Aed7a12b-47a4-4cbe-ae8a-b35fe74a9545 - samples
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ABSTRACT:
PROVIDER: EGAD00001008448 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Purpose: The study aimed to characterize the molecular phenotype of bone marrow macrophages in NHL with RNA sequencing analysis. Methods:RNA of sorted femur macrophages (CD11b+F4/80+) were extracted with an RNA extraction kit (Qiagen). The samples were submitted to Novogene Inc. for library preparation and subsequent RNA sequencing. RNA was used for cDNA library construction using an NEBNext® Ultra RNA Library Prep Kit for Illumina® (New England Biolabs) according to the manufacturer’s protocol. The resulting 250-350 bp insert libraries were quantified using a Qubit 2.0 fluorometer (Thermo Fisher Scientific) and quantitative PCR. Size distribution was analyzed using an Agilent 2100 Bioanalyzer (Agilent Technologies). Qualified libraries were sequenced on an Illumina HiSeq 4000 Platform (Illumina) using a paired-end 150 run (2×150 bases). Reads containing adapter or poly-N and those of low quality were trimmed, after which gene counts were obtained though mapping the clean reads to reference genome mm10 using STAR 2.5.3a.
2021-12-12 | GSE190598 | GEO
Project description:C2C12 cells were UV-irradiated at 400 mJ, and whole cell lysates were harvested from the cells. tRIP was performed as previously described (Masuda et al). Samples were sequenced on the Illumina NovaSeq6000 with 150 bp paired-end read (Macrogen, Japan) or Miseq with 150 bp single-read at the core facility of the Nagoya University. For paired-end read data, only P5 reads were used for analysis. Briefly, after standard HiSeq demultiplexing, reads were adapter-trimmed and reads less than 18 bp were discarded using cutadapt (v1.10). Mapping was first performed against the mouse repetitive elements in RepBase with STAR (v2.5.2b). Repeat-mapped reads were removed, and all others were then mapped against the mouse genome (mm10) with STAR (v 2.5.2b). Multiply mapped reads were filtered out. Duplicates of reads uniquely mapped to the human or mouse genome were removed by Picard (v2.0.1).
2021-07-20 | GSE161602 | GEO
Project description:For ATAC-seq data processing, we used the ENCODE ATAC-seq pipeline (https://www.encodeproject.org/atac-seq/). In detail, for each sample, ATAC-seq reads were first checked for adaptor contamination. Then, adaptor trimmed reads were aligned to hg19 using Bowtie2 under paired-end mode with parameter “-X2000”, which permits 2000 bp for the maximum allowable insert size between two paired ends of each read. Using the ENCODE ATAC-seq pipeline, duplicated reads were properly filtered out. ATAC-seq enriched peaks were called using MACS2 (Zhang et al., 2008) based on remaining unique reads with parameters “-f BAMPE -q 0.05 --nomodel”. In total, we collected 45,569 peaks for the ARID1AWT sample and 27,480 peaks for the ARID1AKO sample.
2020-06-08 | GSE106660 | GEO
Project description:We present a draft genome assembly that includes 200 Gb of Illumina reads, 4 Gb of Moleculo synthetic long-reads and 108 Gb of Chicago libraries, with a final size matching the estimated genome size of 2.7 Gb, and a scaffold N50 of 4.8 Mb. We also present an alternative assembly including 27 Gb raw reads generated using the Pacific Biosciences platform. In addition, we sequenced the proteome of the same individual and RNA from three different tissue types from three other species of squid species (Onychoteuthis banksii, Dosidicus gigas, and Sthenoteuthis oualaniensis) to assist genome annotation. We annotated 33,406 protein coding genes supported by evidence and the genome completeness estimated by BUSCO reached 92%. Repetitive regions cover 49.17% of the genome.
2019-12-16 | PXD016522 | Pride
Project description:A cDNA library was constructed by Novogene (CA, USA) using a Small RNA Sample Pre Kit, and Illumina sequencing was conducted according to company workflow, using 20 million reads. Raw data were filtered for quality as determined by reads with a quality score > 5, reads containing N < 10%, no 5' primer contaminants, and reads with a 3' primer and insert tag. The 3' primer sequence was trimmed and reads with a poly A/T/G/C were removed
2021-07-21 | GSE180131 | GEO
Project description:Purpose: The goals of this study are to obtain the lncRNA landscape in 6 distinct germ cell types during mouse spermatogenesis. Methods: RNA-seq data of lncRNAs from 6 distinct cell types were generated by deep sequencing using Illumina HiSeq 2500. The sequence reads that passed quality filters were analyzed at the transcript isoform level with TopHat followed by Cufflinks. qRT-PCR validation was performed using SYBR Green assays. Results: Using the rRNA minus strategy, we generated a total of 1100.65 Gb paired and sequence reads with a length of 150 bp. Next, we trimmed the adaptor sequence and filtered the sequence data for low-quality reads at a high stringency with an average Phred quality score less than 20, which resulted in a total of 745.67 Gb high-quality sequence reads. After filtering the reads, almost all sequence reads had a Phred quality score higher than 20, and the percentage of Ns in the reads was nearly zero. By using unique whole-genome alignment (mouse genome (mm10)), a total of 14587 mRNA genes and 6921 lncRNA genes were expressed as fragments per kilobase of exon per million reads mapped (FPKM)>0.1 in at least one sample. In addition, to obtain fingerprint lncRNAs during spermatogenesis, we first identified 2327 differentially expressed lncRNAs between every two adjacent stages. Furthermore, we identified 437 fingerprint lncRNAs that were expressed at a FPKM > 10 of its highly expressed stage. Conclusions: Our RNA-seq dataset comprehensively dissects the dynamic expression mode of lncRNAs during mouse spermatogenesis and generated 28 relatively robust potential lncRNA candidates in spermatogenesis.
2020-11-16 | GSE145130 | GEO
Project description:Background: Whole exome sequencing (WES) has been proven to serve as a valuable basis for various applications such as variant calling and copy number variation (CNV) analyses. For those analyses the read coverage should be optimally balanced throughout protein coding regions at sufficient read depth. Unfortunately, WES is known for its uneven coverage within coding regions due to GC-rich regions or off-target enrichment. Results: In order to examine the irregularities of WES within genes, we applied Agilent SureSelectXT exome capture on human samples and sequenced these via Illumina in 2x101 paired-end mode. As we suspected the sequenced insert length to be crucial in the uneven coverage of exome captured samples, we sheared 12 genomic DNA samples to two different DNA insert size lengths, namely 130 and 170 bp. Interestingly, although mean coverages of target regions were clearly higher in samples of 130 bp insert length, the level of evenness was more pronounced in 170 bp samples. Moreover, merging overlapping paired-end reads revealed a positive effect on evenness indicating overlapping reads as another reason for the unevenness. In addition, mutation analysis on a subset of the samples was performed. In these isogenic subclones almost twofold mutations were failed in the 130 bp samples when compared to the 170 bp samples. Visual inspection of the discarded mutation sites exposed low coverages at the sites embedded in high amplitudes of coverage depth in the affected region. Conclusions: Producing longer insert reads could be a good strategy to achieve better uniform read coverage in coding regions and hereby enhancing the effective sequencing yield to provide an improved basis for further variant calling and CNV analyses.
2016-07-28 | E-MTAB-4527 | biostudies-arrayexpress
Project description:Endosperms (with the embryo, aleurone, seed coat, and pericarp removed) at 5, 10, 15, 20, and 25 DPAs of the T. urartu accession PI 428198 were used for RNA-Seq analysis. Each sample had three independent biological replicates. The total RNA of endosperms was isolated and a library with an insert length of ~350 bp was sequenced using the Illumina HiSeq 2000 (San Diego, CA, USA). An integrated reference sequence of T. urartu PI 428198 was prepared by combining the annotated gene set (ftp://ftp.ensemblgenomes.org/pub/plants/release3 9/fasta/triticum_urartu) with cloned low-molecule-weight glutenin subunit genes and gliadin genes after gene redundancy removal. The relative transcript abundance of seed storage protein genes and the 41,507 predicted genes from genome sequences were calculated using the unique aligned reads as the expected number of fragments per kilobase of the transcript sequence per million base pairs sequenced (FPKM).
2021-02-01 | GSE165862 | GEO
Project description:Purpose: Exploring the alteration in histone acetylation colocalized with CREB/CRTC binding, which are important for long-term memory maintenance Methods: The nuclei of Drosophila mushroom bodies were purified and the histone acetylation and CREB/CRTC binding were examined by ChIP-seq using specific antibodies. The sequencing was performed using SOLiD, and aligned to dm3 using Lifescope..The sequence reads that passed quality filters were analyzed by peak calling using PICS and ERD equipped on Strand NGS software. This method using two algorisms excludes false-positive calling of peaks. The PICS and ERD were run using a default setting except for the following parameters; for PICS, 120 bp as an average fragment length, 10 bp as a minimum distance between forward and reverse reads, 200 bp as a minimum distance between forward and reverse reads, 100 bp as a window width, with 5% false discovery rate; for ERD, 2 as an enrichment factor, 100 bp as a window size, 10 bp as a window slide size, 100 bp as a minimum region size. The peaks obtained by ERD analysis were filtered by an enrichment factor of 1.5, and a density of reads at 0.3 for H3K9Ac, 0.15 for H4K16Ac, 0.18 for CREB and 0.15 for CRTC. To determine the histone acetylation-increased sites, the peaks determined from 3 replicates of the trained samples were summed to analyze all peaks. Then the centers of the H3K9Ac and H4K16Ac peaks were extracted, and then used to create neighboring 400 bp windows. The mapped reads were counted in these 400 bp windows for each biological replicate of ChIP-seq analysis. The counted read numbers in individual regions were normalized by elav for H3K9Ac ChIP-seq, and gapdh2 for H4K16Ac ChIP. The normalized read numbers in individual windows obtained from the spaced trained flies and the naïve flies were analyzed by Student’s t test (p < 0.05). Standard deviations of the normalized read numbers in individual windows from 3 replicates of naïve flies were 0.07 in H3K9Ac-ChIP-seq and 0.10 in H4K16Ac-ChIP-seq. Significantly increased peaks showing a more than 1.2-fold increase (>2SD) were determined as sites with an increase in H3K9Ac or H4K16Ac. Results: Using an optimized data analysis workflow, the filtered reads amounted to 12-15 million reads for H3K9Ac in each of 3 replicates, 5-7 million reads for H4K16 in each of 3 replicates, 2.4 million reads for CREB, 2 million reads for CRTC, and 2.9 million reads for input. Using anti-H3K9Ac antibody, we found significant increases in H3K9Ac 1 day after training, and 75.0% of these mapped within 500bps of the transcriptional start sites (TSSs) of 1766 genes. Using anti-H4K16Ac antibody, we found significant increases in H4K16Ac 1 day after spaced training, and 61.6% of these mapped within 500bps of the TSSs of 1320 genes. Epigenetic changes in the vicinity of TSSs suggests that expression of these genes may be increased in LTM maintenance. We also examined CRTC and CREB binding 1 day after training. We identified 2390 CRTC binding sites, of which 79.6% of these were close to CREB binding sites. These CREB/CRTC binding sites mapped to 1394 genes, and were predominately located within 500bp from TSSs (55.4%). Of the 1394 CREB/CRTC target genes, 346 genes showed increases in H3K9Ac, 319 showed increases in H4K16Ac, and 135 showed increases in both. Conclusions: Our study represents the first detailed analysis of chromatin state related to memory in Drosophila mushroom bodied. The optimized data analysis workflows reported here should provide a framework for comparative investigations of tissue-specific epigenetic alterations.
2017-02-09 | GSE73386 | GEO