ABSTRACT
BACKGROUND: Sainfoin (Onobrychis viciifolia) is a highly nutritious tannin-containing forage legume. In the diet of ruminants sainfoin can have anti-parasitic effects and reduce methane emissions under in vitro conditions. Many of these benefits have been attributed to condensed tannins or proanthocyanidins in sainfoin. A combination of increased use of industrially produced nitrogen fertilizer, issues with establishment and productivity in the first year and more reliable alternatives, such as red clover led to a decline in the use of sainfoin since the middle of the last century. In recent years there has been a resurgence of interest in sainfoin due to its potential beneficial nutraceutical and environmental attributes. However, genomic resources are scarce, thus hampering progress in genetic analysis and improvement. To address this we have used next generation RNA sequencing technology to obtain the first transcriptome of sainfoin. We used the library to identify gene-based simple sequence repeats (SSRs) and potential single nucleotide polymorphisms (SNPs). RESULTS: One genotype from each of five sainfoin accessions was sequenced. Paired-end (PE) sequences were generated from cDNA libraries of RNA extracted from 7 day old seedlings. A combined assembly of 92,772 transcripts was produced de novo using the Trinity programme. About 18,000 transcripts were annotated with at least one GO (gene ontology) term. A total of 63 transcripts were annotated as involved in the tannin biosynthesis pathway. We identified 3786 potential SSRs. SNPs were identified by mapping the reads of the individual assemblies against the combined assembly. After stringent filtering a total of 77,000 putative SNPs were identified. A phylogenetic analysis of single copy number genes showed that sainfoin was most closely related to red clover and Medicago truncatula, while Lotus japonicus, bean and soybean are more distant relatives. CONCLUSIONS: This work describes the first transcriptome assembly in sainfoin. The 92 K transcripts provide a rich source of SNP and SSR polymorphisms for future use in genetic studies of this crop. Annotation of genes involved in the condensed tannin biosynthesis pathway has provided the basis for further studies of the genetic control of this important trait in sainfoin.
ABSTRACT
BACKGROUND: DNA methylation is an important regulator of gene expression and chromatin structure. Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is commonly used to identify regions of DNA methylation in eukaryotic genomes. Within MeDIP-Seq libraries, methylated cytosines can be found in both double-stranded (symmetric) and single-stranded (asymmetric) genomic contexts. While symmetric CG methylation has been relatively well-studied, asymmetric methylation in any dinucleotide context has received less attention. Importantly, no currently available software for processing MeDIP-Seq reads is able to resolve these strand-specific DNA methylation signals. Here we introduce DISMISS, a new software package that detects strand-associated DNA methylation from existing MeDIP-Seq analyses. RESULTS: Using MeDIP-Seq datasets derived from Apis mellifera (honeybee), an invertebrate species that contains more asymmetric- than symmetric- DNA methylation, we demonstrate that DISMISS can identify strand-specific DNA methylation signals with similar accuracy as bisulfite sequencing (BS-Seq; single nucleotide resolution methodology). Specifically, DISMISS is able to confidently predict where DNA methylation predominates (plus or minus DNA strands - asymmetric DNA methylation; plus and minus DNA stands - symmetric DNA methylation) in MeDIP-Seq datasets derived from A. mellifera samples. When compared to DNA methylation data derived from BS-Seq analysis of A. mellifera worker larva, DISMISS-mediated identification of strand-specific methylated cytosines is 80 % accurate. Furthermore, DISMISS can correctly (p <0.0001) detect the origin (sense vs antisense DNA strands) of DNA methylation at splice site junctions in A. mellifera MeDIP-Seq datasets with a precision close to BS-Seq analysis. Finally, DISMISS-mediated identification of DNA methylation signals associated with upstream, exonic, intronic and downstream genomic loci from A. mellifera MeDIP-Seq datasets outperforms MACS2 (Model-based Analysis of ChIP-Seq2; a commonly used MeDIP-Seq analysis software) and closely approaches the results achieved by BS-Seq. CONCLUSIONS: While asymmetric DNA methylation is increasingly being found in growing numbers of eukaryotic species and is the predominant pattern observed in some invertebrate genomes, it has been difficult to detect in MeDIP-Seq datasets using existing software. DISMISS now enables more sensitive examinations of MeDIP-Seq datasets and will be especially useful for the study of genomes containing either low levels of DNA methylation or for genomes containing relatively high amounts of asymmetric methylation.
