Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle
Notice bibliographique
Résumé
BACKGROUND: Optimization of an RNA-Sequencing (RNA-Seq) pipeline is critical to maximize power and accuracy to identify genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study used RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures (RFI; n = 6 low-RFI, n = 6 high-RFI). Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by RFI group, iii) merged samples by RFI and tissue group. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR aligner. Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed. RESULTS: On average, total reads detected for Approach i) non-merged samples for liver and muscle, were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), merging samples by RFI group, total reads detected for each merged group was 162,030,705, and for Approach iii), merging samples by RFI group and tissues, was 324,061,410, revealing the highest read depth for Approach iii). Additionally, Approach iii) merging samples by RFI group and tissues, revealed the highest read depth per variant coverage (572.59 ± 3993.11) and encompassed the majority of localized positional genes detected by each approach. This suggests Approach iii) had optimized detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive detection. Approach iii) was then used to detect unique SNPs fixed within low- (12,145) and high-RFI (14,663) groups. Functional annotation of SNPs revealed positional candidate genes, for each RFI group (2886 for low-RFI, 3075 for high-RFI), which were significantly (P < 0.05) associated with immune and metabolic pathways. CONCLUSION: The most optimized RNA-Seq pipeline allowed for more accurate identification of SNPs, associated positional candidate genes, and significantly associated metabolic pathways in muscle and liver tissues, providing insight on the underlying genetic architecture of feed efficiency in beef cattle.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».