Ena-DATASET-POPS-26-07-2017-12:21:19:033-887 - samples
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ABSTRACT:
PROVIDER: EGAD00001003462 | EGA |
REPOSITORIES: EGA
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Project description:Aim: To determine whether mtDNA deletions arise upon the loss of mitochondria fusion in heart tissue Method: DNA was isolated heart mitochondria from control and heart-specific double mitofusin1-2 (dMfn) KO animals and used to generate libraries for sequencing to detect mtDNA deletions. Total genomic DNA from the Deletor mouse was provided by Anu Suomalainen-Wartiovaara and used as a positive control for the detection of mtDNA rearrangements. A standard Illumina TrueSeq paired-end library was prepared with ~500 base pair fragment inserts. Paired-end 100 base pair sequencing was conducted using an Illumina HiSeq 2500. The reads were mapped to the genomic sequence without the mitochondria using bowtie (VN: 2.1.0) to remove nuclear-genomic sequences. The unmapped reads were then mapped to the mitochondrial sequence (GenBank JF286601.1) using bwa (n=0.04, VN: 0.6.2-r126) with unmapped reads undergoing an additional mapping round after trimming fastx_trimmer, VN: 0.0.13.2) to ensure higher mapping results. Using samtools (VN: 1.0: samtools –f1 –F14) reads, where the two paired sequences were observed to be greater than 600 base pair apart were identified as those containing deletion breakpoints. These reads were extracted for additional analysis. Results: Control and dMfn KO samples showed similar frequencys of breakpoints, while the positive control (Deletor mouse) showed a high frequecny of large breakpoints.
2018-12-28 | GSE124420 | GEO
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 is produced as part of the ENCODE Project. It shows high throughput sequencing of RNA samples from tissues or sub cellular compartments from cell lines included in the ENCODE Transcriptome subproject. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf The RNA-Seq data were generated from high quality polyA RNA, and the RNA-Seq libraries were constructed using SOLiD Whole Transcriptome (WT) protocol and reagent kit. Total RNA in good quality was used as starting materials and purified twice through MACs polyT column aimed to enrich polyA and remove any contaminants (e.g., rRNA, tRNA, DNA, protein etc.). A one microgram enriched polyA RNA sample was then fragmented to small pieces, and a gel-based selection method was performed to collect fragmented random polyA at a size-range of 50-150 nt in length. The collected fragmental RNA was then hybridized and ligated to a mix of adapters provided from ABI, followed by reverse transcription to generate corresponding cDNAs. The resulting cDNA library was further amplified by PCR and sequenced by SOLiD platform for single reads at 35 bp length (new version in 50 bp length). Cells were grown according to the approved ENCODE cell culture protocols. Data: The SOLiD-generated RNA-Seq reads were 35 bp in length. An initial filtering process was performed to remove any non-desirable contamination sequences, such as rRNA, tRNA, and repeats etc. A read-split mapping approach was developed to map the 35 bp reads onto the reference genome (GRCh37/hg19) excluding mitochondrion, haplotypes, randoms and chromosome Y. Mapping parameters: Strand specific mapping was done using Applied Biosystems' SOLiD alignment where all the reads were mapped to the genome, and to exon-exon junction database. Seed and extend strategy is adopted where initial seed length of 25 is mapped with maximum of 2 mismatches and then extended to read length, each color space match is awarded a score of +1 and each mismatch is awarded a penalty of -2. After extension each read is trimmed to its maximum score, shortest length. The color space sequences are then converted into base space and checked to ensure that each sequence has a maximum of 2 base pair mismatches. If any sequence has more than 2 mismatches, then that sequence is discarded.
2011-02-14 | E-GEOD-27221 | biostudies-arrayexpress
Project description:This data was produced by the Wold lab at Caltech as part of the ENCODE Project. RNA-Seq is a method for mapping and quantifying the transcriptome of any organism that has a genomic DNA sequence assembly. RNA-Seq is performed by reverse-transcribing an RNA sample into cDNA, followed by high throughput DNA sequencing. The resulting sequence reads are then informatically mapped onto the genome sequence. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf The transcriptome measurements shown on these tracks were performed on polyA selected RNA from total cellular RNA. Data have been produced in two formats: single reads, each of which comes from one end of a randomly primed cDNA molecule; and paired-end reads, which are obtained as pairs from both ends cDNAs resulting from random priming. The resulting sequence reads are then informatically mapped onto the genome sequence (Alignments). Those that don't map to the genome are mapped to known RNA splice junctions (Splice Sites). These mapped reads are then counted to determine their frequency of occurrence at known gene models. Sequence reads that cluster at genome locations that lack an existing transcript model are also identified informatically and they are quantified. RNA-Seq is especially suited for giving information about RNA splicing patterns and for determining unequivocally the presence or absence of lower abundance class RNAs. As performed here, internal RNA standards are used to assist in quantification and to provide internal process controls. This RNA-Seq protocol does not specify the coding strand. As a result, there will be ambiguity at loci where both strands are transcribed. The "randomly primed" reverse transcription is, apparently, not fully random. This is inferred from a sequence bias in the first residues of the read population, and this likely contributes to observed unevenness in sequence coverage across transcripts. These tracks show 1x32 n.t. or 2x75 n.t. sequence reads of cDNA obtained from biological replicate samples (different culture plates) of the ENCODE cell lines. The 32 n.t. sequences were aligned to the human genome (hg18) and UCSC known-gene splice junctions using different sequence alignment programs. The 2x75 n.t. reads were mapped serially, first with the Bowtie program (Langmead et al., 2009) against the genome and UCSC known-gene splice junctions (Splice Sites). Bowtie-unmapped reads were then mapped using BLAT to find evidence of novel splicing, by requiring at least 10 bp on the short-side of the splice.
2010-07-30 | E-GEOD-23316 | biostudies-arrayexpress
Project description:The 3' ends of most Drosophila melanogaster genes are poorly annotated or are determined by only a single EST or cDNA clone. To enhance the annotation of poly(A) site use in Drosophila, we performed deep sequencing on RNA isolated from 29 dissected tissues using an approach designed to enrich for poly(A) spanning reads. From these experiments, we identified 1.4 million poly(A) spanning reads leading to the identification of many new poly(A) sites and the identification of many tissue-specific poly(A) sites. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf RNA from 29 dissected Drosophila melanogaster tissues (in duplicate) were used to prepare polyA enriched RNA-Seq libraries. Briefly, total RNA was poly(A) selected, fragmented, and ligated to 5' and 3' RNA linkers. These libraries were amplified using Illumina paired-end primers, and subsequently reamplified using a 3' primer complementary to the 3' adapter but containing 6 Ts at the 3' end. The libraries were also multiplexed and up to 12 samples mixed per lane and sequenced on an Illumina GAIIx using paired-end 76 bp reads, or an illumina HiSeq 2000 using paired-end 100 bp reads. All reads were mapped to the Drosophila melanogaster genome to identify unmapped reads. Unmapped reads containing at least 10 A residues at the 3' end were identified, the terminal A residues trimmed, realigned to the genome to identify uniquely mapped reads. Such reads were identified as polyA spanning reads
2011-11-23 | E-GEOD-33905 | biostudies-arrayexpress
Project description:Methods: Triplicate RNA samples from morphologically stage-matched embryos were sequenced to compare expression profiles over time. Strand-specific libraries were prepared using the TruSeq stranded total RNA-ribozero kit (Illumina) and 100-bp paired-end sequencing was performed to depth of 10 million reads per library on an Illumina HiSeq 2000. Methods: On average, 19 million 100 bp paired-end reads per library were generated. These were then adapter and quality trimmed using cutadapt and SolexaQA. Each sequencing data set was independently mapped to the zebrafish genome with a bowtie2 index generated from Danio_rerio.Zv9.70 (Ensembl) downloaded from Illumina’s iGenomes collection. Zebrafish genome danRer7was used to provide known transcript annotations from Ensembl using TopHat2 (version 2.0.9) with the following options: “tophat2 --GTF genes.gtf --library-type fr-firststrand -p 24 --mate-inner-dist -8 --mate-std-dev 6 zv9” (on average, 75.38% reads mapped uniquely to the genome). Transcriptomes were assembled with Cufflinks (version 2.2.0) using options: ‘cufflinks -p 32 --GTF genes.gtf’ and differential expression analysis between control and knockdown embryos was performed using Cuffdiff. A FDR corrected p-value of 0.05 was applied as the cut off to identify differentially regulated transcripts Results: We could show that MO assisted depletion of Rad21 and CTCF affected the transcriptional profiles of embryos in different ways.
2017-12-01 | GSE84601 | GEO
Project description:We performed paired-end PRO-Seq and/or PRO-Cap on human foreskin fibroblasts (HFF) infected with human cytomegalovirus (HCMV) for 4 h (strain TB40/E) or 96 h (strain Towne varS). Each sample has matched uninfected controls. Towne experiments were also performed with adapters containing 4 nt of random sequence flanking both sides (8 total bases of randomness) to enable deduplication. Reads were first mapped to the human genome (hg38) (files available on GEO). Unmapped reads were then mapped to HCMV (Genbank FJ616285.1) (track hub available at https://github.com/P-TEFb/trackHub_HCMV).
2019-01-16 | GSE113394 | GEO
Project description:Whole exome sequencing of 5 HCLc tumor-germline pairs. Genomic DNA from HCLc tumor cells and T-cells for germline was used. Whole exome enrichment was performed with either Agilent SureSelect (50Mb, samples S3G/T, S5G/T, S9G/T) or Roche Nimblegen (44.1Mb, samples S4G/T and S6G/T). The resulting exome libraries were sequenced on the Illumina HiSeq platform with paired-end 100bp reads to an average depth of 120-134x. Bam files were generated using NovoalignMPI (v3.0) to align the raw fastq files to the reference genome sequence (hg19) and picard tools (v1.34) to flag duplicate reads (optical or pcr), unmapped reads, reads mapping to more than one location, and reads failing vendor QC.
2016-02-01 | E-MTAB-4313 | biostudies-arrayexpress
Project description:Evaluation of modulation of the innate immune response during H1N1 infection. The modulatory effect of Single-stranded oligonucleotides (ssON) on monocyte-derived dendritic cells (MoDCs) are evaluated. RNAseq data are used to study the effect on the transcriptome of MoDCs, during infection with simultaneous addition of ssON. Further mechanistic information are added via RNAseq data on poly I:C stimulated MoDCs (Toll-Like Receptor 3 agonist). Control samples are included to perform differential expression analysis. Provided are the fastq files, obtained in the following manner: The RNA sequencing was performed with the TruSeq RiboZero kit from Illumina, 25 M reads per sample and 2x125bp. Read quality were assessed using FastQC (Version 0.11.5) Trim Galore (Version 0.3.6) was used for adapter removal and quality trimming with a quality threshold of 20 on the Phred scale. Count files was created out of the trimmed fastq by mapping high-quality reads to Homo sapiens UCSC hg38 (GRCh38.77) reference genome using STAR aligner (version 2.5) with default values and the parameter out Reads Unmapped set to Fastx in order to extract the unmapped reads. After STAR alignment, the count data for the aligned reads were generated with HTSeq-count (version 0.6.1). The-m parameter was set to union.
2019-05-03 | E-MTAB-7803 | biostudies-arrayexpress
Project description:Usually, unmapped reads have been considered as useless and been trashed or ignored. Here, we develop a strategy to mining the full length sequence by unmapped reads combining with specific reverse transcription primers design and high throughput sequencing. In this study, we salvage 36 unmapped reads from standard RNA-Seq data(GSM3188619) and randomly select one 149 bp read as a model(CTGGTGCCATAATTCAGGGAACTGTGTTCTTGATGTACTATCTGAGACATTTGTGCTTCCCCCCATCCAGCTATCAGGCTGTTAGGCAATGCACTTCTAGGAATTAGAATTCTATAAGGAATCTCATGCTGGAAGAACAAAAAGACCCA ). Specific reverse transcription primers(5' end:CTGGTGCCATAATTCAGGGA, 3' end:GGATCTTCACGTAACGGATTGT) are designed to amplify its both ends, followed by next generation sequencing. Then we use a statistical model base on power law distribution to estimate its integrality and significance. Further, we validate it by Sanger sequencing. The result shows that the full length is 1,556 bp, with InDel mutation in microsatellite structure. This would be a useful strategy to extract the sequences information from the unmapped RNA-seq data.
2021-04-22 | GSE172487 | GEO