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ERgene: Python library for screening endogenous reference genes

#Zehua Zeng, Yuzhe Xiong, WenhuanGuo & Hongwo DU

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ERgene: Python library for screening endogenous genes, built by using the computational power of the Pandas library, with high computational efficiency and simple operation steps.

INTRODUCTION

In the analysis, sample differences and experimental operation differences are common, but can cause errors. We have to find suitable internal reference genes to eliminate the errors.Normal methods of gene expression analysis (microarray, RT-PCR, parallel sequencing) require normalization to reduce differences between samples (geNorm, NormFinder, BestKeeper methods).

RESULTS

  1. Take the samples and perform gene expression analysis
  2. Analyse the difference of the sample spectral density and make a box and a density diagram
  3. Look at the difference of the spectral density
  4. Look for stable internal reference gene to normalize the data, using the ERgene.FindERG method

BestKeeper

geNorm

  • Calculate all kinds of Bestkeeper indexes based on the gene of the houskeeper
  • Large calculation with improved accuracy
  • Require researchers to provide genes in advance (limited applications)

NormFinder

  • Identify the most stable reference gene from a tested set
  • Require researchers to provide genes in advance (limited applications)
  • Source for R language and Excel 2003
  • Do not use candidate reference genes, but all genes
  • Construct a methematical model
Mathematical description

Usage of block calculation when the dataset is too large. The results are then combined and sorted. The program will return the top 20 genes

Optimization
Principle

ERgene inverts the internal standard method: the ratio of gene expression quantities is calculated as a relative correction factor (in form of a matrix)

METHODS

ERgene.normalizationdata(data, ERGname): it is used for standardization of a single internal reference gene

ERgene.FindERG(data, depth): It screens internal reference genes. The parameter are "data" and "depth".

FUNCTION DESCRIPTION