ReviewMethods in molecular biology (Clifton, N.J.)2009
A maximum likelihood method for reconstruction of the evolution of eukaryotic gene structure.
Review in Methods in molecular biology (Clifton, N.J.), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Isolation and expression of terminal flower1 (TFL1) gene in clove (Syzygium aromaticum L.).BMC research notes · 2025Article
- Analysis of microbial community evolution, autolysis phenomena, and energy metabolism pathways inFrontiers in microbiology · 2024Article
- Origin of spliceosomal introns and alternative splicing.Cold Spring Harbor perspectives in biology · 2014Article
- Reverse transcriptase and intron number evolution.Stem cell investigation · 2014Article
- Evaluating gene expression dynamics using pairwise RNA FISH data.PLoS computational biology · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Spliceosomal introns are one of the principal distinctive features of eukaryotes. Nevertheless, different large-scale studies disagree about even the most basic features of their evolution. In order to come up with a more reliable reconstruction of intron evolution, we developed a model that is far more comprehensive than previous ones. This model is rich in parameters, and estimating them accurately is infeasible by straightforward likelihood maximization. Thus, we have developed an expectation-maximization algorithm that allows for efficient maximization. Here, we outline the model and describe the expectation-maximization algorithm in detail. Since the method works with intron presence-absence maps, it is expected to be instrumental for the analysis of the evolution of other binary characters as well.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.