Dataset Information

Predicting regulatory variants with composite statistic.

ABSTRACT: MOTIVATION:Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. RESULTS:Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. AVAILABILITY AND IMPLEMENTATION:We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS CONTACT: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Li MJ

PROVIDER: S-EPMC6280872 | biostudies-other | 2016 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Predicting regulatory variants with composite statistic.

Li Mulin Jun MJ Pan Zhicheng Z Liu Zipeng Z Wu Jiexing J Wang Panwen P Zhu Yun Y Xu Feng F Xia Zhengyuan Z Sham Pak Chung PC Kocher Jean-Pierre A JP Li Miaoxin M Liu Jun S JS Wang Junwen J

Bioinformatics (Oxford, England) 20160606 18

<h4>Motivation</h4>Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in re ...[more]

PMID: 27273672

Similar Datasets

Project description:Puberty is a complex physiological event measured by various indicator traits in genetic improvement programs of beef cattle; thus, developing a more complete understanding of the genes and regulatory pathways and networks involved in puberty will provide knowledge to help improve genetic selection strategies. Herein, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed for cattle to achieve puberty (i.e., longissimus dorsi muscle, fat, and liver). These tissues were collected from pre (PRE)- and post (POST)-pubertal Brangus (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) heifers derived from a population of cattle used to identify QTL associated with fertility traits. In order to exploit the power of complementary omics analyses, PRE and POST puberty co-expression gene networks were constructed by combining the results from RNA-Seq, GWAS, and bovine transcription factors. RNA-Seq of 8 tissues among PRE and POST Brangus heifers revealed 1515 differentiallyexpressed and 943 tissue-specific genes within the 17,832 genes confirmed by metrics of RNA-Seq analysis. Combining the results from RNA-Seq and GWAS indentified a total of 25 QTL associated to heifer fertility. The hypothalamus experienced the most notable up-regulation of genes via puberty. Complementary omics procedures revealed 2,450 co-expressed genes across the 8 tissues relative to puberty. The PRE network had 372,861 connections whereas the POST network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, TSG1D1, DACH2, LHX4, PROP1 and SIX6). Results from multiples sources of omics data will facilitate the design of breeding strategies to improve fertility in Bos indicus-influenced composite cattle. Sixty-one samples from PRE and POST pubertal composite beef heifers were analyzed with RNA-Seq. The transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to metabolism andbody morphometrics needed for cattle to achieve puberty (i.e.,) was characterized. These tissues were collected from pre (PRE)- and post (POST)-pubertal Brangus (3/8 Brahman x 5/8 Angus) heifers derived from a population of cattle used to identify QTL associated with fertility. Total RNA was purified using a Trizol protocol (Invitrogen, Carlsbad, CA). Sequencing libraries were made using TruSeq RNA Sample Preparation kit of Illumina (San Diego, CA).

Dataset Information

Predicting regulatory variants with composite statistic.

Publications

Predicting regulatory variants with composite statistic.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure