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Transcriptome analysis of genetic mechanism of growth curve inflection point using a pig model.

ABSTRACT: Animal growth curves play an important role for animal breeders to optimize feeding and management strategies (De Lange et al., 2001 [1]; Brossard et al., 2009 [2]; Strathe et al., 2010 [3]). However, the genetic mechanism of the phenotypic difference between the inflection point and noninflection points of the growth curve remains unclear. Here, we report the differentially expressed gene pattern in pig longissimus dorsi among three typical time points of the growth curve, inflection point (IP), before inflection point (BIP) and after inflection point (AIP). The whole genome RNA-seq data was deposited at GenBank under the accession number PRJNA2284587. The RNA-seq libraries generated 117 million reads of 5.89 gigabases in length. Totals of 21,331, 20,996 and 20,139 expressed transcripts were identified in IP, UIP and AIP, respectively. Furthermore, we identified 757 differentially expressed genes (DEGs) between IP and UIP, and 271 DEGs between AIP and IP. Function enrichment analysis of DEGs found that the highly expressed genes in IP were mainly enriched in energy metabolism, global transcriptional activity and bone development intensity. This study contributes to reveal the genetic mechanism of growth curve inflection point.

SUBMITTER: Shen L

PROVIDER: S-EPMC4664711 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Transcriptome analysis of genetic mechanism of growth curve inflection point using a pig model.

Shen Linyuan L Zhang Shunhua S Zhu Li L

Genomics data 20150902

Animal growth curves play an important role for animal breeders to optimize feeding and management strategies (De Lange et al., 2001 [1]; Brossard et al., 2009 [2]; Strathe et al., 2010 [3]). However, the genetic mechanism of the phenotypic difference between the inflection point and noninflection points of the growth curve remains unclear. Here, we report the differentially expressed gene pattern in pig longissimus dorsi among three typical time points of the growth curve, inflection point (IP) ...[more]

PMID: 26697358

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Project description:BackgroundDuring the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the control capability of COVID-19. How to determine the IP days related to the capability is still unclear. This study aims to (i) build a predictive model based on item response theory (IRT) to determine the IP for countries, and (ii) compare which countries (or continents) are hit most.MethodsWe downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country.ResultsThe top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61-0.86), 0.58 (0.31-0.84), and 0.54 (0.44-0.64), respectively. An online time-event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents.ConclusionAn IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.

Dataset Information

Transcriptome analysis of genetic mechanism of growth curve inflection point using a pig model.

Publications

Transcriptome analysis of genetic mechanism of growth curve inflection point using a pig model.

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