I've written a paper about DNA sequence analysis. This paper attempts to use Bayesian modelling for a set of DNA sequences. It will probably end up either in a statistics journal, or, more likely, in a bioinformatics journal. My concern is that biologists may take exception to some of the language in the introduction.
I'm attempting to make a connection between De Novo motif discovery, and modelling the sequences. Maybe it is a bit of a stretch. E.g. I use language like "analyzing a set of DNA sequences with biological significance solely by focusing on the motifs contained within them potentially discards valuable information, for example, possible long-range correlations between nucleotide positions in the sequences." Also, "An alternative, and possibly complementary approach, is to consider a sequence as a single unit, and try to do direct statistical analysis on it... This approach is used in this paper, which does not use Markovian techniques. Instead, it tries to model correlation structure across the sequence."
So, the question is whether it is better to try to make an explicit connection at the risk of saying things that are incorrect and generally over-stretching, rather than just saying (which seems a little lame) that this sequence classification problem is related to De Novo motif discovery problem and leaving it at that. Comments?
I include the first few paragraphs of the introduction below. This includes all the relevant language.
I'm willing to send my current draft to anyone who is interested in knowing more about the context. I don't want to post a public link to it, though.
DNA sequence motifs are nucleotide sequence patterns that are conjectured to have a biological significance. Often they indicate sequence-specific binding sites for proteins such as nucleases and transcription factors (TF). Others are involved in important processes at the RNA level, including ribosome binding, mRNA processing (splicing, editing, polyadenylation) and transcription termination. Motif discovery is a very active area of research interest. So-called “De novo computational discovery” is perhaps the most popular, where given only a set of DNA sequences, an algorithm is used to identify candidate shared motifs. This can be thought of as the task of finding a set of non- overlapping, approximately matching substrings given a starting set of strings. This is a very difficult problem.
From a more general perspective, DNA sequence analysis is often done using DNA sequence motifs. It is reasonable to ask the question - what makes a sequence a motif? From a biological perspective, a motif is simply the smallest identifiable sequence sub- component of something larger. This subcomponent can be thought of as the smallest identifiable piece of functionality related to the underlying biology, Therefore, sequence analysis often focuses on identifying these motifs. However, these motifs are typically very short, so analyzing a set of DNA sequences with biological significance solely by focusing on the motifs contained within them potentially discards valuable information, for example, possible long-range correlelations between nucleotide positions in the sequences. Note also that the statistical methods used to identify motifs are typically Markovian, like Hidden Markov Models (HMM), which are naturally tailored towards looking at small sequences.
An alternative, and possibly complementary approach, is to consider a sequence as a single unit, and try to do direct statistical analysis on it. This approach is less often used. One reason is that such sequences can quickly grow too large, and are not well suited to Markovian approaches. This approach is used in this paper, which does not use Markovian techniques. Instead, it tries to model correlation structure across the sequence.
We do this by fitting a suitable Bayesian model to that set using Bayesian model selection. As noted above, our major rationale for this model is the assumption that the nucleotide locations of this set are correlated among themselves. With this assumption in mind, we construct a family of probability distributions to capture this correlation information, described in Subsection 2.1.