Functional annotation of livestock genomes is not only important for advancements in precision breeding and agronomics, but also to better understand genome evolution and human disease. To this end, the international FAANG (Functional Annotation of ANimal Genomes) initiative was launched in 2016 [1]. The European GENE-SWitCH (GS) project [2], a key part of FAANG, aims at delivering new underpinning knowledge on the functional genomes of two economically important monogastric farm species, chicken and pig, with the goal of rapid translation into the poultry and pig sectors. 

Toward its first aim, the GS project has sampled 7 tissues (liver, kidney, ileum, lung, heart, skin and cerebellum) from 4 animals across 3 developmental stages (early and late embryogenesis, and newborn) in both chicken and pig. The project applied various sequencing assays to identify reliable transcripts, genes, regulatory elements and their relationships during development.

Using state-of-the-art pipelines, including our TAGADA (Transcripts And Genes Assembly, Deconvolution, and Analysis) pipeline [3], with stringent data filtering and Quality Controls, we produced extensive annotations of both short and long RNAs of both species during development, with ~34k long and ~119k short transcripts for chicken, and ~47k and ~49k for pig. We explored this transcript repertoire by characterizing several features of either long or short RNAs, like alternative splicing, functional potential, chimeric transcripts and short RNA classes. In particular, we show that our novel gene annotations of chicken and pig are richer than the ones provided by generic platforms such as Ensembl or RefSeq, since it also includes overlooked transcripts classes, such as long non-coding RNAs that account for a majority of newly annotated transcripts, or chimeric transcripts supported by ChimPipe [4], a chimera specific tool, and also many more transcript isoforms of known genes. Our small gene annotations, that we obtained using the shortstack tool [5], were composed of genes of known biotypes (miRNA, tRNA, snoRNA, snRNA, rRNA, scaRNA), but also a majority of genes of unknown biotype or that overlapped exons of long genes.

We explored the dynamic transcriptome landscape of chicken and pig during development based on the analysis of long and small RNA sequencing data, and will detail how gene expression changes between developmental stages (the so-called “gene switches”). Comparative analysis of gene expression revealed that a vast majority of both coding and non-coding genes were differentially expressed between developmental stages in at least one of the seven tissues. In addition, differential expression patterns of small and long genes were often similar, potentially highlighting either long RNA degradation products or micro RNA product / precursor relationships. Principal component analysis based on the expression of both long and small genes in the eighty four samples of the two species revealed distinct expression patterns for brain and liver compared to the other tissues.

Finally using an in-house pipeline that we previously benchmarked [3], we identified gene orthology relationships between chicken and pig, both for coding and long non-coding genes, including an increasing number of known coding relationships for increasingly recent Ensembl gene annotations. These results provide new insight into genome organization and regulation during pig and chicken development.

References

[1] Tuggle CK, Giuffra E, White SN, Clarke L, Zhou H, Ross PJ, Acloque H, Reecy JM, Archibald A, Bellone RR, Boichard M. GO‐FAANG meeting: a gathering on functional annotation of an imal genomes. Animal Genetics. 2016 Oct;47(5):528-33.

[2] https://www.gene-switch.eu/ 

[3] Kurylo C, Guyomar C, Foissac S, Djebali S. TAGADA: a scalable pipeline to improve genome annotations with RNA-seq data. NAR Genomics and Bioinformatics. 2023 Dec 1;5(4):lqad089.

[4] Rodríguez-Martín B, Palumbo E, Marco-Sola S, Griebel T, Ribeca P, Alonso G, Rastrojo A, Aguado B, Guigó R, Djebali S. ChimPipe: accurate detection of fusion genes and transcription-induced chimeras from RNA-seq data. BMC genomics. 2017 Jan 3;18(1):7.

[5] Axtell MJ. ShortStack: comprehensive annotation and quantification of small RNA genes. Rna. 2013 Jun 1;19(6):740-51.