Dataset Information

EasyCodeML: A visual tool for analysis of selection using CodeML.

ABSTRACT: The genomic signatures of positive selection and evolutionary constraints can be detected by analyses of nucleotide sequences. One of the most widely used programs for this purpose is CodeML, part of the PAML package. Although a number of bioinformatics tools have been developed to facilitate the use of CodeML, these have various limitations. Here, we present a wrapper tool named EasyCodeML that provides a user-friendly graphical interface for using CodeML. EasyCodeML has a custom running mode in which parameters can be adjusted to meet different requirements. It also offers a preset running mode in which an evolutionary analysis pipeline and publication-quality tables can be exported by a single click. EasyCodeML allows visualized, interactive tree labelling, which greatly simplifies the use of the branch, branch-site, and clade models of selection. The program allows comparison of major codon-based models for analyses of selection. EasyCodeML is a stand-alone package that is supported in Windows, Mac, and Linux operating systems, and is freely available at https://github.com/BioEasy/EasyCodeML.

SUBMITTER: Gao F

PROVIDER: S-EPMC6467853 | biostudies-other | 2019 Apr

REPOSITORIES: biostudies-other

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EasyCodeML: A visual tool for analysis of selection using CodeML.

Gao Fangluan F Chen Chengjie C Arab Daej A DA Du Zhenguo Z He Yehua Y Ho Simon Y W SYW

Ecology and evolution 20190301 7

The genomic signatures of positive selection and evolutionary constraints can be detected by analyses of nucleotide sequences. One of the most widely used programs for this purpose is CodeML, part of the PAML package. Although a number of bioinformatics tools have been developed to facilitate the use of CodeML, these have various limitations. Here, we present a wrapper tool named EasyCodeML that provides a user-friendly graphical interface for using CodeML. EasyCodeML has a custom running mode i ...[more]

PMID: 31015974

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Project description:Multiple sequence alignments (MSAs) are a fundamental analysis tool used throughout biology to investigate relationships between protein sequence, structure, function, evolutionary history, and patterns of disease-associated variants. However, their widespread application in systems biology research is currently hindered by the lack of user-friendly tools to simultaneously visualize, manipulate and query the information conceptualized in large sequence alignments, and the challenges in integrating MSAs with multiple orthogonal data such as cancer variants and post-translational modifications, which are often stored in heterogeneous data sources and formats. Here, we present the Multiple Sequence Alignment Ontology (MSAOnt), which represents a profile or consensus alignment in an ontological format. Subsets of the alignment are easily selected through the SPARQL Protocol and RDF Query Language for downstream statistical analysis or visualization. We have also created the Kinome Viewer (KinView), an interactive integrative visualization that places eukaryotic protein kinase cancer variants in the context of natural sequence variation and experimentally determined post-translational modifications, which play central roles in the regulation of cellular signaling pathways. Using KinView, we identified differential phosphorylation patterns between tyrosine and serine/threonine kinases in the activation segment, a major kinase regulatory region that is often mutated in proliferative diseases. We discuss cancer variants that disrupt phosphorylation sites in the activation segment, and show how KinView can be used as a comparative tool to identify differences and similarities in natural variation, cancer variants and post-translational modifications between kinase groups, families and subfamilies. Based on KinView comparisons, we identify and experimentally characterize a regulatory tyrosine (Y177PLK4) in the PLK4 C-terminal activation segment region termed the P+1 loop. To further demonstrate the application of KinView in hypothesis generation and testing, we formulate and validate a hypothesis explaining a novel predicted loss-of-function variant (D523NPKC?) in the regulatory spine of PKC?, a recently identified tumor suppressor kinase. KinView provides a novel, extensible interface for performing comparative analyses between subsets of kinases and for integrating multiple types of residue specific annotations in user friendly formats.

Project description:BackgroundMicroarray and DNA-sequencing based technologies continue to produce enormous amounts of data on gene expression. This data has great potential to illuminate our understanding of biology and medicine, but the data alone is of limited value without computational tools to allow human investigators to visualize and interpret it in the context of their problem of interest.ResultsWe created a web server called SHOE that provides an interactive, visual presentation of the available evidence of transcriptional regulation and gene co-expression to facilitate its exploration and interpretation. SHOE predicts the likely transcription factor binding sites in orthologous promoters of humans, mice, and rats using the combined information of 1) transcription factor binding preferences (position-specific scoring matrix (PSSM) libraries such as Transfac32, Jaspar, HOCOMOCO, ChIP-seq, SELEX, PBM, and iPS-reprogramming factor), 2) evolutionary conservation of putative binding sites in orthologous promoters, and 3) co-expression tendencies of gene pairs based on 1,714 normal human cells selected from the Gene Expression Omnibus Database.ConclusionSHOE enables users to explore potential interactions between transcription factors and target genes via multiple data views, discover transcription factor binding motifs on top of gene co-expression, and visualize genes as a network of gene and transcription factors on its native gadget GeneViz, the CellDesigner pathway analyzer, and the Reactome database to search the pathways involved. As we demonstrate here when using the CREB1 and Nf-?B datasets, SHOE can reliably identify experimentally verified interactions and predict plausible novel ones, yielding new biological insights into the gene regulatory mechanisms involved. SHOE comes with a manual describing how to run it on a local PC or via the Garuda platform ( www.garuda-alliance.org ), where it joins other popular gadgets such as the CellDesigner pathway analyzer and the Reactome database, as part of analysis workflows to meet the growing needs of molecular biologists and medical researchers. SHOE is available from the following URL http://ec2-54-150-223-65.ap-northeast-1.compute.amazonaws.com A video demonstration of SHOE can be found here: https://www.youtube.com/watch?v=qARinNb9NtE.

Dataset Information

EasyCodeML: A visual tool for analysis of selection using CodeML.

Publications

EasyCodeML: A visual tool for analysis of selection using CodeML.

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