high-throughput, kingdom-wide prediction and annotation of bacterial non-coding rnas高通量,kingdom-wide预测和注释的细菌非编码rna.pdfVIP

High-Throughput, Kingdom-Wide Prediction and Annotation of Bacterial Non-Coding RNAs 1 2 2 3 Jonathan Livny , Hidayat Teonadi , Miron Livny , Matthew K. Waldor * 1 Channing Laboratories, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America, 2 Computer Sciences Department, University of Wisconsin-Madison, Madison, Wisconsin, United States of America, 3 Howard Hughes Medical Institute, Channing Laboratories, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America Abstract Background: Diverse bacterial genomes encode numerous small non-coding RNAs (sRNAs) that regulate myriad biological processes. While bioinformatic algorithms have proven effective in identifying sRNA-encoding loci, the lack of tools and infrastructure with which to execute these computationally demanding algorithms has limited their utilization. Genome- wide predictions of sRNA-encoding genes have been conducted in less than 3% of all sequenced bacterial strains, leading to critical gaps in current annotations. The relative paucity of genome-wide sRNA prediction represents a critical gap in current annotations of bacterial genomes and has limited examination of larger issues in sRNA biology, such as sRNA evolution. Methodology/Principal Findings: We have developed and deployed SIPHT, a high throughput computational tool that utilizes workflow management and distributed computing to effectively conduct kingdom-wide predictions and annotations of intergenic sRNA-encoding genes. Candidate sRNA-encoding loci are identified based on the presence of putative Rho-independent terminators downstream of conserved inte