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1. Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants

   https://doi.org/10.1093/bioinformatics/btab514  

   Song, Shuang; Shan, Nayang; Wang, Geng; Yan, Xiting; Liu, Jun S; Hou, Lin (July 2021, Bioinformatics)

   Schwartz, Russell (Ed.)

   Abstract Motivation Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. Results We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. Availability and implementation The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS. Supplementary information Supplementary data are available at Bioinformatics online. 

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2. Accurate and efficient gene function prediction using a multi-bacterial network

   https://doi.org/10.1093/bioinformatics/btaa885  

   Law, Jeffrey N; Kale, Shiv D; Murali, T M (October 2020, Bioinformatics)

   Lenore, Cowen (Ed.)

   Abstract Motivation Nearly 40% of the genes in sequenced genomes have no experimentally or computationally derived functional annotations. To fill this gap, we seek to develop methods for network-based gene function prediction that can integrate heterogeneous data for multiple species with experimentally based functional annotations and systematically transfer them to newly sequenced organisms on a genome-wide scale. However, the large sizes of such networks pose a challenge for the scalability of current methods. Results We develop a label propagation algorithm called FastSinkSource. By formally bounding its rate of progress, we decrease the running time by a factor of 100 without sacrificing accuracy. We systematically evaluate many approaches to construct multi-species bacterial networks and apply FastSinkSource and other state-of-the-art methods to these networks. We find that the most accurate and efficient approach is to pre-compute annotation scores for species with experimental annotations, and then to transfer them to other organisms. In this manner, FastSinkSource runs in under 3 min for 200 bacterial species. Availability and implementation An implementation of our framework and all data used in this research are available at https://github.com/Murali-group/multi-species-GOA-prediction. Supplementary information Supplementary data are available at Bioinformatics online. 

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3. Supervised biological network alignment with graph neural networks

   https://doi.org/10.1093/bioinformatics/btad241  

   Ding, Kerr; Wang, Sheng; Luo, Yunan (June 2023, Bioinformatics)

   Abstract MotivationDespite the advances in sequencing technology, massive proteins with known sequences remain functionally unannotated. Biological network alignment (NA), which aims to find the node correspondence between species’ protein–protein interaction (PPI) networks, has been a popular strategy to uncover missing annotations by transferring functional knowledge across species. Traditional NA methods assumed that topologically similar proteins in PPIs are functionally similar. However, it was recently reported that functionally unrelated proteins can be as topologically similar as functionally related pairs, and a new data-driven or supervised NA paradigm has been proposed, which uses protein function data to discern which topological features correspond to functional relatedness. ResultsHere, we propose GraNA, a deep learning framework for the supervised NA paradigm for the pairwise NA problem. Employing graph neural networks, GraNA utilizes within-network interactions and across-network anchor links for learning protein representations and predicting functional correspondence between across-species proteins. A major strength of GraNA is its flexibility to integrate multi-faceted non-functional relationship data, such as sequence similarity and ortholog relationships, as anchor links to guide the mapping of functionally related proteins across species. Evaluating GraNA on a benchmark dataset composed of several NA tasks between different pairs of species, we observed that GraNA accurately predicted the functional relatedness of proteins and robustly transferred functional annotations across species, outperforming a number of existing NA methods. When applied to a case study on a humanized yeast network, GraNA also successfully discovered functionally replaceable human–yeast protein pairs that were documented in previous studies. Availability and implementationThe code of GraNA is available at https://github.com/luo-group/GraNA. 

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4. Annotation of protein-coding genes in 49 diatom genomes from the Bacillariophyta clade

   https://doi.org/10.1038/s41597-025-05306-z  

   Nenasheva, Natalia; Pitzschel, Clara; Webster, Cynthia_N; Hart, Alexander_J; Wegrzyn, Jill_L; Bengtsson, Mia_M; Hoff, Katharina_J (June 2025, Scientific Data)

   Abstract Diatoms, a major group of microalgae, play a critical role in global carbon cycling and primary production. Despite their ecological significance, comprehensive genomic resources for diatoms are limited. To address this, we have annotated previously unannotated genome assemblies of 49 diatom species. Genome assemblies were obtained from NCBI Datasets and processed for repeat elements using RepeatModeler2 and RepeatMasker. For gene prediction, BRAKER2 was employed in the absence of transcriptomic data, while BRAKER3 was utilised when transcriptome short read data were available from the Sequence Read Archive. The quality of genome assemblies and predicted protein sets was evaluated using BUSCO, ensuring high-quality genomic resources. Functional annotation was performed using EnTAP, providing insights into the biological roles of the predicted proteins. Our study enhances the genomic toolkit available for diatoms, facilitating future research in diatom biology, ecology, and evolution. 

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5. Digital Microbe: a genome-informed data integration framework for team science on emerging model organisms

   https://doi.org/10.1038/s41597-024-03778-z  

   Veseli, Iva; DeMers, Michelle_A; Cooper, Zachary_S; Schechter, Matthew_S; Miller, Samuel; Weber, Laura; Smith, Christa_B; Rodriguez, Lidimarie_T; Schroer, William_F; McIlvin, Matthew_R; et al (September 2024, Scientific Data)

   Abstract The remarkable pace of genomic data generation is rapidly transforming our understanding of life at the micron scale. Yet this data stream also creates challenges for team science. A single microbe can have multiple versions of genome architecture, functional gene annotations, and gene identifiers; additionally, the lack of mechanisms for collating and preserving advances in this knowledge raises barriers to community coalescence around shared datasets. “Digital Microbes” are frameworks for interoperable and reproducible collaborative science through open source, community-curated data packages built on a (pan)genomic foundation. Housed within an integrative software environment, Digital Microbes ensure real-time alignment of research efforts for collaborative teams and facilitate novel scientific insights as new layers of data are added. Here we describe two Digital Microbes: 1) the heterotrophic marine bacteriumRuegeria pomeroyiDSS-3 with > 100 transcriptomic datasets from lab and field studies, and 2) the pangenome of the cosmopolitan marine heterotrophAlteromonascontaining 339 genomes. Examples demonstrate how an integrated framework collating public (pan)genome-informed data can generate novel and reproducible findings. 

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