Evaluating coverage bias in next-generation sequencing of Escherichia coli.
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ABSTRACT:
SUBMITTER: Gunasekera S
PROVIDER: S-EPMC8224930 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNext-generation sequencing allows genome-wide analysis of changes in chromatin states and gene expression. Data analysis of these increasingly used methods either requires multiple analysis steps, or extensive computational time. 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. We demonstrate its strength and usability by integrating data from several types of sequencing approaches.ResultsWe have developed BAMscale, a one-step tool that processes a wide set of sequencing datasets. 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. 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. BAMScale also includes a visualization module facilitating direct, on-demand quantitative peak comparisons that can be used by experimentalists. Our tool can effectively analyze large sequencing datasets (~?100 Gb size) in minutes, outperforming currently available tools.ConclusionsBAMscale accurately quantifies and normalizes identified peaks directly from BAM files, and creates coverage tracks for visualization in genome browsers. 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. BAMscale is freely available on github (https://github.com/ncbi/BAMscale).
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Project description:The study was designed to investigate the fecal microbiome and resistome of broiler chickens infected with multidrug-resistant (MDR) Escherichia coli (E. coli). Fecal samples (n = 410) from broiler chickens were collected from thirteen randomly selected sites of Khyber Pakhtunkhwa and screened for the presence of MDR E. coli. Upon initial screening, thirteen (13) MDR E. coli isolates were then subjected to shotgun metagenome next-generation sequencing (NGS). NGS based resistome analysis identified the multidrug efflux pump system-related genes at the highest prevalence (36%) followed by aminoglycoside (26.1%), tetracycline (15.9%), macrolide-lincosamide-streptogramin (9.6%), beta-lactam (6.6%), rifampin (2%), sulphonamide (1.3%), phenicol (0.91%), vancomycin (0.62%), trimethoprim (0.34%), colistin (0.30%), and quinolone (0.33%). The most abundant virulence-associated genes (VAGs) identified were iroN, iutA, iss, and iucA. NGS based taxonomic profiling at the phylum level revealed the predominance of Proteobacteria (38.9%) followed by Firmicutes (36.4%), Bacteroidetes (15.8%), and Tenericutes (8.9%). Furthermore, pathobionts such as E. coli, Salmonella enterica, Klebsiella pneumoniae, and Shigella flexneri belonging to the family Enterobacteriaceae were predominantly found. This study revealed the widespread presence of MDR genes, diverse VAGs, and a dysbiotic gut in the broiler chickens infected with MDR E. coli of Khyber Pakhtunkhwa for the first time using NGS.
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Project description:BackgroundMethylation studies are a promising complement to genetic studies of DNA sequence. However, detailed prior biological knowledge is typically lacking, so methylome-wide association studies (MWAS) will be critical to detect disease relevant sites. A cost-effective approach involves the next-generation sequencing (NGS) of single-end libraries created from samples that are enriched for methylated DNA fragments. A limitation of single-end libraries is that the fragment size distribution is not observed. This hampers several aspects of the data analysis such as the calculation of enrichment measures that are based on the number of fragments covering the CpGs.ResultsWe developed a non-parametric method that uses isolated CpGs to estimate sample-specific fragment size distributions from the empirical sequencing data. Through simulations we show that our method is highly accurate. While the traditional (extended) read count methods resulted in severely biased coverage estimates and introduces artificial inter-individual differences, through the use of the estimated fragment size distributions we could remove these biases almost entirely. Furthermore, we found correlations of 0.999 between coverage estimates obtained using fragment size distributions that were estimated with our method versus those that were "observed" in paired-end sequencing data.ConclusionsWe propose a non-parametric method for estimating fragment size distributions that is highly precise and can improve the analysis of cost-effective MWAS studies that sequence single-end libraries created from samples that are enriched for methylated DNA fragments.
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Project description:BackgroundNext-generation sequencing (NGS) technologies has brought an unprecedented amount of genomic data for analysis. Unlike array-based profiling technologies, NGS can reveal the expression profile across a transcript at the base level. Such a base-level read coverage provides further insights for alternative mRNA splicing, single-nucleotide polymorphism (SNP), novel transcript discovery, etc. However, to our best knowledge, none of existing NGS viewers can timely visualize genome-wide base-level read coverages in an interactive environment.ResultsThis study proposes an efficient visualization pipeline and implements a lightweight read coverage viewer, Light-RCV, with the proposed pipeline. Light-RCV consists of four featured designs on the path from raw NGS data to the final visualized read coverage: i) read coverage construction algorithm, ii) multi-resolution profiles, iii) two-stage architecture and iv) storage format. With these designs, Light-RCV achieves a < 0.5s response time on any scale of genomic ranges, including whole chromosomes. Finally, a case study was performed to demonstrate the importance of visualizing base-level read coverage and the value of Light-RCV.ConclusionsCompared with multi-functional genome viewers such as Artemis, Savant, Tablet and Integrative Genomics Viewer (IGV), Light-RCV is designed only for visualization. Therefore, it does not provide advanced analyses. However, its backend technology provides an efficient kernel of base-level visualization that can be easily embedded to other viewers. This viewer is the first to provide timely visualization of genome-wide read coverage at the base level in an interactive environment. The software is available for free at http://lightrcv.ee.ncku.edu.tw.
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