Deep sequencing analysis of transcription-induced chimeras in human prostate adenocarcinoma and reference samples
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE24284 | GEO | 2010/09/23
SECONDARY ACCESSION(S): PRJNA129993
REPOSITORIES: GEO
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE24282: CGH microarray analysis of human prostate adenocarcinoma and normal samples GSE24283: Deep transcriptional sequencing analysis of human prostate adenocarcinoma and reference samples Refer to individual Series
2010-09-22 | E-GEOD-24284 | biostudies-arrayexpress
Project description:BACKGROUND: Readthrough fusions across adjacent genes in the genome, or transcription-induced chimeras (TICs), have been estimated using expressed sequence tag (EST) libraries to involve 4-6% of all genes. Deep transcriptional sequencing (RNA-Seq) now makes it possible to study the occurrence and expression levels of TICs in individual samples across the genome. METHODS: We performed single-end RNA-Seq on three human prostate adenocarcinoma samples and their corresponding normal tissues, as well as brain and universal reference samples. We developed two bioinformatics methods to specifically identify TIC events: a targeted alignment method using artificial exon-exon junctions within 200,000 bp from adjacent genes, and genomic alignment allowing splicing within individual reads. We performed further experimental verification and characterization of selected TIC and fusion events using quantitative RT-PCR and comparative genomic hybridization microarrays. RESULTS: Targeted alignment against artificial exon-exon junctions yielded 339 distinct TIC events, including 32 gene pairs with multiple isoforms. The false discovery rate was estimated to be 1.5%. Spliced alignment to the genome was less sensitive, finding only 18% of those found by targeted alignment in 33-nt reads and 59% of those in 50-nt reads. However, spliced alignment revealed 30 cases of TICs with intervening exons, in addition to distant inversions, scrambled genes, and translocations. Our findings increase the catalog of observed TIC gene pairs by 66%.We verified 6 of 6 predicted TICs in all prostate samples, and 2 of 5 predicted novel distant gene fusions, both private events among 54 prostate tumor samples tested. Expression of TICs correlates with that of the upstream gene, which can explain the prostate-specific pattern of some TIC events and the restriction of the SLC45A3-ELK4 e4-e2 TIC to ERG-negative prostate samples, as confirmed in 20 matched prostate tumor and normal samples and 9 lung cancer cell lines. CONCLUSIONS: Deep transcriptional sequencing and analysis with targeted and spliced alignment methods can effectively identify TIC events across the genome in individual tissues. Prostate and reference samples exhibit a wide range of TIC events, involving more genes than estimated previously using ESTs. Tissue specificity of TIC events is correlated with expression patterns of the upstream gene. Some TIC events, such as MSMB-NCOA4, may play functional roles in cancer.
| S-EPMC3041646 | biostudies-literature
Project description:Prostate adenocarcinoma and matched adjacent normal samples were profiled by deep transcriptional sequencing to analyze transcription-induced chimeras and gene fusions. Reference samples from the MAQC and brain and universal reference libraries were also sequenced.
2010-09-23 | GSE24283 | GEO
Project description:Prostate adenocarcinoma and matched adjacent normal samples were profiled by deep transcriptional sequencing to analyze transcription-induced chimeras and gene fusions. Reference samples from the MAQC and brain and universal reference libraries were also sequenced. Two-condition experiment plus reference samples: Prostate adenocarcinoma versus matched normal from three separate patients, plus brain and universal reference samples from the MAQC project.
2010-09-23 | E-GEOD-24283 | biostudies-arrayexpress
Project description:UNLABELLED: Transcription-induced chimerism, a mechanism involving the transcription and intergenic splicing of two consecutive genes, has recently been estimated to account for approximately 5% of the human transcriptome. Despite this prevalence, the regulation and function of these fused transcripts remains largely uncharacterised. RESULTS: We identified three novel transcription-induced chimeras resulting from the intergenic splicing of a single RNA transcript incorporating the two neighbouring 3p21.3 tumour suppressor locus genes, RBM6 and RBM5, which encode the RNA Binding Motif protein 6 and RNA Binding Motif protein 5, respectively. Each of the three novel chimeric transcripts lacked exons 3, 6, 20 and 21 of RBM6 and exon 1 of RBM5. Differences between the transcripts were associated with the presence or absence of exon 4, exon 5 and a 17 nucleotide (nt) sequence from intron 10 of RBM6. All three chimeric transcripts incorporated the canonical splice sites from both genes (excluding the 17 nt intron 10 insertion). Differential expression was observed in tumour tissue compared to non-tumour tissue, and amongst tumour types. In breast tumour tissue, chimeric expression was associated with elevated levels of RBM6 and RBM5 mRNA, and increased tumour size. No protein expression was detected by in vitro transcription/translation. CONCLUSION: These results suggest that RBM6 mRNA experiences altered co-transcriptional gene regulation in certain cancers. The results also suggest that RBM6-RBM5 transcription-induced chimerism might be a process that is linked to the tumour-associated increased transcriptional activity of the RBM6 gene. It appears that none of the transcription-induced chimeras generates a protein product; however, the novel alternative splicing, which affects putative functional domains within exons 3, 6 and 11 of RBM6, does suggest that the generation of these chimeric transcripts has functional relevance. Finally, the association of chimeric expression with breast tumour size suggests that RBM6-RBM5 chimeric expression may be a potential tumour differentiation marker.
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Project description:Single-molecule long-read sequencing datasets were generated for a son-father-mother trio of Han Chinese descent that is part of the Genome in a Bottle (GIAB) consortium portfolio. The dataset was generated using the Pacific Biosciences Sequel System. The son and each parent were sequenced to an average coverage of 60 and 30, respectively, with N50 subread lengths between 16 and 18 kb. Raw reads and reads aligned to both the GRCh37 and GRCh38 are available at the NCBI GIAB ftp site (ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/). The GRCh38 aligned read data are archived in NCBI SRA (SRX4739017, SRX4739121, and SRX4739122). This dataset is available for anyone to develop and evaluate long-read bioinformatics methods.
| S-EPMC6572837 | biostudies-literature
Project description:With the decreasing cost of next-generation sequencing, deep sequencing of clinical samples provides unique opportunities to understand host-associated microbial communities. Among the primary challenges of clinical metagenomic sequencing is the rapid filtering of human reads to survey for pathogens with high specificity and sensitivity. Metagenomes are inherently variable due to different microbes in the samples and their relative abundance, the size and architecture of genomes, and factors such as target DNA amounts in tissue samples (i.e. human DNA versus pathogen DNA concentration). This variation in metagenomes typically manifests in sequencing datasets as low pathogen abundance, a high number of host reads, and the presence of close relatives and complex microbial communities. In addition to these challenges posed by the composition of metagenomes, high numbers of reads generated from high-throughput deep sequencing pose immense computational challenges. Accurate identification of pathogens is confounded by individual reads mapping to multiple different reference genomes due to gene similarity in different taxa present in the community or close relatives in the reference database. Available global and local sequence aligners also vary in sensitivity, specificity, and speed of detection. The efficiency of detection of pathogens in clinical samples is largely dependent on the desired taxonomic resolution of the organisms. We have developed an efficient strategy that identifies "all against all" relationships between sequencing reads and reference genomes. Our approach allows for scaling to large reference databases and then genome reconstruction by aggregating global and local alignments, thus allowing genetic characterization of pathogens at higher taxonomic resolution. These results were consistent with strain level SNP genotyping and bacterial identification from laboratory culture.
| S-EPMC4218713 | biostudies-literature
Project description:BackgroundTranscriptome sequencing using next-generation sequencing platforms will soon be competing with DNA microarray technologies for global gene expression analysis. As a preliminary evaluation of these promising technologies, we performed deep sequencing of cDNA synthesized from the Microarray Quality Control (MAQC) reference RNA samples using Roche's 454 Genome Sequencer FLX.ResultsWe generated more that 3.6 million sequence reads of average length 250 bp for the MAQC A and B samples and introduced a data analysis pipeline for translating cDNA read counts into gene expression levels. Using BLAST, 90% of the reads mapped to the human genome and 64% of the reads mapped to the RefSeq database of well annotated genes with e-values </= 10-20. We measured gene expression levels in the A and B samples by counting the numbers of reads that mapped to individual RefSeq genes in multiple sequencing runs to evaluate the MAQC quality metrics for reproducibility, sensitivity, specificity, and accuracy and compared the results with DNA microarrays and Quantitative RT-PCR (QRTPCR) from the MAQC studies. In addition, 88% of the reads were successfully aligned directly to the human genome using the AceView alignment programs with an average 90% sequence similarity to identify 137,899 unique exon junctions, including 22,193 new exon junctions not yet contained in the RefSeq database.ConclusionUsing the MAQC metrics for evaluating the performance of gene expression platforms, the ExpressSeq results for gene expression levels showed excellent reproducibility, sensitivity, and specificity that improved systematically with increasing shotgun sequencing depth, and quantitative accuracy that was comparable to DNA microarrays and QRTPCR. In addition, a careful mapping of the reads to the genome using the AceView alignment programs shed new light on the complexity of the human transcriptome including the discovery of thousands of new splice variants.
| S-EPMC2707382 | biostudies-literature
Project description:Comparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies and in different laboratories remains a major challenge. Here we address the need for guidance in choosing algorithms leading to accurate biological interpretations of varied data types acquired with different platforms. Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normalization and batch-effect correction methods at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq dataset characteristics (for example, sample and cellular heterogeneity and platform used) were critical in determining the optimal bioinformatic method. However, reproducibility across centers and platforms was high when appropriate bioinformatic methods were applied. Our findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.
| S-EPMC11245320 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-05-10 | E-GEOD-37765 | biostudies-arrayexpress