in the endoplasmic reticulum as normally observed in eukaryotic cells  (Dudek et al. , 2015; Kober et al. , 2013; Zimmermann et al. , 2011). To validate the absence of the signal peptide in the protein secreted by the hairy roots based expression platform, a N-terminal sequencing using Edman degradation was performed (data not shown). A N-terminal MAXXV pentapeptide was identified with X being a modified amino acid. Considering that the expected N-terminal pentapeptide is MAPPV, this analysis suggested that cleavage of the signal peptide occurred as expected before the Met 24. This demonstrates the efficiency of the hairy root expression system to recognize and cleave successfully the signal peptide of heterologous proteins.
8 kV. Data were acquired using Xcalibur 3. 1 software in a data-dependent mode. MS scans (m/z 350-1600) were recorded at a resolution of R = 70 000 (@ m/z 200) and an AGC target of 3 9 10 6 ions was collected within 100 ms. Dynamic exclusion was set at 30 s and the top 12 ions were selected from fragmentation in HCD mode. MS/MS scans with a target value of 1 9 10 5 ions were collected with a maximum fill time of 100 ms and a resolution of R = 17 500. Additionally, only +2 and +3 charged ions were selected for fragmentation. Other settings were as follows: no sheath and no auxiliary gas flow, capillary heated at 200 °C, normalized HCD collision energy of 27% and isolation width of 2 m/z. Mascot, Sequest and Amanda algorithms through Proteome Discoverer 1. 4 Software  (Thermo Fisher Scientific Inc.
were used for protein identification in batch mode by searching against a merge of protein databases: UniProt Arabidopsis thaliana database (31 587 entries, Reference Proteome Set, release 2015_01) and UniProt Brassica Rapa subsp. pekinensis database (40 809 entries, release 2015_01) from http://www. uniprot. org/ ª 2018 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd. , 17, 505-516.
Acknowledgements We thank  Jean-William Dupuy  ( Plateforme Proteome, University of Bordeaux ) for the nano-LC-MS/MS analysis and database searching,  Philippe Ledent  ( Xpress Biologics ) and  Michel Thiry (Theranor)  for the protein purification,  Laurent Coquet  ( Plateforme PIssaro ,  Universit e de Rouen Normandie ) for technical help with the N-terminal sequencing,  Bruno G€ ugi  ( Laboratoire Glyco-MEV, Universit e de Rouen Normandie ) for help with the monosaccharide composition,  Philippe Chan  ( Normandie Univ, UNIROUEN, Plate-Forme de Prot eomique PISSARO  co-supported by  European Union  and  R egion Normandie. Europe gets involved in Normandie with  European Regional Development Fund (ERDF) ) for Mass spectrometry technical assistance,  Camille Lemasson  and  Emile Rage  for technical assistance, and  BPI France  and the  'R egion Hauts de France'  for partially funding part of this project.
Introduction Any new bioinformatic tool must be made available to its user's community, essentially to biologists, for whom command line interfaces are often cumbersome. This need is mainly satisfied by implementing a dedicated website. Several solutions, such as Galaxy  [3]  or Mobyle  [4] , were developed to ease tool integration within automatically-generated web pages, making them accessible through a generic web user interface. These generic approaches allow the integration of a large variety of bioinformatic tools behind the same interface model. However, each scientific community may also require web applications with specific interfaces adapted to their needs. For instance, phylogeny. fr [1] , a well-known pipeline for phylogeny inference, integrates visualization modules dedicated to specific phylogeny data.
In the same manner, it generates web forms to be directly integrated into any website. Lastly, WAVES provides web service entries in its RESTful API, thus generating services suitable for software interoperability. These web services all share the same API structure. Complete details on API architecture and usage are available in Supplementary Material (supplementary Tables  4  and 5 ). WAVES is highly versatile, as it is compatible with a variety of computing infrastructures. It runs any locally installed tool. By setting the required credentials, WAVES runs remotely installed tools through a secure network connection. It interoperates with most computing resource management systems, provided that they comply with DRMAA  [6]. For interoperability purposes, WAVES interacts with Galaxy, a widely used workflow management system.
Thanks to the BioBlend API  [5] , WAVES lists the tools available in Galaxy instances and offers the ability to import them automatically as new services. WAVES can then run the tools within the Galaxy instance from which it was imported, check computation status, and retrieve results. As WAVES can communicate several Galaxy instances, tool integration from several locations is straightforward. WAVES was written in Python using the Django web framework, which complies with web standards. It is fully portable, as it only requires a Python installation and a running web server. It can thus be installed easily into existing web systems. Moreover, it is released as a reusable Django application and may be integrated into any existing Django project.
WAVES as a tool provider Making tools available on the web is often a thankless and time-consuming task. By taking advantage of WAVES features, this task is greatly simplified, saving time. WAVES can be used as an online service provider. Configuring a tool as a service is achieved by filling out a form in the WAVES administration interface to define the tool settings (supplementary Figure  3 ). Once set up, a tool is immediately available online through web pages, web forms and via the API. This newly created service can be integrated into remote websites by incorporating web forms. At each service upgrade, corresponding web forms are updated immediately and propagated to remote websites. The same mechanism holds for the API, thus allowing service integration into any remote software. The API can also be used to develop dedicated custom web forms, thus providing high-level user web interfaces.
Four micrometre sections were cut followed by hematoxylin and eosin (H&E) staining. Immunohistochemistry analysis was performed on tumour sections to confirm the histopathologic examination. CD3 antibody (#ab5690, 1:400, Abcam, UK) was used to detect T lymphocytes; and B220 (#clone RA3-6B2, 1:100, BD Pharmingen, USA) and paired box transcription 5 (PAX5, # 24/Pax-5, 1:100, BD Pharmingen) antibodies were used to detect B lymphocytes, respectively. All animal experiments were carried out under the respective institutional ethical approval University of Otago (AEC 03/12) and Children's Medical Research Institute (AEC C257). Patient samples. Primary tumour samples were collected from 35 colorectal cancer patients undergoing elective surgery at Dunedin Hospital. A database of clinico-pathological information matched to each of the samples was collected prospectively over a follow-up period of at least 5 years (Supplementary Table  3 ).
These samples were prospectively collected between 1996 and 2007. Informed patient consent was obtained for the use of these tissues and data. Ethical approval had been granted for use of these tissues and data (New Zealand Health & Disability Ethics Committee Reference #14/NTA/33). RNA extraction and RT-qPCR. Total RNA was isolated from snap-frozen tumours as per the manufacturer's protocol using the PureLink RNA mini kit (Invitrogen, USA). RNA (2 µg) extracted from the tumour tissue was reverse transcribed using qScript cDNA SuperMix (Quanta Biosciences, USA), according to the manufacturer's instructions. Primers were designed for specific TP53 transcript subclasses (FL, Δ133TP53 TP53α and β)  18. Three reference genes GAPDH, HPRT and G6PD were used. FLTP53 FP: 5′-CTTCCCTGGATTGGCCA-3′; FLTP53 RP: 5′-TCTGAAAATGTTTCCTGACTCAGA-3′; Δ133TP53 FP: 5′-GCCTGAGTGA-CAGAGCAA-3′; Δ133TP53 RP: 5′-CGTAAGCACCTCCTGCAA-3′; TP53α FP: 5′-TTCACCCTTCAGATCCGTG-3′; TP53α RP: 5′-GGGCATCCTTGAGTTC-CAA-3′; TP53β FP: 5′-TTCAGGACCAGACCAGCTT-3′; TP53β RP: 5′-CAAG-TAGCATCTGTATCAGGCAA-3′; GAPDH FP: 5′-GAAGGTGAAGGTCGGAGTC-3′; GAPDH RP: 5′-GAA-GATGGTGATGGGATTTC-3′; G6PD FP: 5′-ATCGACCACTACCTGGGCAA-3′; G6PD RP: 5′-TTCTGCATCACGTCCCGGA-3′; HPRT1 FP: 5′-GAGCACACA-GAGGGCTACAATGT-3′; HPRT1 RP: 5′-GAAAGGGTGTTTATTCCTCATGGA-3′.
Hybridisation was performed using 5 µg of biotinylated target, which was incubated with the GeneChip Human Exon 1. 0 ST array (Affymetrix) at 45 °C for 16 h. Following hybridisation, arrays were washed and stained using a Fluidics Station 450 (Affymetrix) and the Affymetrix wash and stain kit following the manufacturer's instructions using Affymetrix fluidics protocol FS450_0001. Nonspecifically bound material was removed by washing and specifically bound target was stained using the anti-streptavidin-phycoerythrin stain provided in the wash and stain kit. The arrays were scanned using the GeneChip Scanner 3000 7 G (Affymetrix) and raw data were extracted from the scanned images and analysed with the Affymetrix Power Tools software package (Affymetrix). After the microarrays were scanned, quality checks were performed using the Affymetrix Expression Console Software (2012, available from http://www.
affymetrix. com/estore/browse/level_seven_software_products_only. jsp? productId=131414&categoryId=35623#1_1), the intensity data were subjected to quantile normalisation and summarisation using the RMA algorithm in the R 'affy' 57 and 'aroma. affymetrix' 58 packages. Analysis was then performed on transcript-level probesets using the core annotations. At the transcript level, 18 707 probesets represented different transcripts. Δ133TP53 mRNA expression determined by RT-qPCR was combined with the transcript-level Affymetrix exon array data for each CRC. Spearman's correlation was then performed using the cor() function in R with the method set to 'spearman'  59. Enrichment of gene sets within the input list was determined using the overrepresentation test with default settings using Bonferroni correction in Pantherdb  60.
Transwell assays. MEFs were serum-starved in medium with 0. 5% FBS for 24 h then harvested, resuspended in serum-deficient medium and seeded into Transwell inserts. After 4 h, cells were fixed using 4% paraformaldehyde, stained with 3% crystal violet, imaged using an Olympus DP71 microscope and analysed using ImageJ software. Data shown are from three biological replicates, each with six technical replicates. Cytokine and chemokine analysis. Serum from 9-week-old Δ122/Δ122 IL-6+/+ (n = 3) and 7-week-old Δ122/Δ122 IL-6-/-(n = 3) mice showing no visible pathology, were analysed for the expression of cytokines and chemokines using a Bio-Plex Pro Mouse Cytokine 23-plex Assay (Bio-Rad, USA). Staining of actin filaments with phalloidin. Actin filaments were stained using phalloidin. Cells were seeded into a 24-well plate and a scratch wound introduced using a p200 tip when the cells were confluent.
Detection was performed with diaminobenzidine. H&E staining and counterstaining were operated on a Leica autostainer. Invasion index was calculated as per the formula: invasive index = (invasive cells)/(non-invading cells + invasive cells). Data shown are from three technical replicates with three biological replicates each. Invasion assays. Quantification of cell invasion was done in Transwell chambers containing fluorescence-blocking polycarbonate porous membrane inserts (Fluoroblock; #351152; BD Biosciences, USA; pore size 8 μm). A volume of 100 μl of 2 mg/ml Matrigel with reduced growth factors (a commercially prepared reconstituted BM from Englebreth-Holm-Swarm tumours, #354230; BD Biosciences) were prepared in a Transwell. Cells were transfected and treated with Y-27632 (10 µM, Calbiochem, USA) as monolayers before trypsinisation and plating (1 × 10 5 ) in 2% FBS containing medium on top of a thick layer (around 500 μm) of Matrigel contained within the upper chamber of a Transwell.
Precipitated complexes were washed four times in Tris buffer containing 1% Triton X-100, 150 mM NaCl, 10 mM MgCl 2 , 0. 1 mM PMSF, eluted in SDS sample buffer, immunoblotted and analysed with antibodies specific for RhoA. Scanned autoradiographs were quantified using Aida/2D densitometry software and normalised to RhoA protein. Cell extracts and western blotting. Medium containing rounded cells was centrifuged at 1200 r. p. m. for 5 min and the pellet containing the rounded invasive cells lysed. The remaining adherent cells were gently scraped in lysis buffer. The two populations of cells were analysed separately. Total protein concentration was determined using the BCA kit (Promega, USA). Protein samples were electrophoretically separated on 8% SDS-PAGE gels and an equal amount of total protein (30 µg) was loaded into each lane.