Limits...
GASS: genome structural annotation for Eukaryotes based on species similarity.

Wang Y, Chen L, Song N, Lei X - BMC Genomics (2015)

Bottom Line: The experiment results showed that more than 65% RefSeq exons and splicing junctions were exactly found by GASS.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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation, School of Information Science and Technology, Xiamen University, Xiamen, Fujian, 361005, China. wangying@xmu.edu.cn.

ABSTRACT

Background: With 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.

Results: In 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.

Conclusions: GASS 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.

Show MeSH
GASS processing pipeline. A) exon-sequences of a transcript from AG are aligned to the un-annotated genome with BLAST. And each exon might be aligned to multiple similar regions in UG. B) the search for optimal exon-combination for a transcript is modelled as a shortest path problem. This problem can be solved with dynamic programming algorithm. C) after quality control, the transcript annotations and the corresponding sequences are extracted from the un-annotated genome sequences and organized as UCSC genome format.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4352269&req=5

Fig1: GASS processing pipeline. A) exon-sequences of a transcript from AG are aligned to the un-annotated genome with BLAST. And each exon might be aligned to multiple similar regions in UG. B) the search for optimal exon-combination for a transcript is modelled as a shortest path problem. This problem can be solved with dynamic programming algorithm. C) after quality control, the transcript annotations and the corresponding sequences are extracted from the un-annotated genome sequences and organized as UCSC genome format.

Mentions: GASS produces the genome annotations with the processing pipeline shown in Figure 1. ① Exon-sequences (denoted as ET) of a transcript (denoted as T) of the annotated species (denoted as AG) are aligned to the un-annotated genome (denoted as UG) with BLAST, as in Figure 1(A). The purpose is to find the similar regions in UG to ET in AG. And each exon might have multiple similar regions in UG. ② As in Figure 1(B), the search for optimal exon-combination for a transcript is modelled as a shortest path problem. This problem can be solved with dynamic programming algorithm. ③ As in Figure 1(C), after quality control, the transcript annotations and the corresponding sequences are extracted from the un-annotated genome sequences and organized as UCSC genome format [19]. The detail of each step is described in the following subsections.Figure 1


GASS: genome structural annotation for Eukaryotes based on species similarity.

Wang Y, Chen L, Song N, Lei X - BMC Genomics (2015)

GASS processing pipeline. A) exon-sequences of a transcript from AG are aligned to the un-annotated genome with BLAST. And each exon might be aligned to multiple similar regions in UG. B) the search for optimal exon-combination for a transcript is modelled as a shortest path problem. This problem can be solved with dynamic programming algorithm. C) after quality control, the transcript annotations and the corresponding sequences are extracted from the un-annotated genome sequences and organized as UCSC genome format.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4352269&req=5

Fig1: GASS processing pipeline. A) exon-sequences of a transcript from AG are aligned to the un-annotated genome with BLAST. And each exon might be aligned to multiple similar regions in UG. B) the search for optimal exon-combination for a transcript is modelled as a shortest path problem. This problem can be solved with dynamic programming algorithm. C) after quality control, the transcript annotations and the corresponding sequences are extracted from the un-annotated genome sequences and organized as UCSC genome format.
Mentions: GASS produces the genome annotations with the processing pipeline shown in Figure 1. ① Exon-sequences (denoted as ET) of a transcript (denoted as T) of the annotated species (denoted as AG) are aligned to the un-annotated genome (denoted as UG) with BLAST, as in Figure 1(A). The purpose is to find the similar regions in UG to ET in AG. And each exon might have multiple similar regions in UG. ② As in Figure 1(B), the search for optimal exon-combination for a transcript is modelled as a shortest path problem. This problem can be solved with dynamic programming algorithm. ③ As in Figure 1(C), after quality control, the transcript annotations and the corresponding sequences are extracted from the un-annotated genome sequences and organized as UCSC genome format [19]. The detail of each step is described in the following subsections.Figure 1

Bottom Line: The experiment results showed that more than 65% RefSeq exons and splicing junctions were exactly found by GASS.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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation, School of Information Science and Technology, Xiamen University, Xiamen, Fujian, 361005, China. wangying@xmu.edu.cn.

ABSTRACT

Background: With 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.

Results: In 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.

Conclusions: GASS 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.

Show MeSH