Annotation

Next-generation sequencing has been a huge benefit to investigators studying non-model species. High-throughput gene expression studies, which were once restricted to animals with extensive genomic resources, can now be applied to any species. Transcriptomic studies using RNA-Seq can discover hundreds ...

RNA-seq based on short reads generated by next generation sequencing technologies has become the main approach to study differential gene expression. Until now, the main applications of this technique have been to study the variation of gene expression in a ...

Targeted RNA sequencing (CaptureSeq) uses oligonucleotide probes to capture RNAs for sequencing, providing enriched read coverage, accurate measurement of gene expression, and quantitative expression data. Researchers from the European Bioinformatics Institute applied CaptureSeq to refine transcript annotations in the current ...

Next generation sequencing of cellular RNA is making it possible to characterize genes and alternative splicing in unprecedented detail. However, designing bioinformatics tools to accurately capture splicing variation has proven difficult. Current programs can find major isoforms of a gene ...

RNA editing is one of the post- or co-transcriptional processes that can lead to amino acid substitutions in protein sequences, alternative pre-mRNA splicing, and changes in gene expression levels. Although several methods have been suggested to identify RNA editing sites, ...

The advent of next-generation RNA sequencing (RNA-seq) has greatly advanced transcriptomic studies, including system-wide identification and quantification of mRNA isoforms under various biological conditions. A number of computational methods have been developed to systematically identify mRNA isoforms in a high-throughput ...

Advances in second-generation sequencing of RNA made a near-complete characterization of transcriptomes affordable. However, the reconstruction of full-length mRNAs via de novo RNA-seq assembly is still difficult due to the complexity of eukaryote transcriptomes with highly similar paralogs and multiple ...

SNPs (Single Nucleotide Polymorphisms) are genetic markers whose precise identification is a prerequisite for association studies. Methods to identify them are currently well developed for model species, but rely on the availability of a (good) reference genome, and therefore cannot ...

Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction ...

Sequencing is widely used to discover associations between microRNAs (miRNAs) and diseases. However, the negative binomial distribution (NB) and high dimensionality of data obtained using sequencing can lead to low-power results and low reproducibility. Several statistical learning algorithms have been ...