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Search Results: 1 - 10 of 23378 matches for " Leming Shi "
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Pitfall of genome-wide association studies: Sources of inconsistency in genotypes and their effects  [PDF]
Huixiao Hong, Lei Xu, Zhenqiang Su, Jie Liu, Weigong Ge, Jie Shen, Hong Fang, Roger Perkins, Leming Shi, Weida Tong
Journal of Biomedical Science and Engineering (JBiSE) , 2012, DOI: 10.4236/jbise.2012.510069
Abstract: Personalized medicine will improve heath outcomes and patient satisfaction. However, implementing personalized medicine based on individuals’ biological information is far from simple, requiring genetic biomarkers that are mainly developed and used by the pharmaceutical companies for selecting those patients who benefit more, or have less risk of adverse drug reactions, from a particular drug. Genome-wide Association Studies (GWAS) aim to identify genetic variants across the human genome that might be utilized as genetic biomarkers for diagnosis and prognosis. During the last several years, high-density genotyping SNP arrays have facilitated GWAS that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. The replication studies demonstrated that only a small portion of associated loci in the initial GWAS can be replicated, even within the same populations. Given the complexity of GWAS, multiple sources of Type I (false positive) and Type II (false negative) errors exist. The inconsistency in genotypes that caused either by the genotypeing experiment or by genotype calling process is a major source of the false GWAS findings. Accurate and reproducible genotypes are paramount as inconsistency in genotypes can lead to an inflation of false associations. This article will review the sources of inconsistency in genotypes and discuss its effect in GWAS findings.
mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-Seq studies
Tao Qing,Ying Yu,TingTing Du,LeMing Shi
Science China Life Sciences , 2013, DOI: 10.1007/s11427-013-4437-9
Abstract: RNA-Seq promises to be used in clinical settings as a gene-expression profiling tool; however, questions about its variability and biases remain and need to be addressed. Thus, RNA controls with known concentrations and sequence identities originally developed by the External RNA Control Consortium (ERCC) for microarray and qPCR platforms have recently been proposed for RNA-Seq platforms, but only with a limited number of samples. In this study, we report our analysis of RNA-Seq data from 92 ERCC controls spiked in a diverse collection of 447 RNA samples from eight ongoing studies involving five species (human, rat, mouse, chicken, and Schistosoma japonicum) and two mRNA enrichment protocols, i.e., poly(A) and RiboZero. The entire collection of datasets consisted of 15650143175 short sequence reads, 131603796 (i.e., 0.84%) of which were mapped to the 92 ERCC references. The overall ERCC mapping ratio of 0.84% is close to the expected value of 1.0% when assuming a 2.0% mRNA fraction in total RNA, but showed a difference of 2.8-fold across studies and 4.3-fold among samples from the same study with one tissue type. This level of fluctuation may prevent the ERCC controls from being used for cross-sample normalization in RNA-Seq. Furthermore, we observed striking biases of quantification between poly(A) and RiboZero which are transcript-specific. For example, ERCC-00116 showed a 7.3-fold under-enrichment in poly(A) compared to RiboZero. Extra care is needed in integrative analysis of multiple datasets and technical artifacts of protocol differences should not be taken as true biological findings.
Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile
Zhao Chen,Shi Leming,Tong Weida,Shaughnessy John D
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-s5-s3
Abstract: Background Microarray data have been used for gene signature selection to predict clinical outcomes. Many studies have attempted to identify factors that affect models' performance with only little success. Fine-tuning of model parameters and optimizing each step of the modeling process often results in over-fitting problems without improving performance. Results We propose a quantitative measurement, termed consistency degree, to detect the correlation between disease endpoint and gene expression profile. Different endpoints were shown to have different consistency degrees to gene expression profiles. The validity of this measurement to estimate the consistency was tested with significance at a p-value less than 2.2e-16 for all of the studied endpoints. According to the consistency degree score, overall survival milestone outcome of multiple myeloma was proposed to extend from 730 days to 1561 days, which is more consistent with gene expression profile. Conclusion For various clinical endpoints, the maximum predictive powers of different microarray-based models are limited by the correlation between endpoint and gene expression profile of disease samples as indicated by the consistency degree score. In addition, previous defined clinical outcomes can also be reassessed and refined more coherent according to related disease gene expression profile. Our findings point to an entirely new direction for assessing the microarray-based predictive models and provide important information to gene signature based clinical applications.
Dissecting the Characteristics and Dynamics of Human Protein Complexes at Transcriptome Cascade Using RNA-Seq Data
Geng Chen, Jiwei Chen, Caiping Shi, Leming Shi, Weida Tong, Tieliu Shi
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0066521
Abstract: Human protein complexes play crucial roles in various biological processes as the functional module. However, the expression features of human protein complexes at the transcriptome cascade are poorly understood. Here, we used the RNA-Seq data from 16 disparate tissues and four types of human cancers to explore the characteristics and dynamics of human protein complexes. We observed that many individual components of human protein complexes can be generated by multiple distinct transcripts. Similar with yeast, the human protein complex constituents are inclined to co-express in diverse tissues. The dominant isoform of the genes involved in protein complexes tend to encode the complex constituents in each tissue. Our results indicate that the protein complex dynamics not only correlate with the presence or absence of complexes, but may also be related to the major isoform switching for complex subunits. Between any two cancers of breast, colon, lung and prostate, we found that only a few of the differentially expressed transcripts associated with complexes were identical, but 5–10 times more protein complexes involved in differentially expressed transcripts were common. Collectively, our study reveals novel properties and dynamics of human protein complexes at the transcriptome cascade in diverse normal tissues and different cancers.
Revealing the missing expressed genes beyond the human reference genome by RNA-Seq
Geng Chen, Ruiyuan Li, Leming Shi, Junyi Qi, Pengzhan Hu, Jian Luo, Mingyao Liu, Tieliu Shi
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-590
Abstract: we used two RNA-Seq datasets from human brain tissues and 10 mixed cell lines to investigate the completeness of human reference genome. First, we demonstrated that in previously identified ~5 Mb Asian and ~5 Mb African novel sequences that are absent from the human reference genome of NCBI build 36, ~211 kb and ~201 kb of them could be transcribed, respectively. Our results suggest that many of those transcribed regions are not specific to Asian and African, but also present in Caucasian. Then, we found that the expressions of 104 RefSeq genes that are unalignable to NCBI build 37 in brain and cell lines are higher than 0.1 RPKM. 55 of them are conserved across human, chimpanzee and macaque, suggesting that there are still a significant number of functional human genes absent from the human reference genome. Moreover, we identified hundreds of novel transcript contigs that cannot be aligned to NCBI build 37, RefSeq genes and EST sequences. Some of those novel transcript contigs are also conserved among human, chimpanzee and macaque. By positioning those contigs onto the human genome, we identified several large deletions in the reference genome. Several conserved novel transcript contigs were further validated by RT-PCR.Our findings demonstrate that a significant number of genes are still absent from the incomplete human reference genome, highlighting the importance of further refining the human reference genome and curating those missing genes. Our study also shows the importance of de novo transcriptome assembly. The comparative approach between reference genome and other related human genomes based on the transcriptome provides an alternative way to refine the human reference genome.The latest version of the public human genome assembly NCBI build 37 (also known as GRCh37) has been released and is considered to be the successor to NCBI Build 36. Currently, different types of human genetic variation studies including single-nucleotide polymorphisms (SNPs), deleti
Identifying Unexpected Therapeutic Targets via Chemical-Protein Interactome
Lun Yang,Jian Chen,Leming Shi,Michael P. Hudock,Kejian Wang,Lin He
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0009568
Abstract: Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer's disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover “behind-the-scenes” aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm.
Genomic analysis of microRNA time-course expression in liver of mice treated with genotoxic carcinogen N-ethyl-N-nitrosourea
Zhiguang Li, William S Branham, Stacey L Dial, Yexun Wang, Lei Guo, Leming Shi, Tao Chen
BMC Genomics , 2010, DOI: 10.1186/1471-2164-11-609
Abstract: To explore the possible time-course changes of miRNA expression induced by a carcinogen, we treated mice with one dose of 120 mg/kg N-ethyl-N-nitrosourea (ENU), a model genotoxic carcinogen, and vehicle control. The miRNA expression profiles were assessed in the mouse livers in a time-course design. miRNAs were isolated from the livers at days 1, 3, 7, 15, 30 and 120 after the treatment and their expression was determined using a miRNA PCR Array. Principal component analysis of the miRNA expression profiles showed that miRNA expression at post-treatment days (PTDs) 7 and 15 were different from those at the other time points and the control. The number of differentially expressed miRNAs (DEMs) changed over time (3, 5, 14, 32, 5 and 5 at PTDs 1, 3, 7, 15, 30 and 120, respectively). The magnitude of the expression change varied with time with the highest changes at PTDs 7 or 15 for most of the DEMs. In silico functional analysis of the DEMs at PTDs 7 and 15 indicated that the major functions of these ENU-induced DEMs were associated with DNA damage, DNA repair, apoptosis and other processes related to carcinogenesis.Our results showed that many miRNAs changed their expression to respond the exposure of the genotoxic carcinogen ENU and the number and magnitude of the changes were highest at PTDs 7 to 15. Thus, one to two weeks after the exposure is the best time for miRNA expression sampling.MicroRNA (miRNA) is a class of small nucleic acids that negatively regulate gene expression [1]. They are single-stranded RNA molecules that are not translated into proteins. Each miRNA molecule is partially complementary to one or more mRNA transcripts, and functions to down-regulate gene expression by inhibiting protein translation or destabilizing target transcripts [2]. The expression of miRNAs is regulated developmentally and spatially, and is involved in differentiation and proliferation of cells [3]. Therefore, miRNA molecules can modulate a wide array of growth and different
Studies on abacavir-induced hypersensitivity reaction: a successful example of translation of pharmacogenetics to personalized medicine
YongLi Guo,LeMing Shi,HuiXiao Hong,ZhenQiang Su,James Fuscoe,BaiTang Ning
Science China Life Sciences , 2013, DOI: 10.1007/s11427-013-4438-8
Abstract: Abacavir is an effective nucleoside analog reverse transcriptase inhibitor used to treat human immunodeficiency virus (HIV) infected patients. Its main side effect is hypersensitivity reaction (HSR). The incidence of the HSR is associated with ethnicity among patients exposed to abacavir, and retrospective and prospective studies show a significantly increased risk of abacavir-induced HSR in human leukocyte antigen (HLA)-B*57:01-carrying patients. Immunological studies indicated that abacavir interacts specifically with HLA-B*57:01 and changed the binding specificity between the HLA molecule and the HLA-presented endogenous peptide repertoire, leading to a systemic autoimmune reaction. HLA-B*57:01 screening, combined with patch testing, had clinically predictive value and cost-effective impact in reducing the incidence of abacavir-induced HSR regardless of the HLA-B*57:01 prevalence in the population. Therefore, the US Food and Drug Administration (FDA) and international HIV treatment guidelines recommend a routine HLA-B*57:01 screening prior to abacavir treatment to decrease false positive diagnosis and prevent abacavir-induced HSR. The studies of abacavir-induced HSR and the implementation of the HLA-B*57:01 screening in the clinic represent a successful example of the use of pharmacogenetics for personalized diagnosis and therapy.
Comparative Analysis of Human Protein-Coding and Noncoding RNAs between Brain and 10 Mixed Cell Lines by RNA-Seq
Geng Chen, Kangping Yin, Leming Shi, Yuanzhang Fang, Ya Qi, Peng Li, Jian Luo, Bing He, Mingyao Liu, Tieliu Shi
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0028318
Abstract: In their expression process, different genes can generate diverse functional products, including various protein-coding or noncoding RNAs. Here, we investigated the protein-coding capacities and the expression levels of their isoforms for human known genes, the conservation and disease association of long noncoding RNAs (ncRNAs) with two transcriptome sequencing datasets from human brain tissues and 10 mixed cell lines. Comparative analysis revealed that about two-thirds of the genes expressed between brain and cell lines are the same, but less than one-third of their isoforms are identical. Besides those genes specially expressed in brain and cell lines, about 66% of genes expressed in common encoded different isoforms. Moreover, most genes dominantly expressed one isoform and some genes only generated protein-coding (or noncoding) RNAs in one sample but not in another. We found 282 human genes could encode both protein-coding and noncoding RNAs through alternative splicing in the two samples. We also identified more than 1,000 long ncRNAs, and most of those long ncRNAs contain conserved elements across either 46 vertebrates or 33 placental mammals or 10 primates. Further analysis showed that some long ncRNAs differentially expressed in human breast cancer or lung cancer, several of those differentially expressed long ncRNAs were validated by RT-PCR. In addition, those validated differentially expressed long ncRNAs were found significantly correlated with certain breast cancer or lung cancer related genes, indicating the important biological relevance between long ncRNAs and human cancers. Our findings reveal that the differences of gene expression profile between samples mainly result from the expressed gene isoforms, and highlight the importance of studying genes at the isoform level for completely illustrating the intricate transcriptome.
Shifting from Population-wide to Personalized Cancer Prognosis with Microarrays
Li Shao, Xiaohui Fan, Ningtao Cheng, Leihong Wu, Haoshu Xiong, Hong Fang, Don Ding, Leming Shi, Yiyu Cheng, Weida Tong
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0029534
Abstract: The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment.
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