Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Deep-sequencing influences the results obtained in small-RNA sequencing

ABSTRACT: This study presents a comparison of small RNA sequencing libraries generated from the same cell lines but using different sequencing platforms and protocols. The samples were analyzed and compared at the level of miRNAs expression and as a population of small RNAs derived from repetitive elements. Despite a good correlation between sequencing platforms, there are qualitative and quantitative variations in the results depending on the protocol used. 10 samples were examined: 6 from the ES E14 XY cell type: 1 454, 2 SOLiD from 2 technological versions, and 3 SOLEXA from 3 different protocols, and 4 samples from ES PGK XX cells: 1 454 and 1 SOLEXA sample, and 2 SOLiD samples from 2 technological versions.

ORGANISM(S): Mus musculus

SUBMITTER: Nicolas Servant

PROVIDER: E-GEOD-35368 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Deep-sequencing protocols influence the results obtained in small-RNA sequencing.

Toedling Joern J Servant Nicolas N Ciaudo Constance C Farinelli Laurent L Voinnet Olivier O Heard Edith E Barillot Emmanuel E

PloS one 20120227 2

Second-generation sequencing is a powerful method for identifying and quantifying small-RNA components of cells. However, little attention has been paid to the effects of the choice of sequencing platform and library preparation protocol on the results obtained. We present a thorough comparison of small-RNA sequencing libraries generated from the same embryonic stem cell lines, using different sequencing platforms, which represent the three major second-generation sequencing technologies, and pr ...[more]

PMID: 22384282

Similar Datasets

Project description:We previously reported widespread differential expression of long non-protein-coding RNAs (ncRNAs) in response to virus infection. Here, we expanded the study through small RNA transcriptome sequencing analysis of the host response to both severe acute respiratory syndrome coronavirus (SARS-CoV) and influenza virus infections across four founder mouse strains of the Collaborative Cross, a recombinant inbred mouse resource for mapping complex traits. We observed differential expression of over 200 small RNAs of diverse classes during infection. A majority of identified microRNAs (miRNAs) showed divergent changes in expression across mouse strains with respect to SARS-CoV and influenza virus infections and responded differently to a highly pathogenic reconstructed 1918 virus compared to a minimally pathogenic seasonal influenza virus isolate. Novel insights into miRNA expression changes, including the association with pathogenic outcomes and large differences between in vivo and in vitro experimental systems, were further elucidated by a survey of selected miRNAs across diverse virus infections. The small RNAs identified also included many non-miRNA small RNAs, such as small nucleolar RNAs (snoRNAs), in addition to nonannotated small RNAs. An integrative sequencing analysis of both small RNAs and long transcripts from the same samples showed that the results revealing differential expression of miRNAs during infection were largely due to transcriptional regulation and that the predicted miRNA-mRNA network could modulate global host responses to virus infection in a combinatorial fashion. These findings represent the first integrated sequencing analysis of the response of host small RNAs to virus infection and show that small RNAs are an integrated component of complex networks involved in regulating the host response to infection. IMPORTANCE: Most studies examining the host transcriptional response to infection focus only on protein-coding genes. However, mammalian genomes transcribe many short and long non-protein-coding RNAs (ncRNAs). With the advent of deepsequencing technologies, systematic transcriptome analysis of the host response, including analysis of ncRNAs of different sizes, is now possible. Using this approach, we recently discovered widespread differential expression of host long (>200 nucleotide[nt]) ncRNAs in response to virus infection. Here, the samples described in the previous report were again used, but we sequenced another fraction of the transcriptome to study very short (about 20 to 30 nt) ncRNAs. We demonstrated that virus infection also altered expression of many short ncRNAs of diverse classes. Putting the results of the two studies together, we show that small RNAs may also play an important role in regulating the host response to virus infection. The small RNA transcriptome deep sequencing analysis was performed on lung samples from our previously published study (Unique signatures of long noncoding RNA expression in response to virus infection and altered innate immune signaling , X Peng, MBio. 2010 Oct 26;1(5). pii: e00206-10.). We infected four of the eight founder mouse strains used in generating the Collaborative Cross, a recombinant inbred mouse resource for mapping complex traits (41). These strains included 129S1/SvImJ (129/S1), WSB/EiJ (WSB), PWK/PhJ (PWK), and CAST/EiJ (CAST) mice. Ten-week-old mice were intranasally infected with phosphate-buffered saline (PBS) alone or with 1X10^5 PFU of mouse adapted severe acute respiratory syndrome coronavirus (SARS-CoV; rMA15), or 500 PFU of influenza A virus strain A/Pr/8/34 (H1N1; PR8). To match the previous whole-transcriptome analysis, we performed small RNA transcriptome sequencing analysis on the same eight samples from mice with SARS-CoV infections, including one SARS-CoV rMA15-infected mouse and one matched mock-infected mouse from each of the four strains at 2 days postinfection (dpi). In addition, we sequenced the small RNA transcriptome for 12 samples obtained from influenza virus infected mice, including two PR8-infected mice and one matched mockinfected mouse from each of the four strains at 2 dpi.

Project description:MicroRNAs (miRNAs) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 non-invasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed-sequence-dependent miRNA-target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most non-invasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including mir-98/let-7), with most miRNA changes apparent already in the non-invasive carcinomas. In addition, patients that went on to develop metastasis demonstrated increased expression of mir-423, and triple negative breast carcinomas were most distinct from other tumor subtypes due to up-regulation of the mir-17~92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible, but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested. 21 barcoded sequencing runs, including 185 unique samples and 54 samples in replicate (6 in triplicate and the remaining in duplicate). The details of each sample can be found in Supplementary Tables S1 and S2.

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.

Dataset Information

Deep-sequencing influences the results obtained in small-RNA sequencing

Publications

Deep-sequencing protocols influence the results obtained in small-RNA sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure