Method

Multisource omic alignment and biological feature discovery with Performer encoder and triplet networks

    • 1State Key Laboratory of Animal Biotech Breeding, Frontiers Science Center for Molecular Design Breeding (MOE), College of Animal Science and Technology, China Agricultural University, Beijing 100193, China;
    • 2Institute of Cardiovascular Diseases, Xiamen Cardiovascular Hospital, School of Medicine, Xiamen University, Xiamen 361006, Fujian, China;
    • 3State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Biomedical Pioneering Innovative Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), Peking University, Beijing 100871, China;
    • 4State Key Laboratory of Agrobiotechnology, Beijing Key Laboratory of Growth and Developmental Regulation for Protected Vegetable Crops, College of Horticulture, China Agricultural University, Beijing 100193, China;
    • 5Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China;
    • 6Dermatology Hospital, Southern Medical University, Guangzhou 510091, China;
    • 7Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China
Published July 22, 2026. https://doi.org/10.1101/gr.281629.125
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cover of Genome Research Vol 36 Issue 7
Current Issue:
July 2026, Vol. 36, No. 7
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Abstract

Advances in single-cell sequencing technologies greatly enhance our understanding of molecular and cellular features. However, effectively leveraging these data to uncover key biological factors remains a major challenge in integrative analyses across multiomic types and comparative studies across species, particularly livestock species such as pigs and cattle. To address this, we develop AlignCell, a deep learning model designed to learn robust biological features by integrating multisource omic data across platforms, omic types, and species, thereby facilitating the discovery of key factors, such as conserved and species-specific genes in cross-species comparative studies. Across various applications and benchmarking compared with existing tools, AlignCell performs well. Notably, using AlignCell to integrate female gonad data across four species (human, mouse, pig, and cattle), including the bovine single-cell data generated in this study, AlignCell reveals the unexplored species-conserved gene CCT2 in primordial germ cells (PGCs). Additionally, it identifies unexplored species-specific genes PRICKLE4 and CTSV in pig and cattle PGCs, providing important insights for reproductive and developmental research.

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