GASS: genome structural annotation for Eukaryotes based on species similarity

Abstract

BackgroundWith the development of high-throughput sequencing techniques, more and more genomes were sequenced and assembled. However, annotating a genome’s structure rapidly and expressly remains challenging. Current eukaryotic genome annotations require various, abundant supporting data, such as: species-specific and cross-species protein sequences, ESTs, cDNA and RNA-Seq data. Collecting those data and merging their analytical results to achieve a consistent complete annotation is a complex, time and cost consuming task.ResultsIn our study, we proposed a fast and easy-to-use computational tool: GASS (Genome Annotation based on Species Similarity). It annotates a eukaryotic genome based on only the annotations from another similar species. With aligning the exons’ sequences of an annotated similar species to the un-annotated genome, GASS detects the optimal transcript annotations with a shortest-path model. In our study, GASS was used to achieve the rhesus annotations based on the human annotations. The produced annotations were evaluated by comparing them to the two existing rhesus annotation databases (RefSeq and Ensembl) directly and being aligned with three RNA-Seq data of rhesus. The experiment results showed that more than 65% RefSeq exons and splicing junctions were exactly found by GASS. GASS’s sensitivity was higher than RefSeq’s, and was close to Ensembl’s. GASS had higher specificities than Ensembl at gene, transcript, exon and splicing junction levels. We also found the mis-assemblies of rheMac3 genome, which led to the 2 bp shifts in annotating position on exons’ boundary and then the incomplete splicing canonical sites in Refseq annotations. These detections were further supported by various data sources.ConclusionsGASS quickly produces structural genome annotations in sufficient abundance and accuracy. With simple and rapid running of GASS, small labs can create quick views of genome annotations for an un-annotated species, without the necessity to create, collect, analyze and synthesize extra various data sources, or wait several months for the annotations from professional organizations. GASS can be applied to many study occasions, such as the analysis of RNA-Seq datasets from the unannotated species whose genome drafts are available but the annotations are not.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1353-3) contains supplementary material, which is available to authorized users.

Sequencing and assembly of the Egyptian buffalo genome

“…The second phase was performed using homology-based prediction by transferring the cattle's annotation to our assembly. The program GASS [32] was then used to map the gene sequences to the draft genome. In the final stage, extra genes were identified using the de novo prediction Augustus program [33].…”

Review on the Computational Genome Annotation of Sequences Obtained by Next-Generation Sequencing

“…Ab initio annotations, on the other hand, identify genes and their structures using mathematical models. Nonetheless, the ab initio gene predictors have to be trained using high-quality gene models or organism-specific genome traits, such as codon frequency and intron–exon length distribution [ 45 ]. Further, ab initio models require ESTs, RNA-seq data, and proteins to improve prediction accuracy.…”

Twelve quick steps for genome assembly and annotation in the classroom

“…While Fig 1 and Table 3 provide a summary of useful tools with key features, it is highly recommended to be familiar with the regular update of public databases and pipelines. In addition, understanding the performance and capability of various analysis from a detailed comparison and instructions of common features of annotation tools could be a very important factor for a successful genome annotation, structurally [7,111,[114][115][116][117] and functionally [8,[118][119][120][121][122][123].…”