Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Targeted exome sequencing of 69 human melanoma brain metastases

ABSTRACT: In-depth information regarding the DFCI OncoPanel sequencing panel has been described previously. Briefly, sequencing is performed using an Illumina HiSeq 2500 system (RRID:SCR_016383) with 2×100 paired-end reads. Samples must meet an average 50X coverage and minimum of 30X coverage for 80% of targets for analysis. For all samples, a board-certified molecular pathologist evaluates alterations and writes interpretations of the results. The three generations of OncoPanel use Mutect (RRID:SCR_000559) and GATK (RRID:SCR_001876) to interrogate possible alterations in the complete exonic DNA sequences of 275, 309, and 447 cancer-related genes, respectively, including substitutions, insertions, and deletions. Because tumor tissues are tested without a paired normal from individual patients, additional informatics steps are taken to identify common single nucleotide polymorphisms (SNPs). Any SNP present at >0.1% in Exome Variant Server, NHLBI GO Exome Sequencing Project (ESP) (RRID:SCR_012761), or present in dbSNP (RRID:SCR_002338) is filtered; however, variants also present in at least twice in COSMIC (RRID:SCR_002260) are rescued for manual review. Variants that appear two or more times in a panel of 150 normal samples sequenced in-house and are not present in COSMIC are also filtered. For copy number alteration (CNA) analysis, the workflow employs an in-house algorithm (RobustCNV) to cover exonic regions of targeted genes as well as select intronic regions. The depth of coverage of these regions is determined by counting the number of reads aligning within defined genomic intervals and then normalized against a panel of normals and corrected for GC bias. The copy number for a segmented genomic interval is calculated as a log2 ratio of the depth of coverage of this sample compared to a panel of normal (non-cancer) samples that are run on the same plate. In parallel (samples not included), additional cases were previously sequenced via the MSK-IMPACT platform. To ensure maximum overlap between the platforms, only single nucleotide variants (SNVs), dinucleotide/oligonucleotide variants (DNVs/ONVs), insertions/deletions (indels), high copy gains (amplifications), and homozygous deletions from 182 genes shared between all versions of both platforms were evaluated in our analyses involving samples from both platforms. To filter pathogenic and passenger mutations, missense, truncating (nonsense, frameshift, and splice site), and in-frame indel mutations from the 182 genes overlapping between the DFCI OncoPanel and MSK-IMPACT panels were subjected to analysis in the Cancer Genome Interpreter (CGI) suite (RRID:SCR_023752) with cancer type set to “Skin Cutaneous Melanoma (SKCM)”. Known pathogenic mutations, defined as those listed as pathogenic in ClinVar (RRID:SCR_006169), OncoKB (RRID:SCR_014782), and/or CGI databases, were included in downstream analyses. Mutations which had not been previously annotated but were predicted to be pathogenic mutations by CGI were also included. Missense SNV mutations called passengers in CGI were subjected to another round of analysis in the ChasmPlus suite with cancer type set to SKCM. Missense mutations called pathogenic via this approach (p < 0.05; FDR Q < 0.3) were salvaged and included in further analyses. All other mutations were excluded. TERT promoter mutations were evaluated via FATHMM-MKL. Mutations called pathogenic via this tool were included in downstream analyses. Clinical data for these cases is available in a supplementary file from the associated publication. OF NOTE, THE RAW SEQUENCING DATA FROM THESE SAMPLES CANNOT BE MADE PUBLICLY AVAILABLE BECAUSE THE RESEARCH PARTICIPANT CONSENT DOES NOT INCLUDE AUTHORIZATION TO SHARE IDENTIFIABLE DATA.

INSTRUMENT(S): Illumina HiSeq 2500

ORGANISM(S): Homo sapiens

SUBMITTER:

PROVIDER: E-MTAB-14575 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:This track displays a chromatin state segmentation for each of nine human cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=GM12878,H1-hESC,HepG2,HUVEC,HMEC,HSMM,K562,NHEK,NHLF). A common set of states across the cell types were learned by computationally integrating ChIP-seq data for nine factors plus input (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=CTCF,H3K4me1,H3K4me2,H3K4me3,H3K27ac,H3K9ac,H3K36me3,H4K20me1,H3K27me3,Input) using a Hidden Markov Model (HMM). In total, fifteen states were used to segment the genome, and these states were then grouped and colored to highlight predicted functional elements. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf ChIP-seq data from the Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeBroadChipSeq) track was used to generate this track. Data for nine factors plus input (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=CTCF,H3K4me1,H3K4me2,H3K4me3,H3K27ac,H3K9ac,H3K36me3,H4K20me1,H3K27me3,Input) and nine cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=GM12878,H1-hESC,HepG2,HUVEC,HMEC,HSMM,K562,NHEK,NHLF) was binarized separately at a 200 base pair resolution based on a Poisson background model. The chromatin states were learned from this binarized data using a multivariate Hidden Markov Model (HMM) that explicitly models the combinatorial patterns of observed modifications (Ernst and Kellis, 2010). To learn a common set of states across the nine cell types, first the genomes were concatenated across the cell types. For each of the nine cell types, each 200 base pair interval was then assigned to its most likely state under the model. Detailed information about the model parameters and state enrichments can be found in (Ernst et al, accepted). This is release 1 (Jun 2011) of this track, and it is based on the NCBI36/hg18 release of the Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeBroadChipSeq) track. This track has also been lifted over to GRCh37/hg19 (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHmm). It is anticipated that the HMM methods will be run on the newer GRCh37/hg19 Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHistone) data and will replace the lifted version.

Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Piero Carninci mailto:carninci@riken.jp). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows 5' cap analysis gene expression (CAGE) tags and clusters in RNA extracts (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=rnaExtract) from different sub-cellular localizations (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=localization) in multiple cell lines (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType). A CAGE cluster is a region of overlapping tags with an assigned value that represents the expression level. The data in this track were produced as part of the ENCODE Transcriptome Project. Release 2 has three new downloads only files per experiment (Clusters, TSS Gencode 7 and TSS HMM) and four new cell lines (A459, AG04450, BJ and SK-N-SH_RA). Release 1 on hg19 contained the original data on hg18 (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeRikenCage) that was remapped and indicated in this release as Generation 0 since that data had no replicates. If there is both old and new generation data available for a particular experiment, only the new generation data is displayed and the older data is available for download. The new data for this track was done with a different process and has standard replicate numbers. The replicate labeling in the genome browser view is a counter indicating the total number of replicates submitted. The producing lab has replicate numbers that correspond to their internal bio-replicate numbering. Where these two numbering systems conflict, both are listed in the long label of the specific track. 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 (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell). RNA molecules longer than 200 nt were isolated from each subcellular compartment and then were fractionated into polyA+ and polyA- fractions as described in these protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/general/rnaExtracts.txt). The CAGE tags were sequenced from the 5' ends of cap-trapped cDNAs produced using RIKEN CAGE technology (Kodzius et al. 2006; Valen et al. 2009). To create the tag, a linker was attached to the 5' end of polyA+ or polyA- reverse-transcribed cDNAs which were selected by cap trapping (Carninci et al. 1996). The first 27 bp of the cDNA were cleaved using class II restriction enzymes. A linker was then attached to the 3' end of the cDNA. After PCR amplification, the tags were sequenced (36 bp single reads) using Illumina's Genome analyzer. Tags were mapped to the human genome (hg19) using the program delve (T. Lassmann manuscript in preparation). Delve is a new probabilistic aligner focused on giving the best possible alignment of reads to a genome rather than focusing on speed. It calculates the mapping accuracy (probability of each alignment being true or not) for each alignment. There is no set limit on the number of errors allowed and therefore the mapping rate is commonly 100%. However, for analysis it is recommended to discard alignments with low mapping qualities. Exceptions to the above protocol are the polyA- RNA samples from K562 cytosol, K562 nucleus, and prostate whole cell which were sequenced using ABI SOLiD (http://www.appliedbiosystems.com/absite/us/en/home/applications-technologies/solid-next-generation-sequencing.html) technology. These reads were mapped using Bowtie using default parameters. Clusters were defined as regions of overlapping CAGE reads. The expression level was computed as the number of reads making up the cluster, divided by the total number of reads sequenced, times 1 million.

Project description:We compared the dynamics and mechanisms of resistance development to ceftazidime, meropenem, ciprofloxacin, and ceftolozane-tazobactam in wild-type (PAO1) and mutator (PAOMS, M-bM-^HM-^FmutS) P. aeruginosa. The strains were incubated for 24 h with 0.5 to 64M-CM-^W MICs of each antibiotic in triplicate experiments. The tubes from the highest antibiotic concentration showing growth were reinoculated in fresh medium containing concentrations up to 64M-CM-^W MIC for 7 consecutive days. The susceptibility profiles and resistance mechanisms were assessed in two isolated colonies from each step, antibiotic, and strain. Ceftolozane-tazobactam-resistant mutants were further characterized by whole-genome analysis through RNA sequencing (RNA-seq). The development of high-level resistance was fastest for ceftazidime, followed by meropenem and ciprofloxacin. None of the mutants selected with these antibiotics showed cross-resistance to ceftolozane-tazobactam. On the other hand, ceftolozane-tazobactam resistance development was much slower, and high-level resistance was observed for the mutator strain only. PAO1 derivatives that were moderately resistant (MICs, 4 to 8 ug/ml) to ceftolozane-tazobactam showed only 2 to 4 mutations, which determined global pleiotropic effects associated with a severe fitness cost. High-level-resistant (MICs, 32 to 128 ug/ml) PAOMS derivatives showed 45 to 53 mutations. Major changes in the global gene expression profiles were detected in all mutants, but only PAOMS mutants showed ampC overexpression, which was caused by dacB or ampR mutations. Moreover, all PAOMS mutants contained 1 to 4 mutations in the conserved residues of AmpC (F147L, Q157R, G183D, E247K, or V356I). Complementation studies revealed that these mutations greatly increased ceftolozane-tazobactam and ceftazidime MICs but reduced those of piperacillin-tazobactam and imipenem, compared to those in wild-type ampC. Therefore, the development of high-level resistance to ceftolozane-tazobactam appears to occur efficiently only in a P. aeruginosa mutator background, in which multiple mutations lead to overexpression and structural modifications of AmpC. Mutants of Pseudomonas aeroginosa PAO1 and PAO1 M-bM-^HM-^FmutS against Ceftolozane-tazobactam were generated and analysed using RNA-Seq

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Targeted exome sequencing of 69 human melanoma brain metastases

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