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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Analysis of high-throughput sequencing data.

Shrinivasrao P Mane¹, Thero Modise, Bruno W Sobral

¹Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA. smane@vbi.vt.edu

Methods in Molecular Biology (Clifton, N.J.)

|October 9, 2010

Summary

This summary is machine-generated.

Next-generation sequencing generates vast amounts of short-read sequence (SRS) data. This study demonstrates using Bowtie for ultrafast alignment of SRS data against the Arabidopsis genome to detect SNPs and indels with Maq.

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Area of Science:

Genomics
Bioinformatics

Background:

Next-generation sequencing (NGS) technologies have drastically increased sequencing output and reduced costs.
High-throughput sequencing generates short-read sequence (SRS) data, enabling applications like whole genome resequencing and variant detection.
Various computational tools exist for analyzing large-scale sequencing data.

Purpose of the Study:

To describe the application of the Bowtie alignment program for processing SRS data.
To demonstrate the alignment of SRS data against the Arabidopsis reference genome.
To outline the subsequent use of alignment files for identifying single nucleotide polymorphisms (SNPs) and insertions/deletions (indels).

Main Methods:

Utilizing the ultrafast alignment program, Bowtie.
Aligning short-read sequence (SRS) data to the Arabidopsis reference genome.
Employing the Maq software to identify SNPs and indels from Bowtie-generated alignment files.

Main Results:

Successful alignment of SRS data using Bowtie.
Generation of alignment files suitable for variant analysis.
Identification of SNPs and indels within the Arabidopsis genome.

Conclusions:

Bowtie is an effective tool for the rapid alignment of SRS data.
The described workflow enables efficient variant discovery (SNPs and indels) in genomic data.
This approach facilitates downstream biological research utilizing high-throughput sequencing data.