A novel computational approach for genome-wide prediction of small RNAs in bacteria

Lei Li; Hoi Shan Kwan

doi:10.1101/011668

Home/Archive/bioRxiv

bioRxiv · 10.1101/011668

A novel computational approach for genome-wide prediction of small RNAs in bacteria

Lei Li·Hoi Shan Kwan

View original source ↗Export metadata ↓

Abstract

Small regulatory RNAs (sRNAs) are the most abundant post-transcriptional regulators in bacteria. They serve ubiquitous roles that control nearly every aspects of bacterial physiology. Identification of important features from sRNAs sequences will guide the computational prediction of new sRNA sequences for a better understanding of the pervasive sRNA-mediated regulation in bacteria. In this study, we have performed systematic analyses of many sequence and structural features that are possibly related to sRNA properties and identified a subset of significant features that effectively discriminate sRNAs sequences from random sequences. we then used a neural network model that integrated these subfeatures on unlabeled testing datasets, and it had achieved a 92.2% recall and 89.8% specificity. Finally, we applied this prediction model for genome-wide identification of sRNAs-encoded genes using a sliding-window approach. We recovered multiple known sRNAs and hundreds of predicted new sRNAs. These candidate novel sRNAs deserve extensive study to better understand the sRNA-mediated regulatory network in bacteria.

Explore related subjects

Bioinformatics Preprint

Keep this discovery

BibTeX RIS

Lei Li, Hoi Shan Kwan. 2014-11-19. A novel computational approach for genome-wide prediction of small RNAs in bacteria. https://doi.org/10.1101/011668

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2016

iCARE: An R Package to Build and Apply Absolute Risk Models

This report describes a R package, called the Individualized Coherent Absolute Risk Estimation (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individuals risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: (1) a model for relative risk, (2) an age-specific disease incidence rate, (3) the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.

Bioinformatics↗

bioRxiv2016

deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

SummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.\n\nAvailabilitythe C++ source code of deSPI is available at https://github.com/hitbc/deSPI\n\nContactydwang@hit.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗

bioRxiv2016

De novo assembly of viral quasispecies using overlap graphs

A viral quasispecies, the ensemble of viral strains populating an infected person, can be highly diverse. For optimal assessment of virulence, pathogenesis and therapy selection, determining the haplotypes of the individual strains can play a key role. As many viruses are subject to high mutation and recombination rates, high-quality reference genomes are often not available at the time of a new disease outbreak. We present SAVAGE, a computational tool for reconstructing individual haplotypes of intrahost virus strains without the need for a high-quality reference genome. SAVAGE makes use of either FM-index based data structures or ad-hoc consensus reference sequence for constructing overlap graphs from patient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph. In benchmark experiments on simulated and on real deep coverage data, SAV-AGE drastically outperforms generic de novo assemblers as well as the only specialized de novo viral quasispecies assembler available so far. When run on ad-hoc consensus reference sequence, SAVAGE performs very favorably in comparison with state-of-the-art reference genome guided tools. We also apply SAVAGE on two deep coverage samples of patients infected by the Zika and the hepatitis C virus, respectively, which sheds light on the genetic structures of the respective viral quasispecies.

Bioinformatics↗