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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

BioQueue: a novel pipeline framework to accelerate bioinformatics analysis.

Li Yao¹, Heming Wang², Yuanyuan Song¹

¹College of Life Science, Northeast Forestry University, Harbin 150040, China.

Bioinformatics (Oxford, England)

|June 22, 2017

Summary

This summary is machine-generated.

BioQueue optimizes bioinformatics analysis by automatically estimating resource needs for each step, preventing errors and maximizing computational efficiency for biologists.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-Generation Sequencing generates vast amounts of data.
Existing bioinformatics pipeline frameworks often lack efficient resource management.
User experience with resource estimation can lead to errors like memory overflow.

Purpose of the Study:

To develop an automated resource estimation and job dispatch system for bioinformatics pipelines.
To improve the utilization of computing resources in bioinformatics research.
To provide an accessible tool for biologists with limited programming expertise.

Main Methods:

Developed BioQueue, a web-based framework.
Implemented checkpoints before each analysis step.
Integrated automatic estimation of CPU, memory, and disk requirements.
Designed a shell command-like syntax for ease of use.

Main Results:

BioQueue automatically estimates and dispatches jobs based on resource needs.
The framework prevents common errors such as memory overflow.
Biologists can easily access an efficient queue system without extensive programming knowledge.

Conclusions:

BioQueue enhances the efficiency and reliability of bioinformatics data analysis.
The user-friendly syntax lowers the barrier for biologists to leverage powerful computational resources.
This framework maximizes the potential of available computing infrastructure for genomic research.