BackgroundMicroRNAs (miRNAs) have been known to play an important role in several biological processes in both animals and plants. Although several tools for miRNA and target identification are available, the number of tools tailored towards plants is limited, and those that are available have specific functionality, lack graphical user interfaces, and restrict the number of input sequences. Large-scale computational identifications of miRNAs and/or targets of several plants have been also reported. Their methods, however, are only described as flow diagrams, which require programming skills and the understanding of input and output of the connected programs to reproduce.ResultsTo overcome these limitations and programming complexities, we proposed C-mii as a ready-made software package for both plant miRNA and target identification. C-mii was designed and implemented based on established computational steps and criteria derived from previous literature with the following distinguishing features. First, software is easy to install with all-in-one programs and packaged databases. Second, it comes with graphical user interfaces (GUIs) for ease of use. Users can identify plant miRNAs and targets via step-by-step execution, explore the detailed results from each step, filter the results according to proposed constraints in plant miRNA and target biogenesis, and export sequences and structures of interest. Third, it supplies bird's eye views of the identification results with infographics and grouping information. Fourth, in terms of functionality, it extends the standard computational steps of miRNA target identification with miRNA-target folding and GO annotation. Fifth, it provides helper functions for the update of pre-installed databases and automatic recovery. Finally, it supports multi-project and multi-thread management.ConclusionsC-mii constitutes the first complete software package with graphical user interfaces enabling computational identification of both plant miRNA genes and miRNA targets. With the provided functionalities, it can help accelerate the study of plant miRNAs and targets, especially for small and medium plant molecular labs without bioinformaticians. C-mii is freely available at http://www.biotec.or.th/isl/c-mii for both Windows and Ubuntu Linux platforms.

Background Manual chemical data curation from publications is error-prone, time consuming, and hard to maintain up-to-date data sets. Automatic information extraction can be used as a tool to reduce these problems. Since chemical structures usually described in images, information extraction needs to combine structure image recognition and text mining together. Results We have developed ChemEx, a chemical information extraction system. ChemEx processes both text and images in publications. Text annotator is able to extract compound, organism, and assay entities from text content while structure image recognition enables translation of chemical raster images to machine readable format. A user can view annotated text along with summarized information of compounds, organism that produces those compounds, and assay tests. Conclusions ChemEx facilitates and speeds up chemical data curation by extracting compounds, organisms, and assays from a large collection of publications. The software and corpus can be downloaded from http://www.biotec.or.th/isl/ChemEx.

To exploit social media data in vaccine-related areas, we proposed VaccineWatch, a monitoring system with visualizations and analytics of significant vaccine information from Twitter and RSS feeds. The system was designed and implemented as a web application with following distinguished features. First, it comes with graphical user interfaces that visualize perspectives of vaccine-related information mined from social media data. Second, it provides a set of filters allowing users to focus on their diseases, vaccines, countries, and/or companies of interest. Third, it includes the helper tools for the management of social media data collection and backend processes such as Twitter and RSS crawlers. The prototype of VaccineWatch is available at www.vacciknowlogy .org/VaccineWatch.

Domain combination provides important clues to the roles of protein domains in protein function, interaction and evolution. We have developed a web server d-Omix (a Mixer of Protein Domain Analysis Tools) aiming as a unified platform to analyze, compare and visualize protein data sets in various aspects of protein domain combinations. With InterProScan files for protein sets of interest provided by users, the server incorporates four services for domain analyses. First, it constructs protein phylogenetic tree based on a distance matrix calculated from protein domain architectures (DAs), allowing the comparison with a sequence-based tree. Second, it calculates and visualizes the versatility, abundance and co-presence of protein domains via a domain graph. Third, it compares the similarity of proteins based on DA alignment. Fourth, it builds a putative protein network derived from domain–domain interactions from DOMINE. Users may select a variety of input data files and flexibly choose domain search tools (e.g. hmmpfam, superfamily) for a specific analysis. Results from the d-Omix could be interactively explored and exported into various formats such as SVG, JPG, BMP and CSV. Users with only protein sequences could prepare an InterProScan file using a service provided by the server as well. The d-Omix web server is freely available at http://www.biotec.or.th/isl/Domix.

×The LZW is a lossless data compression algorithm Ref.[5] The compression algorithm starts with a dictionary containing all characters. During the compression, the algorithm dynamically expands the dictionary and outputs codes that refer to strings in the dictionary. Normally, the number of bits of the code is less than that of the variable length string in the dictionary. Data is compressed when the algorithm replaces the whole string with its code.A nice property of LZW is that the dictionary does not have to be packed with a compressed data. LZW decompression does not require a dictionary because the algorithm can reconstruct the dictionary while processing the compressed data. When using LZW to decompress an English text, the dictionary is initialized with all English characters and symbols. However, in this paper, the output of the decompression algorithm is a binary string. Therefore, the dictionary is initialized with the number 0 and 1.A pseudo code for LZW decompression used in LZWGA is shown in Fig 1.