RESUMEN
The main way of analyzing genetic sequences is by finding sequence regions that are related to each other. There are many methods to do that, usually based on this idea: find an alignment of two sequence regions, which would be unlikely to exist between unrelated sequences. Unfortunately, it is hard to tell if an alignment is likely to exist by chance. Also, the precise alignment of related regions is uncertain. One alignment does not hold all evidence that they are related. We should consider alternative alignments too. This is rarely done, because we lack a simple and fast method that fits easily into practical sequence-search software. Here is described a simplest-conceivable change to standard sequence alignment, which sums probabilities of alternative alignments. This makes it easier to tell if a similarity is likely to occur by chance. This approach is better than standard alignment at finding distant relationships, at least in a few tests. It can be used in practical sequence-search software, with minimal increase in implementation difficulty or run time. It generalizes to different kinds of alignment, e.g. DNA-versus-protein with frameshifts. Thus, it can widely contribute to finding subtle relationships between sequences.
RESUMEN
Background: Endogenous retroviruses (ERVs), which blur the boundary between virus and transposable element, are genetic material derived from retroviruses and have important implications for evolution. This study examines the diversity and evolution of human endogenous retroviruses (HERVs) of the HERVL family, which has long terminal repeats (LTRs) named MLT2. Results: By probability-based sequence comparison, we uncover systematic annotation errors that conceal the true complexity and diversity of transposable elements (TEs) in the human genome. Our analysis identifies new subfamilies within the MLT2 group, proposes a refined classification scheme, and constructs new consensus sequences. We present an evolutionary analysis including phylogenetic trees that elucidate the relationships between these subfamilies and their contributions to human evolution. The results underscore the significance of accurate TE annotation in understanding genome evolution, highlighting the potential for misclassified TEs to impact interpretations of genomic studies. Availability and implementation: Not applicable.