HemI: A Toolkit for Illustrating Heatmaps

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Abstract

Recent high-throughput techniques have generated a flood of biological data in all aspects. The transformation and visualization of multi-dimensional and numerical gene or protein expression data in a single heatmap can provide a concise but comprehensive presentation of molecular dynamics under different conditions. In this work, we developed an easy-to-use tool named HemI (Heat map Illustrator), which can visualize either gene or protein expression data in heatmaps. Additionally, the heatmaps can be recolored, rescaled or rotated in a customized manner. In addition, HemI provides multiple clustering strategies for analyzing the data. Publication-quality figures can be exported directly. We propose that HemI can be a useful toolkit for conveniently visualizing and manipulating heatmaps. The stand-alone packages of HemI were implemented in Java and can be accessed at http://hemi.biocuckoo.org/down.php.

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A survey of tools for variant analysis of next-generation genome sequencing data

Stephan Pabinger, Andreas Dander, Maria Fischer … (2013)

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.

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Cooperation between Polycomb and androgen receptor during oncogenic transformation.

Ming Hu, C. Zhao, Jianjun Yu … (2012)

Androgen receptor (AR) is a hormone-activated transcription factor that plays important roles in prostate development and function, as well as malignant transformation. The downstream pathways of AR, however, are incompletely understood. AR has been primarily known as a transcriptional activator inducing prostate-specific gene expression. Through integrative analysis of genome-wide AR occupancy and androgen-regulated gene expression, here we report AR as a globally acting transcriptional repressor. This repression is mediated by androgen-responsive elements (ARE) and dictated by Polycomb group protein EZH2 and repressive chromatin remodeling. In embryonic stem cells, AR-repressed genes are occupied by EZH2 and harbor bivalent H3K4me3 and H3K27me3 modifications that are characteristic of differentiation regulators, the silencing of which maintains the undifferentiated state. Concordantly, these genes are silenced in castration-resistant prostate cancer rendering a stem cell-like lack of differentiation and tumor progression. Collectively, our data reveal an unexpected role of AR as a transcriptional repressor inhibiting non-prostatic differentiation and, upon excessive signaling, resulting in cancerous dedifferentiation.

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An interactive power analysis tool for microarray hypothesis testing and generation.

Jinwook Seo, Heather Gordish-Dressman, Eric Hoffman (2006)

Human clinical projects typically require a priori statistical power analyses. Towards this end, we sought to build a flexible and interactive power analysis tool for microarray studies integrated into our public domain HCE 3.5 software package. We then sought to determine if probe set algorithms or organism type strongly influenced power analysis results. The HCE 3.5 power analysis tool was designed to import any pre-existing Affymetrix microarray project, and interactively test the effects of user-defined definitions of alpha (significance), beta (1-power), sample size and effect size. The tool generates a filter for all probe sets or more focused ontology-based subsets, with or without noise filters that can be used to limit analyses of a future project to appropriately powered probe sets. We studied projects from three organisms (Arabidopsis, rat, human), and three probe set algorithms (MAS5.0, RMA, dChip PM/MM). We found large differences in power results based on probe set algorithm selection and noise filters. RMA provided high sensitivity for low numbers of arrays, but this came at a cost of high false positive results (24% false positive in the human project studied). Our data suggest that a priori power calculations are important for both experimental design in hypothesis testing and hypothesis generation, as well as for the selection of optimized data analysis parameters. The Hierarchical Clustering Explorer 3.5 with the interactive power analysis functions is available at www.cs.umd.edu/hcil/hce or www.cnmcresearch.org/bioinformatics. jseo@cnmcresearch.org

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Author and article information

Contributors

Zhang Zhang: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2014

Publication date (Electronic): 5 November 2014

Volume: 9

Issue: 11

Electronic Location Identifier: e111988

Affiliations

[1]Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

Beijing Institute of Genomics, Chinese Academy of Sciences, China

Author notes

* E-mail: xueyu@ 123456hust.edu.cn

Competing Interests: YX is a PLOS ONE Editorial Board member. This does not alter the authors' adherence to PLOS ONE Editorial policies and criteria.

Conceived and designed the experiments: YX. Performed the experiments: WD YW. Analyzed the data: WD YW YX. Contributed reagents/materials/analysis tools: ZL HC. Wrote the paper: WD YW YX.

Article

Publisher ID: PONE-D-14-26604

DOI: 10.1371/journal.pone.0111988

PMC ID: 4221433

PubMed ID: 25372567

SO-VID: c9a59918-12ca-4300-b7eb-85583a205076

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 30 June 2014

Date accepted : 4 October 2014

Page count

Pages: 5

Funding

The work was supported, in whole or in part, by the National Basic Research Program (973 project) (2012CB910101, and 2013CB9339002), Natural Science Foundation of China (31171263, 81272578 and J1103514), International Science & Technology Cooperation Program of China (2014DFB30020), China Postdoctoral Science Foundation (2014M550392), and Fundamental Research Funds for the Central Universities (HUST: 2013TS080, 2014YQ003). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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HemI: A Toolkit for Illustrating Heatmaps

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Most cited references 8

A survey of tools for variant analysis of next-generation genome sequencing data

Cooperation between Polycomb and androgen receptor during oncogenic transformation.

An interactive power analysis tool for microarray hypothesis testing and generation.

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