MAPT and PAICE: Tools for time series and single time point transcriptionist visualization and knowledge discovery.

Parsa Hosseini,Benjamin F Matthews,Nadim W Alkharouf,Arianne Tremblay

doi:10.6026/97320630008287

Abstract

With the advent of next-generation sequencing, -omics fields such as transcriptomics have experienced increases in data throughput on the order of magnitudes. In terms of analyzing and visually representing these huge datasets, an intuitive and computationally tractable approach is to map quantified transcript expression onto biochemical pathways while employing datamining and visualization principles to accelerate knowledge discovery. We present two cross-platform tools: MAPT (Mapping and Analysis of Pathways through Time) and PAICE (Pathway Analysis and Integrated Coloring of Experiments), an easy to use analysis suite to facilitate time series and single time point transcriptomics analysis. In unison, MAPT and PAICE serve as a visual workbench for transcriptomics knowledge discovery, data-mining and functional annotation. Both PAICE and MAPT are two distinct but yet inextricably linked tools. The former is specifically designed to map EC accessions onto KEGG pathways while handling multiple gene copies, detection-call analysis, as well as UN/annotated EC accessions lacking quantifiable expression. The latter tool integrates PAICE datasets to drive visualization, annotation, and data-mining.AvailabilityThe database is available for free at http://sourceforge.net/projects/paice/http://sourceforge.net/projects/mapt/

Highlights

With the advent of next-generation sequencing, -omics fields such as transcriptomics have experienced increases in data throughput on the order of magnitudes
With next-generation sequencing becoming a mainstay in The above tools provide useful features and are built with solid molecular biology, transcriptomics research will continue to capabilities, we found that these tools are organism make ever-growing leaps and bounds
The Caleydo software [5] utilizes KEGG to PAICE and MAPT are cross-platform standalone applications provide a means of visualizing gene expression in a 3D manner, built using Python 2.7

Summary

Introduction

With the advent of next-generation sequencing, -omics fields such as transcriptomics have experienced increases in data throughput on the order of magnitudes. The former is designed to map EC accessions onto KEGG pathways while handling multiple gene copies, detection-call analysis, as well as UN/annotated EC accessions lacking quantifiable expression. The latter tool integrates PAICE datasets to drive visualization, annotation, and data-mining.

Results

Conclusion