dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole Genome Resequencing of INRAE Isogenic Lines and Use of the INRAE Database for SNP Detection Genomic DNA was extracted from fin clips of 19 rainbow trout INRAE isogenic lines. Whole-genome paired-end sequencing libraries were prepared and sequenced using the Illumina HiSeq 2000, Hi Seq 3000 or HiSeq X-Ten platforms at a depth of genome coverage ranging from 10X to 32X per sample. The 19 isogenic lines were sequenced in two batches that were processed successively. The first batch contained sequencing data from 12 samples (doubled haploid individuals) coming from 11 isogenic lines. The second batch contained sequencing data from 17 samples (doubled haploid individuals) coming from 17 isogenic lines (9 lines already sequenced in batch 1; and 8 lines not previously sequenced). Overall, 10 out of the 19 isogenic lines were sequenced twice.
This resulted in a total of 8,911,630,867 paired reads with a median of 321,575,464 per sample. Sequence reads from each of the 12 samples from the first batch were mapped to the Swanson rainbow trout reference genome (GenBank assembly accession GCA_002163495. 1; Pearse et al. , 2019)  using  BWA MEM v. 0. 7. 12 (Li, 2013). We then ran Samtools sort [v1. 3. 1,  (Danecek et al. , 2021) ] to sort the alignment data by chromosome and scaffold locations. Afterwards, PCR duplicates were marked using Picard Tools (v. 2. 1. 1, Broad Institute, 2019)  MarkDuplicates. Variant calling was then performed for each sample using  GATK (v3. 7; McKenna et al. , 2010)  HaplotypeCaller (options -stand_call_conf 30 -mbq 10), leading to 12 vcf files. A variant reference file containing 1,207,861 high quality SNPs was generated by keeping variants with QUAL ≥ 1,050 from the vcf files.
Prior to sequence alignment, sequences have been filtered using trimmomatic 0. 36  (Bolger et al. , 2014)  to remove Illumina Truseq adapters, trim low quality bases, keep trimmed reads with a sufficient length and average quality. These parameters removed 3. 8% of the reads, keeping 6,349,173,142 reads over the 17 samples. Alignment software was updated to use BWA MEM v. 0. 7. 15. First calling to create a high quality variants set to recalibrate the BAM files was avoided by directly using the final vcf file from the first batch analysis. These recalibrated BAM have been submitted to GATK Haplotype caller as before to generate GVCF files. To increase confidence in the SNP calling, we also added 2 other SNP callers: Samtools mpileup and FreeBayes 1. 1. 0 (Garrison and Marth, 2012). GATK calling results were jointly genotyped using GATK GenotypeGVCFs on the 12 GVCF files from the first batch and the 17 newly generated GVCF files.
This calling procedure resulted in 3 VCF files, one for each caller. Calling from GATK contained 29 samples (from the 19 isogenic lines, i. e. , with 10 lines replicated) and 31,454,943 variants; Freebayes and Mpileup was used only on the second batch and contained 19 samples and 25,805,271 and 30,340,281 variants respectively. The final step for variant calling was to intersect the 3 calling datasets using VCFtools_0. 1. 12a (Danecek et al. , 2011) , to keep only variants called by the 3 callers (genotypes kept were the GATK ones). SNP and INDEL were separated using GATK SelectVariants, and SNP were filtered with GATK VariantFiltration by following the GATK recommendations (QD < 2. 0 || MQ < 40. 0 || FS > 60. 0 || SOR > 3. 0 || MQRankSum < -12. 5 || ReadPosRankSum < -8. 0). This constitutes the INRAE1 variants dataset which includes 14,439,713 SNPs.
Using a vcf_filter. py homemade script to parse VCF file, INRAE1 dataset was filtered to keep only bi-allelic SNP localized on the 29 trout chromosomes or mitochondrial chromosome, fully genotyped for all 29 samples. As 10 isogenic lines were duplicated, we also checked genotype consistency and removed SNP with more than 1 isogenic line genotype discordance. Finally, we kept one sample per isogenic line (with the deepest sequencing) and filtered out SNP with more than 1 heterozygote genotype as they may represent duplicated genome regions. Among the 14,439,713 variants, we kept 10,286,009 SNPs (71. 23%). We merged them using BCFtools with a second dataset INRAE2, containing 14,478,077 SNPs called from 60 samples of a commercial line from "Les Fils de Charles Murgat" (Beaurepaire, France) and whose resequencing was described in  Fraslin et al.
Merging the USDA and INRAE SNP Databases and SNP Preselection A total of 32,372,492 distinct SNPs were selected for consideration for the HD chip, from a combination (BCFtools merge) of the USDA and INRAE databases (https://doi. org/10. 5281/zenodo. 6657091). An overview of the process to detect and select SNPs for inclusion on the array is provided in Figure  1. SNPs were further filtered to be at least 50 base pairs from the closest identified SNP which resulted in a subset of 3,679,547 SNPs. During vcf files merging, additional alleles may be added on shared variant positions and some variants previously removed from the INRAE dataset (on replicate discordance or high isogenic heterozygosity rate) may be reincluded. Thus, for a first SNP preselection, in addition to filtering SNPs with MAF ≥10% in both the USDA and INRAE databases, we applied filters on bi-allelism variant and on a maximum number of 4 heterozygote INRAE isogenic lines.
From this first submission to Affymetrix quality control, only 457,086 SNPs were qualified as recommended to be designable for the HD array and among them, only 351,755 were not ambiguous, meaning they were not of the type [G/C] or [T/A] that would require 4 probes instead of only two to distinguish the alleles. To get sufficient recommended variants and to avoid the selection of markers that will use twice the space used by the others on the HD array, we decided to resubmit a large second set of variants to Affymetrix quality check. The same procedure was applied to produce a second more relaxed preselected set of SNPs by keeping SNPs with a MAF ≥10% in the INRAE dataset only. This second preselection contained 533,637 additional SNPs. Among that additional set, 134,086 SNPs were specific to the INRAE dataset while the others were also present in the USDA dataset but with MAF below 10%.
We merged the first recommended set of 457,086 SNPs with this additional set of 533,637 SNPs. Then we removed all ambiguous SNPs of type [G/C], [C/G], [T/A] or [A/T]. Finally, densities were adjusted such that in regions with more than 30 SNPs retained per 100 kb by the previous filters, we only kept SNPs with MAF ≥15% in at least one of the two INRAE or USDA databases. This procedure resulted in a selection of 815,525 SNPs for the final submission in October 2020 to Affymetrix for assessment of the suitability of the SNPs for inclusion on a custom AXIOM 96HT SNP chip. Of the submitted SNPs, a total of 623,544 SNPs were deemed to be "designable" (recommended or neutral) in either the forward or reverse flanking sequence based on the Affymetrix pconvert score.
Single Nucleotide Polymorphism Mapping on the Arlee Reference Genome Recently USDA/ARS  (Gao et al. , 2021)  released a second reference genome assembly (USDA_OmykA_1. 1; GenBank assembly accession GCA_013265735. 3) for Oncorhynchus mykiss as long reads-based de-novo assembly for a second WSU DH line, named Arlee line, had been performed. Because Arlee lineage was closer from the INRAE isogenic lines  (Palti et al. , 2014)  than Swanson lineage, it was decided to keep for further analysis only the SNPs that were mapped uniquely on one of the 32 chromosomes of this new reference genome. Therefore, the 701,602 SNP probe flanking sequences of the HD chip were realigned to the new Arlee reference genome using BLAST (blastn with default task, i. e. , megablast, and filtering options -evalue 1e-5 -perc_identity 95 -best_hit_overhang 0.
The first round of quality control was done by ThermoFisher software AxiomAnalysisSuite ™ with threshold values of 97% for SNP call rate and 90% for sample call rate. All the 288 individuals of the LB line passed the preliminary control, while 174 out of the 175 individuals from LC line passed the control quality. Following array hybridization and imaging, genotypes were called using default settings in the Axiom Analysis Suite software and exported from the software in PLINK  (Purcell et al. , 2007)  format. In addition, we used the WGS information of 20 samples sequenced in  Gao et al. (2018) ' study (with average genome coverage above ×20) to extract their genotypes for SNPs included in the HD chip and positioned on the Arlee reference genome  (Gao et al. , 2021; GCA_013265735. 3). Those samples came from hatchery (Dworhak, L.
SNP Identification and Characterization Based on the Swanson Reference Genome Density of SNPs varied strongly from one chromosome to another with average SNP density per Mb ranging from 13,200 for Omy26 to 20,132 for Omy22. Across all chromosomes, the average SNP density per Mb was 16,483 SNPs (Figure  3 ). The Mb with the minimum density contained 451 SNPs while the Mb with the highest density contained 31,819 SNPs. SNP identified in USDA or INRAE databases differed in terms of MAF distribution (Figure  4 ): 70% and 49% of SNPs had a MAF below 15% (40% and 15% had a MAF below 5%, respectively).
HD Chip Based on genotyping the 288 LB samples, 65. 34% of markers were polymorphic, had individuals with all three genotypes, and passed Affymetrix filtering metrics in the Axiom Analysis Suite software to be categorized as "PolyHigh Resolution" variants. Of those that "failed" to be in that category, 15. 35% passed filtering metrics but were monomorphic, 10. 71% passed filtering metrics but the minor allele homozygote was missing, and the remainder 8. 60% failed due to low call rates or other quality filters. The total number of best recommended markers was 91. 81% corresponding to 610,115 SNPs out of the 664,531 genotyped variants. Based on genotyping the 175 LC samples, 69. 91% of markers were polymorphic, had individuals with all three genotypes, and passed Affymetrix filtering metrics in the Axiom Analysis Suite software to be categorized as "PolyHigh Resolution" variants.
3), the average SNP density on the chromosomes was one SNP per 3. 8 kb, or 266 SNPs per Mb. The median inter-marker distance was 1. 5 kb with only 7% of the distances between successive  markers being above 10 kb. The largest gap was 4. 16 Mb at the end of chromosome Omy6, the second largest gap was 2. 94 Mb at the end of chromosome Omy10 and the third largest gap was 2. 75 Mb at the end of chromosome Omy13 (Figure  5 ). Only five other gaps were above 2 Mb with values ranging from 2. 3 to 2. 5 Mb on chromosomes Omy7, Omy10, Omy15, and Omy21. Finally, PLINK v1. 9 software (www. cog-genomics. org/plink2) was used for a final SNP filtering based on keeping for further analysis SNPs with call rate above 95% and a deviation test from Hardy-Weinberg equilibrium (HWE) with a p-value < 10e-7 within each population.
, 2016). A similar number of 9. 6M SNPs were identified for the development of a 700 K SNP chip in catfish  (Zeng et al. , 2017). Recently, a set of 82 fish were collected from six different locations of China and re-sequenced to identify 9. 3M SNPs to design a 600K SNP chip for large yellow croaker  (Zhou et al. , 2020). Based on the resequencing of 85 samples by USDA and 79 samples by INRAE, we identified 32,372,492 SNPs that were variants (MAF ≥ 1%) in either the USDA or the INRAE sets. More precisely, 29. 0 and 16. 4 million SNPs were identified in the USDA and INRAE datasets respectively for equivalent number of sequenced individuals. The higher number of SNPs detected in the USDA dataset probably resulted from the larger number of diverse populations included in the USDA dataset. The USDA database included 11 doubled haploid individuals and 50 individuals from 7 commercial, hatchery or wild populations, compared to the INRAE database that included 19 doubled haploid individuals derived from one experimental line and 60 individuals sampled from a single French commercial line.
, 2020) , by either gynogenesis  (Quillet et al. , 2007)  or androgenesis  (Young et al. , 1996)  in rainbow trout. Both USDA and INRAE datasets included the sequencing of 11 and 19 doubled-haploid individuals respectively from 30 different isogenic lines. This number, quite large and unique in fish, makes it possible to take advantage of both the within-line characteristics (homozygosity, isogenicity) and between-line variability. In particular, rainbow trout isogenic lines are being used for the development of genomic tools: the trout genome is the result of a whole genome duplication event that occurred about 96 Mya ago  (Berthelot et al. , 2014). Therefore, many genomic regions remain in a pseudo-tetraploid status, which complicates sequence assembly and development of genetic markers because of the difficulty to distinguish true allelic variants from PSVs.
DATA AVAILABILITY STATEMENT Raw sequence data that were generated for French isogenic lines are deposited in the ENA (Projects PRJEB52016 and PRJEB51847). The VCF file for the database of all the SNPs identified in this study including a file with allele frequency information for each SNP is deposited to the public repository ZENODO for downloading under the name "INRAE_USDA_MAF1. vcf. gz" (https://doi. org/10. 5281/zenodo. 6657091). The sequence and the genotypes of the three French commercial trout lines from "Les Fils de Charles Murgat" (Beaurepaire, France), "Bretagne Truite" (Plouigneau, France) and "Viviers de Sarrance" (Sarrance, France) breeding companies will be made available by request on the recommendation of Pierrick Haffray (SYSAAF, pierrick. haffray@inrae. fr).
ACKNOWLEDGMENTS The SNP chip was developed in cooperation with Thermo Fisher and we particularly thank the following  Thermo Fisher Scientific personnel  for their direct contribution:  Ruth Barral Arca ,  Marie-Laure Schneider , and  Philippe Lavisse. We are also grateful to the  Genotoul bioinformatics platform (Toulouse Occitanie , doi: 10. 15454/1. 5572369328961167E12 ) and the  INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility , doi:  10. 15454/1. 5572390655343293E12 )for providing help, computing, and storage resources. We also thank the 3 French breeding companies " Les fils de Charles Murgat", "Bretagne Truite," and "Viviers de Sarrance " that provided samples for genome resequencing or genotyping on the 665K SNP array.
FUNDING This study was supported by  INRAE, FranceAgrimer  and the  European Maritime and Fisheries Fund  ( Hypotemp  project, n  °P FEA470019FA1000016 , and  NeoBio  project, n  °R FEA470016FA1000008 ). The sequencing of the INRAE rainbow trout isogenic lines were partly funded by  CRB-Anim  ( Biological Resource Centers for Domestic Animals ).
X-ray source A simple geometry of the X-ray tube of the small animal radiotherapy research platform (SARRP-Xstrahl Inc. , Swanee, GA, USA) was created for the simulations (Figure  2 ). The geometry consists of an X-ray source and the internal brass collimator of the tube. When the minibeam collimator was needed, it was placed at the front of the internal collimator. The photons of the source were initiated from the focal plane of the X-ray tube. Their spatial distribution was sampled from a Gaussian with a sigma of 0. 75 and 0. 25 mm for the x-and ydirection and a cutoff value of 3 and 1 mm, respectively. The angular distributions were uniform in both directions, with an angle of 20 •. The spectrum was generated using the SpekPy software toolkit. 31, 32 The tube parameters at treatment mode were selected.
Other minor atmospheric gases, such as O 3 and N 2 O among others, are present in the atmospheric spectrum which also includes nonresonant collision-induced absorption due to several mechanisms. The aim of our research is to improve the characterization of the mm/submm atmospheric spectrum using very stable heterodyne receivers with excellent sideband separation and extremely high (kHz) spectral resolutions at the 5000 m altitude Chajnantor site in northern Chile. This last aspect (spectral resolution) is the main improvement (by more than three orders of magnitude) in the presented data with respect to our previous work conducted ∼20 yr ago from Mauna Kea in Hawai'i. These new measurements have enabled us to identify slight modifications needed in the Atmospheric Transmission at Microwaves (ATM) model to better take into account minor constituent vertical profiles, include a few missing lines, and adjust some high-energy O 3 line frequencies.
79. 128. 5)  or via http://cdsarc. u-strasbg. fr/viz-bin/cat/J/A+A/664/A153 This publication is based on data acquired with the Atacama Pathfinder Experiment (APEX) under programme ID 105. 2084. APEX is a collaboration between the Max-Planck-Institut für Radioastronomie, the European Southern Observatory, and the Onsala Space Observatory. Since the beginning of molecular astrophysics, efforts have been made to measure, characterize, and model atmospheric effects in the frequencies concerned. One of earliest works was conducted in the 1970s from Tenerife at ∼2400 m above sea level by  Hills et al. (1978). Those measurements were the first to show the presence of submm atmospheric windows at high and dry mountain sites, motivating the construction of submm observatories that began operations about one decade later in Mauna Kea  (Hawai'i).
Atmospheric radiative-transfer model The ATM model includes the spectroscopy and reference vertical profiles for all relevant molecular species contributing to the mm and submm atmospheric spectrum as seen from ground-based observatories, along with empirical and theoretical descriptions of the CIA mechanisms such as N 2 -N 2 + O 2 -O 2 + O 2 -N 2 (dry CIA), H 2 O-N 2 + H 2 -O 2 ("foreign" wet CIA), and H 2 O-H 2 O ("self" wet CIA). The data presented and analyzed in this paper belong to a larger study for which the validation and upgrades of the ATM model for the Chajnantor site is one of the goals. ATM is described in detail in ?.
Atmospheric radiative-transfer model The ATM model includes the spectroscopy and reference vertical profiles for all relevant molecular species contributing to the mm and submm atmospheric spectrum as seen from ground-based observatories, along with empirical and theoretical descriptions of the CIA mechanisms such as  ("foreign" wet CIA) , and H 2 O-H 2 O ("self" wet CIA). The data presented and analyzed in this paper belong to a larger study for which the validation and upgrades of the ATM model for the Chajnantor site is one of the goals. ATM is described in detail in  Pardo et al. (2001b). N 2 -N 2 + O 2 -O 2 + O 2 -N 2 (dry CIA), H 2 O-N 2 + H 2 -O 2.
At the time, there were some differences between the two polarizations, but as they were rather small, it was decided to use an average spillover number. This leads to an uncertainty of η cal,660 GHz band of the order 0. 01 which might contribute to the calibration offset discussed later in this section. In order to obtain the T EBB (ν) spectra, we modified the APEX Calibrator software  (Polehampton et al. 2019)  to compute this quantity and write it out in CLASS format  (Pety 2018).
Total precipitable water vapor column The first step is to retrieve the PWVC from the data in some way that does not depend on the calibration issue. To do so, we found that the best strategy consists in using the ortho-H 2 O (J Ka,Kb = 5 3,2 -4 4,1 ) line with central frequency at 620 700. 807 MHz. The upper level of this transition lies at 697. 9 K and its quantum line strength is 0. 1256. These parameters make this line quite sensitive to the PWVC at the site conditions. To avoid the problem of the calibration offset mentioned above, we can use the differential T EBB between the most extreme 2. 0 and 1. 0 airmasses and compare it to the ATM model simulations, assuming the offset does not depend on airmass. By doing so (see Fig. 2 ), it is clear that the PWVC, at least when the skydip around 620 GHz was done, is 0.
For a discussion of the green and blue curves for PWVC=0. 325 mm; see section ??. following linear fit against frequency (D. Muders private communication): η cal,660 GHz band (ν) = 1. 903 • 10 -4 • ν[GHz] -5. 083 • 10 -2. (2) This fit was obtained using the early DSB version of the Band 9 receiver, which was installed in 2016. At the time, there were some differences between the two polarizations, but as they were rather small, it was decided to use an average spillover number. This leads to an uncertainty of η cal,660 GHz band of the order 0. 01 which might contribute to the calibration offset discussed later in this section. In order to obtain the T EBB (ν) spectra, we modified the APEX Calibrator software (?) to compute this quantity and write it out in CLASS format (?).
For a discussion of the green and blue curves for PWVC = 0. 325 mm; see Sect. 4. 1. 2. height = 2. 5 km) provides the best fit to the data. The other two curves correspond to 3. 0 km (green) and 2. 0 km (blue). In the latter two cases, the differences with respect to the data at ∼1. 5 GHz from the line center reach a few Kelvin, whereas the fit is almost perfect for 2. 5 km. Therefore, this value was assumed for the ATM simulations throughout this paper.
Recalibration Once the PWVC retrieval has produced a consistent result, we have to correct the data for the above mentioned offset. We decided to focus on those frequencies where the atmospheric opacity is expected to be larger than 8. 0 at 2. 0 airmasses, that is, around 573. 0, 620. 7, and 740. 0 GHz. Even in the very dry conditions experienced during the observations, the ATM model shows that the measured T EBB should be within 2 K of the physical temperature of the bottom atmospheric layer. We therefore applied a very simple recalibration consisting in shifting the 2. 0 airmass spectrum so that it matches the ATM model calculations at the above-mentioned three frequencies, and we applied the same shift to the spectra at the four other airmasses (see the third panel of Fig. 3 ). This means that our re-calibration consists of a global shift and not of a re-scaling.
Because the first bending ν 2 mode of H 2 O lies at 2322. 92 K, this spectral feature appears much weaker than the above-mentioned 620. 7 GHz H 2 O line (a few Kelvin in brightness from the lowest T EBB level in this atmospheric window) and its use for PWVC retrieval purposes is somewhat complicated because of ozone lines (see below). The J Ka,Kb = 2 1,1 -2 0,2 line of HDO -corresponding to the strongest (µ b ) component of the dipole moment -is detected at 599 926. 76 MHz. The zoom onto the data in panel 1 of Fig. 4  A153, page 4 of 8 A&A proofs: manuscript no. ms43409-22-update Fig. 3. APEX Atmospheric observing run carried out on Dec. 6 th 2020. The panels from top to bottom show: Atmospheric parameters from the observing log (blue and green); uncorrected spectra from the observations CLASS file (black); corrected spectra (blue, see text) plus ATM model prediction for airmass=1.
0 (red) and line positions for N 2 O and O 3 ; ATM model simulations (red) and data vs model residuals according to Equation ?? (blue dots, smoothed to 1 GHz); and atmospheric opacity breakdown according to those model results (several colors in the bottom panel). Zooms on selected frequency ranges, highlighted in yellow, are displayed in Figure  ??. correct opacity terms, it would show up as airmass-dependent quadratic residuals. The fact that no such residuals are seen indicates: 1. The applied recalibration procedure is consistent. The recalibrated data, ATM model results, and reference atmospheric profiles used in the ATM calculations are available in electronic form at the CDS via anonymous ftp to cdsarc. ustrasbg. fr (130. 79. 128. 5) or via http://cdsweb. u-strasbg. fr/cgi-bin/qcat?J/A+A/.