Motivation The accumulation of somatic mutations plays critical roles in cancer development and progression. However, the global patterns of somatic mutations, especially non-coding mutations, and their roles in defining molecular subtypes of cancer have not been well characterized due to the computational challenges in analysing the complex mutational patterns. Results Here, we develop a new algorithm, called MutSpace, to effectively extract patient-specific mutational features using an embedding framework for larger sequence context. Our method is motivated by the observation that the mutation rate at megabase scale and the local mutational patterns jointly contribute to distinguishing cancer subtypes, both of which can be simultaneously captured by MutSpace. Simulation evaluations show that MutSpace can effectively characterize mutational features from known patient subgroups and achieve superior performance compared with previous methods. As a proof-of-principle, we apply MutSpace to 560 breast cancer patient samples and demonstrate that our method achieves high accuracy in subtype identification. In addition, the learned embeddings from MutSpace reflect intrinsic patterns of breast cancer subtypes and other features of genome structure and function. MutSpace is a promising new framework to better understand cancer heterogeneity based on somatic mutations. Availability and implementation Source code of MutSpace can be accessed at: https://github.com/ma-compbio/MutSpace. Supplementary information Supplementary data are available at Bioinformatics online.

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Advances in machine learning (ML) have enabled the development of next-generation prediction models for complex computational biology problems. These developments have spurred the use of interpretable machine learning (IML) to unveil fundamental biological insights through data-driven knowledge discovery. However, in general, standards and guidelines for IML usage in computational biology have not been well-characterized, representing a major gap toward fully realizing the potential of IML. Here, we introduce a workflow on the best practices for using IML methods to perform knowledge discovery which covers verification strategies that bridge data, prediction model, and explanation. We outline a workflow incorporating these verification strategies to increase an IML method's accountability, reliability, and generalizability. We contextualize our proposed workflow in a series of widely applicable computational biology problems. Together, we provide an extensive workflow with important principles for the appropriate use of IML in computational biology, paving the way for a better mechanistic understanding of ML models and advancing the ability to discover novel biological phenomena.

Motivation The spatial positioning of chromosomes relative to functional nuclear bodies is intertwined with genome functions such as transcription. However, the sequence patterns and epigenomic features that collectively influence chromatin spatial positioning in a genome-wide manner are not well understood. Results Here, we develop a new transformer-based deep learning model called UNADON, which predicts the genome-wide cytological distance to a specific type of nuclear body, as measured by TSA-seq, using both sequence features and epigenomic signals. Evaluations of UNADON in four cell lines (K562, H1, HFFc6, HCT116) show high accuracy in predicting chromatin spatial positioning to nuclear bodies when trained on a single cell line. UNADON also performed well in an unseen cell type. Importantly, we reveal potential sequence and epigenomic factors that affect large-scale chromatin compartmentalization in nuclear bodies. Together, UNADON provides new insights into the principles between sequence features and large-scale chromatin spatial localization, which has important implications for understanding nuclear structure and function. Availability and implementation The source code of UNADON can be found at https://github.com/ma-compbio/UNADON.

Hormones play a crucial role both in plants and animals. As we all known, hormones work through specific receptors in different organisms. Therefore, the hormone interaction between plant and animal is an interesting question worth discussing. Recently, food safety has become a common topic concerned by consumers. A widely discussed rumor such as "Fruit bulking agent is some human reproductive hormones" leads us to consider whether animal hormones did regulate plant growth. Based on the large amount of previous research, we demonstrated that animal hormones did affect plant, such as steroid hormones for livestock feed could regulate physiological processes in plant including flowering, senescence and stress resistance after excreted into the environment. Here, we summarize the previous studies and systematically discuss the effects of animal hormones on plant growth and development.