Mining long noncoding RNA in livestock

Weikard, Rosemarie; Demasius, Wiebke; Kühn, Christa

doi:10.1111/age.12493

Cited by 51 publications

(51 citation statements)

References 144 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, if the genes in question are not ubiquitously expressed, they can be absent from the data of the sequenced tissue. The same is true for genes, the expression of which depends on developmental-, diseaseor physiological state, as is the case for many lncRNAs [53].…”

Section: Discussionmentioning

confidence: 82%

pCADD: SNV prioritisation in Sus scrofa

et al. 2020

View full text Add to dashboard Cite

Background: In animal breeding, identification of causative genetic variants is of major importance and high economical value. Usually, the number of candidate variants exceeds the number of variants that can be validated. One way of prioritizing probable candidates is by evaluating their potential to have a deleterious effect, e.g. by predicting their consequence. Due to experimental difficulties to evaluate variants that do not cause an amino-acid substitution, other prioritization methods are needed. For human genomes, the prediction of deleterious genomic variants has taken a step forward with the introduction of the combined annotation dependent depletion (CADD) method. In theory, this approach can be applied to any species. Here, we present pCADD (p for pig), a model to score single nucleotide variants (SNVs) in pig genomes. Results:To evaluate whether pCADD captures sites with biological meaning, we used transcripts from miRNAs and introns, sequences from genes that are specific for a particular tissue, and the different sites of codons, to test how well pCADD scores differentiate between functional and non-functional elements. Furthermore, we conducted an assessment of examples of non-coding and coding SNVs, which are causal for changes in phenotypes. Our results show that pCADD scores discriminate between functional and non-functional sequences and prioritize functional SNVs, and that pCADD is able to score the different positions in a codon relative to their redundancy. Taken together, these results indicate that based on pCADD scores, regions with biological relevance can be identified and distinguished according to their rate of adaptation. Conclusions:We present the ability of pCADD to prioritize SNVs in the pig genome with respect to their putative deleteriousness, in accordance to the biological significance of the region in which they are located. We created scores for all possible SNVs, coding and non-coding, for all autosomes and the X chromosome of the pig reference sequence Sscrofa11.

show abstract

Section: Discussionmentioning

confidence: 82%

pCADD: SNV prioritisation in Sus scrofa

et al. 2020

View full text Add to dashboard Cite

show abstract

“…() have characterized as many as 58 848 long non‐coding RNAs (lncRNAs), of which 79% were unannotated. Adequate functional characterization of functional elements in livestock species will be a daunting task, particularly for lncRNAs that show a poor evolutionary conservation (Weikard et al ., ). Our knowledge about the goat epigenome is also very limited (Yang et al .…”

Section: Pitfalls and Perspectivesmentioning

confidence: 97%

Goat domestication and breeding: a jigsaw of historical, biological and molecular data with missing pieces

2017

View full text Add to dashboard Cite

Domestic goats (Capra hircus) are spread across the five continents with a census of 1 billion individuals. The worldwide population of goats descends from a limited number of bezoars (Capra aegagrus) domesticated 10 000 YBP (years before the present) in the Fertile Crescent. The extraordinary adaptability and hardiness of goats favoured their rapid spread over the Old World, reaching the Iberian Peninsula and Southern Africa 7000 YBP and 2000 YBP respectively. Molecular studies have revealed one major mitochondrial haplogroup A and five less frequent haplogroups B, C, D, F and G. Moreover, the analysis of autosomal and Y-chromosome markers has evidenced an appreciable geographic differentiation. The implementation of new molecular technologies, such as whole-genome sequencing and genome-wide genotyping, allows for the exploration of caprine diversity at an unprecedented scale, thus providing new insights into the evolutionary history of goats. In spite of a number of pitfalls, the characterization of the functional elements of the goat genome is expected to play a key role in understanding the genetic determination of economically relevant traits. Genomic selection and genome editing also hold great potential, particularly for improving traits that cannot be modified easily by traditional selection.

show abstract

“…ALDB contains 12,103 pig lincRNAs (long intergenic non-coding RNA), 8923 chicken lincRNAs, and 8250 cow lincRNAs (htp://www.ibiomedical.net/aldb/, access March, 2017). However, no comprehensive database currently covers available information on lncRNAs from livestock species, therefore the availability of a comprehensive tool will be valuable and helpful for subsequent genomic and functional annotation of lncRNAs and comparative interspecies analyses [198]. Inference of lncRNAs functions can also be done by connecting their expression paterns with speciic cell types or biological processes to draw possible conclusions on their potential roles.…”

Section: Functional Inference Of Lncrnamentioning

confidence: 99%

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

Ngoc¹,

Dudemaine²,

Fomenky³

et al. 2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

View full text Add to dashboard Cite

The recent remarkable development of transcriptomics technologies, especially next generation sequencing technologies, allows deeper exploration of the hidden landscapes of complex traits and creates great opportunities to improve livestock productivity and welfare. Non-coding RNAs (ncRNAs), RNA molecules that are not translated into proteins, are key transcriptional regulators of health and production traits, thus, transcriptomics analyses of ncRNAs are important for a beter understanding of the regulatory architecture of livestock phenotypes. In this chapter, we present an overview of common frameworks for generating and processing RNA sequence data to obtain ncRNA transcripts. Then, we review common approaches for analyzing ncRNA transcriptome data and present current state of the art methods for identiication of ncRNAs and functional inference of identiied ncRNAs, with emphasis on tools for livestock species. We also discuss future challenges and perspectives for ncRNA transcriptome data analysis in livestock species.

show abstract

Mining long noncoding RNA in livestock

Cited by 51 publications

References 144 publications

pCADD: SNV prioritisation in Sus scrofa

pCADD: SNV prioritisation in Sus scrofa

Goat domestication and breeding: a jigsaw of historical, biological and molecular data with missing pieces

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

Contact Info

Product

Resources

About