Does gene fusion happen at genomic level or transcript level ? what is the difference between fusion genes and chimeric transcripts, are they the same?
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In answer to your first question, both. Most fusions of biological interest appear to be driven by genomic rearrangements. However, there are definitely examples in which mutations in splicing or polyadenylation signals lead to the formation of fusion transcripts from unrearranged (but adjacent) genes.
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We have recently sequenced a bacterial transcriptome with 316 chip from IonTorrent (1.5 million sequences). After filtering low quality data and trimming adapters we noticed that only 51.33% sequences were mapped on reference genome. Looking for the unmapped sequences we can see that most of them are chimeric transcripts, so impossible mapping for them an also causing bias on results. Also many of the unmapped are sequences lacking homology in 20% of the starting sequence.
I would like to know your opinion about it.
Should I have to move to 454 or Illumina? Our Sequencing Department have no idea of why we have so many chimeras.
Thank you, Bernardo
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I would recommend illumina as it has more advanced and standardized protocol with high accuracy. Or u can refer
"Direct Comparisons of Illumina vs. Roche 454 Sequencing Technologies on the Same Microbial Community DNA Sample" at
Next-generation sequencing (NGS) is commonly used in metagenomic studies of complex microbial communities but whether or not different NGS platforms recover the same diversity from a sample and their assembled sequences are of comparable quality remain unclear. We compared the two most frequently used platforms, the Roche 454 FLX Titanium and the Illumina Genome Analyzer (GA) II, on the same DNA sample obtained from a complex freshwater planktonic community. Despite the substantial differences in read length and sequencing protocols, the platforms provided a comparable view of the community sampled. For instance, derived assemblies overlapped in ∼90% of their total sequences and in situ abundances of genes and genotypes (estimated based on sequence coverage) correlated highly between the two platforms (R2>0.9). Evaluation of base-call error, frameshift frequency, and contig length suggested that Illumina offered equivalent, if not better, assemblies than Roche 454. The results from metagenomic samples were further validated against DNA samples of eighteen isolate genomes, which showed a range of genome sizes and G+C% content. We also provide quantitative estimates of the errors in gene and contig sequences assembled from datasets characterized by different levels of complexity and G+C% content. For instance, we noted that homopolymer-associated, single-base errors affected ∼1% of the protein sequences recovered in Illumina contigs of 10× coverage and 50% G+C; this frequency increased to ∼3% when non-homopolymer errors were also considered. Collectively, our results should serve as a useful practical guide for choosing proper sampling strategies and data possessing protocols for future metagenomic studies.
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