Integrative genome-wide analysis in non-small cell lung cancer cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE63356 | GEO | 2017/11/20
SECONDARY ACCESSION(S): PRJNA267557
REPOSITORIES: GEO
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Project description:The goals of this study is to compare the whole genome transcriptome of gefitinib-resistant NSCLC cell line (PC9R) with its gefitinib-sensitive counterpart (PC9) using RNA-seq tecnology Methods: Genome-wide mRNA profiles of the PC9R and PC9 cells were generated by deep sequencing, using Illumina Hiseq2000. The sequence reads that passed quality filters were analyzed in the following steps: 1) RNA-seq reads were aligned to the hg19 genome assembly using TopHat (http://bioinformatics.oxfordjournals.org/content/25/9/1105.short) with the default parameters; 2) Expression index was generated using GFOLD V1.0.9 job count (http://bioinformatics.oxfordjournals.org/content/early/2012/08/23/bioinformatics.bts515); 3) Differential expression were calculated using GFOLD V1.0.9 job diff. Gene expression was quantified in rpkm (reads per kilobase of exon per million mapped sequence reads); 4) GFOLD, a generalized fold change, was used to rank the differentially expressed genes from the RNA-seq data. The GFOLD value can be considered as a reliable log2-fold change when only a single biological replicate is available Results: We found that hundreds of genes were either down- or up-regulated in the PC9R cells compared with the PC9 cells. Specifically, 6% of the total detected genes (1487 genes) were up-regulated in the PC9R cells, with a GFOLD value over 1, and 5% of the total detected genes (1112 genes) were down-regulated, with a GFOLD value less than -1. Conclusions: Our study reveals the differentially expressed genes in gefitinib-resistant NSCLC cells comparing with the sensitive cells in a genome-wide scale. This results help to provide the novel insight into the gefitinib-resistant mechanism.
2018-03-14 | GSE74253 | GEO
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Project description:Intent of the experiment: evaluate whether copy number gains and losses occur throughout the processing of passaging, i.e. test the genomic stability of the patient-derived xenograft models. DNA was extracted from frozen xenograft samples of different passages. KAPA DNA Library Preparation Kit was used to prepare DNA libraries, which were sequenced at low coverage on a HiSeq2000 (Illumina) with a V3 flowcell generating 50bp reads. Raw reads were aligned to the human reference genome version hg19 with Burrows-Wheeler Aligner software package and after duplicate removal further analyzed with QDNAseq to exclude known regions with low mapping quality, correct for the genomic wave and to count the reads per bin. Binned data were further segmented with the ASCAT (Allele-Specific Copy number Analysis of Tumours) algorithm.
2019-04-30 | E-MTAB-7826 | biostudies-arrayexpress
Project description:To investigate a potential role for RNF5 in AML, we monitored changes in gene expression profiles in multiple AML cell lines after RNF5 knock-down. PolyA RNA was isolated using the NEBNext Poly(A) mRNA Magnetic Isolation Module, and bar-coded libraries were constructed using the NEBNext Ultra™ Directional RNA Library Prep Kit for Illumina (NEB, Ipswich, MA). Libraries were pooled and single end-sequenced (1×75) on the Illumina NextSeq 500 using the High output V2 kit (Illumina, San Diego, CA). Quality control was performed using Fastqc (v0.11.5, Andrews S. 2010)), reads were trimmed for adapters, low quality 5′ bases, and minimum length of 20 using CUTADAPT (v1.1). The number of reads per sample and the number of aligned reads suggested that read quality and number were good and that the data were valid for analysis. High-quality data were then mapped to human reference genome (hg19) using STAR mapping algorithm (version 2.5.2a). FeatureCounts implemented in Subread (v1.50) was used to count the sequencing reads from mapped BAM files. Analyses of differentially expressed genes (DEGs) were subsequently performed using a negative binomial test method (edgeR) implemented using SARTools R Package. A list of the DEGs was observed and pathway analysis was performed using IPA (http://www.ingenuity.com) based on the following criteria: |log2(fold change)| >0.4 and p value < 0.05.
2021-07-25 | GSE155929 | GEO
Project description: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
2016-01-09 | E-GEOD-72089 | biostudies-arrayexpress
Project description:Rapamycin-sensitive transgenic Arabidopsis lines (BP12) expressing yeast FK506 Binding Protein12 (FKBP12) were developed. Inhibition of TOR in BP12 plants by rapamycin resulted in slower overall root, leaf and shoot growth and development leading to poor nutrient uptake and light energy utilization. Genetic and physiological studies together with RNA-Seq and metabolite analysis of TOR-suppressed lines revealed that TOR regulates development and lifespan in Arabidopsis by restructuring cell growth, carbon and nitrogen metabolism, gene expression, ribosomal RNA and protein synthesis. Arabidopsis WT (Col)and BP12-2 (TOR knockdown line) seedlings at 15 DAG were treated with rapamycin for 3 days by transferring from 0.5 MS medium to 0.5 MS+10 ug/ml rapamycin. Triplicate samples of rapamycin treated WT and BP12-2 seedlings were used for RNA-Seq analysis (Illumina Hiseq 2000). Paired-end alignments were obtained through aligning short reads onto the reference Arabidopsis Genome (TAIR9) using Bowtie. More than 80% of the reads mapped onto the genome. Htseq-count was used to count the reads from the Bowtie derived output files. Differential expressed genes were identified using edgeR. The FDR-corrected P value for differential expression was set to be <=0.05.
2012-12-18 | E-GEOD-42968 | biostudies-arrayexpress
Project description:Raw RNA-seq counts from reads aligned to the mouse genome (mm10 assembly) were generated with Htseq-count (PMID: 25260700). Differentially expressed genes between TPr and Ctl MSCs were calculated by DESeq2 (PMID: 25516281). Pre-ranked gene set enrichment was performed as recommended for RNA-seq data (PMID: 16199517). Custom R scripts were used to filter highly redundant biological processes. A false discovery rate of 0.05 was considered as an acceptable threshold for further investigation. All analyses were conducted in R (v3.6) or Python (v3.7) programming language. The ggplot2, ClusterProfiler and dplyr packages were used for data visualization. Student’s t-test was performed for normally distributed data to compute the p-value and the Benjamini-Hochberg procedure was used for adjusting the statistical inference of multiple comparisons.
2021-02-08 | GSE161773 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Jonathan Preall jpreall@cshl.edu (Generation 0 Data from Hannon Lab), Carrie Davis davisc@cshl.edu (experimental), Alex Dobin dobin@cshl.edu (computational), Wei Lin wlin@cshl.edu (computational), Tom Gingeras gingeras@cshl.edu (primary investigator)). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). hg18: This data was produced by Hannon lab part of Cold Spring Harbor as part of the ENCODE Project. The series depicts NextGen sequencing information for RNAs between the sizes of 20-200 nt isolated from RNA samples from tissues or sub cellular compartments of cell lines. hg19: This track depicts NextGen sequencing information for RNAs between the sizes of 20-200 nt isolated from RNA samples from tissues or sub cellular compartments from ENCODE cell lines. The overall goal of the ENCODE project is to identify and characterize all functional elements in the sequence of the human genome. hg19: This cloning protocol generates directional libraries that are read from the 5' ends of the inserts, which should largely correspond to the 5' ends of the mature RNAs. The libraries were sequenced on a Solexa platform for a total of 36, 50 or 76 cycles however the reads undergo post-processing resulting in trimming of their 3' ends. Consequently, the mapped read lengths are variable. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf hg18: Small RNAs between 20-200 nt were ribominus treated according to the manufacturer's protocol (Invitrogen) using custom LNA probes targeting ribosomal RNAs (some datasets are also depleted of U snRNAs and high abundant microRNAs). The RNA was treated with Tobacco Alkaline Pyrophosphatase to eliminate any 5' cap structure. Poly-A Polymerase was used to catalyze the addition of C's to the 3' end. The 5' ends were phosphorylated using T4 PNK and an RNA linker was ligated onto the 5' end. Reverse transcription was carried out using a poly-G oligo with a defined 5' extension. The inserts were then amplified using oligos targeting the 5' linker and poly-G extension and containing sequencing adapters. The library was sequenced on an Illumina GA machine for a total of 36, 50 or 76 cycles. Initially 1 lane is run. If an appreciable number of mappable reads are obtained, additional lanes are run. Sequence reads underwent quality filtration using Illumina standard pipeline (Gerlad). The read lengths may exceed the insert sizes and consequently introduce 3' adaptor sequence into the 3' end of the reads. The 3' sequencing adaptor was removed from the reads using a custom clipper program, which aligned the adaptor sequence to the short-reads, allowing up to 2 mismatches and no indels. Regions that aligned were "clipped" off from the read. The trimmed portions were collapsed into identical reads, their count noted and aligned to the human genome (NCBI build 36, hg18 unmasked) using Nexalign (Lassmann et al., not published). The alignment parameters are tuned to tolerate up to 2 mismatches with no indels and will allow for trimmed portions as small as 5 nucleotides to be mapped. We report reads that mapped 10 or fewer times. Data obtained from each lane is processed and mapped independently. The processed/mapped data from each lane is then complied as a single track without additional processing and submitted to UCSC. Consequently, identical reads within a lane were collapsed and their value is reported as the "transfrag" signal value. However, the redundancy between lanes has not been eliminated so the same transfrag may appear multiple times within a signal. hg19: Small RNAs between 20-200 nt were ribominus treated according to the manufacturer's protocol (Invitrogen) using custom LNA probes targeting ribosomal RNAs (some datasets are also depleted of U snRNAs and high abundant microRNAs). The RNA was treated with Tobacco Alkaline Pyrophosphatase to eliminate any 5' cap structures. Poly-A Polymerase was used to catalyze the addition of C's to the 3' end. The 5' ends were phosphorylated using T4 PNK and an RNA linker was ligated onto the 5' end. Reverse transcription was carried out using a poly-G oligo with a defined 5' extension. The inserts were then amplified using oligos targeting the 5' linker and poly-G extension and containing sequencing adapters. The library was sequenced on an Illumina GA machine for a total of 36, 50 or 76 cycles. Initially, one lane was run. If an appreciable number of mappable reads were obtained, additional lanes were run. Sequence reads underwent quality filtration using Illumina standard pipeline (GERALD). The Illumina reads were initially trimmed to discard any bases following a quality score less than or equal to 20 and converted into FASTA format, thereby discarding quality information for the rest of the pipeline. As a result, the sequence quality scores in the BAM output are all displayed as "40" to indicate no quality information. The read lengths may exceed the insert sizes and consequently introduce 3' adapter sequence into the 3' end of the reads. The 3' sequencing adapter was removed from the reads using a custom clipper program (available at http://hannonlab.cshl.edu/fastx_toolkit/), which aligned the adapter sequence to the short-reads using up to 2 mismatches and no indels. Regions that aligned were "clipped" off from the read. Terminal C nucleotides introduced at the 3' end of the RNA via the cloning procedure are also trimmed. The trimmed portions were collapsed into identical reads, their count noted and aligned to the human genome (version hg19, using the gender build appropriate to the sample in question - female/male) using Bowtie (Langmead B. et al). The alignment parameter allowed 0, 1, or 2 mismatches iteratively. We report reads that mapped 20 or fewer times. Discrepancies between hg18 and hg19 versions of CSHL small RNA data: The alignment pipeline for the CSHL small RNA data was updated upon the release of the human genome version hg19, resulting in a few noteworthy discrepancies with the hg18 dataset. First, mapping was conducted with the open-source Bowtie algorithm (http://bowtie-bio.sourceforge.net/index.shtml) rather than the custom NexAlign software. As each algorithm uses different strategies to perform alignments, the mapping results may vary even in genomic regions that do not differ between builds. The read processing pipeline also varies slightly, in that we no longer retain information regarding whether a read was 'clipped' off adapter sequence.
2010-10-08 | E-GEOD-24565 | biostudies-arrayexpress
Project description:Raw RNA-seq counts from reads aligned to the mouse genome (mm10 assembly) were generated with Htseq-count (PMID: 25260700). Differentially expressed genes between IPr and Ctl MSCs were calculated by DESeq2 (PMID: 25516281). Pre-ranked gene set enrichment was performed as recommended for RNA-seq data (PMID: 16199517). Custom R scripts were used to filter highly redundant biological processes. A false discovery rate of 0.05 was considered as an acceptable threshold for further investigation. All analyses were conducted in R (v3.6) or Python (v3.7) programming language. The ggplot2, ClusterProfiler and dplyr packages were used for data visualization. Student’s t-test was performed for normally distributed data to compute the p-value and the Benjamini-Hochberg procedure was used for adjusting the statistical inference of multiple comparisons.
2022-01-20 | GSE183055 | GEO
Project description:The goal of this study was to conduct an in-depth analysis of the human placental transcriptome. RNA was extracted from 16 placental samples using TRIzol following the manufacturer’s protocol. All samples were spiked with 96 External RNA Controls Consortium (ERCC) ExFold RNA transcripts. Ribosomal RNAs were depleted from samples using Ribo-Zero Gold and sequencing libraries were prepared using Illumina TruSeq Stranded Total RNA Sample Preparation kits. Sequencing was performed on the Illumina Hi-Seq 2500 using a 100bp paired-end protocol. Sequence adapters were trimmed using AdapterRemoval with options --trimns, --minlength 20. Trimmed RNA-Seq reads were aligned to known UCSC hg19 genes and the hg19 genome using Bowtie 2 v2.1.0 and TopHat v2.0.9 with options --library-type=fr-firststrand --mate-inner-dist -20 --mate-std-dev 180. UCSC hg19 reference genome and transcriptome was obtained through Illumina iGenomes (https://support.illumina.com/sequencing/sequencing_software/igenome.html). Aligned RNA-Seq reads were summarised using the summarizeOverlaps algorithm with the UCSC known genes hg19 GTF file using the the options overlapMode=``Union'', ignoreStrand=FALSE, singleEnd=FALSE, fragments=TRUE to generate a table of unique read counts per gene for each sample. All samples were processed in the same way, with all sequencing libraries created in the same batch and sequenced together.
2016-08-31 | GSE77085 | GEO