dataesr/scientific-paragraphs-categorization huggingface dataset:
ALVE Annotation Assembled Avian Leukosis Virus subgroup E (ALVE) integrations were identified by BLAST v2. 10. 0  [Altschul et al. , 1990]  using the ALVE1 reference sequence (GenBank: AY013303. 1) and annotated for ORFs and miR-155 recognition sites  [Hu et al. , 2016]. Analogous GRCg6a locations were identified using flanking sequences, then compared with known ALVE integration sites and target site duplications (TSD)  [Mason et al. , 2020b, c]. ALVE susceptibility was assessed by identifying the TVB receptor (TNFRSF10B) genotype. We have deposited the primary data underlying these analyses as follows: genome assemblies are deposited in the NCBI assembly archive (GRCg7b -PRJNA660757 and GRCg7w -PRJNA660758), PacBio SMRT reads associated with each reference are found in the SRA under the Bioproject number PRJNA673216.
In addition, all sequence types and files are available in the VGP database (https://genomeark. s3. amazonaws. com/index. html?prefix=species/Gallus_gallus/bGalGal1/). The mitochondrial genome is available under NCBI accession number NC_053523. 1. All Illumina data used in evaluating mapping rates, WGS or RNAseq, are described in various supplemental tables. evolved from dinosaurs" and "birds are related to dinosaurs" require substantial revision. Rather, it is very clear that birds, including our humble chicken, are in fact extant dinosaurs. Omnipresent in literature, film, television, popular culture and the media since the original fossil discoveries, notions that dinosaurs were obliterated entirely by the latest mass extinction event have undergone radical revision. On the contrary, dinosaurs are the great survivors of mass extinction events and we suggest, this may be due, at least in part, to their unique genome organization (in other words, their karyotype).
, 2010]. In recent years, the genomes of a large number of birds and lizards have been assembled and annotated, including zebra finches  [Warren et al. , 2010] , chickens  [International Chicken Genome Sequencing Consortium, 2004] , turkeys  [Dalloul et al. , 2010] , and duck  [Zhu et al. , 2021]. Moreover, large-scale bird genome projects  [Jarvis et al. , 2014; Zhang G et al. , 2014] , and chicken pangenomes  [Wang K et al. , 2021; Li M et al. , 2022]  have also generated considerable genomic data. These large comparative genomic datasets identified hundreds of lost genomic-blocks in the bird genomes, and also suggested that hundreds of genes are missing in birds  [Lovell et al. , 2014; Zhang G et al. , 2014]. The missing genes seem to be directly related to the unique physiological phenomena of birds.
Reconstruction of Missing Genes in the Chicken Genome While the hypothesis of missing genes in birds has been proposed for decades, researchers have found that some of the missing genes were, in fact, present in chickens or other birds. In the presence of large gaps and imperfect gene annotation in the genome, the de novo assembly of gene sequences using RNA-seq is considered to be an efficient way to identify unannotated genes in the genome. Attempts that only used a few tissues/organs have identified many missing genes in birds  [Hron et al. , 2015; Bornelov et al. , 2017; Botero-Castro et al. , 2017]. Recently, we used the raw data from 26 chicken tissues downloaded from the GenBank database to assemble and obtain 2,048,631 transcripts and identified 589 missing genes in birds  [Yin et al.
Reconstruction of Missing Genes from Other Birds In addition to chicken, researchers have reconstructed many genes thought to be missing from other birds. We collected data from various important tissues from duck (24), pigeon (11), goose (8), and zebra finch (22)  [Yin et al. , 2019b] , and an avian transcriptomic database containing a total of 9,296,247 transcripts was constructed by de novo transcriptome assembly. From this, we identified several genes in duck (583), pigeon (558), goose (537), and zebra finch (543) from 806 genes that were thought to be missing in birds [in  Lovell et al. , 2014, see Table S1; in Zhang G et al. , 2014  see Table  S10 ]. Only 135 genes were not found in this bird transcriptome database. The number of missing genes reconstructed in different birds by de novo assembly of large transcriptome data is similar, indicating that these genes thought to be missing exist across different bird species.
Since the first annotated version (Ensembl v22 -May 25, 2004) associated with the Galgal2 assembly, the number of genes and the diversity of their biotypes have increased, especially in 2015 with the introduction of long noncoding RNAs (lncRNAs), which is concurrent with the first initiatives of lncRNA annotation  [Chodroff et al. , 2010; Necsulea et al. , 2014; Li et al. , 2015; Muret et al. , 2017]. LncRNAs represent a large and heterogeneous class of genes defined by transcripts longer than 200 nucleotides without coding-potential capabilities  [Derrien et al. , 2012]. They represent a variety of regulatory elements implied in gene expression and can act at different levels by using diverse biological mechanisms based on DNA, RNA, or protein interactions  [Guh et al. , 2020]. As illustrated in Figure  10 , lncRNAs can interact with DNA, RNA, and proteins and act at different molecular levels: nuclear organization (e.
They are less expressed  [Derrien et al. , 2012; Muret et al. , 2017; Le Béguec et al. , 2018; Jehl et al. , 2020] , explaining why they have been detected only recently -i. e. , this last decade -by highthroughput transcriptome sequencing technologies (RNA-seq). Furthermore, lncRNA expression is more specific to tissues, life stages, and conditions than that of PCGs  [Cabili et al. , 2011; Derrien et al. , 2012; Jehl et al. , 2020]. The identification of these genic entities is therefore dependent on the variety of RNA-seq data available to detect them. After presenting the different chicken genome assemblies developed over the last 2 decades, we discuss the associated genome annotations provided by NCBI's RefSeq and EMBL-EBI's Ensembl, the two reference annotation databases. We characterize them in terms of number of gene and transcript models, variety of biotypes, or in terms of models that are shared by the two reference databases.
We show that lncRNA loci are even less wellknown than PCG ones, although, for the latter, knowledge of their transcripts can be further improved. Finally, we discuss the impacts of these weaknesses and the value of gathering different genome annotation resources, in particular, for a better description of lncRNA loci, and then present two initiatives. The MANE project yet limited to the human genome aims to synergize the NCBI's RefSeq and EMBL-EBI's Ensembl "gene" databases to establish a consensus annotation. The second project, specifically realized for the chicken, is to provide a "gene" database built from various resources including the NC-BI's RefSeq and EMBL-EBI's Ensembl databases and other resources such as FAANG multi-tissue resources and NONCODE database. This gene catalog is maintained at each significant update in chicken genome assembly and.
Evolution of the Two Reference Genome Annotations: A Breakthrough in 2015 with the Apparition of the lncRNA Gene Biotype Genome annotation is not only evolving according to the version of the genome assembly but also to the evolu-tion of annotation bioinformatics pipelines and data resources, mainly composed nowadays of RNA-seq data. In Figure  12a , genome annotations, from 2004 with the Gal-gal2/WUGSC1. 0 assembly through 2022 with the GRC7b assembly, produced by the reference centers, NCBI's Ref-Seq and EMBL-EBI's Ensembl, have been analyzed. As illustrated, the gene number has increased, particularly due to the apparition in 2015 of lncRNAs, with 5,763 and 4,641 lncRNAs modeled by NCBI's RefSeq (v103) and EMBL-EBI  's Ensembl (v94). This increase continues with the last genome annotation v107 (associated with the GRCg7b assembly) provided by EMBL-EBI's Ensembl with 11,944 lncRNAs compared with 5,504 for v106 (associated with GRCg6a).
Differences between the Latest NCBI RefSeq and EMBL-EBI Ensembl Genome Annotations For the same genome assembly, the two genome annotation bioinformatics centers, EMBL-EBI and NCBI, do not provide the same annotations, as illustrated in Figure 13. First, EMBL-EBI's Ensembl provides twice the number of lncRNA gene models compared to NCBI's RefSeq (shown in Fig. 13a ) resulting in a total of 30,108 gene models (associated to 72,689 transcripts) including 17,007 PCGs, 11,944 lncRNAs, and 674 miRNAs compared with 25,638 gene models (associated with 85,704 transcripts) including 18,024 PCGs, 5,791 lncRNAs, and 799 miRNAs for RefSeq. These differences can be explained by the sample datasets and the annotation pipeline thresholds used specifically by the two bioinformatics centers. For example, NCBI's RefSeq does not consider lncRNAs supported by a single mono-exonic transcript in contrast to EMBL-EBI's Ensembl (with 1,157 lncRNA loci).
Second, using the "GffCompare" software  [Pertea and Pertea, 2020] , we observed that most of the transcript models are different between the two ge-DOI: 10. 1159/000529376 nome annotations, as shown in Figure  13b. Among the 72,579 transcripts from EMBL-EBI's Ensembl considered in the analysis, only 17. 8% are strictly equal in the NCBI's RefSeq annotation. More than half (55. 9%) are identified as new isoforms of an existing locus and 26. 1% (18,922) transcripts are associated with 9,958 new gene loci resulting in more than one-third of the 30,108 gene models from EMBL-EBI's Ensembl not being known in NCBI's RefSeq. Important differences exist between PCG and lncRNA transcripts. For PCG, most of transcripts from EMBL-EBI's Ensembl (70. 8%) are new isoforms of the same gene loci existing in the two databases.
These results show that the transcript isoforms are not well described with current RNA-seq resources. Indeed, most of RNA-seq data available in the public database are short-read RNA-seq; the long-read RNA-seq studies using the new technologies such as ONT or PacBio are still very limited  [Kuo et al. , 2017; Guan et al. , 2022]  due to the cost of these technologies and their low sequencing depth. For lncRNA, most of the lncRNA transcripts from EMBL-EBI's Ensembl (77. 4%) are considered as new loci compared to NCBI's RefSeq. The main cause of this very low gene overlap between the two genome annotations is the difficulty in capturing and therefore modeling lncRNAs compared to PCGs, due to specific features of lncRNA. First, lncRNAs are characterized by a global low expression; around less than 10% of the total reads of a sample analyzed by common technologies support lncRNA transcripts  [Lagarrigue et al.
, 2022]. Second, they are tissue-, developmental stage-, and condition-specific  [Cabili et al. , 2011; Derrien et al. , 2012; Jehl et al. , 2020] , conditions which are not covered by the limited number of RNA-seq samples used by the reference genome annotation centers compared to the tens of thousands of short-read RNAseq generated by the avian scientific community which are available in the public database. Moreover, the transcript models from NCBI's RefSeq are significantly longer than those of EMBL-EBI's Ensembl, as shown in Figure  13c , particularly for lncRNAs (almost twice the length), with nearly two supplementary exons by transcript (resp. a median of 5 vs. 10 exons/transcript, p < 10 -16 ) for both PCG and lncRNA models, with median exon sizes which remain similar (∼250 bp). Moreover, NCBI's RefSeq provides a higher extreme distribution of transcripts per gene for PCGs compared to EMBL-EBI's Ensembl (resp.
5 vs. 3 for the third quartile and resp. 9 and 5 for the last decile). Note that these numbers are far below what is described in human (resp. 6, 11, 18 transcripts per gene for the median, the third quartile, and the last decile (EMBL-EBI's Ensembl v107 with the GRCh38. 13 assembly). This discrepancy can be explained in part by the variety of samples each reference used. EM-BL-EBI's Ensembl combines a short-read RNA-seq dataset of 21 tissues from the Roslin Institute (1 stage-condition of a same breed per tissue, 21 samples of individual pool) with a short-read dataset of 7 tissues from the GENE-SWitCH project (3 stages of a same breed, 84 samples) and a long-read dataset of 6 tissues (7 samples)  [EMBL EBI's Ensembl, 2022 ]. NCBI's RefSeq integrates data from various projects representing more than 20 tissues, different development stages and breeds for a total of 100 and 89 samples for short-read and long-read RNAseq, respectively, in addition to "Cap Analysis Gene Expression" (CAGE) data including those from the FAN-TOM project  [Lizio et al.
, 2015] , which have provided a multispecies lncRNA annotation: the first,  Foissac et al. [2019] , used 3 tissues of 4 female and male biological replicates of 4 farm species including the chicken updated to 12 tissues (personal communication); the second one,  Kern et al. [2018] , used 8 tissues of 2 female and male biological replicates of chicken, pig and cattle and was recently updated to 19 tissues for the chicken species  [Guan et al. , 2022]. Nevertheless, for a given species such as chicken, these studies remain limited due to the range of tissues, stage of development, condition that may exist. In this context, we proposed since 2020 to provide a comprehensive gene catalog for chicken by gathering different resources, including EMBL-EBI's Ensembl, NCBI's RefSeq, and other multi-tissue databases, that we update at each important change of chicken genome assembly and annotation.
Since the release of the new GRCg7b chicken genome sequence, we have recently updated the gene catalogue of 52,075 genes published in 2020  [Jehl et al. , 2020] , considering the last NCBI's RefSeq and EMBL-EBI's Ensembl annotations available in June 2022. First, we gathered the two genome annotation references, i. e. , the v106 of RefSeq and the v107 of Ensembl resources. In addition to these two references, we chose to gather the two updated FAANG multi-tissue resources described above  [Foissac et al. , 2019; Guan et al. , 2022] , in which lncRNAs have been modeled in parallel with the PCG loci. The NONCODE resource composed only of lnc RNA loci has also been used, even if this resource has not been updated since 2014 for the chicken  [Zhao et al. , 2021]. As a result, the EMBL-EBI's Ensembl and NCBI's RefSeq references grew respectively from 17,007 to 18,024 to 23,926 PCGs and from 11,944 to 5,791 to 44,428 lnc RNA genes.
Conclusion This review provides an overview of the evolution of chicken genome assemblies from 2004 to June 2022 and their genome annotations provided by the two most widely used annotation databases, NCBI's RefSeq and EMBL-EBI's Ensembl. We show a great evolution of the genome assembly through 6 different versions due to various technical and technological advances, the latest GRCg7b offers a genome reference sequence composed of 42 chromosomes (1-39; W/Z; MT), reaching the number observed in the chicken karyotype with more microchromosomes than the previous versions and with no gap between the ∼250 scaffolds. Moreover, we show that the annotation of the chicken genome is constantly evolving according to the version of the genome assembly, the evolution of bioinformatic annotation pipelines, and the RNA-seq data resources.
However, for properly catching lncRNAs, the low sequencing depths of these long-read technologies compared to short-read RNA-seq require preliminarily capture strategies used to boost the concentration of low-abundance transcripts in cDNA libraries. Such strategies have been applied to 4 human and mouse tissues by the GENCODE consortium  [Lagarde et al. , 2017]. However, the low sequencing depths and the high cost of these technologies limit for the moment their wider use. The main fuel of the genome annotation databases remains the short-read RNA-seq massively generated by the scientific community. In this context, to increase the completeness of the chicken ge-nome annotation, especially lncRNAs, we highlight the interest to combine the two reference NCBI's RefSeq and EMBL-EBI's Ensembl genome annotation databases and even other databases and present two initiatives.
However, embryos are often not consistently developed, thus staged according to the Hamburger and Hamilton series  [Hamburger and Hamilton, 1951]. Interestingly, in single-cell analysis, one embryo consists of a series of cell types and their states covering different developmental stages. Therefore, making multiple developmental atlases at close time points is not essential. In contrast, auxiliary atlases must be generated reflecting different factors such as sex  [Clinton et al. , 2001]  and variable genetic background  [Núñez-León et al. , 2019]. The raw data from and references to scRNA-seq studies are searchable at NCBI's GEO database (https://www. ncbi. nlm. nih. gov/geo/) and several single-cell reference sites (e. g. , https://panglaodb. se/papers. html, https:// www. nxn. se/single-cell-studies/).
To explore the original datasets, some interactive viewers for single-cell data are available (Single Cell Portal, https://singlecell. broadinstitute. org; UCSC Cell Browser, https://cells. ucsc. edu; EM-BL-EBI, https://www. ebi. ac. uk/gxa/sc/home). Thus, the first endeavor toward the chick cell atlas project will be to create an armamentarium, incorporate multimodal data, display those datasets and assist users. Tabula Muris, Tabula Sapiens/Human Cell Atlas (HCA)/HuBMAP, Tabula Drosophilae, and C. elegans atlas are examples of other species  [Tabula Muris Consortium, 2018; HuBMAPConsortium, 2019; Haniffa et al. , 2021; Taylor et al. , 2021; Lindeboom et al. , 2021; Eraslan et al. , 2022; Li H et al. , 2022; Tabula Sapiens Consortium, 2022]. Species-specific community websites like GEISHA (http://geisha.
Standardized Chicken Gene The Chicken Gene Nomenclature Consortium (CGNC) was convened in 2009 to develop and promote standardized gene nomenclature for chicken genes  [Burt et al. , 2009]. The CGNC provides updates following each major annotation update of the chicken genome and has an ongoing manual annotation effort. Nomenclature provided by CGNC is displayed and continually updated in NCBI's Gene Database. CGNC cross-references both NCBI Gene IDs (formerly Entrez Gene IDs) and Ensembl Gene IDs. Nomenclature may be assigned automatically via multiple orthology and homology searches  [Eyre et al. , 2007]  or by manually reviewing gene location, synteny, and published papers. While we make every effort to assign standardized gene symbols and names that are based upon human nomenclature when there is a clear orthology between genes, there are exceptions to this rule.
This project highlights the importance of manual review for gene nomenclature automatically assigned across taxonomic groups, and we are currently reviewing additional gene sets with names that reference X and Y chromosomes. Manually assigning gene nomenclature requires that biocurators have a good working knowledge of how public databases store, integrate, and share information, as well as understanding key principles in genetics, molecular and cellular biology, evolutionary biology, and physiology. CGNC offers research experiences for undergraduate and high school students who wish to complete a DOI: 10. 1159/000529376 research project. Students are introduced to the concept of standardized gene nomenclature and why it is important. They work collaboratively to review automatically assigned gene nomenclature, including reviewing associated publications and completing their own phylogenetic analyses.
Students review each other's work, and all nomenclature is quality checked before data entry to the CGNC database. This allows students the opportunity to see the results of their research reflected in databases which use CGNC information. The current focus for manual curation efforts is to assign nomenclature for gene families and chicken genes that are currently designated with the "LOC" prefix in the NCBI gene set. Gene families frequently have contractions and expansions between species and homology searches often cannot identify orthologs within a family. Many of these LOC genes are similar to (or "-like") genes found in other vertebrates but have no clear ortholog and are either new members of a gene family or represent lineage-specific genes. For example, avian gene families with notable changes compared to mammals include keratins, histones, ribosomal RNAs, cadherins, and olfactory receptors, and many of these gene families are included in the current LOC genes.
RNA-Seq Library Preparation and Data Analysis For each sample, 500 ng of total RNA was used to construct a cDNA library by using the NEBNext ® Ultra TM RNA library prep Kit for Illumina ® (New England Biolabs, Ipswich, MA). In total, there were 64 RNA-seq libraries. The cDNA libraries were quantified by Qubit (ThermoFisher Scientific, Waltham, MA) and validated by Agilent Bioanalyzer High Sensitivity DNA Assay (Agilent, Santa Clara, CA) and then sequenced on the HiSeq4000 platform (Illumina, San Diego, CA) for 100bp paired-end reads (DNA Core Facility, University of California, Davis, CA). Sequencing data can be accessed at GEO (PRJNA896699). Raw reads were trimmed to remove adapter sequences and low-quality bases were removed using the Trim Galore program (https://www. bioinformatics. babraham.
Gene Co-Expression Network Analysis The Weighted Gene Co-expression Network Analysis (WGCNA) package in R was used for gene co-expression network analysis  [Langfelder and Horvath, 2008, 2012]. A soft threshold of 13 was set for generating an adjacency matrix based on co-expression, and the minimum module size was arbitrarily set at 30. To evaluate associations of co-expressed gene clusters with line and treatment, the Leghorn and Fayoumi lines were given nominal values of 1 and 2 and non-treatment and treatment the nominal values of 0 and 1. For the continuous traits collected from the previous physiological study  [Wang Y et al. , 2018a] , association of co-expressed gene clusters with the continuous traits pH, PCO 2 , HCO 3 , BE, PO 2 , sO 2 , Glu, Na + , K + , and iCa 2+ were also evaluated.
Summary of RNA-Seq Analysis Sixty-four chicken cDNA libraries were prepared from hypothalamus and breast muscle samples and sequenced by the Illumina HiSeq 4000, which included four treatment groups: treated and non-treated Leghorn (LT and LC); treated and non-treated Fayoumi (FT and FC), at the acute heat stress (AH) and the chronic heat stress & NDV inoculation (CH&NDV), respectively. Of the 24,357 annotated chicken genes in the chicken Galgal 6. 0 database, more than 80% of the annotated genes were identified in both lines. The detailed mapping statistics are listed in Table  3. The two tissues showed distinct transcriptome profiles. Many genes (2,242 at the AH stage and 3,052 at the CH&NDV stage) were specifically identified in the hypothalamus. For the breast muscle, 627 genes at the AH stage and 384 genes at the CH&NDV stage were specifically expressed compared to the hypothalamus, many of which are related to muscle biogenesis (online suppl.
Annotation of Repeats in Avian Genomes For most bird genomes repeats have been annotated automatically using a repeat database (most often Repbase  [Kojima, 2020] ) and a library-based annotation tool (usually RepeatMasker). The resulting annotations include both full-length and fragmented copies of interspersed repeats. These consist mainly of TEs, tandemly repeated units of simple repeats ((RY) n and homonucleotidic motifs), low complexity repeats (minisatellite/variable number tandem repeat and microsatellite/short tandem repeat), and telomeric and centromeric macrosatellite DNA (large stretches, from thousand to millions of base pairs, of repeated units ranging in size from ∼10 bp to several hundred bp). Some repeated genes encoding small noncoding RNAs such as tRNAs, 7SL RNAs, and ribosomal RNA are also annotated.