Next generation sequence analysis
Ontology highlight
ABSTRACT:
ORGANISM(S): Aspergillus carbonarius
PROVIDER: GSE135950 | GEO | 2019/08/20
REPOSITORIES: GEO
Similar Datasets
Project description:A Modular Open-Source Assembler (AMOS) was designed to offer a modular approach to genome assembly. AMOS includes a wide range of tools for assembly, including the lightweight de novo assemblers Minimus and Minimo, and Bambus 2, a robust scaffolder able to handle metagenomic and polymorphic data. This protocol describes how to configure and use AMOS for the assembly of Next Generation sequence data. Additionally, we provide three tutorial examples that include bacterial, viral, and metagenomic datasets with specific tips for improving assembly quality.
| S-EPMC3072823 | biostudies-literature
Project description:High-throughput sequencing technologies promise to transform the fields of genetics and comparative biology by delivering tens of thousands of genomes in the near future. Although it is feasible to construct de novo genome assemblies in a few months, there has been relatively little attention to what is lost by sole application of short sequence reads. We compared the recent de novo assemblies using the short oligonucleotide analysis package (SOAP), generated from the genomes of a Han Chinese individual and a Yoruban individual, to experimentally validated genomic features. We found that de novo assemblies were 16.2% shorter than the reference genome and that 420.2 megabase pairs of common repeats and 99.1% of validated duplicated sequences were missing from the genome. Consequently, over 2,377 coding exons were completely missing. We conclude that high-quality sequencing approaches must be considered in conjunction with high-throughput sequencing for comparative genomics analyses and studies of genome evolution.
| S-EPMC3115693 | biostudies-literature
Project description:BACKGROUND: Mitochondrial disorders can originate from mutations in one of many nuclear genes controlling the organelle function or in the mitochondrial genome (mitochondrial DNA (mtDNA)). The large numbers of potential culprit genes, together with the little guidance offered by most clinical phenotypes as to which gene may be causative, are a great challenge for the molecular diagnosis of these disorders. METHODS: We developed a novel targeted resequencing assay for mitochondrial disorders relying on microarray-based hybrid capture coupled to next-generation sequencing. Specifically, we subjected the entire mtDNA genome and the exons and intron-exon boundary regions of 362 known or candidate causative nuclear genes to targeted capture and resequencing. We here provide proof-of-concept data by testing one HapMap DNA sample and two positive control samples. RESULTS: Over 94% of the targeted regions were captured and sequenced with appropriate coverage and quality, allowing reliable variant calling. Pathogenic mutations blindly tested in patients' samples were 100% concordant with previous Sanger sequencing results: a known mutation in Pyruvate dehydrogenase alpha 1 subunit (PDHA1), a novel splicing and a known coding mutation in Hydroxyacyl-CoA dehydrogenase alpha subunit (HADHA) were correctly identified. Of the additional variants recognized, 90 to 94% were present in dbSNP while 6 to 10% represented new alterations. The novel nonsynonymous variants were all in heterozygote state and mostly predicted to be benign. The depth of sequencing coverage of mtDNA was extremely high, suggesting that it may be feasible to detect pathogenic mtDNA mutations confounded by low level heteroplasmy. Only one sequencing lane of an eight lane flow cell was utilized for each sample, indicating that a cost-effective clinical test can be achieved. CONCLUSIONS: Our study indicates that the use of next generation sequencing technology holds great promise as a tool for screening mitochondrial disorders. The availability of a comprehensive molecular diagnostic tool will increase the capacity for early and rapid identification of mitochondrial disorders. In addition, the proposed approach has the potential to identify new mutations in candidate genes, expanding and redefining the spectrum of causative genes responsible for mitochondrial disorders.
| S-EPMC2784303 | biostudies-literature
Project description:Next generation sequence analysis
| PRJNA560769 | ENA
Project description:Next-generation sequencing (NGS) studies in cancer are limited by the amount, quality and purity of tissue samples. In this situation, primary xenografts have proven useful preclinical models. However, the presence of mouse-derived stromal cells represents a technical challenge to their use in NGS studies. We examined this problem in an established primary xenograft model of small cell lung cancer (SCLC), a malignancy often diagnosed from small biopsy or needle aspirate samples. Using an in silico strategy that assign reads according to species-of-origin, we prospectively compared NGS data from primary xenograft models with matched cell lines and with published datasets. We show here that low-coverage whole-genome analysis demonstrated remarkable concordance between published genome data and internal controls, despite the presence of mouse genomic DNA. Exome capture sequencing revealed that this enrichment procedure was highly species-specific, with less than 4% of reads aligning to the mouse genome. Human-specific expression profiling with RNA-Seq replicated array-based gene expression experiments, whereas mouse-specific transcript profiles correlated with published datasets from human cancer stroma. We conclude that primary xenografts represent a useful platform for complex NGS analysis in cancer research for tumours with limited sample resources, or those with prominent stromal cell populations.
| S-EPMC3784448 | biostudies-literature
Project description:A variety of extremely challenging biological sequence analyses were conducted on the XSEDE large shared memory resource Blacklight, using current bioinformatics tools and encompassing a wide range of scientific applications. These include genomic sequence assembly, very large metagenomic sequence assembly, transcriptome assembly, and sequencing error correction. The data sets used in these analyses included uncategorized fungal species, reference microbial data, very large soil and human gut microbiome sequence data, and primate transcriptomes, composed of both short-read and long-read sequence data. A new parallel command execution program was developed on the Blacklight resource to handle some of these analyses. These results, initially reported previously at XSEDE13 and expanded here, represent significant advances for their respective scientific communities. The breadth and depth of the results achieved demonstrate the ease of use, versatility, and unique capabilities of the Blacklight XSEDE resource for scientific analysis of genomic and transcriptomic sequence data, and the power of these resources, together with XSEDE support, in meeting the most challenging scientific problems.
| S-EPMC4185199 | biostudies-other
Project description:Purpose: The goal of this study is to investigate the responses of HUVECs after the stimulation of conidia of A. fumigatus Methods: HUVECs were stimulated with conidia of Aspergillus fumigatus for 2 and 6 hours. Three biological repeats of stimulated cells or un-stimulated controls were send for RNA sequencing. Results: Using an optimized data analysis workflow, we mapped about 40 million sequence reads per sample to the human genome (build hg38) and identified round 80,000 transcripts in the HUVECs upon stimulation. Conclusions: Our resutls showed the detailed analysis of HUVECs transcriptomes upton conidia of Aspergillus fumigatus stimulation.
2022-03-29 | GSE191240 | GEO
Project description:During tumor initiation and progression, cancer cells acquire a selective advantage, allowing them to outcompete their normal counterparts. Identification of the genetic changes that underlie these tumor acquired traits can provide deeper insights into the biology of tumorigenesis. Regions of copy number alterations and germline DNA variants are some of the elements subject to selection during tumor evolution. Integrated examination of inherited variation and somatic alterations holds the potential to reveal specific nucleotide alleles that a tumor "prefers" to have amplified. Next-generation sequencing of tumor and matched normal tissues provides a high-resolution platform to identify and analyze such somatic amplicons. Within an amplicon, examination of informative (e.g., heterozygous) sites deviating from a 1:1 ratio may suggest selection of that allele. A naive approach examines the reads for each heterozygous site in isolation; however, this ignores available valuable linkage information across sites. We, therefore, present a novel hidden Markov model-based method-Haplotype Amplification in Tumor Sequences (HATS)-that analyzes tumor and normal sequence data, along with training data for phasing purposes, to infer amplified alleles and haplotypes in regions of copy number gain. Our method is designed to handle rare variants and biases in read data. We assess the performance of HATS using simulated amplified regions generated from varying copy number and coverage levels, followed by amplicons in real data. We demonstrate that HATS infers the amplified alleles more accurately than does the naive approach, especially at low to intermediate coverage levels and in cases (including high coverage) possessing stromal contamination or allelic bias.
| S-EPMC3266043 | biostudies-literature
Project description:Rapidly evolving sequencing technologies produce data on an unparalleled scale. A central challenge to the analysis of this data is sequence alignment, whereby sequence reads must be compared to a reference. A wide variety of alignment algorithms and software have been subsequently developed over the past two years. In this article, we will systematically review the current development of these algorithms and introduce their practical applications on different types of experimental data. We come to the conclusion that short-read alignment is no longer the bottleneck of data analyses. We also consider future development of alignment algorithms with respect to emerging long sequence reads and the prospect of cloud computing.
| S-EPMC2943993 | biostudies-literature
Project description:Next-generation sequencing (NGS) technologies have generated enormous amounts of shotgun read data, and assembly of the reads can be challenging, especially for organisms without template sequences. We study the power of genome comparison based on shotgun read data without assembly using three alignment-free sequence comparison statistics, D(2), D(*)(2) and D(s)(2), both theoretically and by simulations. Theoretical formulas for the power of detecting the relationship between two sequences related through a common motif model are derived. It is shown that both D(*)(2) and D(s)(2), outperform D(2) for detecting the relationship between two sequences based on NGS data. We then study the effects of length of the tuple, read length, coverage, and sequencing error on the power of D(*)(2) and D(s)(2). Finally, variations of these statistics, d(2), d(*)(2) and d(s)(2), respectively, are used to first cluster five mammalian species with known phylogenetic relationships, and then cluster 13 tree species whose complete genome sequences are not available using NGS shotgun reads. The clustering results using d(s)(2) are consistent with biological knowledge for the 5 mammalian and 13 tree species, respectively. Thus, the statistic d(s)(2) provides a powerful alignment-free comparison tool to study the relationships among different organisms based on NGS read data without assembly.
| S-EPMC3581251 | biostudies-literature