CAFU is a Galaxy-based bioinformatics framework for comprehensive assembly and functional annotation of unmapped RNA-seq data from single- and mixed-species samples which integrates plenty of existing next-generation sequencing (NGS) analytical tools and our developed programs, and features an easy-to-use interface to manage, manipulate and most importantly, explore large-scale unmapped reads. Besides the common process of reads cleansing and mapping, unmapped reads extraction and de novo transcription assembly, CAFU optionally offers multiple-level evidence evaluation, sequence and expression characterization, and transcript function annotation. Taking advantages of machine learning (ML) technologies, CAFU also effectively addresses the challenge of classifying species-specific transcripts assembled using unmapped reads from mixed-species samples.
Cui H, Zhai J, Ma C. miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences.[J]. Plos One, 2015, 10(11):e0142753.
Zhang T, Ju L, Zhai J, Song Y, Song J, Ma C. miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences [M]. Plant MicroRNAs: Methods and Protocols, Methods in Molecular Biology, 2019, vol 1932:89-97.