Dataset Information

Computerized polymorphic marker identification: experimental validation and a predicted human polymorphism catalog.

ABSTRACT: A computational system for the prediction of polymorphic loci directly and efficiently from human genomic sequence was developed and verified. A suite of programs, collectively called POMPOUS (polymorphic marker prediction of ubiquitous simple sequences) detects tandem repeats ranging from dinucleotides up to 250 mers, scores them according to predicted level of polymorphism, and designs appropriate flanking primers for PCR amplification. This approach was validated on an approximately 750-kilobase region of human chromosome 3p21.3, involved in lung and breast carcinoma homozygous deletions. Target DNA from 36 paired B lymphoblastoid and lung cancer lines was amplified and allelotyped for 33 loci predicted by POMPOUS to be variable in repeat size. We found that among those 36 predominately Caucasian individuals 22 of the 33 (67%) predicted loci were polymorphic with an average heterozygosity of 0.42. Allele loss in this region was found in 27/36 (75%) of the tumor lines using these markers. POMPOUS provides the genetic researcher with an additional tool for the rapid and efficient identification of polymorphic markers, and through a World Wide Web site, investigators can use POMPOUS to identify polymorphic markers for their research. A catalog of 13,261 potential polymorphic markers and associated primer sets has been created from the analysis of 141,779,504 base pairs of human genomic sequence in GenBank. This data is available on our Web site (pompous.swmed.edu) and will be updated periodically as GenBank is expanded and algorithm accuracy is improved.

SUBMITTER: Fondon JW

PROVIDER: S-EPMC22669 | biostudies-literature | 1998 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computerized polymorphic marker identification: experimental validation and a predicted human polymorphism catalog.

Fondon J W JW Mele G M GM Brezinschek R I RI Cummings D D Pande A A Wren J J O'Brien K M KM Kupfer K C KC Wei M H MH Lerman M M Minna J D JD Garner H R HR

Proceedings of the National Academy of Sciences of the United States of America 19980601 13

A computational system for the prediction of polymorphic loci directly and efficiently from human genomic sequence was developed and verified. A suite of programs, collectively called POMPOUS (polymorphic marker prediction of ubiquitous simple sequences) detects tandem repeats ranging from dinucleotides up to 250 mers, scores them according to predicted level of polymorphism, and designs appropriate flanking primers for PCR amplification. This approach was validated on an approximately 750-kilob ...[more]

PMID: 9636181

Similar Datasets

Project description:Computational methods have been widely used for the prediction of protein subcellular localization. However, these predictions are rarely validated experimentally and as a result remain questionable. Therefore, experimental validation of the predicted localizations is needed to assess the accuracy of predictions so that such methods can be confidently used to annotate the proteins of unknown localization. Previously, we published a method called ngLOC that predicts the localization of proteins targeted to ten different subcellular organelles. In this short report, we describe the accuracy of these predictions using experimental validations.We have experimentally validated the predicted subcellular localizations of 114 human proteins corresponding to nine different organelles in normal breast and breast cancer cell lines using live cell imaging/confocal microscopy. Target genes were cloned into expression vectors as GFP fusions and cotransfected with RFP-tagged organelle-specific gene marker into normal breast epithelial and breast cancer cell lines. Subcellular localization of each target protein is confirmed by colocalization with a co-expressed organelle-specific protein marker. Our results showed that about 82.5% of the predicted subcellular localizations coincided with the experimentally validated localizations. The highest agreement was found in the endoplasmic reticulum proteins, while the cytoplasmic location showed the least concordance. With the exclusion of cytoplasmic location, the average prediction accuracy increased to 90.4%. In addition, there was no difference observed in the protein subcellular localization between normal and cancer breast cell lines.The experimentally validated accuracy of ngLOC method with (82.5%) or without cytoplasmic location (90.4%) nears the prediction accuracy of 89%. These results demonstrate that the ngLOC method can be very useful for large-scale annotation of the unknown subcellular localization of proteins.

Project description:Currently, Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are widely prevalent in the elderly population, and accumulating evidence implies a strong link between them. For example, patients with T2DM have a higher risk of developing neurocognitive disorders, including AD, but the exact mechanisms are still unclear. This time, by combining bioinformatics analysis and in vivo experimental validation, we attempted to find a common biological link between AD and T2DM. We firstly downloaded the gene expression profiling (AD: GSE122063; T2DM: GSE161355) derived from the temporal cortex. To find the associations, differentially expressed genes (DEGs) of the two datasets were filtered and intersected. Based on them, enrichment analysis was carried out, and the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify the specific genes. After verifying in the external dataset and in the samples from the AD and type 2 diabetes animals, the shared targets of the two diseases were finally determined. Based on them, the ceRNA networks were constructed. Besides, the logistic regression and single-sample gene set enrichment analysis (ssGSEA) were performed. As a result, 62 DEGs were totally identified between AD and T2DM, and the enrichment analysis indicated that they were much related to the function of synaptic vesicle and MAPK signaling pathway. Based on the evidence from external dataset and RT-qPCR, CARTPT, EPHA5, and SERPINA3 were identified as the marker genes in both diseases, and their clinical significance and biological functions were further analyzed. In conclusion, discovering and exploring the marker genes that are dysregulated in both 2 diseases could help us better comprehend the intrinsic relationship between T2DM and AD, which may inspire us to develop new strategies for facing the dilemmas of clinical or basic research in cognitive dysfunction.

Project description:BackgroundColorectal cancer is an important death-related disease in the worldwide. However, specific colon cancer tumor markers currently used for diagnosis and treatment are few. The purpose of this study is to screen the potential colon cancer markers by bioinformatics and verify the results with experiments.MethodsGene expression data were downloaded from two different databases: TCGA database and GEO datasets, which were then analyzed by two different methods (difference analysis and WGCNA method). Venn and PPI analysis obtained the potential core genes, which were then performed the GO enrichment and KEGG pathway analysis. Expressions levels of NKD1 in colon carcinoma tissues were further confirmed by immunohistochemical staining and western blot assays. Moreover, the function was measured by MTT, clone formation, and tumor transplantation experiments. Importantly, co-immunoprecipitation, immunofluorescence, and protein stability assays were further performed to explore the underlying mechanism of NKD1 promoting cell proliferation.ResultsNine potential core genes highly expressed in colon cancer samples were screened out by bioinformatics analysis. NKD1, one of the hub genes, highly expressed in the colon carcinoma tissues could enhance the proliferation of colon cancer cells. Mechanism research demonstrated that NKD1 was essential for the combination between Wnt signalosome (DVL) and β-catenin, and that NKD1 knockout remarkably decreased the β-catenin expression. Immunofluorescence assays further implied that NKD1 knockout significantly inhibited β-catenin nuclear accumulation. Importantly, the stability of β-catenin proteins was maintained by NKD1 in the colon cancer cells.ConclusionWe believe that NKD1 well expressed in the colorectal carcinoma tissues can enhance the proliferation of colon cancer cells. Furthermore, the functions that NKD1 may have in colon cancer cells should be different from that NKD1 has played in the zebrafish. Thus, NKD1 could be a specific colorectal cancer marker.

Project description:The abundance and identity of functional variation segregating in natural populations is paramount to dissecting the molecular basis of quantitative traits as well as human genetic diseases. Genome sequencing of multiple organisms of the same species provides an efficient means of cataloging rearrangements, insertion, or deletion polymorphisms (InDels) and single-nucleotide polymorphisms (SNPs). While inbreeding depression and heterosis imply that a substantial amount of polymorphism is deleterious, distinguishing deleterious from neutral polymorphism remains a significant challenge. To identify deleterious and neutral DNA sequence variation within Saccharomyces cerevisiae, we sequenced the genome of a vineyard and oak tree strain and compared them to a reference genome. Among these three strains, 6% of the genome is variable, mostly attributable to variation in genome content that results from large InDels. Out of the 88,000 polymorphisms identified, 93% are SNPs and a small but significant fraction can be attributed to recent interspecific introgression and ectopic gene conversion. In comparison to the reference genome, there is substantial evidence for functional variation in gene content and structure that results from large InDels, frame-shifts, and polymorphic start and stop codons. Comparison of polymorphism to divergence reveals scant evidence for positive selection but an abundance of evidence for deleterious SNPs. We estimate that 12% of coding and 7% of noncoding SNPs are deleterious. Based on divergence among 11 yeast species, we identified 1,666 nonsynonymous SNPs that disrupt conserved amino acids and 1,863 noncoding SNPs that disrupt conserved noncoding motifs. The deleterious coding SNPs include those known to affect quantitative traits, and a subset of the deleterious noncoding SNPs occurs in the promoters of genes that show allele-specific expression, implying that some cis-regulatory SNPs are deleterious. Our results show that the genome sequences of both closely and distantly related species provide a means of identifying deleterious polymorphisms that disrupt functionally conserved coding and noncoding sequences.

Dataset Information

Computerized polymorphic marker identification: experimental validation and a predicted human polymorphism catalog.

Publications

Computerized polymorphic marker identification: experimental validation and a predicted human polymorphism catalog.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets