379 research outputs found
What can we learn from noncoding regions of similarity between genomes?
BACKGROUND: In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to assume that these conserved regions are more likely to contain functional elements than less-conserved portions of the genome. METHODS: Here we used a motif-oriented machine learning method based on the Relevance Vector Machine algorithm to extract the strongest signal from a set of non-coding conserved sequences. RESULTS: We successfully fitted models to reflect the non-coding sequences, and showed that the results were quite consistent for repeated training runs. Using the learned models to scan genomic sequence, we found that they often made predictions close to the start of annotated genes. We compared this method with other published promoter-prediction systems, and showed that the set of promoters which are detected by this method is substantially similar to that detected by existing methods. CONCLUSIONS: The results presented here indicate that the promoter signal is the strongest single motif-based signal in the non-coding functional fraction of the genome. They also lend support to the belief that there exists a substantial subset of promoter regions which share several common features including, but not restricted to, a relative abundance of CpG dinucleotides. This subset is detectable by a variety of distinct computational methods
A machine learning strategy to identify candidate binding sites in human protein-coding sequence
BACKGROUND: The splicing of RNA transcripts is thought to be partly promoted and regulated by sequences embedded within exons. Known sequences include binding sites for SR proteins, which are thought to mediate interactions between splicing factors bound to the 5' and 3' splice sites. It would be useful to identify further candidate sequences, however identifying them computationally is hard since exon sequences are also constrained by their functional role in coding for proteins. RESULTS: This strategy identified a collection of motifs including several previously reported splice enhancer elements. Although only trained on coding exons, the model discriminates both coding and non-coding exons from intragenic sequence. CONCLUSION: We have trained a computational model able to detect signals in coding exons which seem to be orthogonal to the sequences' primary function of coding for proteins. We believe that many of the motifs detected here represent binding sites for both previously unrecognized proteins which influence RNA splicing as well as other regulatory elements
NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence
NestedMICA is a new, scalable, pattern-discovery system for finding transcription factor binding sites and similar motifs in biological sequences. Like several previous methods, NestedMICA tackles this problem by optimizing a probabilistic mixture model to fit a set of sequences. However, the use of a newly developed inference strategy called Nested Sampling means NestedMICA is able to find optimal solutions without the need for a problematic initialization or seeding step. We investigate the performance of NestedMICA in a range scenario, on synthetic data and a well-characterized set of muscle regulatory regions, and compare it with the popular MEME program. We show that the new method is significantly more sensitive than MEME: in one case, it successfully extracted a target motif from background sequence four times longer than could be handled by the existing program. It also performs robustly on synthetic sequences containing multiple significant motifs. When tested on a real set of regulatory sequences, NestedMICA produced motifs which were good predictors for all five abundant classes of annotated binding sites
NestedMICA as an ab initio protein motif discovery tool.
BACKGROUND: Discovering overrepresented patterns in amino acid sequences is an important step in protein functional element identification. We adapted and extended NestedMICA, an ab initio motif finder originally developed for finding transcription binding site motifs, to find short protein signals, and compared its performance with another popular protein motif finder, MEME. NestedMICA, an open source protein motif discovery tool written in Java, is driven by a Monte Carlo technique called Nested Sampling. It uses multi-class sequence background models to represent different "uninteresting" parts of sequences that do not contain motifs of interest. In order to assess NestedMICA as a protein motif finder, we have tested it on synthetic datasets produced by spiking instances of known motifs into a randomly selected set of protein sequences. NestedMICA was also tested using a biologically-authentic test set, where we evaluated its performance with respect to varying sequence length. RESULTS: Generally NestedMICA recovered most of the short (3-9 amino acid long) test protein motifs spiked into a test set of sequences at different frequencies. We showed that it can be used to find multiple motifs at the same time, too. In all the assessment experiments we carried out, its overall motif discovery performance was better than that of MEME. CONCLUSION: NestedMICA proved itself to be a robust and sensitive ab initio protein motif finder, even for relatively short motifs that exist in only a small fraction of sequences. AVAILABILITY: NestedMICA is available under the Lesser GPL open-source license from: http://www.sanger.ac.uk/Software/analysis/nmica/RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are
Metamotifs--a generative model for building families of nucleotide position weight matrices.
BACKGROUND: Development of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms is expanding our understanding of transcription factor binding site motifs. The consequential growth of sequence motif data sets makes it important to systematically group and categorise regulatory motifs. It has been shown that there are familial tendencies in DNA sequence motifs that are predictive of the family of factors that binds them. Further development of methods that detect and describe familial motif trends has the potential to help in measuring the similarity of novel computational motif predictions to previously known data and sensitively detecting regulatory motifs similar to previously known ones from novel sequence. RESULTS: We propose a probabilistic model for position weight matrix (PWM) sequence motif families. The model, which we call the 'metamotif' describes recurring familial patterns in a set of motifs. The metamotif framework models variation within a family of sequence motifs. It allows for simultaneous estimation of a series of independent metamotifs from input position weight matrix (PWM) motif data and does not assume that all input motif columns contribute to a familial pattern. We describe an algorithm for inferring metamotifs from weight matrix data. We then demonstrate the use of the model in two practical tasks: in the Bayesian NestedMICA model inference algorithm as a PWM prior to enhance motif inference sensitivity, and in a motif classification task where motifs are labelled according to their interacting DNA binding domain. CONCLUSIONS: We show that metamotifs can be used as PWM priors in the NestedMICA motif inference algorithm to dramatically increase the sensitivity to infer motifs. Metamotifs were also successfully applied to a motif classification problem where sequence motif features were used to predict the family of protein DNA binding domains that would interact with it. The metamotif based classifier is shown to compare favourably to previous related methods. The metamotif has great potential for further use in machine learning tasks related to especially de novo computational sequence motif inference. The metamotif methods presented have been incorporated into the NestedMICA suite.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are
The Effectiveness of Instructional Methods Based on Learning Style Preferences of Agricultural Students: A Research Tool for Continuous Improvement for Faculty in Career and Technical Education (CTE) Programs
This study was conducted to research learning style preferences of agriculture students. Specifically, the objectives which guided the study were: (1) to determine the learning style preferences of undergraduate agricultural students enrolled in a given Soil Science course and (2) to ascertain if there were differences in the students’ course grade average (CGA) in the given Soil Science course when the treatment group were taught according to their learning style preferences versus the control group. For research question two, there was a hypothesis statement to determine if modifying the instructional approach to the students’ learning style preference for the treatment group produces a higher course grade average (CGA) than the control group. The Productivity Environmental Preference Survey (PEPS) was used to obtain the students’ preferred learning style preferences. Results indicate that modifying the instructional approach to the students’ learning styles preference (Structure) would result in a higher course grade average (CGA) for the treatment group versus the control group
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