We propose a new method for smallRNAs (sRNAs) identification. First we build an effective target genome (ETG) by means of a strand-specific procedure. Then we propose a new bioinformatic pipeline based mainly on the combination of two types of information: the first provides an expression map based on RNA-seq data (Reads Map) and the second applies principles of comparative genomics leading to a Conservation Map. By superimposing these two maps, a robust method for the search of sRNAs is obtained. We apply this methodology to investigate sRNAs in Mycobacterium tuberculosis H37Rv. This bioinformatic procedure leads to a total list of 1948 candidate sRNAs. The size of the candidate list is strictly related to the aim of the study and to the technology used during the verification process. We provide performance measures of the algorithm in identifying annotated sRNAs reported in three recent published studies.

A genome-wide identification analysis of small regulatory RNAs in Mycobacterium tuberculosis by RNA-Seq and conservation analysis

AMBROSI, ALESSANDRO;DI SERIO, MARIACLELIA
2012-01-01

Abstract

We propose a new method for smallRNAs (sRNAs) identification. First we build an effective target genome (ETG) by means of a strand-specific procedure. Then we propose a new bioinformatic pipeline based mainly on the combination of two types of information: the first provides an expression map based on RNA-seq data (Reads Map) and the second applies principles of comparative genomics leading to a Conservation Map. By superimposing these two maps, a robust method for the search of sRNAs is obtained. We apply this methodology to investigate sRNAs in Mycobacterium tuberculosis H37Rv. This bioinformatic procedure leads to a total list of 1948 candidate sRNAs. The size of the candidate list is strictly related to the aim of the study and to the technology used during the verification process. We provide performance measures of the algorithm in identifying annotated sRNAs reported in three recent published studies.
2012
ESCHERICHIA-COLI; HIGH-THROUGHPUT; NONCODING RNAS; ALIGNMENT; BACTERIA; SEQUENCE; REGIONS; EXPRESSION; PREDICTION; DATABASE
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/47439
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