dataesr/scientific-paragraphs-categorization huggingface dataset:
The main limitation of such library-based approaches is that their annotations depend heavily on the quality of the reference database used, including completeness and accuracy of consensus sequences. Furthermore, because these methods are primarily based on sequence similarity, they tend to overestimate the diversity of interspersed repeats by flagging very small non-overlapping hits that often display low sequence complexity. Finally, these methods are inappropriate for annotating satellite DNAs in newly sequenced species because those repeated units are absent from reference databases. Some studies use signature-based methods instead, focusing on traits that are unique to certain TEs or repeats. For example, to detect retrotransposons with large terminal repeats (LTR) at both ends, programs such as LTR Finder, LocaTR, LtrHarvest, or ReroTector can detect specific DNA organization patterns and signatures (motifs) that are specific to retroviruses  [Bolisetty et al.
, 2012; Mason et al. , 2016; Ji and DeWoody, 2016]. Tandem repeat finder (TRF)  [Benson, 1999] , another signature method tool, is dedicated to detecting all types of uncom- The final method for repeat annotation are DNA de novo consensus methods that combine a range of detection tools. The REPET pipeline  [Permal et al. , 2012]  includes the TEdenovo module which uses both de novo (RECON, GROUPER, and PILER) and signature-based tools such as TRF (it can also be set to include a librarybased step). A second REPET module, TEannot, uses the output of the first module to annotate the genome. Such de novo consensus methods have historically been limited by the need for powerful calculation and storage resources, which has restricted their application to small eukaryotic genomes (∼10 Mb to 500 Mb).
However, advances in computing clusters and a recent REPET update have opened the way for the use of this software package with larger genomes such as those of vertebrates. A second de novo package, the RepeatModeler2 pipeline  [Flyn et al. , 2020] , has an architecture that is similar, but simpler than that of REPET. It employs two discovery algorithms, RepeatScout and RECON, followed by consensus building and classification steps. In addition, Repeat-Modeler2 includes two signature-based tools LTRharvest and LTR_retriever. The de novo repeat library produced by RepeatModeler2 is then used to annotate genomes using the RepeatMasker program. The main weakness of these de novo methods is that they are unable to detect repeats with very few copies, such as DNA transposable elements or endogenous viral elements (EVEs).
, 2009; Solinhac et al. , 2010] , the rDNA repeat copy number intra-and inter-individual variability to be 150-250 per haploid genome, and average rDNA array sizes in various chicken populations having a range from 5 to 7 Mb  [Delany and Krupkin, 1999; Delany, 2000; Schmid et al. , 2005]. The latter authors also showed that rDNA repetitive units in the chicken NORs vary from 11 to 50 kb. This difference depends on the IGS size, which is significantly larger in broiler breeds  [Delany and Krupkin, 1999; Schmid et al. , 2005]. Four chicken rDNA repeats identified to date are of 34,497 bp, 26,999 bp, 27,055 bp, and 25,865 bp, the latter detected in the red jungle fowl  [Dyomin et al. , 2019]. A sample of the complete chicken rRNA gene cluster sequence was first assembled from raw reads and annotated as being 11,863 bp long (NCBI accession number KT445934)  [Dyomin et al.
, 2019]. The same is true for the guinea fowl IGS. Some of the internal repeats, such as SV-AL, (CT) n , VAL, were found to be common to the IGS of both species and perhaps to IGS of all Galliformes. At the same time, specific internal IGS repeats exist, such as the Nme repeats for the guinea fowl IGS and the EL repeats for the chicken IGS. The existence of specific repeat types within relatively close taxa of birds, the high degree of degeneracy of SV-AL and VAL, and the low interspecies homology of unique sequences indicate a high rate of evolution of both the entire IGS structure and of its elements. No functionally significant sites associated with RNApolymerase activity were found in the IGS sequences of the chicken and guinea fowl using conventional transcription databases. In the identification of internal regions of IGS with high functional significance, it could be quite useful to study the IGS structure in representatives of all major taxa of birds.
For understanding alleles and haplotypes, we began by assembling the data known from the scientific literature as well as from nucleotide databases (such as NCBI/Gen-Bank)  [Afrache et al. , 2020]. From the literature, 16 complete BF-BL haplotypes were known from sequencing cosmids, bacterial artificial chromosomes (BACs), and long-range PCR products. Other haplotypes could be assembled from complete or partial (exon 2-exon 3 for BF, exon 2 and exon 2-exon 3 for BLB) genomic sequences, as well as complete or partial cDNA sequences. However, many gene and cDNA sequences in the databases had to be ignored, even if published, since they were deposited from single studies and differed by only 1 or 2 nucleotides, with the associated papers revealing that only one amplification had been carried out, so most of those se-quences were not separate alleles but the result of nucleotide misincorporation.
, 2008b; Elferink et al. , 2012]. The Affymetrix 600K array had been further applied to several large-scale population genomic studies. For instance, genotyping of 1,200 chickens, ranging from 41 to 469 samples of five Chinese breeds of Beijing-You, Hongshan, Shouguang, Taihe Silkie, and Tibetan, along with White Leghorn originating from Italy, Houdan chicken from France, and Rhode Island Red from USA, identified a higher genomic diversity in most Chinese breeds compared to exotic ones, while all breeds carried some unique polymorphisms allowing successful assignment of all samples back to the breeds of origins. Local Tibetan chickens with high-altitude adaptability had a high level of genetic admixture from White Leghorn  [Nie et al. , 2019]. The addition of 69 samples (including five Chinese game breeds, Cornish chicken, and red junglefowls) to the 1,200 chicken dataset strengthened the distinct genomic variability present within each breed, with the highest genomic uniqueness observed in White Leghorn, Houdan, and Rhode Island Red.
, 2018 Li F et al. , , 2021]] , genotyping by genome reducing and sequencing (GGRS)  [Liao et al. , 2015 [Liao et al. , , 2016;; Zhao QB et al. , 2018] , and genotyping by sequencing (GBS)  [Pértille et al. , 2016; Zhang M et al. , 2018; Zhang Y et al. , 2021; Habimana et al. , 2021]  proved to be effective in identifying and genotyping some novel SNPs in chickens. However, these methods have not yet been widely applied. De Novo Assembly of the New Chicken Reference Genome Two de novo haplotype assemblies using a trio of samples, including an F 1 hybrid female (bGalGal1) from a broiler hen (bGalGal2) mated with a White Leghorn cock (bGalGal3), were recently released: bGalGal1. mat. broiler. GRCg7b (GenBank and RefSeq assembly accession nos. GCA_016699485. 1 and GCF_016699485. 2, released in January 2021) and bGalGal1.
pat. whiteleghornlayer. GRCg7w (GenBank and RefSeq assembly accession nos. GCA_016700215. 2 and GCF_016700215. 2, released in October 2021). They were assembled using PacBio Sequel I CLR, Illumina NovaSeq, Arima Genomics HiC, and Bionano Genomics DLS reads at 102. 01× genome coverage. bGalGal1. mat. broiler. GRCg7b has a total genome length of 1. 05 Gb in 677 contigs assembled into 214 scaffolds with N50 contigs of 18. 83 Mb and N50 scaffolds of 90. 86 DOI: 10. 1159/000529376 Mb, while bGalGal1. pat. whiteleghornlayer. GRCg7w also had 1. 05 Gb in total length in 685 contigs assembled into 276 scaffolds with N50 contigs of 17. 74 Mb and N50 scaffolds of 90. 56 Mb. There are 17,007 coding and 13,040 noncoding genes in GCA_016699485. 1 and 16,884 coding and 13,294 noncoding genes in GCA_016700215.
2 from the Ensembl release 104 published in January 2022 (Database version 108. 7); while 18,023 coding and 7,330 noncoding genes in GCF_016699485. 2, and 17,981 coding and 7,310 noncoding genes in GCF_016700215. 2 are from the NCBI Gallus gallus Annotation Release 106 published in March 2022. It may be expected that these two new de novo assembled genomes will be of broad interest to the scientific and industrial communities, considering their highest sequencing depth and coverages, most complete annotations, and relevance to commercial chicken production [see article by  Warren et al. , this report].
De Novo Assemblies of Indigenous and Commercial Chicken Breeds Following the reduction in sequencing cost and the advent of new sequencing technologies, de novo genome sequences of chicken breeds of particular interest are being generated to identify the genetic control of their unique phenotypes. These are warranted for comparative genomic analyses of SNPs, insertions, and deletions (IN-DELs), structural variations (SVs), and coding and noncoding transcriptomes. The first de novo draft genome of an indigenous chicken, the Korean Yeonsan Ogye, was released in December 2017 (Ogye1. 0, GenBank Genome Accession no. GCA_002798355. 1). This breed is characterized by having an entirely black external feature and internal organs. Following a hybrid de novo assembly pipeline, the genome was assembled into 8,241 pseudo-contigs and 1,906 scaffolds, which were further aligned and anchored to the GalGal4 chromosomes, based on high-depth Illumina HiSeq short reads (376.
The intensive genomic analyses of domestic chickens also detected several specific genetic admixture events with red junglefowls and other junglefowl species outside the proposed center of domestication  [Wang MS et al. , 2020]. Such admixture and/or introgression events have also been reported in another WGS study  [Lawal et al. , 2020]. Genomic footprints of admixture from indigenous large-sized Asian breeds and egg-laying Mediterranean breeds were identified in the gene pool of commercial meat and laying chickens, supporting their contributions to modern commercial meat and egg industries, respectively  [Guo Y et al. , 2021]. The first, and largest WGS dataset of African indigenous chickens, including 234 genomes from 24 Ethiopian chicken populations dis-DOI: 10. 1159/000529376 tributed in different types of climates and productive systems, was generated to call up to 15 million SNPs mapped to the GRCg6a reference.
Following an intensive evaluation of major topological inconsistencies caused by different genetic/genomic datasets and bioinformatic methods, the genetic contamination of some captive junglefowl flocks is verified, while the importance of large number of genetic variants in different genomic components of many reliable samples (e. g. , collected from their original ranges in the wild) to phylogenetic inference is substantiated  [Tiley et al. , 2020; Wang et al. , 2020; Mariadassou et al. , 2021]. WGS data analysis have also provided new insights on the ROH in chicken genomes. Compared to the red junglefowls with the least number of ROH, white layers carried the largest number of ROH per bird, mostly shorter than 1 Mb, while broilers tended to have relatively more ROH >2 Mb. Both SNP-based Wright's F IS and ROHbased F ROH metrics suggested higher inbreeding in the white layers compared to the broilers and red junglefowls  [Talebi et al.
, 2020]. Candidate genes in the ROH islands may also be associated with QTLs responsible for productive traits  [Talebi et al. , 2020]  and high-altitude adaptation  [Yuan et al. , 2022]. As expected, more CNVs and CNVRs were identified from the WGS data compared to the findings of SNP genotyping arrays. For instance, 12,955 CNVs in 5,467 CNVRs, accounting for 9. 42% of the genome, were found in two Iranian indigenous and commercial chicken breeds, with 34% of these CNVRs overlapping with those identified in SNP-array-based analyses  [Sohrabi et al. , 2018]. Across 51 WGS datasets of Chinese indigenous breeds (Xinghua, Luxi Game, Beijing-You, and Silkie), commercial lines (Recessive White Rock and White Leghorn), and red junglefowls, 19,329 duplications and 98,736 deletions in 11,123 CNVRs, accounting for 7% of the total autosomal size, and overlapping with 2,636 protein-coding genes were identified  [Chen X et al.
Dataset Description At the core of the consortium is a substantial genomic dataset of chickens and junglefowl. At the time of writing (September 2022), the dataset comprises 4,392 chicken and junglefowl genome sequences, of which 2,307 were derived from public databases, and the remainder provided by consortium members. In addition to domesticated chickens and red junglefowl (comprising all 5 subspecies: G. g. gallus, G. g. bankiva, G. g. jabouillei, G. g. murghi, G. g. spadiceus; total n = 291), we also included members of the congeneric Gallus species Gallus varius (n = 21), Gallus lafayettii (n = 12), and Gallus sonneratii (n = 15). Among the domesticated chickens, a wide array of geographical locations are represented (Africa, n = 1,047; East Asia, n = 856; South East Asia, n = 72; South Asia, n = 137; Middle East, n = 219; European fancy breeds, n = 462; North America, n = 835; South America, n = 15; Oceania, n = 24) as well as commercial birds (n = 329) and experimental lines (n = 42).
Processing Pipeline In order to provide complete consistency of analysis, the entire dataset was re-processed from raw reads using a state-of-the-art mapping and processing pipeline implemented on the SuperMUC computing cluster of the Leibniz-Rechenzentrum, Bavarian Academy of Science, Germany (Fig. 39 ). All reads underwent pre-processing (quality trimming, adaptor removing) with Fastp (v 0. 21. 0) then were mapped to the most recent version of the chicken genome (GRCg7b) (GCA_016699485. 1) with BWA (v 0. 7. 17-r1188). The resulting BAM files from the same samples were then merged with samtools (v 1. 9). For the variant calling, we generated gvcf using Elprep (v 5. 1. 1, compiled with go1. 17; a reimplementation of GATK in GO langage). These gvcfs were integrated into a GATK genomicDB (gatk v4.
2. 3. 0). To optimize performance, we built 48 databases corresponding to partitioning the genome into 48 intervals of equal size (∼20 Mb). Variant calling was performed using GATK genotypeGVCFs to obtain a VCF file by interval. We then obtained a global VCF file using GATK GatherVCFs. GATK VariantRecalibrator was then used to recalibrate variants using known SNPs.
Project Timeline The project began in 2021 and is expected to conclude (at least the first tranche of analyses) in 2023. The first phase of the project (Q3-Q4 2021) involved data gathering from both public and private sources and curation of associated metadata. In Q1 2022, the read files were quality checked to remove low quality samples and to check pre-processing from the variety of sequencing platforms included in the dataset. In Q2 2022, read mapping commenced, soon followed by variant calling. At the time of writing (September 2022), mapping and variant calling have been completed and the VCF will shortly be made available for further analyses.
, 2011; Alemu et al. , 2016; Picard Druet et al. , 2020]. In addition to genomic prediction, genotyping enables parentage verification and assignment (for example in identification of hens laying floor eggs  [Wolc et al. , 2021] ) or in the use of cross-classified mating with mixed semen  [Hsu et al. , 2015; Wolc et al. , 2015]  and improved product traceability (verification of correctness of line crosses throughout the breeding pyramid). With decreasing costs of genotyping, multiple genome-wide association studies (GWAS) have been performed, identifying 16,656 QTL across 370 traits as curated in the www. animalgenome. org database (as of 05/11/22). These studies, combined with additional -omics data are starting to give insights into the complex biology of chickens, their health, re-sponse to environment, feed utilization, and production traits.
The N50 length for the contigs is 17. 66 Mb, while the scaffold N50 is 20. 79 Mb. The  NCBI Gallus gallus Annotation Release  104, including 17,477 protein-coding and 6,558 noncoding genes (released in May 2018), and the Ensembl release 94, including 16,878 coding and 7,166 noncoding genes (released in April 2018) (database version 106. 6) complement the GRCg6a assembly.
Lange ,  D. Wright ,  H. Cheng ,  H. Simianer ,  S. Weigend ,  W. Warren ,  R. Crooijmans ,  O. Hanotte ,  J. Smith ,  M. Tixier-Boichard , and  L. A. F. Frantz ) Consortium Description On October 25-26, 2019, a satellite meeting devoted to the preparation of a Chicken Genome Diversity Consor-Funding Sources The computational part of the project was funded by  Super-MUC  grant ID:  23826  awarded by The  Leibniz Supercomputing Centre (LRZ) , of the  Bavarian Academy of Sciences and Humanities. The authors were supported by  European Research Council  grants ( ERC-2013-StG-337574-UNDEAD  and  ERC-2019-StG-853272-PALAEOFARM ) and by the  Wellcome Trust  ( 210119/Z/18/Z ). The authors would like to acknowledge the  Edinburgh Genomics Facility (Edinburgh, UK)  for generation of the sequence data.
Data Availability Statement Data Availability Statement Data have been submitted to the public databases under the following accession numbers: The raw data for assembling the transcriptome database of chicken, duck, goose, pigeon, and zebra finch were deposited in Sequence Read Archive (SRA) database under the accession number SRP141084. The Mallard genome is stored in NCBI under accession number PRJNA554956. The raw data for the Silkie genome assembly can be found in the SRA database under the accession number PRJNA805080 (unpublished data).
The Avian W Chromosome: Simply a Y with a Different Name? P. D. W. is supported by Australian Research Council Discovery Projects (DP180100931, DP210103512, and DP220101429). J. M. G. is supported by Australian Research Council Discovery Projects (DP210103512 and DP220101429). H. R. P. is supported by the Australian National University and NHMRC Synergy Grant (APP2011277). The authors acknowledge the provision of com- puting resources provided by the Australian BioCommons Lead- ership Share (ABLeS) program. This program is co-funded by Bio- platforms Australia (enabled by NCRIS), the National Computa- tional Infrastructure and Pawsey Supercomputing Centre. (Prepared by P. D. Price, T. F. Rogers, and A. E. Wright).
[2022] 10. 1186/s12864-019-5802-2 10. 1016/j. celrep. 2020. 03. 055 GSE143337 GSE130439 10. 7554/eLife. 57779 PRJNA624258 10. 1126/science. abb8598 https://github. com/jiewwwang/ Single-cell-retinal-regeneration 10. 1186/s12864-020-07136-2 CRA002353 10. 7554/eLife. 54279 GSE142244 10. 1038/s41467-021-24157-x GSE166981 10. 7554/eLife. 64819 GSE161905 33,761,346 10. 1038/s41467-021-21892-z GSE149457 10. 7554/eLife. 63907 GSE159107 10. 3389/fphys. 2021. 562817 CRA003604 10. 1016/j. celrep. 2021. 110251 GSE171649 10. 3389/fmicb. 2022. 800618 PRJNA687808 10. 1016/j. csbj. 2022. 03. 040 PRJNA761874 10. 7554/eLife. 74464 GSE131688, GSE181577 Downloaded from http://karger. com/cgr/article-pdf/162/8-9/405/3975130/000529376. pdf by INRAE-Institut National de Recherche pour l'Agriculture user on 26 October 2023 Cochlea SMART-seq, dissociation Janesick et al.
[2021] 10. 1016/j. celrep. 2021. 108900 https://umgear. org/dataset_ explorer. html?search_terms = a 10x, 10x chromium system from 10x Genomics (Pleasanton, CA, USA). Dissociation indicates enzymatic dissociation. Cytogenet Genome Res 2022;162:405-527 DOI: 10. 1159/000529376.
im-ageh2020. eu, funded by the EU Horizon, 2020 research and innovation program No. 677353). Sequencing of the ancient genomes was funded by the Arts and Humanities Research Council (grant AH/L006979/1). O. L. is supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 895107. Prepared by C. Keambou Tiambo, C. Kamidi Muhonja, M. Ogugo, O. Ouko, E. Ilatsia, S. Kemp, A. Djikeng, and M. McGrew) Biobanking DOI.
Methods This article reports data collected as part of a project examining women's experiences of prenatal screening/diagnosis in France, between May 2015 and February 2018. The project used a retrospective, mixed methods sequential approach, as the objectives were to describe, measure and then explore women's experiences of prenatal screening/diagnosis  (Creswell and Plano Clark, 2007). The study comprises an online survey using closed and open questions, followed by in-depth interviews. The use of a mixed methods design was deemed appropriate to address the diverse study aims, namely to gain information from different perspectives and obtain a comprehensive understanding of women's experiences. Furthermore, when used together, quantitative and qualitative methods complement each other and compensate for each other's limitations (i.
Quantitative data were analysed using SAS 9. 4 version. Author 3 predominantly conducted the quantitative analysis. For the qualitative part, participants were recruited among those who indicated, at the end of the survey, being willing to be interviewed (n= 564). Purposive sampling was used to explore a range of experiences: 1) women for whom a fetal anomaly was suspected prenatally and had either: given birth to a healthy baby, or a baby with anomalies, had lost the baby, or terminated the pregnancy; 2) women whose baby was born with anomalies discovered postnatally; 3) women who had refused all/some aspects of DSS. Of the 564 women agreeing to an interview, 99 were invited and 67 (67. 6%) were interviewed. Of those, five had refused MST, which is the focus of this article. Participants were asked to provide a narrative of their experience, and questions were used to explore the reasons why they had refused MST.
Acknowledgements We would like to, first and foremost, acknowledge the contribution to this study of our esteemed colleague and friend,  Sophia Rosman , who sadly passed away at the end of 2017. We would also like to thank  Christopher Hinton  for the translation of this article, our colleagues at the  Collectif Interassociatif Autour de la Naissance (Ciane)  who partnered with us on this project, as well as the  Région Ile de France (France)  for funding this study ( Partenariat Institutions -Citoyens pour la Recherche et l'Innovation ; agreement n°  13020600 ). Finally, we would like to express our gratitude to the women who participated in the research and shared their experience with us.
Data collection, structure determination and, refinement X-ray data collection was performed at the X06DA-PXIII beamline at the Swiss Light Source (Table  II ). A 360° native data set was collected at a wavelength of 1-Å with an oscillation of 0. 2° and an exposure time of 0. 1 s on PILATUS 2M-F detector  25. A single-wavelength anomalous diffraction experiment from intrinsic sulfur atoms (S-SAD) consisting of 12 × 360° scans at varying values of and of the PRIGo multi-axis goniometer  26  , was collected on a single crystal at 100 K at 2. 075-Å wavelength using 0. 2° oscillation 0. 1 s exposure as previously described  27. Data processing, scaling and merging were performed using XDS and XSCALE  28. The highresolution data cutoff was based on the statistical indicators CC 1/2  29. Substructure determination and phasing were performed with SHELX 30,31 using the CRANK2 program  32.