BACKGROUND: The primary determinant of crop yield is photosynthetic capacity, which is under the control of photosynthesis-related genes. Therefore, the mining of genes involved in photosynthesis is important for the study of photosynthesis. MapMan Mercator 4 is a powerful annotation tool for assigning genes into proper functional categories; however, in maize, the functions of approximately 22.15% (9520) of genes remain unclear and are labeled "not assigned", which may include photosynthesis-related genes that have not yet been identified. The fast-increasing usage of the machine learning approach in solving biological problems provides us with a new chance to identify novel photosynthetic genes from functional "not assigned" genes in maize. RESULTS: In this study, we proved the ensemble learning model using a voting eliminates the preferences of single machine learning models. Based on this evaluation, we implemented an ensemble based ML(Machine Learning) methods using a majority voting scheme and observed that including RNA-seq data from multiple photosynthetic mutants rather than only a single mutant could increase prediction accuracy. And we call this approach "A Machine Learning-based Photosynthetic-related Gene Detection approach (PGD)". Finally, we predicted 716 photosynthesis-related genes from the "not assigned" category of maize MapMan annotation. The protein localization prediction (TargetP) and expression trends of these genes from maize leaf sections indicated that the prediction was reliable and robust. And we put this approach online base on google colab. CONCLUSIONS: This study reveals a new approach for mining novel genes related to a specific functional category and provides candidate genes for researchers to experimentally define their biological functions.

MOTIVATION: RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis. RESULTS: In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. AVAILABILITY AND IMPLEMENTATION: An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org

BACKGROUND: Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. RESULTS: The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. CONCLUSION: The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In one experiment, MIV is a better choice than IIV as input to data normalization and statistical analysis methods, as it yields as greater number of statistically significant differentially expressed genes; TM4 does not support the choice of MIV input data. Overall, the more flexible and extensive statistical models of Expresso achieve more accurate analytical results, when judged by the yardstick of qRT-PCR data, in the context of an experimental design of modest complexity.