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Motivation Microbiota analyses have important implications for health and science. These analyses make use of 16S/18S rRNA gene sequencing to identify taxa and predict species diversity. However, most available tools for analyzing microbiota data require adept programming skills and in-depth statistical knowledge for proper implementation. While long-read amplicon sequencing can lead to more accurate taxa predictions and is quickly becoming more common, practitioners have no easily accessible tools with which to perform their analyses. Results We present MOCHI, a GUI tool for microbiota amplicon sequencing analysis. MOCHI preprocesses sequences, assigns taxonomy, identifies different abundant species and predicts species diversity and function. It takes either taxonomic count table or FASTQ of partial 16S/18S rRNA or full-length 16S rRNA gene as input. It performs analyses in real time and visualizes data in both tabular and graphical formats. Availability and implementation MOCHI can be installed to run locally or accessed as a web tool at https://mochi.life.nctu.edu.tw. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: Identification of single motifs and motif pairs that can be used to predict transcription factor localization in ChIP-chip data, and gene expression in tissue-specific microarray data. Results: We describe methodology to identify de novo individual and interacting pairs of binding site motifs from ChIP-chip data, using an algorithm that integrates localization data directly into the motif discovery process. We combine matrix-enumeration based motif discovery with multivariate regression to evaluate candidate motifs and identify motif interactions. When applied to the HNF localization data in liver and pancreatic islets, our methods produce motifs that are either novel or improved known motifs. All motif pairs identified to predict localization are further evaluated according to how well they predict expression in liver and islets and according to how conserved are the relative positions of their occurrences. We find that interaction models of HNF1 and CDP motifs provide excellent prediction of both HNF1 localization and gene expression in liver. Our results demonstrate that ChIP-chip data can be used to identify interacting binding site motifs. Availability: Motif discovery programs and analysis tools are available on request from the authors. Contact: [email protected]