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Modern phenotyping techniques yield vast amounts of data that are challenging to manage and analyze. When thoroughly examined, this type of data can reveal genotype-to-phenotype relationships and meaningful connections among individual traits. However, efficient data mining is challenging for experimental biologists with limited training in curating, integrating, and exploring complex datasets. Additionally, data transparency, accessibility, and reproducibility are important considerations for scientific publication. The need for a streamlined, user-friendly pipeline for advanced phenotypic data analysis is pressing. In this article we present an open-source, online platform for multivariate analysis (MVApp), which serves as an interactive pipeline for data curation, in-depth analysis, and customized visualization. MVApp builds on the available R-packages and adds extra functionalities to enhance the interpretability of the results. The modular design of the MVApp allows for flexible analysis of various data structures and includes tools underexplored in phenotypic data analysis, such as clustering and quantile regression. MVApp aims to enhance findable, accessible, interoperable, and reproducible data transparency, streamline data curation and analysis, and increase statistical literacy among the scientific community., (© 2019 American Society of Plant Biologists. All Rights Reserved.)

Mining patterns of histone modifications interplay from epigenomic profiles are one of the leading research areas these days. Various methods based on clustering approaches and hidden Markov models have been presented so far with some limitations. Here we present ChromClust, a semi-supervised clustering tool for mining commonly occurring histone modifications at various locations of the genome. Applying our method to 11 chromatin marks in nine human cell types recovered 11 clusters based on distinct chromatin signatures mapping to various elements of the genome. Our approach is efficient in respect to time and space usage along with the added facility of maintaining database at the backend. It outperforms the existing methods with respect to mining patterns in a semi-supervised fashion mapping to various functional elements of the genome. It will aid in future by saving the resources of time and space along with efficiently retrieving the hidden interplay of histone combinations., (Copyright © 2015 Elsevier Inc. All rights reserved.)