Comparison of genomic sequences using the Hamming distance

Pinheiro, Hildete Prisco; Pinheiro, Aluísio; Sen, Pranab Kumar

doi:10.1016/j.jspi.2003.03.002

Cited by 45 publications

(62 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Hamming distance is calculated as the number of differences between two vectors and attributes identical weight to each change. It is considered a natural measure and is the simplest way to calculate a distance (Durbin et al, 1998;Pinheiro et al, 2004). Tree drawing was performed with the TreeView program (version 1.6.1; available from R. D. M. Page, University of Glasgow, UK; http://taxonomy.zoology.gla.ac.uk/rod/rod.html).…”

Section: Discussionmentioning

confidence: 99%

Genetic instability and divergence of clonal populations in colon cancer cells in vitro

Masramon

Vendrell

Tarafa

et al. 2006

Journal of Cell Science

View full text Add to dashboard Cite

The accumulation of multiple chromosomal abnormalities is a characteristic of the majority of colorectal cancers and has been attributed to an underlying chromosomal instability. Genetic instability is considered to have a key role in the generation of genetic and phenotypic heterogeneity in cancer cells. To shed light on the dynamics of chromosomal instability in colon cancer cells, we have analyzed genetic divergence in clonal and subclonal derivates of chromosomally unstable (SW480) and stable (HCT116, LoVo) cell lines. Conventional G-banding karyotyping and arbitrarily primed PCR (AP-PCR) fingerprinting were used to calculate genetic distances among clones and parental cells, and to trace tree-type phylogenies among individual cells and clonal cell populations. SW480 cells showed enhanced karyotypic heterogeneity in clones as compared with parental cells. Moreover, genetic clonal divergence was also increased after two consecutive episodes of single-cell cloning, demonstrating that the homogeneity induced by the bottleneck of cloning is disrupted by genetic instability during clonal expansion and, as a consequence, heterogeneity is restored. These results demonstrate genetic drift in clonal populations originated from isolated cells. The generated cell heterogeneity coupled with selection provides the grounds for the reported feasibility of pre-neoplastic and neoplastic cells to generate new phenotypic variants with increased evolutionary potential.

show abstract

Section: Discussionmentioning

confidence: 99%

Genetic instability and divergence of clonal populations in colon cancer cells in vitro

Masramon

Vendrell

Tarafa

et al. 2006

Journal of Cell Science

View full text Add to dashboard Cite

show abstract

“…Under the assumption of an inter-position stochastic dependence, we need to consider another measure of variation. The Gini-Simpson biodiversity index has found useful applications in genetics and in bioinformatics (Sen, 2005;Pinhero et al, 2005). Mostly, categorical data models (without an ordering of the categories) appear, which preempts the use of measures of quantitative diversity analysis.…”

Section: A Pseudo Marginal Modelmentioning

confidence: 99%

“…Mostly, categorical data models (without an ordering of the categories) appear, which preempts the use of measures of quantitative diversity analysis. Without much stringent structural regularity assumptions, the Hamming distance exploits the idea of Gini-Simpson diversity index in a variety of multidimensional setups (Sen, 2005;Pinhero et al, 2005). Consider a set of n vectors X gi = (X gi1 , .…”

Section: A Pseudo Marginal Modelmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Testing in Genomic Sequences Using Hamming Distance

Kang¹

2012

Communications for Statistical Applications and Methods

View full text Add to dashboard Cite

High-dimensional categorical data models with small sample sizes have not been used extensively in genomic sequences that involve count (or discrete) or purely qualitative responses. A basic task is to identify differentially expressed genes (or positions) among a number of genes. It requires an appropriate test statistics and a corresponding multiple testing procedure so that a multivariate analysis of variance should not be feasible. A family wise error rate(FWER) is not appropriate to test thousands of genes simultaneously in a multiple testing procedure. False discovery rate(FDR) is better than FWER in multiple testing problems. The data from the 2002-2003 SARS epidemic shows that a conventional FDR procedure and a proposed test statistic based on a pseudo-marginal approach with Hamming distance performs better.

show abstract

“…This has led to the development of many statistical methods designed to examine such association, including various single-marker tests and the more powerful multiple-marker analyses, also known as gene/set-based analyses, where the functional role of a group of variants or a signaling pathway is of interest. One class of the marker-set methods is based on the nonparametric U-statistic and utilizes similarity or dissimilarity measures to test the existence of association (e.g., Pinheiro et al 2005;Schaid et al 2005;Wessel and Shork 2006;Wei et al 2008;Tzeng et al 2009;Li 2012;Wei et al 2014;Wang et al 2015). The formulation and derivation of these U-statistic-based tests are simple and intuitive; they are useful when not much information is available about the mode of inheritance and phenotype distribution.…”

Section: Introductionmentioning

confidence: 99%

Reexamining Dis/Similarity-Based Tests for Rare-Variant Association with Case-Control Samples

Wang

Tzeng

et al. 2018

Genetics

View full text Add to dashboard Cite

A properly designed distance-based measure can capture informative genetic differences among individuals with different phenotypes and be used to detect variants responsible for the phenotypes. To detect associated variants, various tests have been designed to contrast genetic dis/similarity scores of certain subject groups in different ways, among which the most widely used strategy is to quantify the difference between the within-group genetic dis/similarities (i.e., case-case and control-control similarities) and the between-group dis/similarities (i.e., case-control similarities). While it has been noted that for common variants, the within-group and the between-group measures should all be included, in this work, we show that for rare variants, comparison based on the two within-group measures can more effectively quantify the genetic difference between cases and controls. The between-group measure tends to overlap with one of the two within-group measures for rare variants, although such overlap is not present for common variants. Consequently, a dis/similarity test that includes the between-group information in the contrast tends to attenuate the association signals and leads to power loss. Based on the findings, we propose a dissimilarity test that compares the degree of SNP dissimilarity within cases to that within controls to better characterize the difference between two disease phenotypes.We provide the statistical properties, asymptotic distribution, and computation details for small sample size of the proposed test. We use simulated and real sequence data to assess the performance of the proposed test, comparing with other rare variant methods including those similarity-based tests that utilize both within-group and between-group information. As similarity-based approaches serve as one of the dominating approaches in rare variant analysis, our results would provide some insight for effective detection of rare variants.3

show abstract

Comparison of genomic sequences using the Hamming distance

Cited by 45 publications

References 5 publications

Genetic instability and divergence of clonal populations in colon cancer cells in vitro

Genetic instability and divergence of clonal populations in colon cancer cells in vitro

Multiple Testing in Genomic Sequences Using Hamming Distance

Reexamining Dis/Similarity-Based Tests for Rare-Variant Association with Case-Control Samples

Contact Info

Product

Resources

About