J.Costello_20191003_dataset_exomeseq - samples
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PROVIDER: EGAD00001005386 | EGA |
REPOSITORIES: EGA
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Project description:The paired-end reads were aligned to the human reference genome (hg19) with decoy sequences (as used in the 1000 Genomes Project) using BWA (v0.7.5a) software. The duplicate reads were marked and subsequently removed using Picard (v1.140) (https://broadinstitute.github.io/picard/) and SAMtools (v0.1.19), respectively. Local realignment around insertions/deletions (indels) and base quality recalibration were performed using GATK (v3.6).
| EGAD00001011158 | EGA
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Jonathan Preall jpreall@cshl.edu (Generation 0 Data from Hannon Lab), Carrie Davis davisc@cshl.edu (experimental), Alex Dobin dobin@cshl.edu (computational), Wei Lin wlin@cshl.edu (computational), Tom Gingeras gingeras@cshl.edu (primary investigator)). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). hg18: This data was produced by Hannon lab part of Cold Spring Harbor as part of the ENCODE Project. The series depicts NextGen sequencing information for RNAs between the sizes of 20-200 nt isolated from RNA samples from tissues or sub cellular compartments of cell lines. hg19: This track depicts NextGen sequencing information for RNAs between the sizes of 20-200 nt isolated from RNA samples from tissues or sub cellular 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. hg19: This cloning protocol generates directional libraries that are read from the 5' ends of the inserts, which should largely correspond to the 5' ends of the mature RNAs. The libraries were sequenced on a Solexa platform for a total of 36, 50 or 76 cycles however the reads undergo post-processing resulting in trimming of their 3' ends. Consequently, the mapped read lengths are variable. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf hg18: Small RNAs between 20-200 nt were ribominus treated according to the manufacturer's protocol (Invitrogen) using custom LNA probes targeting ribosomal RNAs (some datasets are also depleted of U snRNAs and high abundant microRNAs). The RNA was treated with Tobacco Alkaline Pyrophosphatase to eliminate any 5' cap structure. Poly-A Polymerase was used to catalyze the addition of C's to the 3' end. The 5' ends were 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 and containing sequencing adapters. The library was sequenced on an Illumina GA machine for a total of 36, 50 or 76 cycles. Initially 1 lane is run. If an appreciable number of mappable reads are obtained, additional lanes are run. Sequence reads underwent quality filtration using Illumina standard pipeline (Gerlad). The read lengths may exceed the insert sizes and consequently introduce 3' adaptor sequence into the 3' end of the reads. The 3' sequencing adaptor was removed from the reads using a custom clipper program, which aligned the adaptor sequence to the short-reads, allowing up to 2 mismatches and no indels. Regions that aligned were "clipped" off from the read. The trimmed portions were collapsed into identical reads, their count noted and aligned to the human genome (NCBI build 36, hg18 unmasked) using Nexalign (Lassmann et al., not published). The alignment parameters are tuned to tolerate up to 2 mismatches with no indels and will allow for trimmed portions as small as 5 nucleotides to be mapped. We report reads that mapped 10 or fewer times. Data obtained from each lane is processed and mapped independently. The processed/mapped data from each lane is then complied as a single track without additional processing and submitted to UCSC. Consequently, identical reads within a lane were collapsed and their value is reported as the "transfrag" signal value. However, the redundancy between lanes has not been eliminated so the same transfrag may appear multiple times within a signal. hg19: Small RNAs between 20-200 nt were ribominus treated according to the manufacturer's protocol (Invitrogen) using custom LNA probes targeting ribosomal RNAs (some datasets are also depleted of U snRNAs and high abundant microRNAs). The RNA was treated with Tobacco Alkaline Pyrophosphatase to eliminate any 5' cap structures. Poly-A Polymerase was used to catalyze the addition of C's to the 3' end. The 5' ends were 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 and containing sequencing adapters. The library was sequenced on an Illumina GA machine for a total of 36, 50 or 76 cycles. Initially, one lane was run. If an appreciable number of mappable reads were obtained, additional lanes were run. Sequence reads underwent quality filtration using Illumina standard pipeline (GERALD). The Illumina reads were initially trimmed to discard any bases following a quality score less than or equal to 20 and converted into FASTA format, thereby discarding quality information for the rest of the pipeline. As a result, the sequence quality scores in the BAM output are all displayed as "40" to indicate no quality information. The read lengths may exceed the insert sizes and consequently introduce 3' adapter sequence into the 3' end of the reads. The 3' sequencing adapter was removed from the reads using a custom clipper program (available at http://hannonlab.cshl.edu/fastx_toolkit/), which aligned the adapter sequence to the short-reads using up to 2 mismatches and no indels. Regions that aligned were "clipped" off from the read. Terminal C nucleotides introduced at the 3' end of the RNA via the cloning procedure are also trimmed. The trimmed portions were collapsed into identical reads, their count noted and aligned to the human genome (version hg19, using the gender build appropriate to the sample in question - female/male) using Bowtie (Langmead B. et al). The alignment parameter allowed 0, 1, or 2 mismatches iteratively. We report reads that mapped 20 or fewer times. Discrepancies between hg18 and hg19 versions of CSHL small RNA data: The alignment pipeline for the CSHL small RNA data was updated upon the release of the human genome version hg19, resulting in a few noteworthy discrepancies with the hg18 dataset. First, mapping was conducted with the open-source Bowtie algorithm (http://bowtie-bio.sourceforge.net/index.shtml) rather than the custom NexAlign software. As each algorithm uses different strategies to perform alignments, the mapping results may vary even in genomic regions that do not differ between builds. The read processing pipeline also varies slightly, in that we no longer retain information regarding whether a read was 'clipped' off adapter sequence.
2010-10-08 | E-GEOD-24565 | biostudies-arrayexpress
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:<p>Genetic analysis of patients with Inherited Retinal Dystrophies (IRDs) was carried out by performing Whole Genome Sequencing (WGS). The main purpose of this study is to identify simple and complex mutations responsible for IRD in patients. </p> <p>WGS was performed on selected affected and unaffected individuals using the Illumina HiSeqX10. The reads were aligned to human genome 19 (hg19) and variant calling was performed using Genome Analysis Toolkit (GATK). The genotyping quality of single nucleotide variants (SNVs) and indels was assessed using the variant quality score recalibration approach implemented in GATK. Copy number variations (CNVs) were called using Genome STRiP and SpeedSeq. </p> <p>This large set of whole genome sequencing data from different ethnicity can be stored and shared through dbGaP. This data could serve as a source for checking frequencies of variants or the pathogenicity of selected variants in different ethnicities. </p>
| phs001619 | dbGaP
Project description:Purpose: The goal of this study was to compare exome sequences of Ackr4-deficient mice with C57BL/6 wildtype sequences to understand phenotypic differences which can not be explained by Ackr4-deficiency. Methods: Tail DNA of one wild-type (WT, C57BL/6) and atypical chemokine receptor 4 knockout (Ackr4−/−) mice was sequenced by exome sequencing using an Illumina NovaSeq 6000 sequencing system (2x 100bp). After demultiplexing (Illumina bcl2fastq 2.19) and adapter trimming (Skewer, 0.2.2), the trimmed reads were aligned with to the murine genome (mm10) with Burrows-Wheeler Aligner (BWA-mem 0.7.2-cegat). Reads aligned to multiple sides with the same mapping score were discarded. Further, duplicated reads were removed with SAMtool 0.1.18. Genetic variants were identified with Verscan 2.4.2-cegat and variants with a frequency of ≥ 2% were annotated with SnpEff (version 4.2 with GRCm38.75). Results: 100,000 to 140,000 million sequence reads per mouse were mapped to the mouse genome (build mm10). Each mouse had approx. 20,000 variants (insertions, deletions, SNPs) compared to the reference genome. In Ackr4-/- mice, 3,367 variants were present in both mice analysed, yet not detected in the C57BL/6 mouse used as control. 1781 of these 'shared' variants were present on the chromosome with the targeted Ackr4 allele (Chr9) and predominantly in close proximity to the Ackr4 locus, indicating that these variants originate from the 129-genetic background which was used for targeted mutagenesis during the generation of these mice. Conclusions: Our study shows that Ackr4-/- mice in generation F12 after backcrossing to the C57BL/6 background are still congenic for 129-derived DNA around the targeted Ackr4 locus, indicating that passenger mutations can influence the phenotype of these mice.
2019-12-21 | GSE138124 | GEO
Project description:Purpose: To identify the genetic basis of posterior polymorphous corneal dystrophy 1 (PPCD1). Methods: Next-generation sequencing was performed on DNA samples from 4 affected and 4 unaffected members of a previously reported family with PPCD1 linked to chromosome 20 between D20S182 and D20S195. Custom capture probes were utilized for targeted region capture of the linked interval. Single nucleotide variants (SNVs) and insertions/deletions (indels) were identified using two bioinformatics pipelines and two annotation databases. Candidate variants met the following criteria: quality score â¥20, read depth â¥5X, heterozygous, novel or rare (minor allele frequency (MAF) ⤠0.05), present in each affected individual and absent in each unaffected individual. Structural variants were detected with two different microarray platforms to identify indels of varying sizes. Results: Sequencing reads aligned to the linked region on chromosome 20, and high coverage was obtained across the sequenced region. The majority of identified variants were detected with both pipelines and annotation databases, although unique variants were identified. Twelve SNVs in 10 genes (2 synonymous variants and 10 noncoding variants) and 9 indels in 7 genes met the filtering criteria and were considered candidate variants for PPCD1. Conclusions: Next-generation sequencing of the PPCD1 interval has identified 17 genes containing novel or rare SNVs and indels that segregate with the affected phenotype in an affected family previously mapped to the PPCD1 locus. We anticipate that screening of these candidate genes in other families previously mapped to the PPCD1 locus will result in the identification of the genetic basis of PPCD1. Four affected and 4 unaffected individuals from a single family were analyzed for copy number variation within the PPCD1 disease locus. Array design and analysis is based on genome build hg19.
2016-06-30 | E-GEOD-72617 | biostudies-arrayexpress
Project description:We report the global occupancy by ChIP-seq of AIB1 in MCF10A, MCF10A-D4, MCFDCIS and MCFDCIS-D4 cells. We performed ChIP and library preparation with the ActiveMotif Low Cell ChIP-seq kit. Data was aligned to hg19 with rsubread, data was sorted and de-duplicated with SAMTOOLS, and peaks were called with CSAW. Analysis was done with ChIP-seeker in R.
2020-11-08 | GSE144632 | GEO
Project description:Small RNA sequencing has been performed using eight commonly used breast cancer cell lines by Illumina as part of the Illumina iDEA Challange 2011. Total RNA of the cell lines was isolated and the sequencing library was prepared using Illuminas Small RNA v1.5 library prep protocol. This data was analyzed with Flicker, an add-on to the Illumina Genome Analyzer Pipeline software that allows for the preliminary analysis of small RNA runs on the GA. Flicker was used in this case for: * Trimming: Attempts to trim the adaptor sequence from small RNA reads Trimming is accomplished by aligning the adaptor with each read, both internally (the adaptor is fully contained within the read) and as a suffix (the adaptor overlaps the 3' end of the read). A similarity matrix is used to generate the matching score, taking into account both substitutions and indels. The alignment with the best matching score is returned, and that best alignment is trimmed from the read. A trimming threshold may be specified, and the adaptor is not trimmed unless the best score exceeds the threshold. * Aligning with Iterated ELAND: Attempts to aligns trimmed reads to targets (squashed genomes or databases such as miRBase) using the ELAND short read aligner. Each read is aligned by ELAND with specified alignment targets. ELAND was designed to align reads of a uniform length, and the trimmed reads from a small RNA run are several different lengths. We currently address this by running ELAND for reads of each length: reads are binned by length, and each bin of a given length is aligned against a specified target. Once all bins are aligned against all targets (in this case, gDNA and miRBase), the results are recombined into a single file with all the annotation information. ===== Key to Experiments HCT ID, Lane, Sample ID, Cell Line, ER +/- Status, Reads HCT20061, Lane 1, HS378, MCF7, +, 14,555,390 HCT20062, Lane 2, HS379, MDA-MB-231, -, 15,147,037 HCT20063, Lane 3, HS381, T47D, +, 14,701,254 HCT20064, Lane 4, HS377, BT-20, -, 16,529,823 HCT20065, Lane 5, HS375, BT-474, +, 16,443,361 HCT20066, Lane 6, HS380, MDA-MB-468, -, 16,746,981 HCT20067, Lane 7, HS382, ZR-75-1, +, 17,631,005 HCT20068, Lane 8, HS376, MCF10A, normal, 19,155,607
2016-03-14 | E-MTAB-4539 | biostudies-arrayexpress
Project description:Purpose:To take a comprehensive effort in characterizing the brain vasculature gene expression upon hyperglycemia. Methods: We extracted mRNA from brain microvasculature fragments isolated from a genetic mouse model of hyperglycemia (Ins2-AKITA) and WT mice and analyzed their transcriptome with RNA sequencing The samples were sequenced on an Illumina HiSeq 2500 sequencer at the SNP&SEQ sequencing facility (Science for Life laboratory (SciLifeLab), Uppsala sequencing node). The reads were aligned to the Ensembl mouse gene assembly (NCBIM37) using Tophat2 software (version 2.0.4). The duplicated reads were removed using the picard tool (version 1.92). To identify the genes significantly enriched in the pericyte samples as compared with microvascular samples, statistical tests were performed using the Cufflinks tool (version 2.2.1) Results: Twenty-three genes were significantly regulated in mutant when compared to WT (False Discovery Rate < 0.05)
2018-12-11 | GSE113919 | GEO
Project description:Small RNAs (<100 bases in length) were purified from total RNA samples using the miRNeasy kit (Qiagen), and multiplexed miRNA libraries were prepared using a previously described protocol and sequenced on a HiSeq 2000 (Illumina). Image analysis, base calling, demultiplexing and conversion of BCL to FASTQ format were carried out using CASAVA 1.8.2 software (Illumina). Adaptor sequences were removed using mirExpress software. Fastq were 3p-adaptor trimmed. The sequences were then aligned on hg19 (GRCh37) human reference genome with STAR (v.2.5.2a), allowing no mismatch with reference. Reads were counted using HTSeq-count (HTSeq v 0.6.1p1 Python package) using “intersection-strict” mode, to count mature miRNA abundance according to miRbase v.20.
2020-08-12 | E-MTAB-6553 | biostudies-arrayexpress