Pre-T cells from control and Zfp36l1, Zfp36l2 double conditional knockout mice. [iCLIP]
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE79178 | GEO | 2016/09/05
SECONDARY ACCESSION(S): PRJNA315111
REPOSITORIES: GEO
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Project description:Purpose: We aimed to identify the targets of the RNA binding protein ZFP36L1 in thymoctes. Methods: Total naïve thymocytes from control or DCKO mice were treated with UV light to crosslink RNA and proteins, then RNA-protein complexes were pulled down with anti-ZFP36L1. RNA was extracted and used to make cDNS libraries that were then sequenced by MiSeq 150bp single-end read (Sample 1) and HiSeq2500 RapidRun 50bp single-end read (sample 2, 3). Results: Sample demultiplexing was performed by identification of the 3 known bases of the 7 bases barcode introduced in the 5â end of the read by the RCLIP primer. The remaining four random bases were used to remove PCR duplicate reads. Reads were trimmed to remove any adaptor sequence and barcodes before mapping reads to genome GRCm38 using Bowtie. After read mapping, the single-nucleotide at position -1 was annotated as unique ZFP36L1 crosslink site. Identification of highly significant ZFP36L1 binding sites was performed using iCount to assign a FDR to each crosslink site. Conclusions: We identified ZFP36L1 binding sites in 8675 thymocyte mRNAs. Individual nucleotide resolution cross linking immunoprecipitation (iCLIP) of ZFP36L1-bound RNAs in total naive thymocytes from C57BL/6 mice.
2016-09-05 | E-GEOD-79178 | biostudies-arrayexpress
Project description:Purpose: We aimed to identify the targets of the RNA binding protein ZFP36L1 in B cells. Methods: Mature B cells were stimulated with LPS, IL-4 and IL-5 for 48 hours to induce ZFP36L1 expression. Cells were treated with UV light to crosslink RNA and proteins then RNA-protein complexes were pulled down with anti-ZFP36L1. RNA was extracted and used to make cDNS libraries that were then sequenced using Illumina’s HiSeq2000 (100 bp single end sequencing). Results: Sample demultiplexing was performed by identification of the 3 known bases of the 7 bases barcode introduced in the 5’ end of the read by the RCLIP primer. The remaining four random bases were used to remove PCR duplicate reads. Reads were trimmed to remove any adaptor sequence and barcodes before mapping reads to genome mm10 using Bowtie. After read mapping, the single-nucleotide at position -1 was annotated as unique ZFP36L1 crosslink site. Identification of highly significant ZFP36L1 binding sites was performed using iCount to assign a FDR to each crosslink site Conclusions: We identified ZFP36L1 binding sites in 1361 B cell mRNAs.
2016-04-21 | GSE78239 | GEO
Project description:Purpose: We aimed to identify the targets of the RNA binding protein TruB1 in HEK293FT cells. Methods: Total RNA from endogenous-Flag KI cells were treated with UV light to crosslink RNA and proteins, then RNA-protein complexes were pulled down with anti-Flag. RNA was extracted and used to make cDNS libraries that were then sequenced by MiSeq 150bp single-end read (Sample1). Results: Reads were trimmed to remove any adaptor sequence and barcodes before mapping reads to genome hg38 using Bowtie2. Conclusions: We identified TruB1 binding sites in tRNAs and noncodingRNAs.
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Project description:Purpose: Conditional knockout of Zfp36l1 Zfp36l2 in pro-B cells perturbs B cell development leading to reduced V(D)J recombination and diminished numbers of cells in successive stages of development. This RNA seq experiment aimed to determine the molecular pathways affected by loss of Zfp36l1 and Zfp36l2, and to deduce direct targets of these RNA binding proteins. Methods: RNAseq libraries were prepared from 0.1 µg of RNA from sorted control and DCKO late pre-B cells using TruSeq RNA sample preparation kit v2 modified to be strand specific using the dUTP method. Libraries were sequenced by an Illumina genome analyzer II measuring 54bp single-end reads. Over 30 million reads were measured from each sample. The reads were trimmed to remove adapter sequences using Trim Galore then mapped using Tophat (version 1.1.4) to the NCBIm37 mouse assembly (April 2007, strain C57BL/6J); reads with an identical sequence to more than one genomic locus were not mapped. Quality control analysis was carried out with FastQC. Results: Read counts for each gene were generated in SeqMonk: transcripts from the same gene were collapsed into a single transcript containing all exons, so total reads were counted without considering alternative splice forms. Since the libraries were strand-specific only reads on the opposing strand were counted. Differences in the abundance of transcripts between DCKO and control late pre-B cells were calculated in the R/Bioconductor program DESeq (version 1.12.1). Adjusted P values for differential expression were calculated in DESeq using a Benjamini-Hochberg correction: genes with an adjusted p-value of less than 5% were considered significant. Differentially expressed mouse transcripts identified using DESeq were analyzed for gene set enrichment using Toppfun. Conclusions: We identified an enrichment of mRNAs involved in cell cycle progression within Zfp36l1 Zfp36l2 double conditional knockouts.
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Project description:HDMYZ cells were treated with 2ug/ml ActD for 0, 4 and 12 hours. Small RNAs of 15-40 bases were gel-purified from 10 ug total RNA, and subjected to multiplex Illumina small RNA library preparation. Small RNA libraries were sequenced on a HiSeq2000 (Illumina) with 3 samples per lane. To quantify miRNA and isoform abundance, sequence reads were processed by the miRDeep2 package, with the following modifications. First, to remove adaptor sequence, we removed both the main adaptor sequence present in the sequencing reads, as well as the second most abundant adaptor variant. In addition, we did not restrict the size of small RNAs during adaptor removal. Second, we used miRBase v18 for mapping the reads. Third, for quantifying miRNA and isoform frequency, we limited reads to more or equal to 15 bases in length with zero mis-match during mapping. The number of reads that were mapped to known miRNAs was used to normalize read frequencies for each miRNA or each miRNA isoform. For quantification purposes, we only considered miRNAs or isoforms that had frequency >= 1x10e-6 in samples without ActD treatment, which correspond to ~21-30 reads in raw count. These miRNAs or isoforms were referred to as reliably quantifiable.To analyze mapping to the genome, we removed reads that mapped to miRNA precursors. The rest of the reads were then mapped to the genome with Bowtie.
2017-09-02 | GSE46968 | GEO
Project description:This track depicts high throughput sequencing of long RNAs (>200 nt) from RNA samples from tissues or subcellular 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. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Sample preparation and sequencing: K562 and GM12878 total cell, total RNA: Standard Illumina Pair-end kit with the sole exception that a "tagged" random hexamer was used to prime the 1st strand synthesis: 5'-ACTGTAGGN6-3'. The addition of this tag is what permits us to make strand assignments for the reads. The sequence of the tag is reported in the 5' end of the read. Asymmetric PCR can place the tag on either the 1st or 2nd read depending on which strand it used as a template. Strand assignments are made by looking for the tag at the 5' end of either read 1 or read 2. Read 1 is physically linked to read 2. Therefore, if a tag is present on one end strand assignments are made for both ends. We noted during analysis that the tags are generally 5' truncated. We only "strand" reads that contain ACTGTAGG, CTGTAGG, TGTAGG, GTAGG. Between 63-68% of reads could be stranded in these libraries. It is possible to cull additional stranded reads that contain non-templated TAGG, AGG, GG, or G sequences at their 5' end. The peak in insert size distribution is between 200-250 nucleotides. K562 cytosol, polyA+ RNA: Oligo-dT selected poly-A+ RNA was RiboMinus-treated according to the manufacturer's protocol (Invitrogen). The RNA was treated with tobacco alkaline pyrophosphatase to eliminate any 5' cap structures and hydrolyzed to ~200 bases via alkaline hydrolysis. The 3' end was repaired using calf intestinal alkaline phosphatase, and poly-A polymerase was used to catalyze the addition of Cs to the 3' end. The 5' end was 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. This cloning protocol generated stranded reads that were read from the 5' ends of the inserts. The library was sequenced on a Solexa platform for a total of 36 cycles; however, the reads underwent post-processing, resulting in trimming of their 3' ends. Consequently, the mapped read lengths are variable. Analysis: K562 and GM12878 total cell, total RNA: Tags were removed from the 5' ends of the reads in accordance to their lengths and strand assignments made. Subsequently, the reads were trimmed from their 3' ends to a final length of 50 nucleotides and were mapped using NexAlign, a program developed by Timo Lassman, RIKEN. We allowed up to 2 mismatches across the entire length and only report reads that mapped to a single/unique locus in the assembled hg18 genome. K562 cytosol, polyA+ RNA: Reads were mapped to the human (hg18, March 2006) assembly using Nexalign, with only uniquely mapping (one loci), exactly matching (no mis-matches) aligned reads reported in the processed files, as follows: 1) Collect the read sequences from Illumina non-filtered output files. 2) Filter out all reads that contain undefined nucleotides ('N'). 3) Perform iterative alignment/C-tail chopping algorithm (below). On each alignment step, the reads are aligned to the genome with 100% identity. All reads that align to a single locus are withdrawn from the alignment pool and only the reads that could not be aligned continue to the next step. a) Align to the hg18 genome using Nexalign 1.3.3 (© Timo Lassmann) without chopping off any nucleotides. b) Chop off any C-blocks (until the first non-C) at the ends of the reads. c) Align to the genome -> remove and save those that align. d) Chop off any non-Cs until the next C. e) Chop off C-block until the next non-C. f) Align to the genome -> remove and save those that align. g) Repeat steps d, e, and f until the reads align to the genome, or chopping results in the reduction of the reads' lengths to below 16 (default), or there are no non-Cs left.
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