An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues

Genetic regulation of gene expression across multiple tissues in chickens

The chicken is a valuable model for understanding fundamental biology and vertebrate evolution and is a major global source of nutrient-dense and lean protein. Despite being the first non-mammalian amniote to have its genome sequenced, a systematic characterization of functional variation on the chicken genome remains lacking. Here, we integrated bulk RNA sequencing (RNA-seq) data from 7,015 samples, single-cell RNA-seq data from 127,598 cells and 2,869 whole-genome sequences to present a pilot atlas of regulatory variants across 28 chicken tissues. This atlas reveals millions of regulatory effects on primary expression (protein-coding genes, long non-coding RNA and exons) and post-transcriptional modifications (alternative splicing and 3'-untranslated region alternative polyadenylation). We highlighted distinct molecular mechanisms underlying these regulatory variants, their context-dependent behavior and their utility in interpreting genome-wide associations for 39 chicken complex traits. Finally, our comparative analyses of gene regulation between chickens and mammals demonstrate how this resource can facilitate cross-species gene mapping of complex traits.

Transcriptome analysis of multiple tissues and identification of tissue-specific genes in Lueyang black-bone chicken

Systematically constructing a gene expression atlas of poultry tissues is critically important for advancing poultry research and production. In this study, the gene expression profiles of 9 major tissues of Lueyang black-bone chicken were successfully constructed by transcriptome sequencing technology. Through in-depth analysis of transcriptome data, a total of 10 housekeeping genes (HKGs) and 87 marker genes (MGs) were identified. Furthermore, by applying weighted gene co-expression network analysis (WGCNA), we delineated nine tissue-specific modules and 90 hub genes, offering novel insights into the regulatory networks underlying tissue-specific gene expression. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that HKGs were predominantly involved in maintaining fundamental cellular functions, with significant enrichment in pathways related to oxidative phosphorylation, cell cycle regulation, and DNA replication. MGs were closely associated with tissue-specific physiological functions, providing valuable insights into the molecular mechanisms governing tissue functionality. Notably, through multidimensional validation, EEF1A1 and FTH1 were confirmed to exhibit cross-tissue expression stability, establishing them as ideal reference genes for multi-tissue qPCR experiments in chickens. Additionally, we successfully identified tissue marker genes, including TNNT2, PIT54, SFTPC, and PGM1, which are specific to the heart, liver, lung, and breast muscle, respectively. The results of this study have important scientific value in expanding reference gene selection and elucidating tissue-specific molecular mechanisms, and provide solid theoretical support and technical guidance for poultry breeding improvement and production practice optimization.

Comparative Genomics and Epigenomics of Transcriptional Regulation

Transcriptional regulation in response to diverse physiological cues involves complicated biological processes. Recent initiatives that leverage whole genome sequencing and annotation of regulatory elements significantly contribute to our understanding of transcriptional gene regulation. Advances in the data sets available for comparative genomics and epigenomics can identify evolutionarily constrained regulatory variants and shed light on noncoding elements that influence transcription in different tissues and developmental stages across species. Most epigenomic data, however, are generated from healthy subjects at specific developmental stages. To bridge the genotype-phenotype gap, future research should focus on generating multidimensional epigenomic data under diverse physiological conditions. Farm animal species offer advantages in terms of feasibility, cost, and experimental design for such integrative analyses in comparison to humans. Deep learning modeling and cutting-edge technologies in sequencing and functional screening and validation also provide great promise for better understanding transcriptional regulation in this dynamic field.

Transcriptomic Profiling Reveals Altered Expression of Genes Involved in Metabolic and Immune Processes in NDV-Infected Chicken Embryos

OBJECTIVE

The poultry industry is significantly impacted by viral infections, particularly Newcastle Disease Virus (NDV), which leads to substantial economic losses. It is essential to comprehend how the sequence of development affects biological pathways and how early exposure to infections might affect immune responses.

METHODS

This study employed transcriptome analysis to investigate host-pathogen interactions by analyzing gene expression changes in NDV-infected chicken embryos' lungs.

RESULT

RNA-Seq reads were aligned with the chicken reference genome (Galgal7), revealing 594 differentially expressed genes: 264 upregulated and 330 downregulated. The most overexpressed genes, with logFC between 8.15 and 8.75, included C8A, FGG, PIT54, FETUB, APOC3, and FGA. Notably, downregulated genes included BPIFB3 (-4.46 logFC) and TRIM39.1 (-4.26 logFC). The analysis also identified 29 novel transcripts and 20 lncRNAs that were upregulated. Gene Ontology and KEGG pathways' analyses revealed significant alterations in gene expression related to immune function, metabolism, cell cycle, nucleic acid processes, and mitochondrial activity due to NDV infection. Key metabolic genes, such as ALDOB (3.27 logFC), PRPS2 (2.66 logFC), and XDH (2.15 logFC), exhibited altered expression patterns, while DCK2 (-1.99 logFC) and TK1 (-2.11 logFC) were also affected. Several immune-related genes showed significant upregulation in infected lung samples, including ALB (6.15 logFC), TLR4 (1.86 logFC), TLR2 (2.79 logFC), and interleukin receptors, such as IL1R2 (3.15 logFC) and IL22RA2 (1.37 logFC). Conversely, genes such as CXCR4 (-1.49 logFC), CXCL14 (-2.57 logFC), GATA3 (-1.51 logFC), and IL17REL (-2.93 logFC) were downregulated. The higher expression of HSP genes underscores their vital role in immune responses.

CONCLUSION

Comprehension of these genes' interactions is essential for regulating viral replication and immune responses during infections, potentially aiding in the identification of candidate genes for poultry breed improvement amidst NDV challenges.

The first chicken oocyte nucleus whole transcriptomic profile defines the spectrum of maternal mRNA and non-coding RNA genes transcribed by the lampbrush chromosomes

Lampbrush chromosomes, with their unusually high rate of nascent RNA synthesis, provide a valuable model for studying mechanisms of global transcriptome up-regulation. Here, we obtained a whole-genomic profile of transcription along the entire length of all lampbrush chromosomes in the chicken karyotype. With nuclear RNA-seq, we obtained information about a wider set of transcripts, including long non-coding RNAs retained in the nucleus and stable intronic sequence RNAs. For a number of protein-coding genes, we visualized their nascent transcripts on the lateral loops of lampbrush chromosomes by RNA-FISH. The set of genes transcribed on the lampbrush chromosomes is required for basic cellular processes and is characterized by a broad expression pattern. We also present the first high-throughput transcriptome characterization of miRNAs and piRNAs in chicken oocytes at the lampbrush chromosome stage. Major targets of predicted piRNAs include CR1 and long terminal repeat (LTR) containing retrotransposable elements. Transcription of tandem repeat arrays was demonstrated by alignment against the whole telomere-to-telomere chromosome assemblies. We show that transcription of telomere-derived RNAs is initiated at adjacent LTR elements. We conclude that hypertranscription on the lateral loops of giant lampbrush chromosomes is required for synthesizing large amounts of transferred to the embryo maternal RNA for thousands of genes.

Comprehensive Annotation and Expression Profiling of C2H2 Zinc Finger Transcription Factors across Chicken Tissues

As the most abundant class of transcription factors in eukaryotes, C2H2-type zinc finger proteins (C2H2-ZFPs) play critical roles in various biological processes. Despite being extensively studied in mammals, C2H2-ZFPs remain poorly characterized in birds. Recent accumulation of multi-omics data for chicken enables the genome-wide investigation of C2H2-ZFPs in birds. The purpose of this study is to reveal the genomic occurrence and evolutionary signature of chicken C2H2-ZFPs, and further depict their expression profiles across diverse chicken tissues. Here, we annotated 301 C2H2-ZFPs in chicken genome, which are associated with different effector domains, including KRAB, BTB, HOMEO, PHD, SCAN, and SET. Among them, most KRAB-ZFPs lack orthologues in mammals and tend to form clusters by duplication, supporting their fast evolution in chicken. We also annotated a unique and previously unidentified SCAN-ZFP, which is lineage-specific and highly expressed in ovary and testis. By integrating 101 RNA-seq datasets for 32 tissues, we found that most C2H2-ZFPs have tissue-specific expression. Particularly, 74 C2H2-ZFPs-including 27 KRAB-ZFPs-show blastoderm-enriched expression, indicating their association with early embryo development. Overall, this study performs comprehensive annotation and expression profiling of C2H2 ZFPs in diverse chicken tissues, which gives new insights into the evolution and potential function of C2H2-ZFPs in avian species.

GEGA (Gallus Enriched Gene Annotation): an online tool providing genomics and functional information across 47 tissues for a chicken gene-enriched atlas gathering Ensembl and Refseq genome annotations

GEGA is a user-friendly tool designed to navigate through various genomic and functional information related to an enriched gene atlas in chicken that integrates the gene catalogues from the two reference databases, NCBI-RefSeq and EMBL-Ensembl/GENCODE, along with four additional rich resources such as FAANG and NONCODE. Using the latest GRCg7b genome assembly, GEGA encompasses a total of 78 323 genes, including 24 102 protein-coding genes (PCGs) and 44 428 long non-coding RNAs (lncRNAs), significantly increasing the number of genes provided by each resource independently. However, GEGA is more than just a gene database. It offers a range of features that allow us to go deeper into the functional aspects of these genes. Users can explore gene expression and co-expression profiles across 47 tissues from 36 datasets and 1400 samples, discover tissue-specific variations and their expression as a function of sex or age and extract orthologous genes or their genomic configuration relative to the closest gene. For the communities interested in a specific gene, a list of genes or a quantitative trait locus region in chicken, GEGA's user-friendly interface facilitates efficient gene analysis, easy downloading of results and a multitude of graphical representations, from genomic information to detailed visualization of expression levels.

Enriched atlas of lncRNA and protein-coding genes for the GRCg7b chicken assembly and its functional annotation across 47 tissues

Gene atlases for livestock are steadily improving thanks to new genome assemblies and new expression data improving the gene annotation. However, gene content varies across databases due to differences in RNA sequencing data and bioinformatics pipelines, especially for long non-coding RNAs (lncRNAs) which have higher tissue and developmental specificity and are harder to consistently identify compared to protein coding genes (PCGs). As done previously in 2020 for chicken assemblies galgal5 and GRCg6a, we provide a new gene atlas, lncRNA-enriched, for the latest GRCg7b chicken assembly, integrating "NCBI RefSeq", "EMBL-EBI Ensembl/GENCODE" reference annotations and other resources such as FAANG and NONCODE. As a result, the number of PCGs increases from 18,022 (RefSeq) and 17,007 (Ensembl) to 24,102, and that of lncRNAs from 5789 (RefSeq) and 11,944 (Ensembl) to 44,428. Using 1400 public RNA-seq transcriptome representing 47 tissues, we provided expression evidence for 35,257 (79%) lncRNAs and 22,468 (93%) PCGs, supporting the relevance of this atlas. Further characterization including tissue-specificity, sex-differential expression and gene configurations are provided. We also identified conserved miRNA-hosting genes with human counterparts, suggesting common function. The annotated atlas is available at gega.sigenae.org.

TAGADA: a scalable pipeline to improve genome annotations with RNA-seq data

Genome annotation plays a crucial role in providing comprehensive catalog of genes and transcripts for a particular species. As research projects generate new transcriptome data worldwide, integrating this information into existing annotations becomes essential. However, most bioinformatics pipelines are limited in their ability to effectively and consistently update annotations using new RNA-seq data. Here we introduce TAGADA, an RNA-seq pipeline for Transcripts And Genes Assembly, Deconvolution, and Analysis. Given a genomic sequence, a reference annotation and RNA-seq reads, TAGADA enhances existing gene models by generating an improved annotation. It also computes expression values for both the reference and novel annotation, identifies long non-coding transcripts (lncRNAs), and provides a comprehensive quality control report. Developed using Nextflow DSL2, TAGADA offers user-friendly functionalities and ensures reproducibility across different computing platforms through its containerized environment. In this study, we demonstrate the efficacy of TAGADA using RNA-seq data from the GENE-SWiTCH project alongside chicken and pig genome annotations as references. Results indicate that TAGADA can substantially increase the number of annotated transcripts by approximately [Formula: see text] in these species. Furthermore, we illustrate how TAGADA can integrate Illumina NovaSeq short reads with PacBio Iso-Seq long reads, showcasing its versatility. TAGADA is available at github.com/FAANG/analysis-TAGADA.

Prediction of transcript isoforms in 19 chicken tissues by Oxford Nanopore long-read sequencing

To identify and annotate transcript isoforms in the chicken genome, we generated Nanopore long-read sequencing data from 68 samples that encompassed 19 diverse tissues collected from experimental adult male and female White Leghorn chickens. More than 23.8 million reads with mean read length of 790 bases and average quality of 18.2 were generated. The annotation and subsequent filtering resulted in the identification of 55,382 transcripts at 40,547 loci with mean length of 1,700 bases. We predicted 30,967 coding transcripts at 19,461 loci, and 16,495 lncRNA transcripts at 15,512 loci. Compared to existing reference annotations, we found ∼52% of annotated transcripts could be partially or fully matched while ∼47% were novel. Seventy percent of novel transcripts were potentially transcribed from lncRNA loci. Based on our annotation, we quantified transcript expression across tissues and found two brain tissues (i.e., cerebellum and cortex) expressed the highest number of transcripts and loci. Furthermore, ∼22% of the transcripts displayed tissue specificity with the reproductive tissues (i.e., testis and ovary) exhibiting the most tissue-specific transcripts. Despite our wide sampling, ∼20% of Ensembl reference loci were not detected. This suggests that deeper sequencing and additional samples that include different breeds, cell types, developmental stages, and physiological conditions, are needed to fully annotate the chicken genome. The application of Nanopore sequencing in this study demonstrates the usefulness of long-read data in discovering additional novel loci (e.g., lncRNA loci) and resolving complex transcripts (e.g., the longest transcript for the TTN locus).

LncRNAs in domesticated animals: from dog to livestock species

Animal genomes are pervasively transcribed into multiple RNA molecules, of which many will not be translated into proteins. One major component of this transcribed non-coding genome is the long non-coding RNAs (lncRNAs), which are defined as transcripts longer than 200 nucleotides with low coding-potential capabilities. Domestic animals constitute a unique resource for studying the genetic and epigenetic basis of phenotypic variations involving protein-coding and non-coding RNAs, such as lncRNAs. This review presents the current knowledge regarding transcriptome-based catalogues of lncRNAs in major domesticated animals (pets and livestock species), covering a broad phylogenetic scale (from dogs to chicken), and in comparison with human and mouse lncRNA catalogues. Furthermore, we describe different methods to extract known or discover novel lncRNAs and explore comparative genomics approaches to strengthen the annotation of lncRNAs. We then detail different strategies contributing to a better understanding of lncRNA functions, from genetic studies such as GWAS to molecular biology experiments and give some case examples in domestic animals. Finally, we discuss the limitations of current lncRNA annotations and suggest research directions to improve them and their functional characterisation.

Advances in Non-Coding RNA Sequencing

Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.

Opportunity to improve livestock traits using 3D genomics

The advent of high-throughput chromosome conformation capture and sequencing (Hi-C) has enabled researchers to probe the 3D architecture of the mammalian genome in a genome-wide manner. Simultaneously, advances in epigenomic assays, such as chromatin immunoprecipitation and sequencing (ChIP-seq) and DNase-seq, have enabled researchers to study cis-regulatory interactions and chromatin accessibility across the same genome-wide scale. The use of these data has revealed many unique insights into gene regulation and disease pathomechanisms in several model organisms. With the advent of these high-throughput sequencing technologies, there has been an ever-increasing number of datasets available for study; however, this is often limited to model organisms. Livestock species play critical roles in the economies of developing and developed nations alike. Despite this, they are greatly underrepresented in the 3D genomics space; Hi-C and related technologies have the potential to revolutionise livestock breeding by enabling a more comprehensive understanding of how production traits are controlled. The growth in human and model organism Hi-C data has seen a surge in the availability of computational tools for use in 3D genomics, with some tools using machine learning techniques to predict features and improve dataset quality. In this review, we provide an overview of the 3D genome and discuss the status of 3D genomics in livestock before delving into advancing the field by drawing inspiration from research in human and mouse. We end by offering future directions for livestock research in the field of 3D genomics.

Watch Out for a Second SNP: Focus on Multi-Nucleotide Variants in Coding Regions and Rescued Stop-Gained

Most single-nucleotide polymorphisms (SNPs) are located in non-coding regions, but the fraction usually studied is harbored in protein-coding regions because potential impacts on proteins are relatively easy to predict by popular tools such as the Variant Effect Predictor. These tools annotate variants independently without considering the potential effect of grouped or haplotypic variations, often called "multi-nucleotide variants" (MNVs). Here, we used a large RNA-seq dataset to survey MNVs, comprising 382 chicken samples originating from 11 populations analyzed in the companion paper in which 9.5M SNPs- including 3.3M SNPs with reliable genotypes-were detected. We focused our study on in-codon MNVs and evaluate their potential mis-annotation. Using GATK HaplotypeCaller read-based phasing results, we identified 2,965 MNVs observed in at least five individuals located in 1,792 genes. We found 41.1% of them showing a novel impact when compared to the effect of their constituent SNPs analyzed separately. The biggest impact variation flux concerns the originally annotated stop-gained consequences, for which around 95% were rescued; this flux is followed by the missense consequences for which 37% were reannotated with a different amino acid. We then present in more depth the rescued stop-gained MNVs and give an illustration in the SLC27A4 gene. As previously shown in human datasets, our results in chicken demonstrate the value of haplotype-aware variant annotation, and the interest to consider MNVs in the coding region, particularly when searching for severe functional consequence such as stop-gained variants.

Transcriptomes of an Array of Chicken Ovary, Intestinal, and Immune Cells and Tissues

While the chicken (Gallus gallus) is the most consumed agricultural animal worldwide, the chicken transcriptome remains understudied. We have characterized the transcriptome of 10 cell and tissue types from the chicken using RNA-seq, spanning intestinal tissues (ileum, jejunum, proximal cecum), immune cells (B cells, bursa, macrophages, monocytes, spleen T cells, thymus), and reproductive tissue (ovary). We detected 17,872 genes and 24,812 transcripts across all cell and tissue types, representing 73% and 63% of the current gene annotation, respectively. Further quantification of RNA transcript biotypes revealed protein-coding and lncRNAs specific to an individual cell/tissue type. Each cell/tissue type also has an average of around 1.2 isoforms per gene, however, they all have at least one gene with at least 11 isoforms. Differential expression analysis revealed a large number of differentially expressed genes between tissues of the same category (immune and intestinal). Many of these differentially expressed genes in immune cells were involved in cellular processes relating to differentiation and cell metabolism as well as basic functions of immune cells such as cell adhesion and signal transduction. The differential expressed genes of the different segments of the chicken intestine (jejunum, ileum, proximal cecum) correlated to the metabolic processes in nutrient digestion and absorption. These data should provide a valuable resource in understanding the chicken genome.

RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species

In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call-rate and read number per GT) on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT (call rate ≥ 50%) and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.

Tissue Resources for the Functional Annotation of Animal Genomes

In order to generate an atlas of the functional elements driving genome expression in domestic animals, the Functional Annotation of Animal Genome (FAANG) strategy was to sample many tissues from a few animals of different species, sexes, ages, and production stages. This article presents the collection of tissue samples for four species produced by two pilot projects, at INRAE (National Research Institute for Agriculture, Food and Environment) and the University of California, Davis. There were three mammals (cattle, goat, and pig) and one bird (chicken). It describes the metadata characterizing these reference sets (1) for animals with origin and selection history, physiological status, and environmental conditions; (2) for samples with collection site and tissue/cell processing; (3) for quality control; and (4) for storage and further distribution. Three sets are identified: set 1 comprises tissues for which collection can be standardized and for which representative aliquots can be easily distributed (liver, spleen, lung, heart, fat depot, skin, muscle, and peripheral blood mononuclear cells); set 2 comprises tissues requiring special protocols because of their cellular heterogeneity (brain, digestive tract, secretory organs, gonads and gametes, reproductive tract, immune tissues, cartilage); set 3 comprises specific cell preparations (immune cells, tracheal epithelial cells). Dedicated sampling protocols were established and uploaded in https://data.faang.org/protocol/samples. Specificities between mammals and chicken are described when relevant. A total of 73 different tissues or tissue sections were collected, and 21 are common to the four species. Having a common set of tissues will facilitate the transfer of knowledge within and between species and will contribute to decrease animal experimentation. Combining data on the same samples will facilitate data integration. Quality control was performed on some tissues with RNA extraction and RNA quality control. More than 5,000 samples have been stored with unique identifiers, and more than 4,000 were uploaded onto the Biosamples database, provided that standard ontologies were available to describe the sample. Many tissues have already been used to implement FAANG assays, with published results. All samples are available without restriction for further assays. The requesting procedure is described. Members of FAANG are encouraged to apply a range of molecular assays to characterize the functional status of collected samples and share their results, in line with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.

Systematic screening of long intergenic noncoding RNAs expressed during chicken embryogenesis

Long noncoding RNAs (lncRNAs) have emerged as important regulators of many biological processes, including embryogenesis and development. To provide a systematic analysis of lncRNAs expressed during chicken embryogenesis, we used Iso-Seq and RNA-Seq to identify potential lncRNAs at embryonic stages from d 1 to d 8 of incubation: sequential stages covering gastrulation, somitogenesis, and organogenesis. The data characterized an expanded landscape of lncRNAs, yielding 45,410 distinct lncRNAs (31,282 genes). Amongst these, a set of 13,141 filtered intergenic lncRNAs (lincRNAs) transcribed from 9803 lincRNA gene loci, of which, 66.5% were novel, were further analyzed. These lincRNAs were found to share many characteristics with mammalian lincRNAs, including relatively short lengths, fewer exons, lower expression levels, and stage-specific expression patterns. Functional studies motivated by "guilt-by-association" associated individual lincRNAs with specific GO functions, providing an important resource for future studies of lincRNA function. Most importantly, a weighted gene co-expression network analysis suggested that genes of the brown module were specifically associated with the day 2 stage. LincRNAs within this module were co-expressed with proteins involved in hematopoiesis and lipid metabolism. This study presents the systematic identification of lincRNAs in developing chicken embryos and will serve as a powerful resource for the study of lincRNA functions.