5/29/2023 0 Comments Peaks 4 step seq![]() The order of genome replication can also be measured with replication-timing sequencing, which involves identifying copy-number state differences between diploid G1-phase and replicating S-phase (or asynchronous-AS) cells. These approaches are useful to pinpoint where DNA replication is initiated in the genome. They are either based on sequencing newly synthesized and RNA-primed DNA, such as Okazaki fragment sequencing (OK-seq) for the lagging strand or nascent-strand sequencing (NS-seq) for the leading strand. To understand DNA replication patterns across the genome, next-generation sequencing methods are increasingly used. By enabling detection of DNA breaks that occur in a small fraction of a cell population, END-seq can be used to understand how breaks occur and are repaired. Recently, a new method has been introduced for genome-wide mapping of DNA double-strand breaks (END-seq). Integrating these analyses with gene expression data such as RNA-seq, it is possible to gain better understanding of the architecture and regulation of the genome. Other analyses focus on identifying open-chromatin and DNA-accessible regions, which are useful to classify enhancer regions, and transcription factor footprints. By mapping protein-bound DNA, we can determine transcription factor binding sites or histone modification distributions across the genome. Ĭurrently many studies focus on identifying protein–DNA interactions through sequencing (ChIP-seq). These integrative multi-omics studies elucidate the functionalities of coding and non-coding parts of the genome, their influence on development of complex disease such as cancers and their translational implications. Improved technologies and decreasing sequencing costs enable in-depth analyses of chromatin and gene expression changes for genome-wide comparisons. BAMscale is freely available on github ( ). BAMScale can be implemented for a wide set of methods for calculating coverage tracks, including ChIP-seq and ATAC-seq, as well as methods that currently require specialized, separate tools for analyses, such as splice-aware RNA-seq, END-seq and OK-seq for which no dedicated software is available. ![]() ConclusionsīAMscale accurately quantifies and normalizes identified peaks directly from BAM files, and creates coverage tracks for visualization in genome browsers. Our tool can effectively analyze large sequencing datasets (~ 100 Gb size) in minutes, outperforming currently available tools. BAMScale also includes a visualization module facilitating direct, on-demand quantitative peak comparisons that can be used by experimentalists. The outputs consist of raw and normalized peak scores (multiple normalizations) in text format and scaled bigWig coverage tracks that are directly accessible to data visualization programs. To demonstrate the usefulness of BAMscale, we analyzed multiple sequencing datasets from chromatin immunoprecipitation sequencing data (ChIP-seq), chromatin state change data (assay for transposase-accessible chromatin using sequencing: ATAC-seq, DNA double-strand break mapping sequencing: END-seq), DNA replication data (Okazaki fragments sequencing: OK-seq, nascent-strand sequencing: NS-seq, single-cell replication timing sequencing: scRepli-seq) and RNA-seq data. We have developed BAMscale, a one-step tool that processes a wide set of sequencing datasets. We demonstrate its strength and usability by integrating data from several types of sequencing approaches. We sought to develop a tool for rapid quantification of sequencing peaks from diverse experimental sources and an efficient method to produce coverage tracks for accurate visualization that can be intuitively displayed and interpreted by experimentalists with minimal bioinformatics background. Data analysis of these increasingly used methods either requires multiple analysis steps, or extensive computational time. Next-generation sequencing allows genome-wide analysis of changes in chromatin states and gene expression.
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