motif

What a motif is

In science, motif does not mean just any repeated shape. In the supplied sources, it is used for at least two concrete kinds of patterns. In networks, a motif is a small subgraph, meaning a small set of connected nodes and edges, that recurs more often than expected by chance. In DNA studies, a motif is a sequence pattern of nucleotides, the letters A, C, G, and T, that is recognized by a transcription factor, or TF, which is a protein that binds DNA and helps control gene expression.

[2] Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors [3] Network motif detection using hidden markov models

What scientists measure

For network motifs, scientists measure how often a subgraph appears in a real network and compare that to randomized versions of the same network. They use statistics such as p-values, which estimate how surprising an observation is if chance were acting, and z-scores, which measure how far a result is from an expected average in standard-deviation units. For DNA motifs, scientists measure binding specificity, meaning how selectively a TF prefers certain DNA sequences, and use models such as a PWM, or position weight matrix, which scores the contribution of each nucleotide at each position.

[2] Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors [3] Network motif detection using hidden markov models

How motifs matter

Network motifs can act like building blocks of function. The sources describe feedforward loops, self-loops, and feedback loops as motifs that can affect speed of response, variability, pulses, filtering of brief signals, and oscillations. Another source distinguishes a functional motif as a network part whose presence has consequences that the rest of the system cannot undo. In ecology, some motifs relate to stability or reactivity, where reactivity means how strongly a system’s deviation grows after a small disturbance. In genomics, motifs help annotate regulatory regions and predict where TFs bind.

[1] Functional motifs in food webs and networks - PMC - NIH [2] Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors [3] Network motif detection using hidden markov models

Limits and cautions

A motif is not automatically a full explanation of a system. In network work, some motif effects depend on the question being asked, such as stability versus reactivity. Statistical motif discovery also depends on how the randomized comparison is built and on how overlaps are handled. In genomic studies, many candidate motifs do not transfer well across datasets, and artifacts such as simple repeats or contaminants can create false signals. The sources also note that some benchmark tests use synthetic networks, which do not capture all problems in real data.

[1] Functional motifs in food webs and networks - PMC - NIH [2] Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors [3] Network motif detection using hidden markov models

Sources and further reading

This general lesson was generated from public source material. It explains a concept; it does not interpret a private paper or provide medical advice.

1. Functional motifs in food webs and networks - PMC - NIH
2. Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors
3. Network motif detection using hidden markov models