MPP: a microarray-to-phylogeny pipeline for analysis of gene and marker content datasets

نویسندگان

  • Robert Davey
  • George Savva
  • Jo L. Dicks
  • Ian N. Roberts
چکیده

UNLABELLED MPP is a Java application, encompassing both new and established algorithms, for the analysis of gene and marker content datasets arising from high-throughput microarray techniques. MPP analyses flat file output from microarray experiments to determine the probability of the presence or absence of genes or markers within a genome. MPP can construct gene or marker content datasets for a number of genomes and can use the data to estimate an evolutionary tree or network. Results from gene content analyses may be validated by comparing them to known gene contents. MPP was initially developed to analyse data derived from comparative genome hybridization (CGH) microarray experiments in fungi and bacteria. It has recently been adapted to analyse retrotransposon-based insertion polymorphism (RBIP) marker scores derived from tagged microarray marker (TAM) experiments in pea. New analytical procedures may be added easily to MPP as plugins in order to increase the scope of the software. AVAILABILITY MPP source code, executables and online help are available at http://cbr.jic.ac.uk/dicks/software/

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modification of the Fast Global K-means Using a Fuzzy Relation with Application in Microarray Data Analysis

Recognizing genes with distinctive expression levels can help in prevention, diagnosis and treatment of the diseases at the genomic level. In this paper, fast Global k-means (fast GKM) is developed for clustering the gene expression datasets. Fast GKM is a significant improvement of the k-means clustering method. It is an incremental clustering method which starts with one cluster. Iteratively ...

متن کامل

SFLA Based Gene Selection Approach for Improving Cancer Classification Accuracy

 In this paper, we propose a new gene selection algorithm based on Shuffled Frog Leaping Algorithm that is called SFLA-FS. The proposed algorithm is used for improving cancer classification accuracy. Most of the biological datasets such as cancer datasets have a large number of genes and few samples. However, most of these genes are not usable in some tasks for example in cancer classification....

متن کامل

Integration and Reduction of Microarray Gene Expressions Using an Information Theory Approach

The DNA microarray is an important technique that allows researchers to analyze many gene expression data in parallel. Although the data can be more significant if they come out of separate experiments, one of the most challenging phases in the microarray context is the integration of separate expression level datasets that have gathered through different techniques. In this paper, we prese...

متن کامل

Identification of specific gene expression after exposure to low dose ionizing radiation revealed through integrative analysis of cDNA microarray data and the interactome

Background: Accumulating reports suggest that the biological effects of low- and high- dose ionizing radiation (LDIR and HDIR) are qualitatively different and might cause different effects in human skin. Materials and Methods: To better understand the potential risks of LDIR, we analyzed three cDNA microarray datasets from the Gene Expression Omnibus database. Results: A pathway analysis showed...

متن کامل

Gene Regulation Network Based Analysis Associated with TGF-beta Stimulation in Lung Adenocarcinoma Cells

Background: Transforming growth factor (TGF)-β is over-expressed in a wide variety of cancers such as lung adenocarcinoma. TGF-β plays a major role in cancer progression through regulating cancer cell proliferation and remodeling of the tumor micro-environment. However, it is still a great challenge to explain the phenotypic effects caused by TGF-β stimulation and the effect of TGF-β stimulatio...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Bioinformatics

دوره 23 8  شماره 

صفحات  -

تاریخ انتشار 2007