Project description:Species identification of fragmentary bones remains a challenging task in archeology and forensics. A species identification method for such fragmentary bones that has recently attracted interest is the use of bone collagen proteins. We developed a method similar to DNA barcoding that reads collagen protein sequences in bone and automatically determines the species by performing sequence database searches. We tested our method using bone samples from 30 vertebrate species ranging from mammals to fish.
Project description:Purpose: The goal of this study is to compare endothelial small RNA transcriptome to identify the target of OASL under basal or stimulated conditions by utilizing miRNA-seq. Methods: Endothelial miRNA profilies of siCTL or siOASL transfected HUVECs were generated by illumina sequencing method, in duplicate. After sequencing, the raw sequence reads are filtered based on quality. The adapter sequences are also trimmed off the raw sequence reads. rRNA removed reads are sequentially aligned to reference genome (GRCh38) and miRNA prediction is performed by miRDeep2. Results: We identified known miRNA in species (miRDeep2) in the HUVECs transfected with siCTL or siOASL. The expression profile of mature miRNA is used to analyze differentially expressed miRNA(DE miRNA). Conclusions: Our study represents the first analysis of endothelial miRNA profiles affected by OASL knockdown with biologic replicates.
Project description:A cDNA library was constructed by Novogene (CA, USA) using a Small RNA Sample Pre Kit, and Illumina sequencing was conducted according to company workflow, using 20 million reads. Raw data were filtered for quality as determined by reads with a quality score > 5, reads containing N < 10%, no 5' primer contaminants, and reads with a 3' primer and insert tag. The 3' primer sequence was trimmed and reads with a poly A/T/G/C were removed
Project description:We used our newly ultra deep sequence data and bioinformatics to re-annotate P. xylostella genome for high confidence miRNAs with the correct 5p and 3p arm features. Furthermore, the whole genome was screened to identify potential miRNA binding sites using three target-predicting algorithms. Totally, 203 mature miRNAs were annotated, including 33 novel miRNAs. Two geographical populations of Diamondback moth larvae from Queensland (Gatton) and South Australia (Waite) were collected and reared on the cabbage plant at the University of Queensland in Australia. Total RNA was extracted from fifteen 3rd instar larval samples using Triazol® following the manufacturerâs protocol (Life Technologies). The small RNA libraries were generated from both populations with three biological replicates using the Illumina Truseq small RNA preparation kit at the Australian Genome Research Facility (AGRF-Melbourne, Australia). The purified cDNA libraries were sequenced on Illumina HiSeq and raw sequencing reads (50 nt) were obtained using Illuminaâs Sequencing Control Studio software.
Project description:Purpose: In the current study, we used RNA-Seq to determine the transcriptional changes and understand the genetic reprogramming of the diaphragm in the chronic phase post-myocardial infarction. Methods: Rat diaphragm mRNA profiles 16 weeks post Sham and Myocardial infarction (MI) surgeries were generated by deep sequencing using llumina NextSeq 500. The sequence reads that passed quality filters were analyzed at the transcript level with TopHat followed by Cufflinks. Results: After quality control and data filtering, on average, 33,972,308 raw reads per sample were obtained. For each sample, 88.49 % to 91.70 % reads were uniquely mapped to the rat reference genome. Median CV coverage uniformity was 0.45± 0.01. Distribution of mapped reads over DNA regions showed 48% of reads mapped to coding region, 20% of reads mapped to untranslated region (UTR), 12% of reads mapped to intron region, and 20% reads mapped to intergenic region. The resulting Sham and MI transcriptomes, created from generated transcriptomes of each individual sample in each group and merged transcriptomes within the same group, were used for differential gene expression analysis. A total of 112 differentially expressed genes (DEGs) were identified out of a total of 9664 genes with measured expression in MI and Sham groups. Among DEGs, 42 were upregulated and 70 were downregulated in the MI group. Hierarchical clustering heat map, pathway enrichment and gene ontology (GO) analyses, and analysis of DEGs in the framework skeletal muscle-specific biological networks were performed. Conclusions: Our study represents the first detailed analysis of diaphragm transcriptomes post-myocardial infarction generated by RNA-Seq technology.