Bioinformatics framework for genotyping microarray data analysis

Functional genomics is a flourishing science enabled by recent technological breakthroughs in high-throughput instrumentation and microarray data analysis. Genotyping microarrays establish the genotypes of DNA sequences containing single nucleotide polymorphisms (SNPs), and can help biologists probe the functions of different genes and/or construct complex gene interaction networks. The enormous amount of data from these experiments makes it infeasible to perform manual processing to obtain accurate and reliable results in daily routines. Advanced algorithms as well as an integrated software toolkit are needed to help perform reliable and fast data analysis. The author developed a MatlabTM based software package, called TIMDA (a Toolkit for Integrated Genotyping Microarray Data Analysis), for fully automatic, accurate and reliable genotyping microarray data analysis. The author also developed new algorithms for image processing and genotype-calling. The modular design of TIMDA allows satisfactory extensibility and maintainability. TIMDA is open source (URL: http://timda.SF.net and can be easily customized by users to meet their particular needs. The quality and reproducibility of results in image processing and genotype-calling and the ease of customization indicate that TIMDA is a useful package for genomics research

Fully automatic classification of breast cancer microarray images

AbstractA microarray image is used as an accurate method for diagnosis of cancerous diseases. The aim of this research is to provide an approach for detection of breast cancer type. First, raw data is extracted from microarray images. Determining the exact location of each gene is carried out using image processing techniques. Then, by the sum of the pixels associated with each gene, the amount of “genes expression” is extracted as raw data. To identify more effective genes, information gain method on the set of raw data is used. Finally, the type of cancer can be recognized via analyzing the obtained data using a decision tree. The proposed approach has an accuracy of 95.23% in diagnosing the breast cancer types

Automatic gridding of DNA microarray images.

Microarray (DNA chip) technology is having a significant impact on genomic studies. Many fields, including drug discovery and toxicological research, will certainly benefit from the use of DNA microarray technology. Microarray analysis is replacing traditional biological assays based on gels, filters and purification columns with small glass chips containing tens of thousands of DNA and protein sequences in agricultural and medical sciences. Microarray functions like biological microprocessors, enabling the rapid and quantitative analysis of gene expression patterns, patient genotypes, drug mechanisms and disease onset and progression on a genomic scale. Image analysis and statistical analysis are two important aspects of microarray technology. Gridding is necessary to accurately identify the location of each of the spots while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. Due to the deficiencies of the equipment that is used to print the arrays, rotations, misalignments, high contaminations with noise and artifacts, solving the grid segmentation problem in an automatic system is not trivial. The existing techniques to solve the automatic grid segmentation problem cover only limited aspect of this challenging problem and requires the user to specify or make assumptions about the spotsize, rows and columns in the grid and boundary conditions. An automatic gridding and spot quantification technique is proposed, which takes a matrix of pixels or a microarray image as input and makes no assumptions about the spotsize, rows and columns in the grid and is found to effective on datasets from GEO, Stanford genomic laboratories and on images obtained from private repositories. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .V53. Source: Masters Abstracts International, Volume: 43-03, page: 0891. Adviser: Luis Rueda. Thesis (M.Sc.)--University of Windsor (Canada), 2004

A fully automatic gridding method for cDNA microarray images

<p>Abstract</p> <p>Background</p> <p>Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing the underlying images, accurately separating the sub-grids and spots is extremely important for subsequent steps that include segmentation, quantification, normalization and clustering.</p> <p>Results</p> <p>We propose a parameterless and fully automatic approach that first detects the sub-grids given the entire microarray image, and then detects the locations of the spots in each sub-grid. The approach, first, detects and corrects rotations in the images by applying an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm used to find the positions of the sub-grids in the image and the positions of the spots in each sub-grid. Additionally, a new validity index is proposed in order to find the correct number of sub-grids in the image, and the correct number of spots in each sub-grid. Moreover, a refinement procedure is used to correct possible misalignments and increase the accuracy of the method.</p> <p>Conclusions</p> <p>Extensive experiments on real-life microarray images and a comparison to other methods show that the proposed method performs these tasks fully automatically and with a very high degree of accuracy. Moreover, unlike previous methods, the proposed approach can be used in various type of microarray images with different resolutions and spot sizes and does not need any parameter to be adjusted.</p

Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images

Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive data sets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches to fully automate microarray segmentation. Automated artifact removal is also accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of data sets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies.Camille and Henry Dreyfus Foundation (Camille Dreyfus Teacher-Scholar Award

Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays

Novel pattern recognition approaches for transcriptomics data analysis

We proposed a family of methods for transcriptomics and genomics data analysis based on multi-level thresholding approach, such as OMTG for sub-grid and spot detection in DNA microarrays, and OMT for detecting significant regions based on next generation sequencing data. Extensive experiments on real-life datasets and a comparison to other methods show that the proposed methods perform these tasks fully automatically and with a very high degree of accuracy. Moreover, unlike previous methods, the proposed approaches can be used in various types of transcriptome analysis problems such as microarray image gridding with different resolutions and spot sizes as well as finding the interacting regions of DNA with a protein of interest using ChIP-Seq data without any need for parameter adjustment. We also developed constrained multi-level thresholding (CMT), an algorithm used to detect enriched regions on ChIP-Seq data with the ability of targeting regions within a specific range. We show that CMT has higher accuracy in detecting enriched regions (peaks) by objectively assessing its performance relative to other previously proposed peak finders. This is shown by testing three algorithms on the well-known FoxA1 Data set, four transcription factors (with a total of six antibodies) for Drosophila melanogaster and the H3K4ac antibody dataset. Finally, we propose a tree-based approach that conducts gene selection and builds a classifier simultaneously, in order to select the minimal number of genes that would reliably predict a given breast cancer subtype. Our results support that this modified approach to gene selection yields a small subset of genes that can predict subtypes with greater than 95%overall accuracy. In addition to providing a valuable list of targets for diagnostic purposes, the gene ontologies of the selected genes suggest that these methods have isolated a number of potential genes involved in breast cancer biology, etiology and potentially novel therapeutics

Microarray spot partitioning by autonoumsly organising maps thorugh contour model

In cDNA microarray image analysis, classification of pixels as forefront area and the area covered by background is very challenging. In microarray experimentation, identifying forefront area of desired spots is nothing but computation of forefront pixels concentration, area covered by spot and shape of the spots. In this piece of writing, an innovative way for spot partitioning of microarray images using autonomously organizing maps (AOM) method through C-V model has been proposed. Concept of neural networks has been incorpated to train and to test microarray spots.In a trained AOM the comprehensive information arising from the prototypes of created neurons are clearly integrated to decide whether to get smaller or get bigger of contour. During the process of optimization, this is done in an iterative manner. Next using C-V model, inside curve area of trained spot is compared with test spot finally curve fitting is done.The presented model can handle spots with variations in terms of shape and quality of the spots and meanwhile it is robust to the noise. From the review of experimental work, presented approach is accurate over the approaches like C-means by fuzzy, Morphology sectionalization