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| Status |
Public on May 23, 2023 |
| Title |
Dsim_wXD1_sRNA_replicate2 |
| Sample type |
SRA |
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| Source name |
testis
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| Organism |
Drosophila simulans |
| Characteristics |
tissue: testis
genotype: wXD1
treatment: none
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| Extracted molecule |
total RNA |
| Extraction protocol |
For D. melanogaster testis dissections dcr-2 mutant testis were collected from wIR; dcr-2[L811fsx]/dcr-2[R416X] trans-heterozygotes and wIR; dcr-2[R416X]/+ heterozygous flies were used as controls. For D. simulans, we collected testis from dcr2[DsRed]/[white+] trans-heterozygous mutants, and used the parental strain w[XD1] as control. Briefly, testis from 3 days old flies were extracted in TRIzol (Invitrogen) in batches of 10 flies at a time and the testis samples were flash frozen in liquid nitrogen. RNA was extracted from 25-50 testis per genotype.
RNA extraction was performed as described in (Lin et al., 2018), and the quality of RNA samples were assessed with the Agilent Bioanalyzer. RNA samples with RIN >6.5 were used for library preparation using the Illumina TruSeq Total RNA library Prep Kit LT. Briefly, for RNA-seq libraries we used 650 ng of total RNA, and we used the Manufacturer’s protocol except for reducing the number of PCR cycles from 15 as recommended to 8, to minimize artifacts that may arise from PCR amplification. We prepared stranded RNA-seq libraries for D. simulans and unstranded libraries for D. melanogaster as RNA samples were extracted and processed in different time points. Samples were pooled using barcoded adapters provided by the manufacturer and the paired-end sequencing was performed at New York Genome Center using PE75 in the Illumina HiSeq2500 sequencer. We prepared small RNA libraries used ~20 μg total RNA, as previously described in Lin et al. 2018. To the total RNA pool, we added a set of 52 RNA spike-ins, spanning a range of concentrations (QIAseq miRNA Library Spike-In kit #800100). Briefly, small RNAs of size 18- to 29-nt-long small RNAs were purified by preparative PAGE. Next, the 3′ linker (containing four random nucleotides) was ligated overnight using T4 RNA ligase 2, truncated K227Q (NEB), after which the products were recovered by a second PAGE purification. 5′ RNA linkers with four terminal random nucleotides were then ligated to the small RNAs using T4 RNA ligase (NEB) followed by another round of PAGE purification. The cloned small RNAs were then reverse transcribed, PCR amplified and sequenced using P50 single-end sequencing on the Illumina HiSeq 2500 sequencer. We prepared small RNA libraries used ~20 μg total RNA, as previously described in Lin et al. 2018. To the total RNA pool, we added a set of 52 RNA spike-ins, spanning a range of concentrations (QIAseq miRNA Library Spike-In kit #800100). Briefly, small RNAs of size 18- to 29-nt-long small RNAs were purified by preparative PAGE. Next, the 3′ linker (containing four random nucleotides) was ligated overnight using T4 RNA ligase 2, truncated K227Q (NEB), after which the products were recovered by a second PAGE purification. 5′ RNA linkers with four terminal random nucleotides were then ligated to the small RNAs using T4 RNA ligase (NEB) followed by another round of PAGE purification. The cloned small RNAs were then reverse transcribed, PCR amplified and sequenced using P50 single-end sequencing on the Illumina HiSeq 2500 sequencer. To map 5' ends, we used the parallel analysis of RNA 5′ ends from low-input RNA (nanoPARE) strategy (Schon et al., 2018). For Dsim libraries, testis was extracted from <1-week males and total RNA was extracted using TRIzol. cDNA was prepared using Smart-seq2 (Picelli et al., 2013) and tagmented using the Illumina Nextera DNA library preparation kit, purified using the Zymo 5x DNA Clean and Concentrator kit (Zymo Research), and eluted with resuspension buffer. For 5’-end enrichment PCR, the purified reaction was split and amplified either Tn5.1/TSO or Tn5.2/TSO enrichment oligonucleotide primer sets. PCR reaction products with Tn5.1/TSO enrichment oligonucleotide and Tn5.2/TSO enrichment oligonucleotide primer sets were pooled and purified using AMPureXP DNA beads. Final libraries were checked for quality on an Agilent DNA HS Bioanalyzer chip. Libraries with size ranges between 150 and 800 bp were diluted and sequenced to 10–15 million single-end 50-bp reads per sample using a custom sequencing primer (TSO_Seq) and a custom P5/P7 index primer mix on an Illumina HiSeq 2500 instrument. To annotate 3' transcript termini, we used the QuantSeq 3’ mRNA-seq library preparation REV kit for Illumina (Lexogen) with a starting material of 50 ng total RNA from Dmel and Dsim control and dcr-2 mutant samples, according to manufacturer’s instructions. cDNA libraries were sequenced on Illumina HiSeq-1000 sequencer with single-end SE 50 mode.
RNA-sequencing was done with the paired-end sequencing mode, while the small RNA sequencing was peformed with the single-end sequencing protocol.
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| Library strategy |
ncRNA-Seq |
| Library source |
transcriptomic |
| Library selection |
size fractionation |
| Instrument model |
Illumina HiSeq 2500 |
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| Description |
Dsim_sRNA_seq_wildtype
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| Data processing |
RNA sequencing analysis. Paired-end RNA-seq reads from wild-type and mutant dcr-2 samples in Dmel and Dsim were mapped to dm6 (FlyBase) and Dsim PacBio assemblies (Chakraborty et al., 2021), respectively using hisat2 aligner (Kim et al., 2015; Pertea et al., 2016).The resulting alignments in SAM format was converted to BAM using SAMtools software (Li et al., 2009) for downstream analyses. Mapping quality and statistics were determined using the bam_stat.py script provided in the RSeQC software (Wang et al., 2012). Transcript abundance was determined using FeatureCounts software from the subread package (Liao et al., 2014), using Dmel gene annotations from FlyBase r6.25. For Dsim, we used both gene annotations from FlyBase and de novo transcript annotation using StringTie software (see details below) (Pertea et al., 2015). As FlyBase gene annotations for Dsim correspond to Dsim r2.02 assembly, we converted the FlyBase assembly annotations to Dsim PacBio coordinates using the UCSC liftover tool implemented in the KentUtils toolkit from UCSC (https://github.com/ENCODE-DCC/kentUtils). We combined FlyBase liftover and de novo annotations in Dsim to determine transcript abundance for RNA-seq analyses. The following description for differential gene expression (DFE) analysis is the same for Dmel and Dsim data. DFE comparing control and dcr-2 mutant data was performed using the DEseq2 package in R (Love et al., 2014). Genes with low read counts and/or high variability among technical or biological replicates can lead to log fold change differences that are not representative of true differences. Therefore, to minimize variance, we used the log fold change (LFC) shrinkage implemented in the DEseq2 package using the ‘normal’ method described in (Love et al., 2014). For visualization of mapped reads, the BAM alignment files were converted to bigwig format using bam2wig.py script from RSeQC (Wang et al., 2012) and the bigwig tracks were visualized on the IGV genome browser (Robinson et al., 2011).
Small RNA sequencing analysis. Adapters were trimmed from small RNA sequences using Cutadapt software (https://github.com/marcelm/cutadapt); then the 5’ and 3’ 4-nt linkers (total 8 bp) were removed using sRNA_linker_removal.sh script described in (Vedanayagam et al., 2021) (https://github.com/Lai-Lab-Sloan-Kettering/Dox_evolution). The adapter and linker removed sequences were then filtered to remove < 15 nt reads. We mapped > 15 nt reads from Dmel and Dsim genotypes to dm6 reference genome assembly and Dsim PacBio assembly, respectively, with Bowtie (Langmead et al., 2009) using the following mapping options: bowtie -q -p 4 -v 3 -k 20 --best –strata. The resulting BAM alignments from bowtie mapping were converted to bigwig for visualization using bam2wig.py script from the RSeQC software (Wang et al., 2012).
In addition to previously annotated transcripts/genes from the FlyBase annotation, we performed de novo annotation of our transcriptome data to identify additional, novel testis-expressed transcripts in D. melanogaster and D. simulans. The novel annotated transcripts were then supplemented with known annotations to make a combined set of 17285 transcripts in D. melanogaster and 15119 transcripts in D. simulans. We employed two independent, genome assembly guided transcript prediction algorithms, Cufflinks (Trapnell et al., 2012) and StringTie (Pertea et al., 2015). For both methods, de novo transcripts were predicted for each RNA-seq dataset, and a merged transcript model was generated encompassing the transcriptome from WT and mutant datasets. hpRNAs were predicted using the scheme shown in Supplementary Figure 2, and visualized using the Integrated Genomics Viewer (IGV) (Thorvaldsdottir et al., 2013). The termini of primary hpRNA transcripts were refined using the 5'-seq and 3'-seq data.
Assembly: For D. melanogaster, we used the dm6 genome assembly (Flybase) and for D. simulans, we used Dsim PacBio genome assembly (Chakraborty et al. 2021)
Supplementary files format and content: The processed data files for IGV visualization are in bigiwg format
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| Submission date |
Apr 20, 2023 |
| Last update date |
May 24, 2023 |
| Contact name |
Jeffrey Vedanayagam |
| E-mail(s) |
[email protected]
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| Organization name |
Sloan-Kettering Institute
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| Department |
Department of Developmental Biology
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| Lab |
Eric Lai
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| Street address |
1275 York Avenue
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| City |
New Yor |
| State/province |
NY |
| ZIP/Postal code |
10065 |
| Country |
USA |
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| Platform ID |
GPL22293 |
| Series (1) |
| GSE230111 |
Regulatory logic of endogenous RNAi in silencing de novo genomic conflicts |
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| Relations |
| BioSample |
SAMN34258555 |
| SRA |
SRX20020291 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM7187705_Dsim_wXD1_sRNA_rep2.forward.bw |
37.7 Mb |
(ftp)(http) |
BW |
| GSM7187705_Dsim_wXD1_sRNA_rep2.reverse.bw |
32.4 Mb |
(ftp)(http) |
BW |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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