Dataset Information

Detecting novel low-abundant transcripts in Drosophila.

ABSTRACT: Increasing evidence suggests that low-abundant transcripts may play fundamental roles in biological processes. In an attempt to estimate the prevalence of low-abundant transcripts in eukaryotic genomes, we performed a transcriptome analysis in Drosophila using the SAGE technique. We collected 244,313 SAGE tags from transcripts expressed in Drosophila embryonic, larval, pupae, adult, and testicular tissue. From these SAGE tags, we identified 40,823 unique SAGE tags. Our analysis showed that 55% of the 40,823 unique SAGE tags are novel without matches in currently known Drosophila transcripts, and most of the novel SAGE tags have low copy numbers. Further analysis indicated that these novel SAGE tags represent novel low-abundant transcripts expressed from loci outside of currently annotated exons including the intergenic and intronic regions, and antisense of the currently annotated exons in the Drosophila genome. Our study reveals the presence of a significant number of novel low-abundant transcripts in Drosophila, and highlights the need to isolate these novel low-abundant transcripts for further biological studies.

SUBMITTER: Lee S

PROVIDER: S-EPMC1370778 | biostudies-literature | 2005 Jun

REPOSITORIES: biostudies-literature

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Detecting novel low-abundant transcripts in Drosophila.

Lee Sanggyu S Bao Jingyue J Zhou Guolin G Shapiro Joshua J Xu Jinhua J Shi Run Zhang RZ Lu Xuemei X Clark Terry T Johnson Deborah D Kim Yeong C YC Wing Claudia C Tseng Charles C Sun Min M Lin Wei W Wang Jun J Yang Huanming H Wang Jian J Du Wei W Wu Chung-I CI Zhang Xiuqing X Wang San Ming SM

RNA (New York, N.Y.) 20050601 6

Increasing evidence suggests that low-abundant transcripts may play fundamental roles in biological processes. In an attempt to estimate the prevalence of low-abundant transcripts in eukaryotic genomes, we performed a transcriptome analysis in Drosophila using the SAGE technique. We collected 244,313 SAGE tags from transcripts expressed in Drosophila embryonic, larval, pupae, adult, and testicular tissue. From these SAGE tags, we identified 40,823 unique SAGE tags. Our analysis showed that 55% o ...[more]

PMID: 15923377

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Project description:Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them.We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/.

Dataset Information

Detecting novel low-abundant transcripts in Drosophila.

Publications

Detecting novel low-abundant transcripts in Drosophila.

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