Modelling of the breadth of expression from promoter architectures identifies pro-housekeeping transcription factors

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science

Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.

Models of the Gene Must Inform Data-Mining Strategies in Genomics

The gene is a fundamental concept of genetics, which emerged with the Mendelian paradigm of heredity at the beginning of the 20th century. However, the concept has since diversified. Somewhat different narratives and models of the gene developed in several sub-disciplines of genetics, that is in classical genetics, population genetics, molecular genetics, genomics, and, recently, also, in systems genetics. Here, I ask how the diversity of the concept impacts data-integration and data-mining strategies for bioinformatics, genomics, statistical genetics, and data science. I also consider theoretical background of the concept of the gene in the ideas of empiricism and experimentalism, as well as reductionist and anti-reductionist narratives on the concept. Finally, a few strategies of analysis from published examples of data-mining projects are discussed. Moreover, the examples are re-interpreted in the light of the theoretical material. I argue that the choice of an optimal level of abstraction for the gene is vital for a successful genome analysis.

A Contemporary Message from Mendel's Logical Empiricism

The gene is one of the most fundamental concepts in life sciences, having been developed in the mold of the Mendelian paradigm of heredity, which shaped genetics across 150 years. How could Mendel possibly be so prophetic in the middle of 19^th century, using only the small garden of the monastery as his experimental breeding field? I believe that we are indebted to Mendel's mastery of the scientific method, which was far ahead of his time. Although his experimental technology was literally garden-variety, Mendel's excellence in the method of science, algebra, and logical analysis helped him in designing the right experiment and in interpreting the results insightfully. This may be valuable to recall in today's technology-focused culture, where the center of interest tends to be on the generation and description of high-throughput datasets from specialized genomics screens. As Mendel's story suggests, progress in 21^st century genetics may also depend on the development of robust concepts and generalizations.