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Protocol publication

[…] Three different RNA-seq library preparation methods were used for this study: SMART-seq, which uses poly-A selection ( and ), NuGEN Ovation, which uses random priming (, top), and Truseq combined with Ribozero to remove ribosomal RNAs (all other figures). The first two methods allow library construction from <10 ng of total RNA, whereas the later method requires >50 ng total RNA. We compared SMART-seq and Truseq-Ribozero performance on L1 PGCs isolated from wild-type and nos-1(gv5)nos-2(RNAi) and observed identical trends, with an overall higher number of misregulated genes identified with Truseq-Ribozero (Compare (SMART-seq) and (Truseq/Ribozero). For the experiment shown in (top panels) where we compared RNA levels between embryonic PGCs and an oocyte library reference, we used Nugen Ovation libraries which avoids any bias due to poly-A selection while allowing library construction from <3 ng of RNA. For all experiments, control and experimental libraries were made using the same method. contains lists of misregulated genes from analyses. lists all the RNA-seq libraries used in this study and the corresponding figures.SMART-seq libraries: libraries were made from 2 ng of total RNA isolated from sorted PGCs from worms grown at 25°C. Libraries were constructed using SMART-seq v4 Ultra Low input RNA kit (Clontech, Cat. No. 634888) followed by Low Input Library Prep Kit (Clontech, Cat. No. 634947). The cDNAs were then fragmented using Covaris AFA system at the Johns Hopkins University microarray core and cloned using the Low Input library prep Kit.NuGEN Ovation libraries: libraries were made from 3 ng of total RNA isolated from sorted cells from worms grown at 25°C. Libraries were constructed using Nugen Ovation system V2 (#7102–08) followed by Nugen Ultralow library system.TruSeq libraries: 50 ng of total RNA isolated from sorted PGCs from L1 worms grown at 20°C were subjected to Ribozero kit (illumina, MRZE706) to remove rRNA. Libraries were constructed using Truseq Library Prep Kit V2.All cDNA libraries were sequenced using the Illumina Hiseq2000/2500 platform. Differential expression analysis was done using Tophat (V.2.0.8) and Cufflink (V.2.0.2). Cuffdiff accepts multiple biological replicates and uses Benjamini–Hochberg multiple hypothesis to compute false discovery rate (FDR). The cutoff of FDR(q value)=0.05 was used as a significance cutoff for all the analyses in this study. The command lines for Tuxedo suit are listed as below:For each biological sample, sequencing reads were first mapped to ce10 reference genome using tophat2:$ tophat2 -p 12 g 1 --output-dir segment-length 20 --min-intron-length 10 --max-intron-length 25000 G < gene.gtf> --transcriptome-indexFor differential gene expression analysis, sets of independent mutant and control mapped reads (e.g biological replicates) were used in cuffdiff analysis:$ cuffdiff -p 12 -o < output > compatible-hits-norm --upper-quartile-norm -b < genome.fa>  Gene set enrichment analysis for four different categories and correlation of gene expression were done using R functions. R function intersect() was used to extract overlapping lists. Plots were drawn using R package and Prism software.For correlation plots of gene expression shown in , information from different pairs of cuffdiff analyses (WT vs mes-2, WT vs mes-4 and WT vs nos-1/2) was used. Genes with sufficient aligned reads to pass statistical test (OK status in test status from cuffdiff output) were kept, and those without enough alignments (NOTEST, LOWDATA in test status), or other conditions prevent statistical testing were excluded. Values of Log2 fold change were extracted from each cuffdiff output file and list of genes were further consolidated to generated correlation plots. The data process results in different number of genes in selected categories (1173 vs 1250 in X-linked genes, and 1063 vs 1092 in autosomal oocyte genes). However, majority of genes were overlapped between comparisons (1117 for X-linked genes and 1062 for autosomal oocyte genes)In , the area-proportional Venn diagram was created using the VennDiagram R package. For comparisons shown in , oocyte transcriptome data was extracted from , and embryonic soma and germ cells expression profiles were from this study (). Expression of each gene was log10 transformed, ranked and ordered. Correlations were plotted using custom R codes and can be found in source code. [...] Principal component Analysis (PCA) was used to evaluate reproducibility of RNA-seq experiments. PCA revealed clustering of biological replicates with the same library preparation procedure as shown in . In , two different sets of libraries (one set was made with NuGEN protocol and the other was made with SMART-seq protocol) were generated using the same RNA and clustered differently, suggesting different library making procedures could introduce biases. Sequence reads were mapped to transcriptome version ce10 using Hisat2. HTseq-count was used to generate raw counts for each gene. The command lines are listed as below.$hisat2 -x < hisat2-index> -S < output file> -q < iinput file> --known-splicesite-infile --no-softclip$htseq-count -s no > outputfile.genecountThe gene count information from HTseq-count () was subject to regularized log transformation (rlog) and plotPCA in DEseq2 package. […]

Pipeline specifications

Software tools TopHat, Cufflinks, Tuxedo, HISAT2, HTSeq, DESeq2
Application RNA-seq analysis
Organisms Caenorhabditis elegans