dataesr/scientific-paragraphs-categorization huggingface dataset:
The general consensus is that at least two reference genes should be used for accurate normalization; however, the speci c genes used for normalization are dependent on organism and experimental conditions. Cusick et al. (22)  used combined stability values from NormFinder and Best Keeper software to show that vma genes (V-type proton ATPase catalytic subunits) were the most stable transcripts for qPCR studies in the model fungus Neurospora crassa under varying environmental conditions and that actin was the least stable of 12 genes examined. In an examination of eleven potential reference genes, Song et al. (23)  found that ubiquitin-conjugating enzyme (UBC) and elongation factor 2 (EFTu) were the most stable reference genes for qPCR studies in Puccinia helianthin; they also concluded that actin and tubulin were expressed in extremely low levels in certain tissue types and were not appropriate reference genes.
Lyu et al. (24)  had similar ndings in their selection of reference genes for qPCR in Trichoderma afroharzianum using ve different stability analyses; EF1 was found to be the most stably expressed reference gene, whereas alpha tubulin and actin showed the least stable expression. Llanos et al. (25)  analyzed 12 functionally unrelated candidate reference genes for Talaromyces versatilis by RT-qPCR assays over more than 30 relevant culture conditions using geNorm and found that six of these (ubcB, sac7, s1, sarA, TFC1 and UBC6) were stable across all conditions. They suggest geometric averaging of at least three of these genes (or their homologues) and propose their use in all lamentous fungi  (25). Using the NormFinder and geNorm software, Steiger et al. (26) evaluated six potential reference genes in 34 samples from diverse conditions for Trichoderma reesei and found that sar1, which encodes a small GTPase, was the most stable gene, whereas act (encoding actin) was not amongst the best validated ones; they suggest that sar1 and at least one other reference gene be used to normalize transcript analysis.
When selecting appropriate reference genes, several characteristics are important to consider. Relatively stable expression across various treatments ensures that the expression does not vary due to nutrient source or physiological condition; a relatively high expression level ensures that changes in expression will be detectable; and annotation provides some context for why the expression of these genes remains stable. Screening transcriptome datasets has been demonstrated as a quick and e cient means of selecting candidate reference genes that meet these criteria for a particular species (50; 51). In the work reported here, our objective was to select and validate appropriate reference genes for transcript abundance studies by ddPCR in O. maius. Using in-silico analysis to mine RNA-seq data from O.
Results And Discussion Transcriptome sequencing to identify genes with stable expression across three carbon sources and in symbiosis Analysis of three RNA-Seq datasets from O. maius free living mycelium (FLM) grown on modi ed MMN supplemented with glucose, BSA and peat (E. Feldman, unpublished) and two RNA-Seq data sets from O. maius grown on modi ed MMN supplemented with BSA alone (FLM) or in symbiosis (MYC) with Vaccinium myrtillus (44) revealed a list of 251 putative reference genes that met several criteria: (1) stable expression across the ve treatments (based on ratio of RPKM Means); (2) valid annotation; and (3) relatively high expression level (RPKM means > 15). The resultant shortlist of 251 candidate reference genes will provide an excellent resource for researchers looking to examine transcript abundance in O.
maius  (46) (47) (48) (49). This absence of typical housekeeping genes supports the need to validate reference genes in the context of each experimental system. Three of the shortlisted candidate reference genes were selected for further study, based on homology to reference genes previously used in quantitative expression studies in fungi  (22) (23) (24) (25). The genes chosen for this study (Table  1 ) were selected based on InterPro Descriptions; these homology-based annotations indicate that transcript 102649 codes for a translation elongation factor 2 (EFTu) with GTPase activity, potentially involved in translation, ribosomal structure and biogenesis; transcript 18772 codes for a small GTPase (ARF/SAR) subunit SAR1/ GTP-binding ADP-ribosylation factor Arf1, involved in GTP binding and intracellular protein transport; and transcript 32032 codes for a proton-transporting two-sector ATPase complex (ATPase_F1/V1/A1) with hydrogen ion transmembrane transporter activity.
Stability of candidate ddPCR reference genes evaluated for Oidiodendron maius cultured on a variety of carbon sources It is critical that reference genes maintain invariable expression across a wide variety of carbon sources. To evaluate whether the selected candidate reference genes could be utilized as internal controls for normalization of ddPCR expression data, their transcript levels were quanti ed across eleven carbon treatments. The resulting analysis showed variability among and between treatments, as expected, in response to variable amounts of starting material (Fig. 3 ). While a few studies have examined reference gene stability for use in ddPCR, these analyses have been conducted solely on qPCR data (10; 53). The geNorm algorithm was designed for qPCR data and, as such, the input data used is raw expression data.
Conclusions This work is the rst study to show that a data set derived from a massive RNA sequencing effort for several culture conditions can be used for the identi cation of reference genes in O. maius. In this study, a list was generated of 251 annotated O. maius genes with invariant expression during cultivation on varying carbon sources, as well as during symbiosis. Because O. maius is considered a model species for studies of ericoid mycorrhizal fungi, this list will be a valuable resource for selecting reference genes for future gene expression studies. Furthermore, the work reported here enhances the limited information available on this organism. Included in this list, the genes EfTu (transcript 102649), vma (transcript 32032), and sar (transcript 18847) were expressed at stable levels in all samples studied and were examined as candidate reference genes for transcript abundance studies.
Droplet digital PCR is a powerful tool to analyze transcript abundance pro les across multiple treatments and when fold-changes are low; however, like all transcript abundance quanti cation methods, it requires a stable and reproducible normalization method. Using two different algorithms to determine their stability, EfTu (transcript 102649), vma (transcript 32032), and sar (transcript 18847) were demonstrated to be stable reference genes, though their rank order differed between NormFinder and geNorm. Both algorithms demonstrate, however, that the geometric mean of all three candidate reference genes increased their stability appreciably. Therefore, we propose the geometric mean of these three genes be used as an appropriate normalization factor for future studies of transcriptional response across different developmental stages and physiological conditions in O.
Strain The Oidiodendron maius (MUT1381/ATCC MYA-4765) isolate used here had been isolated from roots of Vaccinium myrtillus growing in zinc-contaminated experimental plots in the Niepolomice Forest, Poland according to  Pearson and Read (58; 59). The isolate is capable of forming typical ericoid mycorrhizae with axenic Calluna vulgaris (60) and Vaccinium myrtillus  (40)  seedlings. The genome of O. maius as well as transcriptomes for both the free-living mycelium and the fungus in symbiosis with Vaccinium myrtillus, have been sequenced  (44). In-silico analysis of RNA-seq data Five RNA-seq datasets from O. maius Zn were analyzed in the current study: three unpublished data sets (E. Feldman, unpublished) and two from Kohler et al. (44;  the complete data sets have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE63947).
The tubes were centrifuged for an additional 5 minutes, the supernatant discarded and the pellet was dried on ice in the fume hood. Once completely dry, the pellet was resuspended in 25 µL DEPC water, then the concentration and quality were determined using a NanoDrop ND-1000 UV-visible light spectrophotometer (NanoDrop, Wilmington, DE, USA (optimum = 55%), GC clamps on both ends (3' GC clamp = 2 nt), 50 mM salt concentration, 300 nM oligonucleotide concentration and minimum overlap of 4 nt at either end. Three primer pairs were chosen for each transcript based on forward and reverse primers having similar GC content and melting temperature, where the target sequence had a single hit when BLASTed against the O. maius model ltered transcript dataset  (40). cDNA from the MMN + Peat treatment was used for all primer validation by conventional PCR.
Sequenced amplicons were reBLASTed against the O. maius model ltered transcript dataset  (40)  to ensure a single hit with the intended target. ddPCR Assay with EvaGreen Validated primers were used in a ddPCR Assay using a QX200™ ddPCR™ system (Bio-Rad Laboratories, Hercules, CA, USA) according to the manufacturer's standard EvaGreen® protocol. Brie y, each reaction contained 2 µL of cDNA, 100 nM of each forward and reverse primer, 1X ddPCR EvaGreen Supermix, 5% DMSO and molecular-grade water to 20 µl. Reactions were loaded into the sample wells of a DG8 droplet generation cartridge (Bio-Rad). Seventy µl of Droplet Generation Oil for EvaGreen (Bio-Rad) were loaded into the oil wells, and the cartridge was placed in the QX200Droplet Generator (Bio-Rad). The resulting droplets were transferred to a 96-well Bio-Rad PCR plate.
The PCR plate was then heat-sealed with a foil seal and placed in the thermocycler. Reaction conditions consisted of initial enzyme activation period at 95 °C for 5 min; followed by 40 cycles of denaturing at 95 °C for 30 s and annealing/extension for 1 min; dye stabilization at 4 °C for 5 min and 90 °C for 5 min; the ramp rate was 2. 5 °C/sec. Optimal annealing temperature was determined by running a temperature gradient for each primer pair ranging from 54 °C to 60 °C. After the ampli cation, plates were loaded into the Bio-Rad QX200 DropletReader for enumeration of the number of positive and negative droplets based on uorescence. The number of template molecules per microliter of starting material was estimated by the QuantaSoft®AP software (version 1. 6. 6. 0320, Bio-Rad) using an internal Poisson algorithm to analyze clusters; only droplets above a minimum amplitude threshold were counted as positive.
Funding: The research and data analysis by ECF was supported by  Natural Sciences and Engineering Research Council of Canada   Discovery Grant Program grants   RGPIN 170627-2013  to MDJ and  RGPIN 05340-2016  to DMD. The contributions of AK were supported by the  Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ), the  Region Lorraine  and the  European Regional Development Fund. The contributions of EM were supported by the  Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ) and by local funding from the  University of Turin. ECF received graduate student travel and research dissemination funding from the  University of British Columbia 's Okanagan campus.
Liquid Chromatography-Coupled Tandem Mass Spectrometry An electrospray ionization (ESI)-quadrupole-time-of-flight (QTOF; Compact, Bruker) coupled with an Ultimate 3,000 nano-HPLC system (Dionex) were used for liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data acquisition. A PepMap100 C-18 trap column (300 μm × 5 mm) and PepMap100 C-18 analytic column (75 μm × 250 mm) were used for reverse phase (RP) chromatographic separation with a flow rate of 500 nl/min. The two buffers used for the RP chromatography were 0. 1% FA/water and 0. 08% FA/80% acetonitrile/water with gradient condition for 2 h. Eluted peptides were then directly sprayed into the mass spectrometer and the MS/MS spectra were interpreted with the Mascot search engine (version 2. 4. 1, Matrix Science, London, United Kingdom) against Swissprot database (559,077 sequences, released in January 2019) and the taxonomy was restricted to Homo sapiens (human; 20,414 sequences) for human EAAT3 from HEK293 cells as well as to rodentia (rodents; 26,759 sequences) for EAAT3-associated rat brain proteins.
The search parameters were used with a mass tolerance of 10 ppm and an MS/MS tolerance of 0. 1 Da. Carbamidomethylation on Cys, oxidation on Met were allowed with two missing cleavage sites and additionally phosphorylation on Ser/Thr was allowed for human EAAT3 protein searches. The Mascot cut-off score was set to 10 and proteins identified with two or more peptides were considered  (Yang et al. , 2019). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al. , 2019)  partner repository with the dataset identifier PXD024524 and 10. 6019/PXD024524.
bands M and C in Figure  6B ) The ions score for an MS/MS match is based on the calculated probability, P, that the observed match between the experimental data and the database sequence is a random event. The reported score is -10Log(P) (http://www. matrixscience. com/help/interpretation_help. html). TABLE 1 | Identification of ARF-GAP1 resulting in an altered ratio (Figure  6C ). Overexpression of wild type ARF results in a stochiometric imbalance, which can also impede anterograde trafficking  (Peters et al. , 1995). Accordingly, both, CFP-tagged ARF1 and GFP-tagged EAAT3 accumulated in a perinuclear membrane compartment (Figure  6D , left hand set of images). In contrast, ARF1-T31N precludes delivery of COPI vesicles to the Golgi and thus traps cargo in the endoplasmic reticulum  (Dascher and Balch, 1994).
Methods Patients. This is an ancillary study using data from a prospective observational cohort. The study objectives and procedures were approved by the local ethics committee (Comité de Protection des Personnes Nord-Ouest II CHU-Place V. Pauchet, 80054 AMIENS Cedex 1). All subjects or next of kin (closest parent, legal guardian) if the patient was unable to be informed, received written information about the study and provided their informed consent to participate. The study was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki. This prospective, observational study was conducted in the intensive care unit (ICU) of the Amiens University Hospital between July 2016 and March 2017. We included patients with a diagnosis of vasoplegic syndrome, treated with norepinephrine, and for whom the attending physician decided to decrease the norepinephrine dosage.
Statistical analyses. Retrospective power analysis shows that a sample of 39 patients enable to demonstrate an area under the receiver-operating-characteristic curve greater than 0. 77, a power of 80%, and an α risk of 0. 05. The distribution of variables was assessed using a Shapiro-Wilk test. Data are expressed as numbers, proportions (in percent), medians [interquartile range] or as means (standard deviation). The coefficient of variation (CV), precision and least significant change (LSC) for MAP were calculated on the overall population. The LSC was 7% (confidence interval 95% (CI 95% ): 4-10). Based on LSC, we defined a positive response (pressure responders) as a decrease in MAP of over 10%  8. Qualitative data were compared with a chi-squared test or Fisher's test, and quantitative data were assessed with a Student's t test or Kruskal-Wallis sum rank test, as appropriate.
Data availability All relevant data are within the paper. Raw data are available after notification and authorization of the competent authorities. In France, all computer data (including databases, particular patient data) are protected by the National Commission on Informatics and Liberty (CNIL), the national data protection authority for France. CNIL is an independent French administrative regulatory body whose mission is to ensure that data privacy law is applied to the collection, storage, and use of personal data. As the database of this study was authorized by the CNIL, we cannot make available data without prior agreement of the CNIL. Requests may be sent to: elisabeth. laillet@chu-dijon. fr.
Assembly Genome assembly was conducted using the DeNovoMAGIC TM software platform  (NRGene, Ness Ziona, Israel). This is a De Bruijn graph-based assembler, designed to efficiently extract the underlying information in the raw reads to solve the complexity of the DeBruijn graph due to genome polyploidy, heterozygosity, and repetitiveness. This task is accomplished using accuratereads-based traveling in the graph that iteratively connects consecutive phased contigs over local repeats to generate long phased scaffolds  (Lu et al. 2015; Hirsch et al. 2016; Avni et al. 2017; Zimin et al. 2017; Zhao et al. 2017). In brief, the algorithm included the following steps: 1) Preprocessing: PCR duplicates, Illumina adaptor AGATCGGAAGAGC, and Nextera linkers (for MP libraries) were removed. The PE 450 bp 2 Â 265 bp libraries overlapping reads were merged with minimal required overlap of 10 bp to create the stitched reads.
Linkage and syntenic maps Linkage and synteny mapping approaches were used to cluster and order the assembled scaffolds into draft pseudomolecules. Linkage mapping was performed using genotyping-bysequencing (GBS) data on 163 F 2 progeny from a cross between cultivars Cypress and Jackson Wonder. 192-plex sequencing libraries were constructed following the protocol by  Manching et al. (2017)  using RASP-2. 0 adapters redesigned for Csp6I/MspI and Csp6I/Taq a I pairs of restriction enzymes. Samples included the F 2 progeny along with replicate samples of the parents and F 1 plants as well as a negative control with no DNA. Separate libraries were constructed for Csp6I/MspI and Csp6I/Taq a I. Sequencing was performed at the University of Delaware's Sequencing and Genotyping Center on a Hiseq2500 run in rapid mode at 1 Â 151 bp.
Processing of GBS data was performed using RedRep (https:// github. com/UD-CBCB/RedRep). First, FASTQ files from two lanes of sequencing were merged for the corresponding libraries. Following quality control and barcode deconvolution, FASTQ files from the same barcode were merged and then mapped to the NRGene sequence assembly. Variants were typed using HaplotypeCaller, and the genotype matrix was filtered as follows. VCFtools  (Danecek et al. 2011)  was used to set genotype calls to missing if fewer than three reads supported the call. The resulting genotype matrix was processed in R version 3. 4. 1 (R Core Team 2018) with custom scripts to filter markers that (1) had greater than 75% missing data; (2) had inconsistent genotype calls between parental replicates; (3) were heterozygous in either parent; (4) did not have the expected parent-hybrid trio genotypes; and (5) had F 2 allele frequencies less than 15% or greater than 85%.
Using sequencing scaffolds containing at least five markers, missing data were imputed using LB-Impute  (Fragoso et al. 2016). A linkage map was constructed with QTL IciMapping software v 4. 1. 0. 0  (Meng et al. 2015)  [run settings: DIS (20 cM) grouping function; RECORD ordering algorithm; SARF (window size ¼ 5) rippling criterion]. Chromosomer  (Tamazian et al. 2016 ) was used to align the NRgene scaffolds against the P. vulgaris reference genome  (Schmutz et al. 2014)  downloaded from JGI ("Pvulgaris_442_v2. 0. softmasked. fa. gz"). First, while retaining softmasked sequences in the reference genome identified by analysis with RepeatMasker, LAST  (Kiełbasa et al. 2011 ) was used to identify and softmask additional repeat sequences using the NEAR seeding scheme (run settings: -uNEAR and -R11).
Following guidelines for human-ape alignments (https:// github. com/mcfrith/last-genome-alignments, last accessed December 2019), substitution and gap frequencies were determined with last-train (run settings: -revsym -matsym -gapsym -E0. 05 -C2) and lastal alignment was performed (run settings: -fMAF -K2 -m50 -E0. 05 -C2, and -p corresponded to the output from last-train). Alignments between repeat sequences were discarded with lastpostmask and a python script was used to retain only the top two LAST matches per query sequence. The resulting output was used to run fragmentmap and assembler routines of Chromosomer. Finally, LAST was used to align the Chromosomer assembled P. lunatus genome to the P. vulgaris reference genome (run settings: -m50 -E0. 05 -C2).
To account for differences in stem length, lesion length was standardized by plant height. Adjusted means for sporulation rating and height-standardized lesion length on the F 2:3 progeny was used as an estimate of the corresponding F 2 parent phenotype for QTL analysis. QTL IciMapping software v 4. 1. 0. 0  (Meng et al. 2015)  was used to perform inclusive composite interval mapping with an LOD threshold of 2. 5 and step size of 1 cM.
Gene annotation Prior to structural annotation, repetitive elements were identified and masked (Supplementary File S1) using RepeatMasker  (Smit et al. 2013 ) with a custom library that included all Viridiplantae entries from Repbase  (Bao et al. 2015)  along with repetitive elements from P. vulgaris  (Gao et al. 2014). Using the masked assembly, gene predictions were generated by AUGUSTUS with the Arabidopsis thaliana training set  (Keller et al. 2011; Scalzitti et al. 2020). The completeness of the genome assembly and AUGUSTUS gene models was analyzed with BUSCO, which measures completeness in terms of evolutionarily informed expectations of gene content  (Simão et al. 2015). The BUSCO Embryophyta dataset (1440 single-copy conserved genes) was used as a reference. Annotation of the predicted genes was undertaken with multiple tools.
First, BLASTp searches (-evalue 1e-10) were performed against a custom BLAST database that included all Viridiplantae sequences from the NCBI nr database  (Wheeler et al. 2008). For sequences that had no hit, a second BLASTp search was performed against all sequences in the nr database. There were 14 RNA-Seq libraries in PRJNA596114 (P. lunatus genome), generated from pods (12 libraries), leaves (1 library), and flowers (1 library). We used MagicBlast at NCBI to map these reads to the predicted genes. The resulting SAM file was converted to BAM format and indexed, followed by samtools idxstats to identify predicted genes with either no transcript coverage or those that were covered over their full length. For the remaining genes with partial coverage, bedtools genomecov was used to calculate the fraction of transcript coverage across each gene.
2014). We found that $50% (32,505) of the P. lunatus proteins had hits to P. vulgaris, which encompassed 96% of the P. vulgaris proteins (Supplementary File S6). Using public transcriptome data for lima bean, we found that the vast majority of P. vulgaris homologs had transcript evidence in P. lunatus (Figure  2 ; Supplementary File S7), while most, but not all, of the nonhomologous genes were likely to be errors in de novo gene prediction. Of the 32,036 predicted P. lunatus proteins without a match in P. vulgaris, 28,317 proteins had no hits to any sequence in the nr database and tended to be shorter (mean of 237 aa) than those with hits (mean of 514 aa). However, 1356 had hits to other Viridiplantae species, and another subset of 2363 had hits to non-Viridiplantae entries in the nr database that were predominantly bacterial sequences.
Data Availability The genome sequence data for this studyis available under the NCBI BioProject PRJNA647124, for BioSample SAMN15394833 (P. lunatus cv. Bridgeton). Raw read data from the individual libraries at the Sequence Read Archive include: SRX9040258, SRX9040257, SRX9040256, SRX9040255, SRX9040254, SRX9040253, SRX9040252, SRX9040251. Supplementary material is available at figshare: https://doi. org/10. 25387/g3. 14398910.