Evaluating coverage bias in next-generation sequencing of Escherichia coli

Gunasekera, Samantha; Abraham, Sam; Stegger, Marc; Pang, Stanley; Wang, Penghao; Sahibzada, Shafi; O’Dea, Mark

doi:10.1371/journal.pone.0253440

Cited by 11 publications

(12 citation statements)

References 56 publications

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“…characterized by low GC-content ( Uelze et al, 2020b ). Furthermore, in a recent study by Gunasekera et al, Nextera XT coverage bias was found in E. coli ( Gunasekera et al, 2021 ), although the authors argued that the benefit of using Nextera Flex/DNA Prep instead of Nextera XT was modest when sequencing the GC-neutral species E. coli .…”

Section: Discussionmentioning

confidence: 99%

The efficiency of Nextera XT tagmentation depends on G and C bases in the binding motif leading to uneven coverage in bacterial species with low and neutral GC-content

et al. 2022

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Whole-genome sequencing (WGS) is becoming the new standard for bacterial high-resolution typing and the performance of laboratories is being evaluated in interlaboratory comparisons. The use of the Illumina Nextera XT library preparation kit has been found to be associated with poorer performance due to a GC-content-dependent coverage bias. The bias is especially strong when sequencing low GC-content species. Here, we have made an in-depth analysis of the Nextera XT coverage bias problem using data from a proficiency test of the low GC-content species Campylobacter jejuni. We have compared Nextera XT with Nextera Flex/DNA Prep and examined the consequences on downstream WGS analysis when using different quantities of raw data. We have also analyzed how the coverage bias relates to differential usage of tagmentation cleavage sites. We found that the tagmentation site was characterized by a symmetrical motif with a central AT-rich region surrounded by Gs and Cs. The Gs and Cs appeared to be the main determinant for cleavage efficiency and the genomic regions that were associated with low coverage only contained low-efficiency cleavage sites. This explains why low GC-content genomes and regions are more subjected to coverage bias. We furthermore extended our analysis to other datasets representing other bacterial species. We visualized how the coverage bias was large in low GC-content species such as C. jejuni, C. coli, Staphylococcus aureus, and Listeria monocytogenes, whereas species with neutral GC-content such as Salmonella enterica and Escherichia coli were only affected in certain regions. Species with high GC-content such as Mycobacterium tuberculosis and Pseudomonas aeruginosa were hardly affected at all. The coverage bias associated with Nextera XT was not found when Nextera Flex/DNA Prep had been used.

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Section: Discussionmentioning

confidence: 99%

The efficiency of Nextera XT tagmentation depends on G and C bases in the binding motif leading to uneven coverage in bacterial species with low and neutral GC-content

et al. 2022

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“…Mapping fortuitously revealed lower coverage (~14,000 to 32,000 reads) between nucleotide positions 178 and 507 versus the flanking regions (~73,000 reads) probably due to library preparation bias. Coverage bias or unevenness has been attributed to low-GC target regions, library preparation enzymes, library PCR amplification, cluster amplification, and sequencing [ 23 , 24 , 25 ]. Zooming in allowed visualization of the sequences down to the nucleotide level for comparison to the reference genome and detection of variants.…”

Section: Resultsmentioning

confidence: 99%

HPV DeepSeq: An Ultra-Fast Method of NGS Data Analysis and Visualization Using Automated Workflows and a Customized Papillomavirus Database in CLC Genomics Workbench

et al. 2021

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Next-generation sequencing (NGS) has actualized the human papillomavirus (HPV) virome profiling for in-depth investigation of viral evolution and pathogenesis. However, viral computational analysis remains a bottleneck due to semantic discrepancies between computational tools and curated reference genomes. To address this, we developed and tested automated workflows for HPV taxonomic profiling and visualization using a customized papillomavirus database in the CLC Microbial Genomics Module. HPV genomes from Papilloma Virus Episteme were customized and incorporated into CLC “ready-to-use” workflows for stepwise data processing to include: (1) Taxonomic Analysis, (2) Estimate Alpha/Beta Diversities, and (3) Map Reads to Reference. Low-grade (n = 95) and high-grade (n = 60) Pap smears were tested with ensuing collective runtimes: Taxonomic Analysis (36 min); Alpha/Beta Diversities (5 s); Map Reads (45 min). Tabular output conversion to visualizations entailed 1–2 keystrokes. Biodiversity analysis between low- (LSIL) and high-grade squamous intraepithelial lesions (HSIL) revealed loss of species richness and gain of dominance by HPV-16 in HSIL. Integrating clinically relevant, taxonomized HPV reference genomes within automated workflows proved to be an ultra-fast method of virome profiling. The entire process named “HPV DeepSeq” provides a simple, accurate and practical means of NGS data analysis for a broad range of applications in viral research.

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“…During the enrichment for bacteria in our study prior to DNA extraction and metagenomic sequencing, most filamentous bacteria (if present) were probably lost and hence their BGCs not included in our dataset. In addition, some NRPS BGCs may be difficult to recover by next-generation sequencing approaches (e.g., from low-GC or high-AT species) due to biases of sequencing or assembly methods [ 114 , 115 , 116 , 117 , 118 ].…”

Section: Discussionmentioning

confidence: 99%

Diversity of Bacterial Secondary Metabolite Biosynthetic Gene Clusters in Three Vietnamese Sponges

et al. 2022

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Recent reviews have reinforced sponge-associated bacteria as a valuable source of structurally diverse secondary metabolites with potent biological properties, which makes these microbial communities promising sources of new drug candidates. However, the overall diversity of secondary metabolite biosynthetic potential present in bacteria is difficult to access due to the fact that the majority of bacteria are not readily cultured in the laboratory. Thus, use of cultivation-independent approaches may allow accessing “silent” and “cryptic” secondary metabolite biosynthetic gene clusters present in bacteria that cannot yet be cultured. In the present study, we investigated the diversity of secondary metabolite biosynthetic gene clusters (BGCs) in metagenomes of bacterial communities associated with three sponge species: Clathria reinwardti, Rhabdastrella globostellata, and Spheciospongia sp. The results reveal that the three metagenomes contain a high number of predicted BGCs, ranging from 282 to 463 BGCs per metagenome. The types of BGCs were diverse and represented 12 different cluster types. Clusters predicted to encode fatty acid synthases and polyketide synthases (PKS) were the most dominant BGC types, followed by clusters encoding synthesis of terpenes and bacteriocins. Based on BGC sequence similarity analysis, 363 gene cluster families (GCFs) were identified. Interestingly, no GCFs were assigned to pathways responsible for the production of known compounds, implying that the clusters detected might be responsible for production of several novel compounds. The KS gene sequences from PKS clusters were used to predict the taxonomic origin of the clusters involved. The KS sequences were related to 12 bacterial phyla with Actinobacteria, Proteobacteria, and Firmicutes as the most predominant. At the genus level, the KSs were most related to those found in the genera Mycolicibacterium, Mycobacterium, Burkholderia, and Streptomyces. Phylogenetic analysis of KS sequences resulted in detection of two known ‘sponge-specific’ BGCs, i.e., SupA and SwfA, as well as a new ‘sponge-specific’ cluster related to fatty acid synthesis in the phylum Candidatus Poribacteria and composed only by KS sequences of the three sponge-associated bacterial communities assessed here.

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Evaluating coverage bias in next-generation sequencing of Escherichia coli

Cited by 11 publications

References 56 publications

The efficiency of Nextera XT tagmentation depends on G and C bases in the binding motif leading to uneven coverage in bacterial species with low and neutral GC-content

The efficiency of Nextera XT tagmentation depends on G and C bases in the binding motif leading to uneven coverage in bacterial species with low and neutral GC-content

HPV DeepSeq: An Ultra-Fast Method of NGS Data Analysis and Visualization Using Automated Workflows and a Customized Papillomavirus Database in CLC Genomics Workbench

Diversity of Bacterial Secondary Metabolite Biosynthetic Gene Clusters in Three Vietnamese Sponges

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