Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Validation of THS-seq method, and comparison of published ENCODE DNase-seq data, self-generated ATAC-seq data and published ATAC-seq data, and THS-seq data for quantitation of chromatin accessibility.

ABSTRACT: Chromatin accessibility captures the binding status of protein factors to chromosomes in vivo, and has been considered a highly informative proxy for functional protein-DNA interactions. Existing DNase I and Tn5 transposase based assays generally require tens of thousands to millions of fresh cells. Applying Tn5 tagmentation to single cells yields very sparse maps. Here we present a transposome hypersensitive sites sequencing assay (THS-seq) for highly sensitive characterizations of chromatin accessibility. Validation of THS-seq method, and comparison of DNase-seq, ATAC-seq and THS-seq methods for quantitation of chromatin accessibility. GSE47753, GSM1155957 GM12878_ATACseq_50k_Rep1, data downsampled to 8,351,125 reads for analysis: pub_SRR891268_ATAC-seq_50k_cells_Rep1_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155958 GM12878_ATACseq_50k_Rep2, data downsampled to 8,351,125 reads for analysis: pub_SRR891269_ATAC-seq_50k_cells_Rep2_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155959 GM12878_ATACseq_50k_Rep3, data downsampled to 8,351,125 reads for analysis: pub_SRR891270_ATAC-seq_50k_cells_Rep3_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155960 GM12878_ATACseq_50k_Rep4, data downsampled to 8,351,125 reads for analysis: pub_SRR891271_ATAC-seq_50k_cells_Rep4_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155961 GM12878_ATACseq_500_Rep1, data downsampled to 8,351,125 reads for analysis: pub_SRR891272_ATAC-seq_500_cells_Rep1_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155962 GM12878_ATACseq_500_Rep2, data downsampled to 8,351,125 reads for analysis: pub_SRR891273_ATAC-seq_500_cells_Rep2_downsampled_dfilter_peaks.bed.gz raw data files were merged, alignment with bowtie 1.3 using parameters, bowtie -n1 -k1 --best --chunkmbs 10240 --strata -l32 -m1 -p4 --nomaqround --sam, followed by clonal read removal for the final data file for further analysis. GSM816665, raw data obtained from UCSC genome browser, https://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeOpenChromDnase: wgEncodeOpenChromDnaseGm12878RawData_merged_unique_dfilter_peaks.bed.gz raw data files were merged, alignment with bowtie 1.3 using parameters, bowtie -n1 -k1 --best --chunkmbs 10240 --strata -l32 -m1 -p4 --nomaqround --sam, followed by clonal read removal for the final data file for further analysis. GSM864360, raw data obtained from UCSC genome browser, https://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeOpenChromFaire: wgEncodeOpenChromFaireGm12878RawData_merged_unique_dfilter_peaks.bed.gz raw data files were merged, alignment with bowtie 1.3 using parameters, bowtie -n1 -k1 --best --chunkmbs 10240 --strata -l32 -m1 -p4 --nomaqround --sam, followed by clonal read removal for the final data file for further analysis. GSM736496, GSM736620, raw data obtained from UCSC genome browser, https://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeUwDnase: wgEncodeUwDnaseGm12878RawData_merged_unique_dfilter_peaks.bed.gz

ORGANISM(S): Homo sapiens

SUBMITTER: Kun Zhang

PROVIDER: E-GEOD-72089 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Florencia Pauli mailto:fpauli@hudsonalpha.org). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track is produced as part of the ENCODE Project. RNA-seq is a method for mapping and quantifying the transcriptome of any organism that has a genomic DNA sequence assembly (Mortazavi et al., 2008). Biological replicates of ENCODE cell lines were grown on separate culture plates, total RNA was purified and polyA selected two times. mRNA was then fragmented by magnesium-catalyzed hydrolysis, reverse transcribed to cDNA by random priming and amplified. The cDNA was sequenced on an Illumina Genome Analyzer (GAI or GAIIx). The DNA sequences were aligned to the NCBI Build37 (hg19) version of the human genome using the sequence alignment programs ELAND (Illumina) or Bowtie (Langmead et al., 2009). The first 10 residues of sequencing have a weak characteristic nucleotide bias of unknown origin. This RNA-seq protocol does not specify the coding strand. As a result, there will be ambiguity at loci where both strands are transcribed. This is the first NCBI Build37 (hg19) release of this track (Jan 2012). This release includes the 3 datasets (Jurkat, A549/DEX100nm, and A549/EtOH2pct) previously released on NCBI Build36 (hg18) and adds data for several more cell types and growth conditions in replicate. Four types of download files are available for each replicate including the Raw Data (fastq), Transcripts GencodeV7 (gtf), Raw Signal (bigwig), and Alignments (bam). For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Experimental Procedures Cells were grown according to the approved ENCODE cell culture protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell) except for H1-hESC for which frozen cell pellets were purchased from Cellular Dynamics. Cells were lysed in RLT buffer (Qiagen RNEasy kit) and processed on RNEasy midi columns according to the manufacturer's protocol, with the inclusion of the "on-column" DNase digestion step to remove residual genomic DNA. mRNA was isolated from at least 10 ug of total RNA with oligo(dT) two times (Dynabeads mRNA PurificationgKit, Invitrogen). Alternatively, cells were lysed and mRNA was purified directly two times with oligo(dT) (Dynabeads mRNA DIRECT Kit, Invitrogen). 100 ng of mRNA was fragmented by magnesium-catalyzed hydrolysis and reverse transcribed to cDNA by random priming according to the protocol in Mortazavi et al. (2008). cDNA was prepared for sequencing on the Genome Analyzer flowcell according to the protocol for the ChIPSeq DNA genomic DNA kit (Illumina). The sequencing libraries were size-selected around 225 bp and amplified with 15 rounds of PCR. Libraries were sequenced with an Illumina Genome Analyzer I or an Illumina Genome Analyzer IIx according to the manufacturer's recommendations. Single end reads of 36 nt in length were obtained. Data Processing and Analysis Fastq files were made from qseq files generated by the Illumina pipeline (Casava 1.7). The Raw Signal files (bigWig) were generated from bedgraph files and the score was calculated as the number of reads at that position divided by the total number of reads divided by one million. Casava export files were aligned to the NCBI Build37 (hg19) version of the human genome with ELAND (Illumina), generating SAM files. Fastq files of experiments that were previously aligned to NCBI Build36 (hg18) were aligned to NCBI Build37 (hg19) using Bowtie (Langmead et al., 2009; parameters: -S -n 2 -k 11 -m 10 --best), also generating SAM files. SAM files were converted to BAM with SAMtools (Li et al., 2009). Gene expression within Gencode.v7 (Harrow et al., 2006) gene models was estimated using Cufflinks v0.9.3 (Roberts et al., 2011). Estimates of transcript abundance were reported in Fragments Per Kilobase of exon per Million fragments mapped (FPKM). FPKM is calculated by dividing the total number of fragments that align to the gene model by the size of the spliced transcript (exons) in kilobases. This number is then divided by the total number of reads in millions for the experiment. FPKM is reported in the last column of the gtf (TranscriptGencV7) files. Raw Data (fastq), Raw Signal (bigWig), Alignments (bam) and Transcript Gencode V7 (gtf) files are available from the Downloads (http://hgwdev.cse.ucsc.edu/cgi-bin/hgFileUi?g=wgEncodeHaibRnaSeq) page.

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived thyroid cancer cell line k1 transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis Methods: mRNA profiles of shCtrl and shTBX3 cells were generated by deep sequencing, in duplicate, using BGISEQ-500. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods:Bowtie2 to map clean reads to reference gene and use HISAT to reference genome.Bowtie2 parameters forSE reads:-q--phred64--sensitive--dpad0--gbar and HISTAparameters forSE reads:-p8--phred64--sensitive-I1-X 1000. qRT–PCR validation was performed using SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 24 million sequence reads per sample to the human genome (build hg19) and identified 20511 transcripts in K1-shCtrl and K1-shTBX3 cells with RSEM workflow. Approximately 5% of the transcripts showed differential expression between K1-shCtrl and K1-shTBX3 cells, with a fold change ≥1.5 and p value <0.01. Altered expression of 16 candidate genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to thyroid cancer function. Conclusions: Our study represents the first detailed analysis of thyroid cancer cell line k1 transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

Dataset Information

Validation of THS-seq method, and comparison of published ENCODE DNase-seq data, self-generated ATAC-seq data and published ATAC-seq data, and THS-seq data for quantitation of chromatin accessibility.

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