NDLI: Integrating RNA-seq and ChIP-seq data to characterize long non-coding RNAs in Drosophila melanogaster

Content Provider	Springer Nature : BioMed Central
Author	Chen, Mei-Ju May Chen, Li-Kai Lai, Yu-Shing Lin, Yu-Yu Wu, Dung-Chi Tung, Yi-An Liu, Kwei-Yan Shih, Hsueh-Tzu Chen, Yi-Jyun Lin, Yan-Liang Ma, Li-Ting Huang, Jian-Long Wu, Po-Chun Hong, Ming-Yi Chu, Fang-Hua Wu, June-Tai Li, Wen-Hsiung Chen, Chien-Yu
Abstract	Background Recent advances in sequencing technology have opened a new era in RNA studies. Novel types of RNAs such as long non-coding RNAs (lncRNAs) have been discovered by transcriptomic sequencing and some lncRNAs have been found to play essential roles in biological processes. However, only limited information is available for lncRNAs in Drosophila melanogaster, an important model organism. Therefore, the characterization of lncRNAs and identification of new lncRNAs in D. melanogaster is an important area of research. Moreover, there is an increasing interest in the use of ChIP-seq data (H3K4me3, H3K36me3 and Pol II) to detect signatures of active transcription for reported lncRNAs. Results We have developed a computational approach to identify new lncRNAs from two tissue-specific RNA-seq datasets using the poly(A)-enriched and the ribo-zero method, respectively. In our results, we identified 462 novel lncRNA transcripts, which we combined with 4137 previously published lncRNA transcripts into a curated dataset. We then utilized 61 RNA-seq and 32 ChIP-seq datasets to improve the annotation of the curated lncRNAs with regards to transcriptional direction, exon regions, classification, expression in the brain, possession of a poly(A) tail, and presence of conventional chromatin signatures. Furthermore, we used 30 time-course RNA-seq datasets and 32 ChIP-seq datasets to investigate whether the lncRNAs reported by RNA-seq have active transcription signatures. The results showed that more than half of the reported lncRNAs did not have chromatin signatures related to active transcription. To clarify this issue, we conducted RT-qPCR experiments and found that ~95.24 % of the selected lncRNAs were truly transcribed, regardless of whether they were associated with active chromatin signatures or not. Conclusions In this study, we discovered a large number of novel lncRNAs, which suggests that many remain to be identified in D. melanogaster. For the lncRNAs that are known, we improved their characterization by integrating a large number of sequencing datasets (93 sets in total) from multiple sources (lncRNAs, RNA-seq and ChIP-seq). The RT-qPCR experiments demonstrated that RNA-seq is a reliable platform to discover lncRNAs. This set of curated lncRNAs with improved annotations can serve as an important resource for investigating the function of lncRNAs in D. melanogaster.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-016-2457-0.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/s12864-016-2457-0
Journal	BMC Genomics
Issue Number	1
Volume Number	17
Language	English
Publisher	BioMed Central
Publisher Date	2016-03-11
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Drosophila melanogaster Long non-coding RNA Active transcription ChIP-seq RNA-seq
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

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