Project description:The Yeonsan Ogye (Ogye) is the rare black chicken breed domesticated in Korean peninsula, which has been noted for entire black color upon its appearances including feather, skin, comb, eyes, shank, claws and internal organs. In this study, whole genome, transcriptome and epigenome sequencings of Ogye were performed using high-throughput NGS sequencing platforms. We have produced Illumina short-reads (Paired-End, Mate-Pair and FOSMID) and PacBio long-reads for whole genome sequencing (WGS), 1.4 billion reads for RNA-seq, and 123 million reads for RRBS (reduced representation bisulfite sequencing) data. Using WGS data, Ogye genome has been assembled, and coding/non-coding transcriptome maps were constructed on Ogye genome given largescale sequencing data. We have predicted 17,472 (3,550 newly annotated and 13,922 known) protein-coding transcripts, and 9,443 (6,689 novel and 2,754 known) long non-coding RNAs (lncRNAs).
Project description:The Yeonsan Ogye (Ogye) is the rare black chicken breed domesticated in Korean peninsula, which has been noted for entire black color upon its appearances including feather, skin, comb, eyes, shank, claws and internal organs. In this study, whole genome, transcriptome and epigenome sequencings of Ogye were performed using high-throughput NGS sequencing platforms. We have produced Illumina short-reads (Paired-End, Mate-Pair and FOSMID) and PacBio long-reads for whole genome sequencing (WGS), 1.4 billion reads for RNA-seq, and 123 million reads for RRBS (reduced representation bisulfite sequencing) data. Using WGS data, Ogye genome has been assembled, and coding/non-coding transcriptome maps were constructed on Ogye genome given largescale sequencing data. We have predicted 17,472 (3,550 newly annotated and 13,922 known) protein-coding transcripts, and 9,443 (6,689 novel and 2,754 known) long non-coding RNAs (lncRNAs).
Project description:We developed a software package STITCH (https://github.com/snijderlab/stitch) to perform template-based assembly of de novo peptide reads from antibody samples. As a test case we generated de novo peptide reads from protein G purified whole IgG from COVID-19 patients.
Project description:Whole exome sequencing of 5 HCLc tumor-germline pairs. Genomic DNA from HCLc tumor cells and T-cells for germline was used. Whole exome enrichment was performed with either Agilent SureSelect (50Mb, samples S3G/T, S5G/T, S9G/T) or Roche Nimblegen (44.1Mb, samples S4G/T and S6G/T). The resulting exome libraries were sequenced on the Illumina HiSeq platform with paired-end 100bp reads to an average depth of 120-134x. Bam files were generated using NovoalignMPI (v3.0) to align the raw fastq files to the reference genome sequence (hg19) and picard tools (v1.34) to flag duplicate reads (optical or pcr), unmapped reads, reads mapping to more than one location, and reads failing vendor QC.
Project description:Omics approaches are broadly used to explore endocrine and toxicity-related pathways and functions. Nevertheless, there is still a significant gap in knowledge in terms of understanding the endocrine system and its numerous connections and intricate feedback loops, especially in non-model organisms. The fathead minnow (Pimephales promelas) is a widely used small fish model for aquatic toxicology and regulatory testing, particularly in North America. A draft genome has been published but the amount of available genomic or transcriptomic information is still far behind that of other more broadly studied species, such as the zebrafish. Here, we surveyed the tissue-specific proteome and transcriptome profiles in adult male fathead minnow. To do so, we generated a draft transcriptome using short and long sequencing reads. We also performed RNA sequencing and proteomics analysis on the telencephalon, hypothalamus, liver, and gut of male fish. The main purpose of this analysis was to generate tissue-specific omics data in order to support future aquatic ecotoxicogenomic and endocrine-related studies as well as to improve our understanding of the fathead minnow as an ecological model.
Project description:Purpose: This study aimed to generate a whole transcriptome dataset for children with acute myeloid leukemia Methods: RNA-seq data were generated by deep sequencing using Illumina. The sequence reads that passed quality filters were analyzed. Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the human genome (buildhg38) and annotated with ensemble release 100.
Project description:Purpose: To study the alteration of whole transcriptome of Lewis lung carcinoma (LLC) cells after the decreasing of malignant properties of tumor by treatment of tumor-bearing mice with RNase A. Methods: Whole transcriptome profile of Lewis lung carcinoma before and after RNase A treatment were generated by deep sequencing using SOLiD 5.5. The sequence reads were mapped by Bioscope 1.3 software, differential expression was evaluated by Cufflinks v.2.0.1 package. Results: Difference in expression was found for 966 genes. Conclusions: Our study represents the first detailed analysis of alteration of transcriptome of Lewis lung carcinoma after the decrease of malignant prtoperties of the tumor (proliferation and invasion) by RNase A. Whole transcriptome profile of Lewis lung carcinoma before and after RNase A treatment were generated by deep sequencing using SOLiD 5.5.