Dataset Information

MiRNA Digger: a comprehensive pipeline for genome-wide novel miRNA mining.

ABSTRACT: MicroRNAs (miRNAs) are important regulators of gene expression. The recent advances in high-throughput sequencing (HTS) technique have greatly facilitated large-scale detection of the miRNAs. However, thoroughly discovery of novel miRNAs from the available HTS data sets remains a major challenge. In this study, we observed that Dicer-mediated cleavage sites for the processing of the miRNA precursors could be mapped by using degradome sequencing data in both animals and plants. In this regard, a novel tool, miRNA Digger, was developed for systematical discovery of miRNA candidates through genome-wide screening of cleavage signals based on degradome sequencing data. To test its sensitivity and reliability, miRNA Digger was applied to discover miRNAs from four organs of Arabidopsis. The results revealed that a majority of already known mature miRNAs along with their miRNA*s expressed in these four organs were successfully recovered. Notably, a total of 30 novel miRNA-miRNA* pairs that have not been registered in miRBase were discovered by miRNA Digger. After target prediction and degradome sequencing data-based validation, eleven miRNA-target interactions involving six of the novel miRNAs were identified. Taken together, miRNA Digger could be applied for sensitive detection of novel miRNAs and it could be freely downloaded from http://www.bioinfolab.cn/miRNA_Digger/index.html.

SUBMITTER: Yu L

PROVIDER: S-EPMC4702050 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

miRNA Digger: a comprehensive pipeline for genome-wide novel miRNA mining.

Yu Lan L Shao Chaogang C Ye Xinghuo X Meng Yijun Y Zhou Yincong Y Chen Ming M

Scientific reports 20160106

MicroRNAs (miRNAs) are important regulators of gene expression. The recent advances in high-throughput sequencing (HTS) technique have greatly facilitated large-scale detection of the miRNAs. However, thoroughly discovery of novel miRNAs from the available HTS data sets remains a major challenge. In this study, we observed that Dicer-mediated cleavage sites for the processing of the miRNA precursors could be mapped by using degradome sequencing data in both animals and plants. In this regard, a ...[more]

PMID: 26732371

Similar Datasets

Project description:DNA methylation is a crucial epigenetic modification involved in multiple biological processes and diseases. Current approaches for measuring genome-wide DNA methylation via bisulfite sequencing (BS-seq) include whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and enzymatic methyl-seq (EM-seq). The computational analysis tools available for BS-seq data include customized aligners for mapping bisulfite-converted reads and computational pipelines for downstream data analysis. Current post-alignment methylation tools are specialized for the interpretation of CG methylation, which is known to dominate mammalian genomes, however, non-CG methylation (CHG and CHH, where H refers to A, C, or T) is commonly observed in plants and fungi and is closely associated with gene regulation, transposon silencing, and plant development. Thus, we have developed a MethylC-analyzer to analyze and visualize post-alignment WGBS, RRBS, and EM-seq data focusing on CG. The tool is able to also analyze non-CG sites to enhance deciphering genomes of plants and fungi. By processing aligned data and gene location files, MethylC-analyzer generates a genome-wide view of methylation levels and methylation in user-specified genomic regions. The meta-plot, for example, allows the investigation of DNA methylation within specific genomic elements. Moreover, our tool identifies differentially methylated regions (DMRs) and investigates the enrichment of genomic features associated with variable methylation. MethylC-analyzer functionality is not limited to specific genomes, and we demonstrated its performance on both plant and human BS-seq data. MethylC-analyzer is a Python- and R-based program designed to perform comprehensive downstream analyses of methylation data, providing an intuitive analysis platform for scientists unfamiliar with DNA methylation analysis. It is available as either a standalone version for command-line uses or a graphical user interface (GUI) and is publicly accessible at https://github.com/RitataLU/MethylC-analyzer .

Project description:BackgroundUpstream open reading frames (uORFs) in the 5'-untranslated regions (5'-UTRs) of certain eukaryotic mRNAs encode evolutionarily conserved functional peptides, such as cis-acting regulatory peptides that control translation of downstream main ORFs (mORFs). For genome-wide searches for uORFs with conserved peptide sequences (CPuORFs), comparative genomic studies have been conducted, in which uORF sequences were compared between selected species. To increase chances of identifying CPuORFs, we previously developed an approach in which uORF sequences were compared using BLAST between Arabidopsis and any other plant species with available transcript sequence databases. If this approach is applied to multiple plant species belonging to phylogenetically distant clades, it is expected to further comprehensively identify CPuORFs conserved in various plant lineages, including those conserved among relatively small taxonomic groups.ResultsTo efficiently compare uORF sequences among many species and efficiently identify CPuORFs conserved in various taxonomic lineages, we developed a novel pipeline, ESUCA. We applied ESUCA to the genomes of five angiosperm species, which belong to phylogenetically distant clades, and selected CPuORFs conserved among at least three different orders. Through these analyses, we identified 89 novel CPuORF families. As expected, ESUCA analysis of each of the five angiosperm genomes identified many CPuORFs that were not identified from ESUCA analyses of the other four species. However, unexpectedly, these CPuORFs include those conserved across wide taxonomic ranges, indicating that the approach used here is useful not only for comprehensive identification of narrowly conserved CPuORFs but also for that of widely conserved CPuORFs. Examination of the effects of 11 selected CPuORFs on mORF translation revealed that CPuORFs conserved only in relatively narrow taxonomic ranges can have sequence-dependent regulatory effects, suggesting that most of the identified CPuORFs are conserved because of functional constraints of their encoded peptides.ConclusionsThis study demonstrates that ESUCA is capable of efficiently identifying CPuORFs likely to be conserved because of the functional importance of their encoded peptides. Furthermore, our data show that the approach in which uORF sequences from multiple species are compared with those of many other species, using ESUCA, is highly effective in comprehensively identifying CPuORFs conserved in various taxonomic ranges.

Project description:The number of metagenomic studies conducted each year is growing dramatically. Storage and analysis of such big data is difficult and time-consuming. Interestingly, analysis shows that environmental and human metagenomes include a significant amount of non-annotated sequences, representing a 'dark matter.' We established a bioinformatics pipeline that automatically detects metagenome reads matching query sequences from a given set and applied this tool to the detection of sequences matching large and giant DNA viral members of the proposed order Megavirales or virophages. A total of 1,045 environmental and human metagenomes (? 1 Terabase) were collected, processed, and stored on our bioinformatics server. In addition, nucleotide and protein sequences from 93 Megavirales representatives, including 19 giant viruses of amoeba, and 5 virophages, were collected. The pipeline was generated by scripts written in Python language and entitled MG-Digger. Metagenomes previously found to contain megavirus-like sequences were tested as controls. MG-Digger was able to annotate 100s of metagenome sequences as best matching those of giant viruses. These sequences were most often found to be similar to phycodnavirus or mimivirus sequences, but included reads related to recently available pandoraviruses, Pithovirus sibericum, and faustoviruses. Compared to other tools, MG-Digger combined stand-alone use on Linux or Windows operating systems through a user-friendly interface, implementation of ready-to-use customized metagenome databases and query sequence databases, adjustable parameters for BLAST searches, and creation of output files containing selected reads with best match identification. Compared to Metavir 2, a reference tool in viral metagenome analysis, MG-Digger detected 8% more true positive Megavirales-related reads in a control metagenome. The present work shows that massive, automated and recurrent analyses of metagenomes are effective in improving knowledge about the presence and prevalence of giant viruses in the environment and the human body.

Dataset Information

MiRNA Digger: a comprehensive pipeline for genome-wide novel miRNA mining.

Publications

miRNA Digger: a comprehensive pipeline for genome-wide novel miRNA mining.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets