Introduction

Whole genome sequencing (WGS) has undergone significant advancements in recent years, driven by improvements in sequencing technologies, computational tools, and data analysis methods. This mini-review examines key trends and advancements in the field over the past five years, focusing on methodological improvements in genome assembly and analysis, and the application of WGS in diverse fields such as infectious disease surveillance, cancer genomics, and population genetics.

Methodological Advancements in Genome Assembly and Analysis

Significant progress has been made in refining methods for genome assembly and analysis. In 2020, Anton Korobeynikov's group provided a protocol for using the SPAdes de novo assembler (Andrey D. Prjibelski et al., 2020, Current Protocols in Bioinformatics). Mile Šikić's lab developed Raven, a time- and memory-efficient genome assembler, demonstrating advancements in computational efficiency (Robert Vaser et al., 2021, Nature Computational Science). The Kathryn E. Holt group benchmarked long-read assemblers for prokaryotic WGS, providing valuable insights into the performance of different tools (Ryan R. Wick et al., 2021, F1000Research). Furthermore, Ryan R. Wick and Kathryn E. Holt's team published a guide on assembling perfect bacterial genomes using Oxford Nanopore and Illumina sequencing, highlighting the benefits of hybrid approaches (Ryan R. Wick et al., 2023, PLoS Computational Biology). Mengsu Yang and Runsheng Li's group benchmarked Nanopore R10.4 and R9.4.1 flow cells in single-cell whole-genome amplification and WGS, contributing to the optimization of long-read sequencing workflows (Ying Ni et al., 2023, Computational and Structural Biotechnology Journal). Kevin Vanneste's group demonstrated that Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens, showing the increasing utility of long-read sequencing in clinical microbiology (Bert Bogaerts et al., 2024, Journal of Clinical Microbiology). Kai Wang's team developed a signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing, expanding the applications of long-read sequencing to epigenetics (Mian Umair Ahsan et al., 2024, Nature Communications). Yannis Nevers's group developed OMArk, a tool for quality assessment of gene repertoire annotations, improving the accuracy of genome annotation pipelines (Yannis Nevers et al., 2024, Nature Biotechnology). Xiaodong Fang's group created NGenomeSyn, a tool for visualizing syntenic relationships across multiple genomes, facilitating comparative genomics studies (Weiming He et al., 2023, Bioinformatics).

Application of WGS in Infectious Disease Surveillance

WGS has become a crucial tool for tracking and understanding infectious diseases. Richard A. Neher's group developed Nextclade, a tool for clade assignment, mutation calling, and quality control of viral genomes, which has been widely used for SARS-CoV-2 surveillance (Ivan Aksamentov et al., 2020, The Journal of Open Source Software; Ivan Aksamentov et al., 2021, The Journal of Open Source Software). Sebastian Maurer‐Stroh's group highlighted the role of GISAID in pandemic response, emphasizing the importance of data sharing in tracking viral evolution (Shruti Khare et al., 2021, China CDC Weekly). Erik Volz's team assessed the transmissibility of the SARS-CoV-2 lineage B.1.1.7 in England, demonstrating the utility of WGS in understanding viral dynamics (Erik Volz et al., 2021, Nature). Priya Abraham's group characterized SARS-CoV-2 spike mutations in India, providing insights into viral evolution during the second wave of COVID-19 (Sarah Cherian et al., 2021, Microorganisms). Ravindra K. Gupta's group investigated SARS-CoV-2 evolution during treatment of chronic infection, revealing mechanisms of viral adaptation (Steven A. Kemp et al., 2021, Nature). Arnold W. Lambisia's team optimized the SARS-CoV-2 ARTIC Network V4 primers and WGS protocol, improving the efficiency of viral sequencing (Arnold W. Lambisia et al., 2022, Front. med. (Lausanne)). Anderson F. Brito's group highlighted global disparities in SARS-CoV-2 genomic surveillance, underscoring the need for equitable access to sequencing technologies (Anderson F. Brito et al., 2022, Nature Communications). Min Yue's group used WGS to assess antimicrobial resistance and virulence potential of Salmonella isolates from dead poultry in China, demonstrating the application of WGS in food safety (Yan Li et al., 2022, Microbiology Spectrum). Michael J. Satlin's group conducted a prospective cohort study on carbapenem-resistant Pseudomonas aeruginosa, using WGS to understand the epidemiology and clinical outcomes of this important pathogen (Jinnethe Reyes et al., 2023, The Lancet Microbe). Joshua Carter's team used WGS to identify genetic determinants of antibiotic resistance and susceptibility in Mycobacterium tuberculosis, providing insights into the mechanisms of drug resistance (Ivan Barilar et al., 2024, Nature Communications).

Application of WGS in Cancer Genomics

WGS has also significantly advanced our understanding of cancer genomics. Christian von Mering's group contributed to pan-cancer analyses of whole genomes, providing comprehensive insights into the genomic landscape of cancer (Lauri A. Aaltonen et al., 2020, Nature). Núria López-Bigas's team characterized the repertoire of mutational signatures in human cancer, revealing the processes that drive tumorigenesis (Núria López-Bigas et al., 2020, Nature). Christian von Mering's group also analyzed patterns of somatic structural variation and chromothripsis in human cancer genomes, providing insights into the mechanisms of genomic instability (Ofer Shapira et al., 2020, Nature; Ruibin Xi et al., 2020, Nature Genetics). Nischalan Pillay's team investigated signatures of copy number alterations in human cancer, providing a comprehensive view of genomic alterations in cancer (Christopher D. Steele et al., 2022, Nature). Serena Nik‐Zainal's group analyzed substitution mutational signatures in whole-genome-sequenced cancers in the UK population, revealing the diversity of mutational processes in cancer (Andrea Degasperi et al., 2022, Science). Francisco Martínez-Jiménez's team performed a pan-cancer whole-genome comparison of primary and metastatic solid tumors, providing insights into the genomic evolution of cancer metastasis (Francisco Martínez-Jiménez et al., 2023, Nature). John F.X. Diffley's group showed that cyclin E-induced replicative stress drives p53-dependent whole-genome duplication, revealing a mechanism of genome instability in cancer (Jingkun Zeng et al., 2023, Cell). Nirupa Murugaesu's team integrated genomic and clinical data of 13,880 tumors from the 100,000 Genomes Cancer Programme, providing insights for precision oncology (Alona Sosinsky et al., 2024, Nature Medicine). Adam Widman's group developed an ultrasensitive plasma-based monitoring method of tumor burden using machine-learning-guided signal enrichment, improving the detection of cancer recurrence (Adam Widman et al., 2024, Nature Medicine).

Application of WGS in Population and Evolutionary Genomics

WGS has also been instrumental in advancing population and evolutionary genomics. Ryan D. Hernandez's group sequenced 53,831 diverse genomes from the NHLBI TOPMed Program, providing a valuable resource for population genetics studies (Daniel Taliun et al., 2020, Nature; Daniel Taliun et al., 2021, Nature). Marta Byrska-Bishop and Michael C. Zody's team performed high-coverage WGS of the expanded 1000 Genomes Project cohort, improving the resolution of human genetic variation (Marta Byrska-Bishop et al., 2021, Cell; Marta Byrska-Bishop et al., 2022, Cell). Bjarni V. Halldórsson's group sequenced 150,119 genomes in the UK Biobank, providing a large-scale resource for genetic studies (Bjarni V. Halldórsson et al., 2021, Nature; Bjarni V. Halldórsson et al., 2022, Nature). Runyang Nicolas Lou and Nina Overgaard Therkildsen's group provided a beginner's guide to low-coverage WGS for population genomics, making this technique more accessible to researchers (Runyang Nicolas Lou et al., 2021, Molecular Ecology). Olivier Delaneau's team developed methods for efficient phasing and imputation of low-coverage sequencing data, improving the accuracy of genetic analyses (Simone Rubinacci et al., 2021, Nature Genetics; Robin J. Hofmeister et al., 2023, Nature Genetics). Siwei Chen's group created a genomic mutational constraint map using variation in 76,156 human genomes, providing insights into the selective pressures shaping the human genome (Siwei Chen et al., 2022, Nature; Siwei Chen et al., 2023, Nature). Pierrick Wainschtein, Jian Yang, and Peter M. Visscher's group assessed the contribution of rare variants to complex trait heritability from WGS data, improving our understanding of the genetic basis of complex traits (Pierrick Wainschtein et al., 2022, Nature Genetics). Tarjinder Singh's team identified rare coding variants in ten genes that confer substantial risk for schizophrenia, providing insights into the genetic architecture of this disorder (Tarjinder Singh et al., 2022, Nature). Lukas F. K. Kuderna, Jeffrey Rogers, Kyle Kai‐How Farh, and Tomàs Marquès‐Bonet's group created a global catalog of whole-genome diversity from 233 primate species, providing a valuable resource for comparative genomics and evolutionary studies (Lukas F. K. Kuderna et al., 2023, Science). Zhe Zhang's group created a compendium of genetic regulatory effects across pig tissues, providing insights into the genetic basis of phenotypic variation in pigs (Jinyan Teng et al., 2023, Nature Genetics; Jinyan Teng et al., 2024, Nature Genetics). Jianlin Han and Yu Jiang's group revealed the global genetic diversity, introgression, and evolutionary adaptation of indicine cattle using WGS (Ningbo Chen et al., 2023, Nature Communications). Xiao Feng's team provided genomic evidence for rediploidization and adaptive evolution following whole-genome triplication (Xiao Feng et al., 2024, Nature Communications). Joana G. P. Jacinto's group identified multiple independent de novo mutations associated with the development of schistosoma reflexum, a lethal syndrome in cattle (Joana G. P. Jacinto et al., 2024, The Veterinary Journal). Xiao Ma and Yves Van de Peer's group revealed that seagrass genomes show ancient polyploidy and adaptations to the marine environment (Xiao Ma et al., 2024, Nature Plants). Hong Mā's group studied phylogenomic profiles of whole-genome duplications in Poaceae and the landscape of differential duplicate retention and losses among major Poaceae lineages (Taikui Zhang et al., 2024, Nature Communications).

Conclusion

The past five years have witnessed substantial advancements in WGS technologies and their applications. Methodological improvements have enhanced the accuracy, efficiency, and accessibility of WGS. The application of WGS in infectious disease surveillance has been crucial for tracking and understanding viral evolution during the COVID-19 pandemic. In cancer genomics, WGS has provided comprehensive insights into the genomic landscape of cancer and has the potential to improve precision oncology. WGS has also revolutionized population and evolutionary genomics, providing valuable resources for understanding human genetic variation and the genetic basis of complex traits. As WGS technologies continue to evolve, we can expect even more exciting discoveries in the years to come.

✨ About This POST
This mini-review post was generated through Scinapse. Scinapse provides reliable research trend analysis using citation analysis and AI technology.
Check out the trends in your field too!
Get started at https://scinapse.io