Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach
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Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach. / Mármol-Sánchez, Emilio; Cirera, Susanna; Quintanilla, Raquel; Pla, Albert; Amills, Marcel.
In: Genomics, Vol. 112, No. 3, 2020, p. 2107-2118.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach
AU - Mármol-Sánchez, Emilio
AU - Cirera, Susanna
AU - Quintanilla, Raquel
AU - Pla, Albert
AU - Amills, Marcel
PY - 2020
Y1 - 2020
N2 - Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miRNA identification in the yet poorly annotated porcine genome and demonstrated the usefulness of implementing a motif search positional refinement strategy for the accurate determination of precursor miRNA boundaries. The small RNA fraction from gluteus medius skeletal muscle of 48 Duroc gilts was sequenced and used for the prediction of novel miRNA loci. Additionally, we selected the human miRNA annotation for a homology-based search of porcine miRNAs with orthologous genes in the human genome. A total of 20 novel expressed miRNAs were identified in the porcine muscle transcriptome and 27 additional novel porcine miRNAs were also detected by homology-based search using the human miRNA annotation. The existence of three selected novel miRNAs (ssc-miR-483, ssc-miR484 and ssc-miR-200a) was further confirmed by reverse transcription quantitative real-time PCR analyses in the muscle and liver tissues of Göttingen minipigs. In summary, the eMIRNA pipeline presented in the current work allowed us to expand the catalogue of porcine miRNAs and showed better performance than other commonly used miRNA prediction approaches. More importantly, the flexibility of our pipeline makes possible its application in other yet poorly annotated non-model species.
AB - Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miRNA identification in the yet poorly annotated porcine genome and demonstrated the usefulness of implementing a motif search positional refinement strategy for the accurate determination of precursor miRNA boundaries. The small RNA fraction from gluteus medius skeletal muscle of 48 Duroc gilts was sequenced and used for the prediction of novel miRNA loci. Additionally, we selected the human miRNA annotation for a homology-based search of porcine miRNAs with orthologous genes in the human genome. A total of 20 novel expressed miRNAs were identified in the porcine muscle transcriptome and 27 additional novel porcine miRNAs were also detected by homology-based search using the human miRNA annotation. The existence of three selected novel miRNAs (ssc-miR-483, ssc-miR484 and ssc-miR-200a) was further confirmed by reverse transcription quantitative real-time PCR analyses in the muscle and liver tissues of Göttingen minipigs. In summary, the eMIRNA pipeline presented in the current work allowed us to expand the catalogue of porcine miRNAs and showed better performance than other commonly used miRNA prediction approaches. More importantly, the flexibility of our pipeline makes possible its application in other yet poorly annotated non-model species.
KW - MicroRNA discovery
KW - Motif search
KW - Porcine skeletal muscle
KW - Semi-supervised learning
KW - Small RNA-Seq
U2 - 10.1016/j.ygeno.2019.12.005
DO - 10.1016/j.ygeno.2019.12.005
M3 - Journal article
C2 - 31816430
AN - SCOPUS:85076485820
VL - 112
SP - 2107
EP - 2118
JO - Genomics
JF - Genomics
SN - 0888-7543
IS - 3
ER -
ID: 234276633