RNA-sequencing and proteomics data of "Targeting palmitoylation of SLC11A2 for treatment of iron overload and osteoporosis"
Description
This dataset includes the raw RNA-sequencing and proteomics data of osteoclast differentiation generated in the study. Eight-week-old mice were euthanized, and hindlimb bones were harvested and flushed with PBS. Bone marrow was filtered through a 0.4 μm mesh, centrifuged, and resuspended in α-MEM containing 10 ng/mL MCSF and 10% FBS, then plated in 10 cm dishes. After 2-4 days, cells were passaged and cultured in media supplemented with 30 ng/mL MCSF and 50 ng/mL RANKL, with medium changes every two days. Cells were collected after three days for subsequent RNA-sequencing and proteomics.
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Steps to reproduce
For transcriptomincs data，fastp software (https://github.com/OpenGene/fastp) were used to remove the reads that contained adaptor contamination, low quality bases and undetermined bases with default parameter. Then sequence quality was also verified using fastp. We used HISAT2 (https://ccb.jhu.edu/software/hisat2) to map reads to the reference genome of Mus musculus GRCm39. The mapped reads of each sample were assembled using StringTie (https://ccb.jhu.edu/software/stringtie) with default parameters. Then, all transcriptomes from all samples were merged to reconstruct a comprehensive transcriptome using gffcompare (https://github.com/gpertea/gffcompare/). After the final transcriptome was generated, StringTie and was used to estimate the expression levels of all transcripts. StringTie was used to perform expression level for mRNAs by calculating FPKM (FPKM = [total_exon_fragments / mapped_reads(millions) × exon_length(kB)]). The differentially expressed mRNAs were selected with fold change > 2 or fold change < 0.5 and with parametric F-test comparing nested linear models (p value < 0.05) by R package edgeR (https://bioconductor.org/packages/release/bioc/html/edgeR.html).