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  • 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 ~95.24Â % 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.
    Data Types:
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  • 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 ~95.24Â % 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.
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  • Supplementary figures: Fig. S1: Gene names for the data shown in Fig. 1aâ c, Fig. S2: Transcription factor motifs enriched in top ChIP-seq and ATAC-seq regions, Fig. S3: Differential H3K27ac analysis of ATAC-seq regions is an effective method to identify tissue-specific enhancers, Fig. S4: Genes near putative ATAC-seq derived enhancers are differentially regulated across tissues, Fig. S5: The identified putative DV enhancer regions derived from ATAC-seq are enriched for known DV transcription factor motifs, Fig. S6: Number of genes with one or multiple assigned enhancers, Fig. S7: Transcription factor ChIP-seq signal is preferentially found at the expected corresponding binding motifs present within putative MEs and DEEs. (PDF 2673 kb)
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  • Supplementary figures: Fig. S1: Gene names for the data shown in Fig. 1aâ c, Fig. S2: Transcription factor motifs enriched in top ChIP-seq and ATAC-seq regions, Fig. S3: Differential H3K27ac analysis of ATAC-seq regions is an effective method to identify tissue-specific enhancers, Fig. S4: Genes near putative ATAC-seq derived enhancers are differentially regulated across tissues, Fig. S5: The identified putative DV enhancer regions derived from ATAC-seq are enriched for known DV transcription factor motifs, Fig. S6: Number of genes with one or multiple assigned enhancers, Fig. S7: Transcription factor ChIP-seq signal is preferentially found at the expected corresponding binding motifs present within putative MEs and DEEs. (PDF 2673 kb)
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  • Supplementary material: Includes references for known DV enhancers, ChIP-seq and ATAC-seq replicate correlations, and an overview of how some known DV enhancers were assigned to potential target genes. (DOCX 3548 kb)
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  • Supplementary material: Includes references for known DV enhancers, ChIP-seq and ATAC-seq replicate correlations, and an overview of how some known DV enhancers were assigned to potential target genes. (DOCX 3548 kb)
    Data Types:
    • Document
  • Replicate concordance for ChIP-seq for various histone modifications. ChIP-seq replicate concordance is shown with Pearsonâ s correlation coefficient (r-values) calculated on Log (1â +â ngs.plot) enrichment values for all six histone marks. (PDF 7315 kb)
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  • Replicate concordance for ChIP-seq for various histone modifications. ChIP-seq replicate concordance is shown with Pearsonâ s correlation coefficient (r-values) calculated on Log (1â +â ngs.plot) enrichment values for all six histone marks. (PDF 7315 kb)
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  • Additional file 2: Figure S1. Comparison of ChIP results in SuUR mutants and in wild type obtained with H3K27me3 antibodies from different vendors and with the antibodies against H3K27me2. A—scatter plot of ChIP-chip signals obtained with the Abcam #6002 antibodies in SuUR mutants (abscissa) and in wild type (ordinate) [13]. B—scatter plot showing H3K27me3 ChIP-seq signals obtained with Cell Signaling Technology #9733 (CST #9733) antibodies in the same genotypes. C—the same analysis performed with Millipore #07-452 antibodies against H3K27me2. Datapoints inside 193 SSRs are shown in red. In both cases (A and B) H3K27me3 antibodies produce the characteristic skew (arrows): SSRs systematically show stronger signal in wild type strain as compared to SuUR mutants. This tendency is absent in case of H3K27me2 (C).
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  • Distribution of lncRNA types in the different euchromatin regions. Figure S2. Occupied regions for each chromatin signature. Table S1. The length of lncRNAs. Table S2. RNA-seq datasets. Table S3. Statistics of exon numbers in lncRNA and mRNA genes from different sources. Table S4. Raw Ct values of RT-qPCR experiments for un-transcribed regions and the selected lncRNAs. Table S5. ChIP-seq datasets. Table S6. The primer list of the selected lncRNAs for RT-qPCR experiments. (PDF 356 kb)
    Data Types:
    • Document