Dataset Information

Rapid and inexpensive preparation of genome-wide nucleosome footprints from model and non-model organisms.

ABSTRACT: MNase-seq (micrococcal nuclease sequencing) is used to map nucleosome positions in eukaryotic genomes to study the relationship between chromatin structure and DNA-dependent processes. Current protocols require at least two days to isolate nucleosome-protected DNA fragments. We have developed a streamlined protocol for S. cerevisiae and other fungi which takes only three hours. Modified protocols were developed for wild fungi and mammalian cells. This method for rapidly producing sequencing-ready nucleosome footprints from several organisms makes MNase-seq faster and easier, with less chemical waste.

SUBMITTER: McKnight LE

PROVIDER: S-EPMC8141940 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Rapid and inexpensive preparation of genome-wide nucleosome footprints from model and non-model organisms.

McKnight Laura E LE Crandall Johnathan G JG Bailey Thomas B TB Banks Orion G B OGB Orlandi Kona N KN Truong Vi N VN Donovan Drake A DA Waddell Grace L GL Wiles Elizabeth T ET Hansen Scott D SD Selker Eric U EU McKnight Jeffrey N JN

STAR protocols 20210518 2

MNase-seq (micrococcal nuclease sequencing) is used to map nucleosome positions in eukaryotic genomes to study the relationship between chromatin structure and DNA-dependent processes. Current protocols require at least two days to isolate nucleosome-protected DNA fragments. We have developed a streamlined protocol for <i>S. cerevisiae</i> and other fungi which takes only three hours. Modified protocols were developed for wild fungi and mammalian cells. This method for rapidly producing sequenci ...[more]

PMID: 34041500

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Project description:BackgroundRNA-seq is being increasingly adopted for gene expression studies in a panoply of non-model organisms, with applications spanning the fields of agriculture, aquaculture, ecology, and environment. For organisms that lack a well-annotated reference genome or transcriptome, a conventional RNA-seq data analysis workflow requires constructing a de-novo transcriptome assembly and annotating it against a high-confidence protein database. The assembly serves as a reference for read mapping, and the annotation is necessary for functional analysis of genes found to be differentially expressed. However, assembly is computationally expensive. It is also prone to errors that impact expression analysis, especially since sequencing depth is typically much lower for expression studies than for transcript discovery.ResultsWe propose a shortcut, in which we obtain counts for differential expression analysis by directly aligning RNA-seq reads to the high-confidence proteome that would have been otherwise used for annotation. By avoiding assembly, we drastically cut down computational costs - the running time on a typical dataset improves from the order of tens of hours to under half an hour, and the memory requirement is reduced from the order of tens of Gbytes to tens of Mbytes. We show through experiments on simulated and real data that our pipeline not only reduces computational costs, but has higher sensitivity and precision than a typical assembly-based pipeline. A Snakemake implementation of our workflow is available at: https://bitbucket.org/project_samar/samar .ConclusionsThe flip side of RNA-seq becoming accessible to even modestly resourced labs has been that the time, labor, and infrastructure cost of bioinformatics analysis has become a bottleneck. Assembly is one such resource-hungry process, and we show here that it can be avoided for quick and easy, yet more sensitive and precise, differential gene expression analysis in non-model organisms.

Dataset Information

Rapid and inexpensive preparation of genome-wide nucleosome footprints from model and non-model organisms.

Publications

Rapid and inexpensive preparation of genome-wide nucleosome footprints from model and non-model organisms.

Similar Datasets

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