Genome-wide identification of the Phaseolus vulgaris sRNAome using small RNA and degradome sequencing

Background

MiRNAs and phasiRNAs are negative regulators of gene expression. These small RNAs have been extensively studied in plant model species but only 10 mature microRNAs are present in miRBase version 21, the most used miRNA database, and no phasiRNAs have been identified for the model legume Phaseolus vulgaris. Thanks to the recent availability of the first version of the common bean genome, degradome data and small RNA libraries, we are able to present here a catalog of the microRNAs and phasiRNAs for this organism and, particularly, we suggest new protagonists in the symbiotic nodulation events.

Results

We identified a set of 185 mature miRNAs, including 121 previously unpublished sequences, encoded by 307 precursors and distributed in 98 families. Degradome data allowed us to identify a total of 181 targets for these miRNAs. We reveal two regulatory networks involving conserved miRNAs: those known to play crucial roles in the establishment of nodules, and novel miRNAs present only in common bean, suggesting a specific role for these sequences. In addition, we identified 125 loci that potentially produce phased small RNAs, with 47 of them having all the characteristics of being triggered by a total of 31 miRNAs, including 14 new miRNAs identified in this study.

Conclusions

We provide here a set of new small RNAs that contribute to the broader knowledge of the sRNAome of Phaseolus vulgaris. Thanks to the identification of the miRNA targets from degradome analysis and the construction of regulatory networks between the mature microRNAs, we present here the probable functional regulation associated with the sRNAome and, particularly, in N₂-fixing symbiotic nodules.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1639-5) contains supplementary material, which is available to authorized users.     

Exome sequencing as a tool for Mendelian disease gene discovery

Exome sequencing - the targeted sequencing of the subset of the human genome that is protein coding - is a powerful and cost-effective new tool for dissecting the genetic basis of diseases and traits that have proved to be intractable to conventional gene-discovery strategies. Over the past 2 years, experimental and analytical approaches relating to exome sequencing have established a rich framework for discovering the genes underlying unsolved Mendelian disorders. Additionally, exome sequencing is being adapted to explore the extent to which rare alleles explain the heritability of complex diseases and health-related traits. These advances also set the stage for applying exome and whole-genome sequencing to facilitate clinical diagnosis and personalized disease-risk profiling.     

ACE-it: a tool for genome-wide integration of gene dosage and RNA expression data

SUMMARY: We describe a tool, called ACE-it (Array CGH Expression integration tool). ACE-it links the chromosomal position of the gene dosage measured by array CGH to the genes measured by the expression array. ACE-it uses this link to statistically test whether gene dosage affects RNA expression. AVAILABILITY: ACE-it is freely available at http://ibivu.cs.vu.nl/programs/acewww/.     

Protein microarrays as a discovery tool for studying protein-protein interactions

Exploring the function of the genome and the encoded proteins has emerged as a new and exciting challenge in the postgenomic era. Novel technologies come into view that promise to be valuable for the investigation not only of single proteins, but of entire protein networks. Protein microarrays are the innovative assay platform for highly parallel in vitro studies of protein-protein interactions. Due to their flexibility and multiplexing capacity, protein microarrays benefit basic research, diagnosis and biomedicine. This review provides an overview on the basic principles of protein microarrays and their potential to multiplex protein-protein interaction studies.     

miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs

miRDeepFinder is a software package developed to identify and functionally analyze plant microRNAs (miRNAs) and their targets from small RNA datasets obtained from deep sequencing. The functions available in miRDeepFinder include pre-processing of raw data, identifying conserved miRNAs, mining and classifying novel miRNAs, miRNA expression profiling, predicting miRNA targets, and gene pathway and gene network analysis involving miRNAs. The fundamental design of miRDeepFinder is based on miRNA biogenesis, miRNA-mediated gene regulation and target recognition, such as perfect or near perfect hairpin structures, different read abundances of miRNA and miRNA*, and targeting patterns of plant miRNAs. To test the accuracy and robustness of miRDeepFinder, we analyzed a small RNA deep sequencing dataset of Arabidopsis thaliana published in the GEO database of NCBI. Our test retrieved 128 of 131 (97.7%) known miRNAs that have a more than 3 read count in Arabidopsis. Because many known miRNAs are not associated with miRNA*s in small RNA datasets, miRDeepFinder was also designed to recover miRNA candidates without the presence of miRNA*. To mine as many miRNAs as possible, miRDeepFinder allows users to compare mature miRNAs and their miRNA*s with other small RNA datasets from the same species. Cleaveland software package was also incorporated into miRDeepFinder for miRNA target identification using degradome sequencing analysis. Using this new computational tool, we identified 13 novel miRNA candidates with miRNA*s from Arabidopsis and validated 12 of them experimentally. Interestingly, of the 12 verified novel miRNAs, a miRNA named AC1 spans the exons of two genes (UTG71C4 and UGT71C3). Both the mature AC1 miRNA and its miRNA* were also found in four other small RNA datasets. We also developed a tool, ??miRNA primer designer?? to design primers for any type of miRNAs. miRDeepFinder provides a powerful tool for analyzing small RNA datasets from all species, with or without the availability of genome information. miRDeepFinder and miRNA primer designer are freely available at http://www.leonxie.com/DeepFinder.php and at http://www.leonxie.com/miRNAprimerDesigner.php, respectively. A program (called RefFinder: http://www.leonxie.com/referencegene.php) was also developed for assessing the reliable reference genes for gene expression analysis, including miRNAs.     

Genephony: a knowledge management tool for genome-wide research

Background  

One of the consequences of the rapid and widespread adoption of high-throughput experimental technologies is an exponential increase of the amount of data produced by genome-wide experiments. Researchers increasingly need to handle very large volumes of heterogeneous data, including both the data generated by their own experiments and the data retrieved from publicly available repositories of genomic knowledge. Integration, exploration, manipulation and interpretation of data and information therefore need to become as automated as possible, since their scale and breadth are, in general, beyond the limits of what individual researchers and the basic data management tools in normal use can handle. This paper describes Genephony, a tool we are developing to address these challenges.     

GCTA: a tool for genome-wide complex trait analysis

For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.     

Construction of small RNA cDNA libraries for deep sequencing

Small RNAs (21-24 nucleotides) including microRNAs (miRNAs) and small interfering RNAs (siRNAs) are potent regulators of gene expression in both plants and animals. Several hundred genes encoding miRNAs and thousands of siRNAs have been experimentally identified by cloning approaches. New sequencing technologies facilitate the identification of these molecules and provide global quantitative expression data in a given biological sample. Here, we describe the methods used in our laboratory to construct small RNA cDNA libraries for high-throughput sequencing using technologies such as MPSS, 454 or SBS.     

Mining small RNA sequencing data: a new approach to identify small nucleolar RNAs in Arabidopsis

Small nucleolar RNAs (snoRNAs) are noncoding RNAs that direct 2′-O-methylation or pseudouridylation on ribosomal RNAs or spliceosomal small nuclear RNAs. These modifications are needed to modulate the activity of ribosomes and spliceosomes. A comprehensive repertoire of snoRNAs is needed to expand the knowledge of these modifications. The sequences corresponding to snoRNAs in 18–26-nt small RNA sequencing data have been rarely explored and remain as a hidden treasure for snoRNA annotation. Here, we showed the enrichment of small RNAs at Arabidopsis snoRNA termini and developed a computational approach to identify snoRNAs on the basis of this characteristic. The approach successfully uncovered the full-length sequences of 144 known Arabidopsis snoRNA genes, including some snoRNAs with improved 5′- or 3′-end annotation. In addition, we identified 27 and 17 candidates for novel box C/D and box H/ACA snoRNAs, respectively. Northern blot analysis and sequencing data from parallel analysis of RNA ends confirmed the expression and the termini of the newly predicted snoRNAs. Our study especially expanded on the current knowledge of box H/ACA snoRNAs and snoRNA species targeting snRNAs. In this study, we demonstrated that the use of small RNA sequencing data can increase the complexity and the accuracy of snoRNA annotation.     

nWayComp: a genome-wide sequence comparison tool for multiple strains/species of phylogenetically related microorganisms

The increasing number of whole genomic sequences of microorganisms has led to the complexity of genome-wide annotation and gene sequence comparison among multiple microorganisms. To address this problem, we have developed nWayComp software that compares DNA and protein sequences of phylogenetically-related microorganisms. This package integrates a series of bioinformatics tools such as BLAST, ClustalW, ALIGN, PHYLIP and PRIMER3 for sequence comparison. It searches for homologous sequences among multiple organisms and identifies genes that are unique to a particular organism. The homologous gene sets are then ranked in the descending order of the sequence similarity. For each set of homologous sequences, a table of sequence identity among homologous genes along with sequence variations such as SNPs and INDELS is developed, and a phylogenetic tree is constructed. In addition, a common set of primers that can amplify all the homologous sequences are generated. The nWayComp package provides users with a quick and convenient tool to compare genomic sequences among multiple organisms at the whole-genome level.