RASER: reads aligner for SNPs and editing sites of RNA.
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ABSTRACT:
SUBMITTER: Ahn J
PROVIDER: S-EPMC4692970 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new aligner based on ideas from the Magic pipeline. RESULTS:Magic-BLAST uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. CONCLUSIONS:We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.
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Project description:We show that RNA editing sites can be called with high confidence using RNA sequencing data from multiple samples across either individuals or species, without the need for matched genomic DNA sequence. We identified many previously unidentified editing sites in both humans and Drosophila; our results nearly double the known number of human protein recoding events. We also found that human genes harboring conserved editing sites within Alu repeats are enriched for neuronal functions.
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Project description:RNA editing generates post-transcriptional sequence changes that can be deduced from RNA-seq data, but detection typically requires matched genomic sequence or multiple related expression data sets. We developed the GIREMI tool (genome-independent identification of RNA editing by mutual information; https://www.ibp.ucla.edu/research/xiao/GIREMI.html) to predict adenosine-to-inosine editing accurately and sensitively from a single RNA-seq data set of modest sequencing depth. Using GIREMI on existing data, we observed tissue-specific and evolutionary patterns in editing sites in the human population.
| S-EPMC4382388 | biostudies-literature
Project description:Although long-read RNA-seq is increasingly applied to characterize full-length transcripts it can also enable detection of nucleotide variants, such as genetic mutations or RNA editing sites, which is significantly under-explored. Here, we present an in-depth study to detect and analyze RNA editing sites in long-read RNA-seq. Our new method, L-GIREMI, effectively handles sequencing errors and read biases. Applied to PacBio RNA-seq data, L-GIREMI affords a high accuracy in RNA editing identification. Additionally, our analysis uncovered novel insights about RNA editing occurrences in single molecules and double-stranded RNA structures. L-GIREMI provides a valuable means to study nucleotide variants in long-read RNA-seq.
| S-EPMC10360234 | biostudies-literature
Project description:MOTIVATION:Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. RESULTS:To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. AVAILABILITY AND IMPLEMENTATION:STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
| S-EPMC3530905 | biostudies-literature
Project description:A-to-I RNA editing is an essential gene regulatory mechanism. Once thought to be a rare phenomenon only occurring in a few transcripts, the emergence of high-throughput RNA sequencing has facilitated the identification of over 2 million RNA editing sites in the human transcriptome. In this review, we survey the current RNA-seq based methods as well as historical methods used to identify RNA editing sites.
| S-EPMC5014717 | biostudies-literature
Project description:ADAR (adenosine deaminase that acts on RNA) editing enzymes target coding and noncoding double-stranded RNA (dsRNA) and are essential for neuronal function. Early studies showed that ADARs preferentially target adenosines with certain 5' and 3' neighbours. Here we use current Sanger sequencing protocols to perform a more accurate and quantitative analysis. We quantified editing sites in an ?800-bp dsRNA after reaction with human ADAR1 or ADAR2, or their catalytic domains alone. These large data sets revealed that neighbour preferences are mostly dictated by the catalytic domain, but ADAR2's dsRNA-binding motifs contribute to 3' neighbour preferences. For all proteins, the 5' nearest neighbour was most influential, but adjacent bases also affected editing site choice. We developed algorithms to predict editing sites in dsRNA of any sequence, and provide a web-based application. The predictive power of the algorithm on fully base-paired dsRNA, compared with biological substrates containing mismatches, bulges and loops, elucidates structural contributions to editing specificity.
| S-EPMC3113232 | biostudies-literature
Project description:RNA editing is an important co/post-transcriptional molecular process able to modify RNAs by nucleotide insertions/deletions or substitutions. In human, the most common RNA editing event involves the deamination of adenosine (A) into inosine (I) through the adenosine deaminase acting on RNA proteins. Although A-to-I editing can occur in both coding and non-coding RNAs, recent findings, based on RNA-seq experiments, have clearly demonstrated that a large fraction of RNA editing events alter non-coding RNAs sequences including untranslated regions of mRNAs, introns, long non-coding RNAs (lncRNAs), and low molecular weight RNAs (tRNA, miRNAs, and others). An accurate detection of A-to-I events occurring in non-coding RNAs is of utmost importance to clarify yet unknown functional roles of RNA editing in the context of gene expression regulation and maintenance of cell homeostasis. In the last few years, massive transcriptome sequencing has been employed to identify putative RNA editing changes at genome scale. Despite several efforts, the computational prediction of A-to-I sites in complete eukaryotic genomes is yet a challenging task. We have recently developed a software package, called REDItools, in order to simplify the detection of RNA editing events from deep sequencing data. In the present work, we show the potential of our tools in recovering A-to-I candidates from RNA-Seq experiments as well as guidelines to improve the RNA editing detection in non-coding RNAs, with specific attention to the lncRNAs.
| S-EPMC4257104 | biostudies-literature
Project description:The relationship between human inherited genomic variations and phenotypic differences has been the focus of much research effort in recent years. These studies benefit from millions of single-nucleotide polymorphism (SNP) records available in public databases, such as dbSNP. The importance of identifying false dbSNP records increases with the growing role played by SNPs in linkage analysis for disease traits. In particular, the emerging understanding of the abundance of DNA and RNA editing calls for a careful distinction between inherited SNPs and somatic DNA and RNA modifications. In order to demonstrate that some of the SNP database records are actually somatic modification, we focus on one type of these modifications, namely A-to-I RNA editing, and present evidence for hundreds of dbSNP records that are actually editing sites. We provide a list of 102 RNA editing sites previously annotated in dbSNP database as SNPs, and experimentally validate seven of these. Interestingly, we show how dbSNP can serve as a starting point to look for new editing sites. Our results, for this particular type of RNA editing, demonstrate the need for a careful analysis of SNP databases in light of the increasing recognition of the significance of somatic sequence modifications.
| S-EPMC1185576 | biostudies-literature
Project description:BackgroundThe widely observed RNA-DNA differences (RDDs) have been found to be due to nucleotide alteration by RNA editing. Canonical RNA editing (i.e., A-to-I and C-to-U editing) mediated by the adenosine deaminases acting on RNA (ADAR) family and apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) family during the transcriptional process is considered common and essential for the development of an individual. To date, an increasing number of RNA editing sites have been reported in human, rodents, and some farm animals; however, genome-wide detection of RNA editing events in sheep has not been reported. The aim of this study was to identify RNA editing events in sheep by comparing the RNA-seq and DNA-seq data from three biological replicates of the kidney and spleen tissues.ResultsA total of 607 and 994 common edited sites within the three biological replicates were identified in the ovine kidney and spleen, respectively. Many of the RDDs were specific to an individual. The RNA editing-related genes identified in the present study might be evolved for specific biological functions in sheep, such as structural constituent of the cytoskeleton and microtubule-based processes. Furthermore, the edited sites found in the ovine BLCAP and NEIL1 genes are in line with those in previous reports on the porcine and human homologs, suggesting the existence of evolutionarily conserved RNA editing sites and they may play an important role in the structure and function of genes.ConclusionsOur study is the first to investigate RNA editing events in sheep. We screened out 607 and 994 RNA editing sites in three biological replicates of the ovine kidney and spleen and annotated 164 and 247 genes in the kidney and spleen, respectively. The gene function and conservation analysis of these RNA editing-related genes suggest that RNA editing is associated with important gene function in sheep. The putative functionally important RNA editing sites reported in the present study will help future studies on the relationship between these edited sites and the genetic traits in sheep.
| S-EPMC6419479 | biostudies-literature