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Computational Sequence- and NGS-Based MicroRNA Prediction
Published in Ervin Sejdić, Tiago H. Falk, Signal Processing and Machine Learning for Biomedical Big Data, 2018
miRanalyzer [55] uses an ensemble of five random forest classifiers in order to predict novel miRNA. As a pre-screening step, bowtie [70] is used to identify reads that match to known miRNAs and known mRNAs. miRanalyzer uses a set of pre-processed genome files for alignment during its prediction pipeline; 25 animal and 6 plant genomes are available. Feature sets used for classification differ for plant and animal species. No novel miRNAs have been identified by miRanalyzer.
Circular RNA expression profiles following MC-LR treatment in human normal liver cell line (HL7702) cells using high-throughput sequencing analysis
Published in Journal of Toxicology and Environmental Health, Part A, 2019
Shuilin Zheng, Cong Wen, Shu Yang, Yue Yang, Fei Yang
Raw data (raw reads) of FASTQ format were initially processed by Novogene (Beijing, China) through in-house Perl scripts, and high-quality clean data (clean reads) obtained by removing raw reads containing adapter, ploy-N or low-quality reads Further Q20, Q30 and GC content of the clean data were calculated as previously described by Yang et al. (2018c). Subsequently, index of the reference genome was constructed using bowtie2 and paired-end clean reads were aligned to the reference genome using Bowtie (Langmead et al. 2009). The mapped reads of each sample were assembled and full-length transcripts representing multiple splice variants for each gene locus quantitated by StringTie (Pertea et al. 2016). To obtain a candidate set of circRNA, transcripts with potential coding were filtered out and the remaining ones without potential coding were selected as our candidate set of circRNA. Finally, the circRNAs were detected and identified using findcirc (Memczak et al. 2013) and CIRI2 (Gao, Zhang, and Zhao 2018).