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

Overview of attention for article published in BMC Genomics, March 2016
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  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
Integrating RNA-seq and ChIP-seq data to characterize long non-coding RNAs in Drosophila melanogaster
Published in
BMC Genomics, March 2016
DOI 10.1186/s12864-016-2457-0
Pubmed ID
Authors

Mei-Ju May Chen, Li-Kai Chen, Yu-Shing Lai, Yu-Yu Lin, Dung-Chi Wu, Yi-An Tung, Kwei-Yan Liu, Hsueh-Tzu Shih, Yi-Jyun Chen, Yan-Liang Lin, Li-Ting Ma, Jian-Long Huang, Po-Chun Wu, Ming-Yi Hong, Fang-Hua Chu, June-Tai Wu, Wen-Hsiung Li, Chien-Yu Chen

Abstract

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. 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. 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Taiwan 1 1%
Unknown 80 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 27%
Researcher 19 23%
Student > Master 11 13%
Student > Bachelor 8 10%
Lecturer 4 5%
Other 12 15%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 38%
Biochemistry, Genetics and Molecular Biology 29 35%
Immunology and Microbiology 2 2%
Computer Science 2 2%
Environmental Science 2 2%
Other 7 9%
Unknown 9 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 April 2016.
All research outputs
#7,500,672
of 23,577,654 outputs
Outputs from BMC Genomics
#3,526
of 10,777 outputs
Outputs of similar age
#103,337
of 301,215 outputs
Outputs of similar age from BMC Genomics
#69
of 212 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 10,777 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 301,215 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 212 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.