You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

DNA barcoding in plants: Evolution and applications of in silico approaches and resources. Bioinformatics has played an important role in the analysis of DNA barcoding data. The process of DNA barcoding initially involves the available data collection from the existing databases. Many databases have been developed in recent years, e.g. MMDBD [Medicinal Materials DNA Barcode Database], BioBarcode, etc. In case of non-availability of sequences, sequencing has to be done in vitro for which a recently developed software ecoPrimers can be helpful. This is followed by multiple sequence alignment. Further, basic sequence statistics computation and phylogenetic analysis can be performed by MEGA and PHYLIP/PAUP tools respectively. Some of the recent tools for in silico and statistical analysis specifically designed for barcoding viz. CAOS (Character Based DNA Barcoding), BRONX (DNA Barcode Sequence Identification Incorporating Taxonomic Hierarchy and within Taxon Variability), Spider (Analysis of species identity and evolution, particularly DNA barcoding), jMOTU and Taxonerator (Turning DNA Barcode Sequences into Annotated OTUs), OTUbase (Analysis of OTU data and taxonomic data), SAP (Statistical Assignment Package), etc. have been discussed and analysed in this review. The paper presents a comprehensive overview of the various in silico methods, tools, softwares and databases used for DNA barcoding of plants.
{"Chemical": []}