Bioinformatics and Computational Biology

Bioinformatics and computational biology represent inter‐related disciplines that harness computational methods, statistical models and algorithmic innovations to decode, organise and interpret complex biological data. These fields drive our understanding of molecular structures, gene regulation and cellular interactions by integrating high‐throughput experimental data with advanced analytical pipelines. Recent methodological advances—ranging from machine learning approaches for structure prediction to scalable data processing pipelines—have not only enhanced reproducibility but also are pivotal in bridging experimental observations with computational insights, ultimately fueling developments in personalised medicine and systems biology.

Research in Nature Index

Several major developments showcase the dynamic progress in bioinformatics and computational biology. One key milestone is the construction of a novel human pangenome reference that captures diverse structural variations, reducing error rates in variant discovery and improving precision in genome-based workflows [1]. In parallel, integrative single-cell atlases have opened new avenues to identify cell states and regulatory networks across healthy and diseased tissues. For instance, an in-depth atlas of the lung has uncovered how genetic and environmental factors shape cellular heterogeneity [2]. Similarly, high-dimensional 3D imaging coupled with machine learning has provided unprecedented spatial maps of tumours, revealing molecular gradients and immune cell interactions in colorectal cancer [3]. Beyond these data-driven advances, structural biology also converges with computational methods. Recent work on non-LTR retrotransposons has resolved key mechanistic features of target-primed reverse transcription, reinforcing how computational analyses guide experimental pursuits [4]. Finally, foundational strategies for single-cell data integration continue to evolve, ensuring that datasets from disparate modalities—gene expression, accessible chromatin, and protein abundance—can be harmonised and interpreted collectively [5].

Topic trend for the past 5 years

Technical terms

Single-cell transcriptomics: A suite of techniques that profiles gene expression at the level of individual cells, enabling the discovery of rare cell types and dynamic states.

Pangenome: A reference encompassing variations and alleles found across multiple individuals, exceeding the coverage of a single “reference” genome.

Spatiotemporal atlas: A three-dimensional, time-resolved map illustrating the distribution and state transitions of cells or molecules within tissues or organisms.

Machine learning integration: The use of computational algorithms, such as neural networks or random forests, to analyse and predict biological patterns across multidimensional datasets.

References

1. A draft human pangenome reference. Nature (2023).
2. An integrated cell atlas of the lung in health and disease. Nature Medicine (2023).
3. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer. Cell (2023).
4. Structure of the R2 non-LTR retrotransposon initiating target-primed reverse transcription. Science (2023).
5. Comprehensive Integration of Single-Cell Data. Cell (2019).

Position of Bioinformatics and Computational Biology in Nature Index by Count

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