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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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SLUR(M)-py: a SLURM powered Pythonic pipeline for parallel processing of 3D (Epi)genomic profiles.

Cullen Roth¹, Vrinda Venu¹, Sasha Bacot¹

¹Los Alamos National Laboratory, Genomics and Bioanalytics, Los Alamos, NM, USA.

|October 3, 2025

Summary

This summary is machine-generated.

SLUR(M)-py is a new bioinformatics platform that uses Simple Linux Utility for Resource Management (SLURM) to rapidly process complex epigenomic sequencing data. This multi-omic tool streamlines analysis for chromatin characterization experiments, outperforming current high-performance computing pipelines.

Keywords:

ATAC-seq Bioinformatics Hi-C Python SLURM epigenetics genomics parallelization

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Area of Science:

Genomics and Bioinformatics
Computational Biology
Epigenetics

Background:

Epigenomics research generates large, complex multi-omic datasets requiring advanced bioinformatics.
High-performance computing (HPC) and parallelization are crucial for efficient data processing.

Purpose of the Study:

To develop a pythonic computational platform, SLUR(M)-py, that leverages SLURM for efficient multi-omic sequencing data processing.
To automate data analysis for various chromatin characterization experiments, reducing the need for multiple pipelines.

Main Methods:

Development of SLUR(M)-py, a platform integrating with SLURM for automated processing of paired-end sequencing data.
Application of SLUR(M)-py to ATAC-seq and Hi-C data from viral infection studies and the ENCODE project.
Evaluation of processing speed, completeness, quality control, and artifact detection capabilities.

Main Results:

SLUR(M)-py demonstrates superior processing speed and completeness compared to existing HPC pipelines.
The platform effectively handles multi-omic data, including whole-genome, ChIP-seq, ATAC-seq, and Hi-C.
Analysis of ATAC-seq duplicate reads and Hi-C artifacts from viral infections was successfully performed.

Conclusions:

SLUR(M)-py offers a multi-omic, system-agnostic solution to reduce the computational burden in epigenomics research.
The platform provides accurate and reliable data analytics, facilitating the exploration of chromosomal contact dynamics and other epigenomic features.