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Background/Aim: RNA sequencing (RNA-seq) has become an exemplary technology in biology and clinical applications, outperforming other transcriptomic profiling protocols due to its precision and accuracy in characterising transcriptomes. As RNA-seq analysis gains popularity and becomes an essential component of future precision medicine, examining the software development and open science practices used by RNA-seq tools is necessary to ensure and improve the accuracy of generated results. Methods. We performed a comprehensive assessment of the software development and open sciences practices of RNA-seq tools, and benchmarking practices across more than 319 RNA-seq tools developed between the years of 2008 to 2024 by manually data extraction. We extracted features of the RNA seq tools including availability of package managers, number of releases and the benchmarking practises adapted by the researchers. We plan to access the archive stability and engagement of the scientific community to software repositories like Github and Bitbucket to better measure recognition of the tools. Results. Here we show that good software development increases the number of citations for the tool with a statistically significant p-value of 9.1×10−6 (Mann-Whitney U test). Conclusions. In addition to software development practices, it is vital to follow best practices in benchmarking and validation of developed tools. Ideally, a newly developed tool would be compared against all previously developed tools1, but it is currently unknown if this practice is widely adopted in the bioinformatics community. Examining these studies is vital to develop RNA-seq tools that can not only enhance the precision and thoroughness of all future benchmarking studies, but also encourage tool developers to engage in more comprehensive benchmarking when releasing their tools. By highlighting promising trends in usability, accessibility, maintainability, and benchmarking practices, we aim to encourage wider adoption of these best practices within the biomedical research community. This collective effort will ultimately lead to the development of more accurate, robust, and user-friendly RNA-seq tools in the future [ABSTRACT FROM AUTHOR]

Aim. To address the scarcity of high-quality experimental benchmarks in metagenomics by developing a cost-effective, semi-real benchmark generation tool, MetaWiz, which can modify existing experimental data to create diverse and realistic metagenomic benchmarks. Methods. In this study, we employed a series of steps to modify mock data representing known microbial genomes or strains. These steps involved selection, downloading, preprocessing, mapping, calculating mapping statistics, selecting uniquely mapped reads, and making modifications to create diverse benchmarks. The data was downloaded in fasta/fastq formats from the open source repositories like NCBI, MG-RAST, and Github. We preserved the original data quality and length without filtering or trimming to maintain uniformity. For mapping, we used bwa-mem and Minimap2, running both tools with default parameters. We discarded unmapped reads and selected uniquely mapped reads, we filtered out supplementary and alternative alignment. To modify the benchmark dataset, we implemented several functionalities, including the adjustment of strain frequencies, converting long Pacbio reads into short reads, correcting erroneous bases in Nanopore ONT reads using genomes, followed by read length adjustment similar to Pacbio reads. Results. MetaWiz successfully generated diverse benchmarks from real metagenomic data, encompassing various biological and technological characteristics. MetaWiz effectively reduced raw data error rates through mapping and error correction. Specifically, we were able to modify the following datasets into multiple benchmarks: Dataset #1: 9 strains of HIV and 2 strains of HIV (Illumina). We altered the frequency of viral strains and removed strain-specific reads. Dataset #2: 11 strains of bacterial species (Illumina). We altered the frequency of strains and species and removed species-specific reads. Dataset #3: 26 strains of Bacteria and Archaea (Illumina). We selected some of the species/strains from this dataset to mix with Dataset #2, to create multiple benchmarks with different combinations. Dataset #4: 12 strains of bacterial strains (Pacbio and Nanopore). For Pacbio reads, we shortened the read length to 100bp, generating up to 40 million short reads. For Nanopore ONT reads, we corrected the errors using the genomes and shortened the read length to 100bp, generating up to 60 million short reads. Conclusions. MetaWiz offers a cost-effective solution to the challenge of generating high-quality benchmarks in metagenomics. The tool’s ability to create semi-real benchmarks from existing experimental data maintains crucial technological and biological aspects, providing advantages over purely computational simulations. This approach can be extended beyond viral sequences to various sequencing data, enhancing the comprehensiveness of metagenomics benchmarking studies across different biological contexts. [ABSTRACT FROM AUTHOR]

Computational data-centric research techniques play a prevalent and multi-disciplinary role in life science research. In the past, scientists in wet labs generated the data, and computational researchers focused on creating tools for the analysis of those data. Computational researchers are now becoming more independent and taking leadership roles within biomedical projects, leveraging the increased availability of public data. We are now able to generate vast amounts of data, and the challenge has shifted from data generation to data analysis. Here we discuss the pitfalls, challenges, and opportunities facing the field of data-centric research in biology. We discuss the evolving perception of computational data-driven research and its rise as an independent domain in biomedical research while also addressing the significant collaborative opportunities that arise from integrating computational research with experimental and translational biology. Additionally, we discuss the future of data-centric research and its applications across various areas of the biomedical field., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation . The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon., Competing Interests: ET was employed by the company Pacific Biosciences (United States). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Deshpande, Chhugani, Chang, Karlsberg, Loeffler, Zhang, Muszyńska, Munteanu, Yang, Rotman, Tao, Balliu, Tseng, Eskin, Zhao, Mohammadi, P. Łabaj and Mangul.)

Conflicts and natural disasters affect entire populations of the countries involved and, in addition to the thousands of lives destroyed, have a substantial negative impact on the scientific advances these countries provide. The unprovoked invasion of Ukraine by Russia, the devastating earthquake in Turkey and Syria, and the ongoing conflicts in the Middle East are just a few examples. Millions of people have been killed or displaced, their futures uncertain. These events have resulted in extensive infrastructure collapse, with loss of electricity, transportation, and access to services. Schools, universities, and research centers have been destroyed along with decades' worth of data, samples, and findings. Scholars in disaster areas face short- and long-term problems in terms of what they can accomplish now for obtaining grants and for employment in the long run. In our interconnected world, conflicts and disasters are no longer a local problem but have wide-ranging impacts on the entire world, both now and in the future. Here, we focus on the current and ongoing impact of war on the scientific community within Ukraine and from this draw lessons that can be applied to all affected countries where scientists at risk are facing hardship. We present and classify examples of effective and feasible mechanisms used to support researchers in countries facing hardship and discuss how these can be implemented with help from the international scientific community and what more is desperately needed. Reaching out, providing accessible training opportunities, and developing collaborations should increase inclusion and connectivity, support scientific advancements within affected communities, and expedite postwar and disaster recovery., (© The Author(s) 2023. Published by Oxford University Press GigaScience.)

Advances in whole-genome sequencing (WGS) promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from WGS data presents a substantial number of challenges and a plethora of SV detection methods have been developed. Currently, evidence that investigators can use to select appropriate SV detection tools is lacking. In this article, we have evaluated the performance of SV detection tools on mouse and human WGS data using a comprehensive polymerase chain reaction-confirmed gold standard set of SVs and the genome-in-a-bottle variant set, respectively. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of the SV detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance as the SV detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low- and ultralow-pass sequencing data as well as for different deletion length categories., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19.