Dataset Information

Chimeric transcript discovery by paired-end transcriptome sequencing.

ABSTRACT: Recurrent gene fusions are a prevalent class of mutations arising from the juxtaposition of 2 distinct regions, which can generate novel functional transcripts that could serve as valuable therapeutic targets in cancer. Therefore, we aim to establish a sensitive, high-throughput methodology to comprehensively catalog functional gene fusions in cancer by evaluating a paired-end transcriptome sequencing strategy. Not only did a paired-end approach provide a greater dynamic range in comparison with single read based approaches, but it clearly distinguished the high-level "driving" gene fusions, such as BCR-ABL1 and TMPRSS2-ERG, from potential lower level "passenger" gene fusions. Also, the comprehensiveness of a paired-end approach enabled the discovery of 12 previously undescribed gene fusions in 4 commonly used cell lines that eluded previous approaches. Using the paired-end transcriptome sequencing approach, we observed read-through mRNA chimeras, tissue-type restricted chimeras, converging transcripts, diverging transcripts, and overlapping mRNA transcripts. Last, we successfully used paired-end transcriptome sequencing to detect previously undescribed ETS gene fusions in prostate tumors. Together, this study establishes a highly specific and sensitive approach for accurately and comprehensively cataloguing chimeras within a sample using paired-end transcriptome sequencing.

SUBMITTER: Maher CA

PROVIDER: S-EPMC2708976 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundHouttuynia cordata Thunb. is an important traditional medical herb in China and other Asian countries, with high medicinal and economic value. However, a lack of available genomic information has become a limitation for research on this species. Thus, we carried out high-throughput transcriptomic sequencing of H. cordata to generate an enormous transcriptome sequence dataset for gene discovery and molecular marker development.Principal findingsIllumina paired-end sequencing technology produced over 56 million sequencing reads from H. cordata mRNA. Subsequent de novo assembly yielded 63,954 unigenes, 39,982 (62.52%) and 26,122 (40.84%) of which had significant similarity to proteins in the NCBI nonredundant protein and Swiss-Prot databases (E-value <10(-5)), respectively. Of these annotated unigenes, 30,131 and 15,363 unigenes were assigned to gene ontology categories and clusters of orthologous groups, respectively. In addition, 24,434 (38.21%) unigenes were mapped onto 128 pathways using the KEGG pathway database and 17,964 (44.93%) unigenes showed homology to Vitis vinifera (Vitaceae) genes in BLASTx analysis. Furthermore, 4,800 cDNA SSRs were identified as potential molecular markers. Fifty primer pairs were randomly selected to detect polymorphism among 30 samples of H. cordata; 43 (86%) produced fragments of expected size, suggesting that the unigenes were suitable for specific primer design and of high quality, and the SSR marker could be widely used in marker-assisted selection and molecular breeding of H. cordata in the future.ConclusionsThis is the first application of Illumina paired-end sequencing technology to investigate the whole transcriptome of H. cordata and to assemble RNA-seq reads without a reference genome. These data should help researchers investigating the evolution and biological processes of this species. The SSR markers developed can be used for construction of high-resolution genetic linkage maps and for gene-based association analyses in H. cordata. This work will enable future functional genomic research and research into the distinctive active constituents of this genus.

Project description:BACKGROUND: Biofuels extracted from the seeds of Camelina sativa have recently been used successfully as environmentally friendly jet-fuel to reduce greenhouse gas emissions. Camelina sativa is genetically very close to Arabidopsis thaliana, and both are members of the Brassicaceae. Although public databases are currently available for some members of the Brassicaceae, such as A. thaliana, A. lyrata, Brassica napus, B. juncea and B. rapa, there are no public Expressed Sequence Tags (EST) or genomic data for Camelina sativa. In this study, a high-throughput, large-scale RNA sequencing (RNA-seq) of the Camelina sativa transcriptome was carried out to generate a database that will be useful for further functional analyses. RESULTS: Approximately 27 million clean "reads" filtered from raw reads by removal of adaptors, ambiguous reads and low-quality reads (2.42 gigabase pairs) were generated by Illumina paired-end RNA-seq technology. All of these clean reads were assembled de novo into 83,493 unigenes and 103,196 transcripts using SOAPdenovo and Trinity, respectively. The average length of the transcripts generated by Trinity was 697 bp (N50 = 976), which was longer than the average length of unigenes (319 bp, N50 = 346 bp). Nonetheless, the assembly generated by SOAPdenovo produced similar number of non-redundant hits (22,435) with that of Trinity (22,433) in BLASTN searches of the Arabidopsis thaliana CDS sequence database (TAIR). Four public databases, the Kyoto Encyclopedia of Genes and Genomes (KEGG), Swiss-prot, NCBI non-redundant protein (NR), and the Cluster of Orthologous Groups (COG), were used for unigene annotation; 67,791 of 83,493 unigenes (81.2%) were finally annotated with gene descriptions or conserved protein domains that were mapped to 25,329 non-redundant protein sequences. We mapped 27,042 of 83,493 unigenes (32.4%) to 119 KEGG metabolic pathways. CONCLUSIONS: This is the first report of a transcriptome database for Camelina sativa, an environmentally important member of the Brassicaceae. We showed that C. savita is closely related to Arabidopsis spp. and more distantly related to Brassica spp. Although the majority of annotated genes had high sequence identity to those of A. thaliana, a substantial proportion of disease-resistance genes (NBS-encoding LRR genes) were instead more closely similar to the genes of other Brassicaceae; these genes included BrCN, BrCNL, BrNL, BrTN, BrTNL in B. rapa. As plant genomes are under long-term selection pressure from environmental stressors, conservation of these disease-resistance genes in C. sativa and B. rapa genomes implies that they are exposed to the threats from closely-related pathogens in their natural habitats.

Project description:BackgroundA cancer genome is derived from the germline genome through a series of somatic mutations. Somatic structural variants - including duplications, deletions, inversions, translocations, and other rearrangements - result in a cancer genome that is a scrambling of intervals, or "blocks" of the germline genome sequence. We present an efficient algorithm for reconstructing the block organization of a cancer genome from paired-end DNA sequencing data.ResultsBy aligning paired reads from a cancer genome - and a matched germline genome, if available - to the human reference genome, we derive: (i) a partition of the reference genome into intervals; (ii) adjacencies between these intervals in the cancer genome; (iii) an estimated copy number for each interval. We formulate the Copy Number and Adjacency Genome Reconstruction Problem of determining the cancer genome as a sequence of the derived intervals that is consistent with the measured adjacencies and copy numbers. We design an efficient algorithm, called Paired-end Reconstruction of Genome Organization (PREGO), to solve this problem by reducing it to an optimization problem on an interval-adjacency graph constructed from the data. The solution to the optimization problem results in an Eulerian graph, containing an alternating Eulerian tour that corresponds to a cancer genome that is consistent with the sequencing data. We apply our algorithm to five ovarian cancer genomes that were sequenced as part of The Cancer Genome Atlas. We identify numerous rearrangements, or structural variants, in these genomes, analyze reciprocal vs. non-reciprocal rearrangements, and identify rearrangements consistent with known mechanisms of duplication such as tandem duplications and breakage/fusion/bridge (B/F/B) cycles.ConclusionsWe demonstrate that PREGO efficiently identifies complex and biologically relevant rearrangements in cancer genome sequencing data. An implementation of the PREGO algorithm is available at http://compbio.cs.brown.edu/software/.

Dataset Information

Chimeric transcript discovery by paired-end transcriptome sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets