Biomedical subjects

Najma Khalid

Publications and source records attributed to Najma Khalid.

5 recordsLinked to original sources

PubMed2006

A statistical method for predicting splice variants between two groups of samples using GeneChip expression array data.

BACKGROUND: Alternative splicing of pre-messenger RNA results in RNA variants with combinations of selected exons. It is one of the essential biological functions and regulatory components in higher eukaryotic cells. Some of these variants are detectable with the Affymetrix GeneChip that uses multiple oligonucleotide probes (i.e. probe set), since the target sequences for the multiple probes are adjacent within each gene. Hybridization intensity from a probe correlates with abundance of the corresponding transcript. Although the multiple-probe feature in the current GeneChip was designed to assess expression values of individual genes, it also measures transcriptional abundance for a sub-region of a gene sequence. This additional capacity motivated us to develop a method to predict alternative splicing, taking advance of extensive repositories of GeneChip gene expression array data. RESULTS: We developed a two-step approach to predict alternative splicing from GeneChip data. First, we clustered the probes from a probe set into pseudo-exons based on similarity of probe intensities and physical adjacency. A pseudo-exon is defined as a sequence in the gene within which multiple probes have comparable probe intensity values. Second, for each pseudo-exon, we assessed the statistical significance of the difference in probe intensity between two groups of samples. Differentially expressed pseudo-exons are predicted to be alternatively spliced. We applied our method to empirical data generated from GeneChip Hu6800 arrays, which include 7129 probe sets and twenty probes per probe set. The dataset consists of sixty-nine medulloblastoma (27 metastatic and 42 non-metastatic) samples and four cerebellum samples as normal controls. We predicted that 577 genes would be alternatively spliced when we compared normal cerebellum samples to medulloblastomas, and predicted that thirteen genes would be alternatively spliced when we compared metastatic medulloblastomas to non-metastatic ones. We checked the consistency of some of our findings with information in UCSC Human Genome Browser. CONCLUSION: The two-step approach described in this paper is capable of predicting some alternative splicing from multiple oligonucleotide-based gene expression array data with GeneChip technology. Our method employs the extensive repositories of gene expression array data available and generates alternative splicing hypotheses, which can be further validated by experimental studies.

Algorithms↗

PubMed2005

A class of models for analyzing GeneChip gene expression analysis array data.

BACKGROUND: Various analytical methods exist that first quantify gene expression and then analyze differentially expressed genes from Affymetrix GeneChip gene expression analysis array data. These methods differ in the choice of probe measure (quantification of probe hybridization), summarizing multiple probe intensities into a gene expression value, and analysis of differential gene expression. Research papers that describe these methods focus on performance, and how their approaches differ from others. To better understand the common features and differences between various methods, and to evaluate their impact on the results of gene expression analysis, we describe a class of models, referred to as generalized probe models (GPMs), which encompass various currently available methods. RESULTS: Using an empirical dataset, we compared different formulations of GPMs, and GPMs with three other commonly used methods, i.e. MAS 5.0, dChip, and RMA. The comparison shows that, on a genome-wide scale , different methods yield similar results if the same probe measures are chosen. CONCLUSION: In this paper we present a general framework, i.e. GPMs, which encompasses various methods. GPMs permit the use of a wide range of probe measures and facilitate appropriate comparison between commonly used methods. We demonstrate that the dissimilar results stem primarily from different choice of probe measures, rather than other factors.

Algorithms↗

PubMed2004

Single-stranded linear amplification protocol results in reproducible and reliable microarray data from nanogram amounts of starting RNA.

The range of scientific questions utilizing DNA microarray techniques is limited by the fact that these methods require 5-40 microg of high-quality total RNA. Thus, methods that reliably amplify the starting RNA amount could expand the applicability of DNA microarray technology. We developed a single-stranded linear amplification protocol (SLAP) that combines the reproducibility of in vitro transcription and the amplification robustness of polymerase chain reactions. We compared SLAP to the NIH-IVT amplification protocol. SLAP displayed excellent conservation of the 5'/3' signal and demonstrated the most robust amplification, producing the recommended amounts of biotin-labeled RNA with as little as 0.002 microg of starting RNA. Both SLAP and NIH-IVT methods demonstrated good reproducibility, but SLAP maintained the highest level of reliability with RNA starting amounts of <0.05 microg. These results suggest that SLAP is an excellent alternative to IVT-based amplification protocols when RNA is limited by small sample size.

Biotin↗

PubMed2003

A method for the assessment of disease associations with single-nucleotide polymorphism haplotypes and environmental variables in case-control studies.

The rough draft of the human genome map has been used to identify most of the functional genes in the human genome, as well as to identify nucleotide variations, known as "single-nucleotide polymorphisms" (SNPs), in these genes. By use of advanced biotechnologies, researchers are beginning to genotype thousands of SNPs from biological samples. Among the many possible applications, one of them is the study of SNP associations with complex human diseases, such as cancers or coronary heart diseases, by using a case-control study design. Through the gathering of environmental risk factors and other lifestyle factors, such a study can be effectively used to investigate interactions between genes and environmental factors in their associations with disease phenotype. Earlier, we developed a method to statistically construct individuals' haplotypes and to estimate the distribution of haplotypes of multiple SNPs in a defined population, by use of estimating-equation techniques. Extending this idea, we describe here an analytic method for assessing the association between the constructed haplotypes along with environmental factors and the disease phenotype. This method is also robust to the model assumptions and is scalable to a large number of SNPs. Asymptotic properties of estimations in the method are proved theoretically and are tested for finite sample sizes by use of simulations. To demonstrate the use of the method, we applied it to assess the possible association between apolipoprotein CIII (six coding SNPs) and restenosis by using a case-control data set. Our analysis revealed two haplotypes that may reduce the risk of restenosis.

Apolipoprotein C-III↗

PubMed2003

Estimating haplotype frequencies and standard errors for multiple single nucleotide polymorphisms.

Estimating haplotype frequencies becomes increasingly important in the mapping of complex disease genes, as millions of single nucleotide polymorphisms (SNPs) are being identified and genotyped. When genotypes at multiple SNP loci are gathered from unrelated individuals, haplotype frequencies can be accurately estimated using expectation-maximization (EM) algorithms (Excoffier and Slatkin, 1995; Hawley and Kidd, 1995; Long et al., 1995), with standard errors estimated using bootstraps. However, because the number of possible haplotypes increases exponentially with the number of SNPs, handling data with a large number of SNPs poses a computational challenge for the EM methods and for other haplotype inference methods. To solve this problem, Niu and colleagues, in their Bayesian haplotype inference paper (Niu et al., 2002), introduced a computational algorithm called progressive ligation (PL). But their Bayesian method has a limitation on the number of subjects (no more than 100 subjects in the current implementation of the method). In this paper, we propose a new method in which we use the same likelihood formulation as in Excoffier and Slatkin's EM algorithm and apply the estimating equation idea and the PL computational algorithm with some modifications. Our proposed method can handle data sets with large number of SNPs as well as large numbers of subjects. Simultaneously, our method estimates standard errors efficiently, using the sandwich-estimate from the estimating equation, rather than the bootstrap method. Additionally, our method admits missing data and produces valid estimates of parameters and their standard errors under the assumption that the missing genotypes are missing at random in the sense defined by Rubin (1976).

Algorithms↗