Development of personalized tumor biomarkers using massively parallel sequencing.
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ABSTRACT:
SUBMITTER: Leary RJ
PROVIDER: S-EPMC2858564 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Our variant ascertainment algorithm, VAAL, uses massively parallel DNA sequence data to identify differences between bacterial genomes with high sensitivity and specificity. VAAL detected approximately 98% of differences (including large insertion-deletions) between pairs of strains from three species while calling no false positives. VAAL also pinpointed a single mutation between Vibrio cholerae genomes, identifying an antibiotic's site of action by identifying sequence differences between drug-sensitive strains and drug-resistant derivatives.
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Project description:Resequencing of genomic regions that have been implicated by linkage and/or association studies to harbor genetic susceptibility loci represents a necessary step to identify causal variants. Massively parallel sequencing (MPS) offers the possibility of SNP discovery and frequency determination among pooled DNA samples. The strategies of pooling DNA samples and pooling PCR amplicons generated from individual DNA samples were evaluated, and both were found to return accurate estimates of SNP frequencies across varying levels of sequence coverage.
| S-EPMC2986170 | biostudies-literature
Project description:BACKGROUND: Massively parallel sequencing allows for genome-wide hypothesis-free investigation of for instance transcription factor binding sites or histone modifications. Although nucleotide resolution detailed information can easily be generated, biological insight often requires a more general view of patterns (footprints) over distinct genomic features such as transcription start sites, exons or repetitive regions. The construction of these footprints is however a time consuming task. METHODS: The presented software generates a binary representation of the signals enabling fast and scalable lookup. This representation allows for footprint generation in mere minutes on a desktop computer. Several different input formats are accepted, e.g. the SAM format, bed-files and the UCSC wiggle track. CONCLUSIONS: Hypothesis-free investigation of genome wide interactions allows for biological data mining at a scale never before seen. Until recently, the main focus of analysis of sequencing data has been targeted on signal patterns around transcriptional start sites which are in manageable numbers. Today, focus is shifting to a wider perspective and numerous genomic features are being studied. To this end, we provide a system allowing for fast querying in the order of hundreds of thousands of features.
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Project description:The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools (BWA, SAMtools, and GATK), we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications.
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Project description:BackgroundTumor mutation burden is an emerging biomarker for immunotherapy. Although several clinical trials for immunotherapy in lymphoma have been carried out, the mutation burden of various lymphomas is not well known yet. Thus, the objective of this study was to compare tumor mutation burden of various non-Hodgkin lymphomas using panel based massively parallel sequencing.MethodsWe conducted 405 gene panel based massively parallel sequencing of 300 non-Hodgkin lymphomas and investigate the number of SNV/Indel in each lymphoma.ResultsThe number of SNV/Indel was higher in mature B-cell lymphoma than in mature T- and NK-cell lymphoma. (P < 0.001) The number of SNV/Indel in primary mediastinal large B-cell lymphoma and primary diffuse large B-cell lymphoma of the central nervous system was the highest, which was significantly higher than that in diffuse large B-cell lymphoma, not otherwise specified (DLBCL NOS).(P = 0.030 and P = 0.008, respectively) The SNV/Indel number in EBV-positive DLBCL NOS was significantly lower than that in DLBCL NOS. (P = 0.048) Peripheral T-cell lymphoma, NOS showed no significant difference in the number of SNV/Indel from extranodal NK/T-cell lymphoma, nasal type (P = 0.942) or angioimmunoblastic T-cell lymphoma (P = 0.739). The number of SNV/Indel in anaplastic large cell lymphoma, ALK-positive was significantly lower than that in anaplastic large cell lymphoma, ALK-negative (P = 0.049). It was the lowest among all the lymphomas considered.ConclusionVarious lymphomas have different mutation burdens. Thus, tumor mutation burden can be used as a promising biomarker for immunotherapy in lymphomas.
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Project description:The extreme genetic heterogeneity of nonsyndromic hearing loss (NSHL) makes genetic diagnosis expensive and time consuming using available methods. To assess the feasibility of target-enrichment and massively parallel sequencing technologies to interrogate all exons of all genes implicated in NSHL, we tested nine patients diagnosed with hearing loss. Solid-phase (NimbleGen) or solution-based (SureSelect) sequence capture, followed by 454 or Illumina sequencing, respectively, were compared. Sequencing reads were mapped using GSMAPPER, BFAST, and BOWTIE, and pathogenic variants were identified using a custom-variant calling and annotation pipeline (ASAP) that incorporates publicly available in silico pathogenicity prediction tools (SIFT, BLOSUM, Polyphen2, and Align-GVGD). Samples included one negative control, three positive controls (one biological replicate), and six unknowns (10 samples total), in which we genotyped 605 single nucleotide polymorphisms (SNPs) by Sanger sequencing to measure sensitivity and specificity for SureSelect-Illumina and NimbleGen-454 methods at saturating sequence coverage. Causative mutations were identified in the positive controls but not in the negative control. In five of six idiopathic hearing loss patients we identified the pathogenic mutation. Massively parallel sequencing technologies provide sensitivity, specificity, and reproducibility at levels sufficient to perform genetic diagnosis of hearing loss.
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Project description:BackgroundVariation of microorganism communities in the rumen of cattle (Bos taurus) is of great interest because of possible links to economically or environmentally important traits, such as feed conversion efficiency or methane emission levels. The resolution of studies investigating this variation may be improved by utilizing untargeted massively parallel sequencing (MPS), that is, sequencing without targeted amplification of genes. The objective of this study was to develop a method which used MPS to generate "rumen metagenome profiles", and to investigate if these profiles were repeatable among samples taken from the same cow. Given faecal samples are much easier to obtain than rumen fluid samples; we also investigated whether rumen metagenome profiles were predictive of faecal metagenome profiles.ResultsRather than focusing on individual organisms within the rumen, our method used MPS data to generate quantitative rumen micro-biome profiles, regardless of taxonomic classifications. The method requires a previously assembled reference metagenome. A number of such reference metagenomes were considered, including two rumen derived metagenomes, a human faecal microflora metagenome and a reference metagenome made up of publically available prokaryote sequences. Sequence reads from each test sample were aligned to these references. The "rumen metagenome profile" was generated from the number of the reads that aligned to each contig in the database. We used this method to test the hypothesis that rumen fluid microbial community profiles vary more between cows than within multiple samples from the same cow. Rumen fluid samples were taken from three cows, at three locations within the rumen. DNA from the samples was sequenced on the Illumina GAIIx. When the reads were aligned to a rumen metagenome reference, the rumen metagenome profiles were repeatable (P?<?0.00001) by cow regardless of location of sampling rumen fluid. The repeatability was estimated at 9%, albeit with a high standard error, reflecting the small number of animals in the study. Finally, we compared rumen microbial profiles to faecal microbial profiles. Our hypothesis, that there would be a stronger correlation between faeces and rumen fluid from the same cow than between faeces and rumen fluid from different cows, was not supported by our data (with much greater significance of rumen versus faeces effect than animal effect in mixed linear model).ConclusionsWe have presented a simple and high throughput method of metagenome profiling to assess the similarity of whole metagenomes, and illustrated its use on two novel datasets. This method utilises widely used freeware. The method should be useful in the exploration and comparison of metagenomes.
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Project description:Next-generation sequencing (NGS) technologies have transformed genomic research and have the potential to revolutionize clinical medicine. However, the background error rates of sequencing instruments and limitations in targeted read coverage have precluded the detection of rare DNA sequence variants by NGS. Here we describe a method, termed CypherSeq, which combines double-stranded barcoding error correction and rolling circle amplification (RCA)-based target enrichment to vastly improve NGS-based rare variant detection. The CypherSeq methodology involves the ligation of sample DNA into circular vectors, which contain double-stranded barcodes for computational error correction and adapters for library preparation and sequencing. CypherSeq is capable of detecting rare mutations genome-wide as well as those within specific target genes via RCA-based enrichment. We demonstrate that CypherSeq is capable of correcting errors incurred during library preparation and sequencing to reproducibly detect mutations down to a frequency of 2.4 × 10(-7) per base pair, and report the frequency and spectra of spontaneous and ethyl methanesulfonate-induced mutations across the Saccharomyces cerevisiae genome.
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Project description:Massively parallel single-cell genome sequencing is required to further understand genetic diversities in complex biological systems. Whole genome amplification (WGA) is the first step for single-cell sequencing, but its throughput and accuracy are insufficient in conventional reaction platforms. Here, we introduce single droplet multiple displacement amplification (sd-MDA), a method that enables massively parallel amplification of single cell genomes while maintaining sequence accuracy and specificity. Tens of thousands of single cells are compartmentalized in millions of picoliter droplets and then subjected to lysis and WGA by passive droplet fusion in microfluidic channels. Because single cells are isolated in compartments, their genomes are amplified to saturation without contamination. This enables the high-throughput acquisition of contamination-free and cell specific sequence reads from single cells (21,000 single-cells/h), resulting in enhancement of the sequence data quality compared to conventional methods. This method allowed WGA of both single bacterial cells and human cancer cells. The obtained sequencing coverage rivals those of conventional techniques with superior sequence quality. In addition, we also demonstrate de novo assembly of uncultured soil bacteria and obtain draft genomes from single cell sequencing. This sd-MDA is promising for flexible and scalable use in single-cell sequencing.
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