Fuli:Research

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In modern terms, natural selection operates on genetic variations, which provide both evidences to support the mechanism of natural selection and the materials for it to act upon. The selection pressure interacts with individual phenotypes, but ultimately the objects of selection exist within the DNA variations.<BR>
In modern terms, natural selection operates on genetic variations, which provide both evidences to support the mechanism of natural selection and the materials for it to act upon. The selection pressure interacts with individual phenotypes, but ultimately the objects of selection exist within the DNA variations.<BR>
Natural selection has played an enormous role in all aspects of biology. The interplay between the environment, and the phenotypes and genotypes of organisms has increased the complexity of biology. More intriguingly, the loci under natural selection are functionally important and relevant to disease studies. Differences in selective pressures that challenged human populations left different signatures in the functionally important loci of the human genome. Therefore, a new approach to localize disease genes is to explore these evolutionarily selected loci and the underlying alleles in normal populations.<BR><BR>
Natural selection has played an enormous role in all aspects of biology. The interplay between the environment, and the phenotypes and genotypes of organisms has increased the complexity of biology. More intriguingly, the loci under natural selection are functionally important and relevant to disease studies. Differences in selective pressures that challenged human populations left different signatures in the functionally important loci of the human genome. Therefore, a new approach to localize disease genes is to explore these evolutionarily selected loci and the underlying alleles in normal populations.<BR><BR>
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=Press=
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Atlas-SNP2<BR>
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*2009-12-23 'GenomeWeb' <BR>
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This Week in Genome Research
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December 23, 2009
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'''Meanwhile, a group of researchers from the Baylor College of Medicine, Rice University, and Washington University report that they have come up with a way to sift through large amounts of high-throughput re-sequencing data and pick out genetic variants without getting duped by sequencing errors. Their computational tool — called Atlas-SNP2 — takes into account sequence context in training datasets to help distinguish between errors and authentic SNPs with a less than 10 percent false-positive error rate and a false-negative error rate of five percent or so.'''
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[[http://openwetware.org/wiki/Fuli%27s_Lab home page]]
[[http://openwetware.org/wiki/Fuli%27s_Lab home page]]

Revision as of 12:19, 23 December 2009


Contents

Research interests

My research interest lies in utilizing large-scale genomics technologies, and computational and statistical tools to systematically study medical and population genetics/genomics. In medical genetics, I am particularly interested in understanding the genetic etiology of common complex human diseases. In population genetics, I am interested in studying the human evolutionary history indicated by genetic signatures in the human genome.

Development of 1000Genomes data processing tools

  • Fuli Yu, Yong (Tony) Wang, Zhengzheng Wan, Jin Yu, Aixiang Geng
  • in collaboration with Jeff Reid, Cristi Coarfa and Rui Chen

Reliable identification of genetic variants in re-sequencing data is the essential goal of the 1000 Genomes Project and is particular crucial in the exon-region sequencing endeavor. In the full-scale 1,000 Genomes Project, it is planned that the exonic regions will be sequenced at a much higher coverage than the rest of the genome. This presents a unique opportunity and challenge for bioinformatics pipeline development, as there is yet no software designed specifically for processing high coverage targeted sequencing data. Building on our extensive experience in analyzing the high coverage data from the 1000 Genomes Pilot 3 project, we aim to develop an integrated data processing pipeline and to develop a set of metrics in order to identify genomic variations for downstream analysis.

Medical genetics of common complex diseases

Common complex diseases such as cardiovascular disease, cerebrovascular disease, cancer, and diabetes account for most of the mortalities and morbidities in modern societies. Studies suggested strong influence of genetic variants in disease susceptibilities. Recent advances by large-scale association studies have uncovered many underlying predisposed regions for most of the common diseases. These efforts were paving the way to precisely pinpoint the causal genetic variants and understand the pathogenesis of complex diseases.

Population genomics—Signature of recent positive selection in the human genome

In modern terms, natural selection operates on genetic variations, which provide both evidences to support the mechanism of natural selection and the materials for it to act upon. The selection pressure interacts with individual phenotypes, but ultimately the objects of selection exist within the DNA variations.
Natural selection has played an enormous role in all aspects of biology. The interplay between the environment, and the phenotypes and genotypes of organisms has increased the complexity of biology. More intriguingly, the loci under natural selection are functionally important and relevant to disease studies. Differences in selective pressures that challenged human populations left different signatures in the functionally important loci of the human genome. Therefore, a new approach to localize disease genes is to explore these evolutionarily selected loci and the underlying alleles in normal populations.


Press

Atlas-SNP2

  • 2009-12-23 'GenomeWeb'

This Week in Genome Research December 23, 2009

... Meanwhile, a group of researchers from the Baylor College of Medicine, Rice University, and Washington University report that they have come up with a way to sift through large amounts of high-throughput re-sequencing data and pick out genetic variants without getting duped by sequencing errors. Their computational tool — called Atlas-SNP2 — takes into account sequence context in training datasets to help distinguish between errors and authentic SNPs with a less than 10 percent false-positive error rate and a false-negative error rate of five percent or so. ...


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