Project description:Purpose: The goal of this study is to identify the miRNA clusters that are regulated by EGFR under normoxia or hypoxia. Method: Total RNAs were extracted from HeLa cells expressing scrambled control or EGFR shRNA-E1 that cultured under normoxia or hypoxia (1% O2) for 24h. Customized Next-Generation RNA deep sequencing, including both small RNA application and whole transcriptome analysis, was performed according to the standard procedure instructed by Applied Biosystems. For small RNA analysis, library inserts were size selected between 18 and 40nts and analyzed using CLC Genomics Workbench 4.7.1. 35nt colorspace reads were trimmed of adaptor sequence and mapped against human pre-miR sequences (miRBase version 16.0). Values of reads per million mapped reads (RPM) were based on mapped reads with no more than 2 mismatches total. A read was considered to come from a mature miRNA if it mapped to pre-miRNA sequences with no more than three upstream or downstream bases, and missing no more than two upstream or downstream bases from predicted mature or mature* sequences as defined in miRBase version 16.0. All the other pre-miRNA mapped reads were assigned as pre-miRNA signal. qRT–PCR validation was performed using TaqMan and SYBR Green assays. Results: Deep sequencing analysis identified specific miRNA clusters that their maturation (miRNA processing efficacy was reflected by the relative expression of precursor miRNAs affected by EGFR compared with the relative expression of mature miRNAs affected by EGFR) were regulated by EGFR under normixa or specifically in response to hypoxia. Top miRNA candidates that regulated by EGFR in response to hypoxia were further verified by TaqMan and SYBR Green qRT-PCR assays. Conclusion: Next-Generation Deep Sequencing for small RNA analysis revealed a novel function of EGFR involved in miRNA maturation in response to hypoxic stress. RNA profiles of HeLa cells expressing scrambled control (S) or EGFR shRNA-E1 (A1) that cultured under normoxia or hypoxia (1% O2) for 24h were generated by AB SOLiD next-generation sequencing. S: HeLa expressing scrambled control cultured under normoxia; A1: HeLa expressing EGFR shRNA-E1 cultured under normoxia; HS: HeLa expressing scrambled control cultured under hypoxia for 24h; HA1: HeLa expressing EGFR shRNA-E1 cultured under hypoxia for 24h. In total, 4 biological samples with no replicates resulted in 4 small RNA profiles.

Project description:Purpose: The goal of this study is to identify the mRNA clusters that are regulated by EGFR under normoxia or hypoxia. Method: Total RNAs were extracted from HeLa cells expressing scrambled control or EGFR shRNA-E1 that cultured under normoxia or hypoxia (1% O2) for 24h. Customized Next-Generation RNA Deep Sequencing, including both small RNA application and whole transcriptome analysis, was performed according to the standard procedure instructed by Applied Biosystems. For whole transcriptome analysis, SOLiD fragment colorspace transcriptome reads (50nt) were mapped to the human genome (hg19) and assigned to ensemble transcripts using Bioscope 1.3.1 (Life Technologies). The values of reads per kilobase per million reads (RPKM) were determined by Bioscope 1.3.1 CountTags tool using default parameters. Primary alignments with a minimum mapping quality of 10 and minimum alignment score of 10 were counted. Results: Deep sequencing analysis identified subclasses of mRNAs that were affected by EGFR either under normoxia or hypoxia. EGFR-regulated mRNAs (with Log2 fold-change affected by EGFR M-bM-^IM-% 0.4 or M-bM-^IM-$ -0.4) were sorted and over-lapped with mRNAs that were targeted (based on published data and TargetScan prediction with total context score M-bM-^IM-$ -0.20) by the top miRNA candidates affected by EGFR under hypoxia, resulting in 439 mRNAs that regulated by EGFR and likely targeted by the miRNA candidates in response to hypoxia. Conclusion: Whole transcriptome analysis revealed a novel cluster of mRNAs that are likely regulated by EGFR through miRNAs in response to hypoxic stress. RNA profiles of HeLa cells expressing scrambled control (S) or EGFR shRNA-E1 (A1) that cultured under normoxia or hypoxia (1% O2) for 24h were generated by AB SOLiD curstomarized next-generation sequencing, including both small RNA application and whole transcriptome analysis. S: HeLa expressing scrambled control cultured under normoxia; A1: HeLa expressing EGFR shRNA-E1 cultured under normoxia; HS: HeLa expressing scrambled control cultured under hypoxia for 24h; HA1: HeLa expressing EGFR shRNA-E1 cultured under hypoxia for 24h. In total, 4 biological samples with no replicates resulted in 4 whole transcriptome RNA profiles.

Project description:Purpose: microRNA profiles were generated from NIH-3T3 cells control and thapsigargin treated, in duplicate. The goal of this study was to compare microRNA profiles of untreated and thapsigargin treated NIH-3T3 fibroblast cells. Methods: NIH-3T3 cells were grown to confluency and either untreated or treated with 500 nM thapsigargin in media for 24 hours. Cells were harvested with TriZol and RNA isolated according to manufacturers protocol Analysis Outline: Short reads in fastq format were assembled using BclToFastq.pl script from Illumina CASAVA 1.8.1 software pipeline.Read quality was examined using FastQC program (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). Adapters were trimmed at the 3'end using Btrim prgram (PMID:21651976), only sequences equal to and longer than 18nt were retained, leading N base was trimmed at the 5' end. Unique reads were collapsed using Raw_data_parse program from miRExpress suite (PMID:19821977) (the result of this process is a file that contains unique sequences in one column and number of times this sequence was found in the library in another). They can be found in *.merge files in trimmed_reads directory. Collapsed reads were reformatted and uploaded into miRanalyzer web-based pipeline (http://bioinfo2.ugr.es/miRanalyzer/miRanalyzer.php; PMID:21515631) and matched to known mature miRNA (miRBase vesion 16), RFAM database (version 15) of known non-coding RNAs and known gene transcripts. The purpose of miRAnalyzer analysis was to only detect known miRNAs, prediction of novel miRNAs was not performed; search parameters were kept at default. MiRanalyzer output is saved in miRanalyzer folder with detailed information about mapping to known miRNA. Known miRNAs were divided into mature, maturestar (star sequences), maturestarunobs (star sequences not in miRBase) and hairpin. For each of the libraries there are files with unique and ambiguous mappings. Differentional expression analysis was based on unique alignments to known miRNAs (mature_unique.txt file in miRanalyzer folder). Mature_unique.txt has following columns: name: mature miRNA ID from miRBase; #unique reads: number of unique reads mapped; readCount: number of reads mapped; norm_expressed_all: normalized to all reads; norm_expressed_mapped: normalized to mapped reads. miRNA expression profiling was performed using edgeR bioconductor package (PMID:20478825). For differential expression analysis, used TMM normalization and analysis using common disperion (using tagwise dispersion yielded the same results). FDR was calculated according to Hochberg-Benjamini procedure (PMID:2218183). Results of differential expression analysis were saved in diff_exp folder as diff_exp.txt. diff_exp.txt contains miRNA concentrations in log scale, log2 ratio of WT to KO; p-values and FDR corrected p-values. miRNAs were sorted by p-value. NIH-3T3 cells grown to confluency and treated with 500 nM thapsigargin in media for 24 hours

Project description:Purpose: microRNA profiles were generated from NIH-3T3 cells control and thapsigargin treated, in duplicate. The goal of this study was to compare microRNA profiles of untreated and thapsigargin treated NIH-3T3 fibroblast cells. Methods: NIH-3T3 cells were grown to confluency and either untreated or treated with 500 nM thapsigargin in media for 24 hours. Cells were harvested with TriZol and RNA isolated according to manufacturers protocol Analysis Outline: Short reads in fastq format were assembled using BclToFastq.pl script from Illumina CASAVA 1.8.1 software pipeline.Read quality was examined using FastQC program (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). Adapters were trimmed at the 3'end using Btrim prgram (PMID:21651976), only sequences equal to and longer than 18nt were retained, leading N base was trimmed at the 5' end. Unique reads were collapsed using Raw_data_parse program from miRExpress suite (PMID:19821977) (the result of this process is a file that contains unique sequences in one column and number of times this sequence was found in the library in another). They can be found in *.merge files in trimmed_reads directory. Collapsed reads were reformatted and uploaded into miRanalyzer web-based pipeline (http://bioinfo2.ugr.es/miRanalyzer/miRanalyzer.php; PMID:21515631) and matched to known mature miRNA (miRBase vesion 16), RFAM database (version 15) of known non-coding RNAs and known gene transcripts. The purpose of miRAnalyzer analysis was to only detect known miRNAs, prediction of novel miRNAs was not performed; search parameters were kept at default. MiRanalyzer output is saved in miRanalyzer folder with detailed information about mapping to known miRNA. Known miRNAs were divided into mature, maturestar (star sequences), maturestarunobs (star sequences not in miRBase) and hairpin. For each of the libraries there are files with unique and ambiguous mappings. Differentional expression analysis was based on unique alignments to known miRNAs (mature_unique.txt file in miRanalyzer folder). Mature_unique.txt has following columns: name: mature miRNA ID from miRBase; #unique reads: number of unique reads mapped; readCount: number of reads mapped; norm_expressed_all: normalized to all reads; norm_expressed_mapped: normalized to mapped reads. miRNA expression profiling was performed using edgeR bioconductor package (PMID:20478825). For differential expression analysis, used TMM normalization and analysis using common disperion (using tagwise dispersion yielded the same results). FDR was calculated according to Hochberg-Benjamini procedure (PMID:2218183). Results of differential expression analysis were saved in diff_exp folder as diff_exp.txt. diff_exp.txt contains miRNA concentrations in log scale, log2 ratio of WT to KO; p-values and FDR corrected p-values. miRNAs were sorted by p-value.

Dataset Information

Next-Generation Sequencing for Small RNA Application in Scrambled Control and EGFR Knockdown Cells Cultured under Normoxia and Hypoxia

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