A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes.
Ontology highlight
ABSTRACT:
SUBMITTER: KaramiNejadRanjbar M
PROVIDER: S-EPMC7228773 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Intra-tumor heterogeneity is a phenomenon in which mutation profiles differ from cell to cell within the same tumor and is observed in almost all tumors. Understanding intra-tumor heterogeneity is essential from the clinical perspective. Numerous methods have been developed to predict this phenomenon based on variant allele frequency. Among the methods, CloneSig models the variant allele frequency and mutation signatures simultaneously and provides an accurate clone decomposition. However, this method has limitations in terms of clone number selection and modeling. We propose SigTracer, a novel hierarchical Bayesian approach for analyzing intra-tumor heterogeneity based on mutation signatures to tackle these issues. We show that SigTracer predicts more reasonable clone decompositions than the existing methods against artificial data that mimic cancer genomes. We applied SigTracer to whole-genome sequences of blood cancer samples. The results were consistent with past findings that single base substitutions caused by a specific signature (previously reported as SBS9) related to the activation-induced cytidine deaminase intensively lie within immunoglobulin-coding regions for chronic lymphocytic leukemia samples. Furthermore, we showed that this signature mutates regions responsible for cell-cell adhesion. Accurate assignments of mutations to signatures by SigTracer can provide novel insights into signature origins and mutational processes.
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Project description:Unique molecular identifiers (MIDs) have been demonstrated to effectively improve immune repertoire sequencing (IR-seq) accuracy, especially to identify somatic hypermutations in antibody repertoire sequencing. However, evaluating the sensitivity to detect rare T cells and the degree of clonal expansion in IR-seq has been difficult due to the lack of knowledge of T cell receptor (TCR) RNA molecule copy number and a generalized approach to estimate T cell clone size from TCR RNA molecule quantification. This limited the application of TCR repertoire sequencing (TCR-seq) in clinical settings, such as detecting minimal residual disease in lymphoid malignancies after treatment, evaluating effectiveness of vaccination and assessing degree of infection. Here, we describe using an MID Clustering-based IR-Seq (MIDCIRS) method to quantitatively study TCR RNA molecule copy number and clonality in T cells. First, we demonstrated the necessity of performing MID sub-clustering to eliminate erroneous sequences. Further, we showed that MIDCIRS enables a sensitive detection of a single cell in as many as one million naïve T cells and an accurate estimation of the degree of T cell clonal expression. The demonstrated accuracy, sensitivity, and wide dynamic range of MIDCIRS TCR-seq provide foundations for future applications in both basic research and clinical settings.
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Project description:Medical resequencing of candidate genes in individual patient samples is becoming increasingly important in the clinic and in clinical research. Medical resequencing requires the amplification and sequencing of many candidate genes in many patient samples. Here we introduce Nested Patch PCR, a novel method for highly multiplexed PCR that is very specific, can sensitively detect SNPs and mutations, and is easy to implement. This is the first method that couples multiplex PCR with sample-specific DNA barcodes and next-generation sequencing to enable highly multiplex mutation discovery in candidate genes for multiple samples in parallel. In our pilot study, we amplified exons from colon cancer and matched normal human genomic DNA. From each sample, we successfully amplified 96% (90 of 94) targeted exons from across the genome, totaling 21.6 kbp of sequence. Ninety percent of all sequencing reads were from targeted exons, demonstrating that Nested Patch PCR is highly specific. We found that the abundance of reads per exon was reproducible across samples. We reliably detected germline SNPs and discovered a colon tumor specific nonsense mutation in APC, a gene causally implicated in colorectal cancer. With Nested Patch PCR, candidate gene mutation discovery across multiple individual patient samples can now utilize the power of second-generation sequencing.
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Project description:Accurate DNA sequencing is crucial in biomedicine. Underlying the most accurate methods is the assumption that a mutation is true if altered bases are present on both strands of the DNA duplex. We now show that this assumption can be wrong. We establish that current methods to prepare DNA for sequencing, via 'End Repair/dA-Tailing,' may substantially resynthesize strands, leading amplifiable lesions or alterations on one strand to become indiscernible from true mutations on both strands. Indeed, we discovered that 7-17% and 32-57% of interior 'duplex base pairs' from cell-free DNA and formalin-fixed tumor biopsies, respectively, could be resynthesized in vitro and potentially introduce false mutations. To address this, we present Duplex-Repair, and show that it limits interior duplex base pair resynthesis by 8- to 464-fold, rescues the impact of induced DNA damage, and affords up to 8.9-fold more accurate duplex sequencing. Our study uncovers a major Achilles' heel in sequencing and offers a solution to restore high accuracy.
| S-EPMC8755016 | biostudies-literature
Project description:BackgroundCancer mutations accumulate through replication errors and DNA damage coupled with incomplete repair. Individual mutational processes often show nucleotide sequence and functional region preferences. As a result, some sequence contexts mutate at much higher rates than others, with additional variation found between functional regions. Mutational hotspots, with recurrent mutations across cancer samples, represent genomic positions with elevated mutation rates, often caused by highly localized mutational processes.MethodsWe count the 11-mer genomic sequences across the genome, and using the PCAWG set of 2583 pan-cancer whole genomes, we associate 11-mers with mutational signatures, hotspots of single nucleotide variants, and specific genomic regions. We evaluate the mutation rates of individual and combined sets of 11-mers and derive mutational sequence motifs.ResultsWe show that hotspots generally identify highly mutable sequence contexts. Using these, we show that some mutational signatures are enriched in hotspot sequence contexts, corresponding to well-defined sequence preferences for the underlying localized mutational processes. This includes signature 17b (of unknown etiology) and signatures 62 (POLE deficiency), 7a (UV), and 72 (linked to lymphomas). In some cases, the mutation rate and sequence preference increase further when focusing on certain genomic regions, such as signature 62 in transcribed regions, where the mutation rate is increased up to 9-folds over cancer type and mutational signature average.ConclusionsWe summarize our findings in a catalog of localized mutational processes, their sequence preferences, and their estimated mutation rates.
| S-EPMC10436389 | biostudies-literature
Project description:On average, an approved drug currently costs US$2-3 billion and takes more than 10 years to develop1. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (Kd) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins.
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Project description:BackgroundAs the use of nanopore sequencing for metagenomic analysis increases, tools capable of performing long-read taxonomic classification (ie. determining the composition of a sample) in a fast and accurate manner are needed. Existing tools were either designed for short-read data (eg. Centrifuge), take days to analyse modern sequencer outputs (eg. MetaMaps) or suffer from suboptimal accuracy (eg. CDKAM). Additionally, all tools require command line expertise and do not scale in the cloud.ResultsWe present BugSeq, a novel, highly accurate metagenomic classifier for nanopore reads. We evaluate BugSeq on simulated data, mock microbial communities and real clinical samples. On the ZymoBIOMICS Even and Log communities, BugSeq (F1 = 0.95 at species level) offers better read classification than MetaMaps (F1 = 0.89-0.94) in a fraction of the time. BugSeq significantly improves on the accuracy of Centrifuge (F1 = 0.79-0.93) and CDKAM (F1 = 0.91-0.94) while offering competitive run times. When applied to 41 samples from patients with lower respiratory tract infections, BugSeq produces greater concordance with microbiological culture and qPCR compared with "What's In My Pot" analysis.ConclusionBugSeq is deployed to the cloud for easy and scalable long-read metagenomic analyses. BugSeq is freely available for non-commercial use at https://bugseq.com/free .
| S-EPMC7993542 | biostudies-literature
Project description:BackgroundWithin the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required.ResultsIn this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown Swiss cattle. Using four popular imputation programs (Beagle, FImpute, Impute2, Minimac) and various compositions of reference panels, the accuracy of the imputed sequence variant genotypes was high and differences between the programs and scenarios were small. We imputed sequence variant genotypes for more than 1600 Brown Swiss bulls and performed genome-wide association studies for milk fat percentage at two stages of lactation. We found one and three quantitative trait loci for early and late lactation fat content, respectively. Known causal variants that were imputed from the sequenced reference panel were among the most significantly associated variants of the genome-wide association study.ConclusionsOur study demonstrates that whole-genome sequence information can be imputed at high accuracy in cattle populations. Using imputed sequence variant genotypes in genome-wide association studies may facilitate causal variant detection.
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Project description:Multiplexed quantitative analyses of complex proteomes enable deep biological insight. While a multitude of workflows have been developed for multiplexed analyses, the most quantitatively accurate method (SPS-MS3) suffers from long acquisition duty cycles. We built a new, real-time database search (RTS) platform, Orbiter, to combat the SPS-MS3 method's longer duty cycles. RTS with Orbiter eliminates SPS-MS3 scans if no peptide matches to a given spectrum. With Orbiter's online proteomic analytical pipeline, which includes RTS and false discovery rate analysis, it was possible to process a single spectrum database search in less than 10 ms. The result is a fast, functional means to identify peptide spectral matches using Comet, filter these matches, and more efficiently quantify proteins of interest. Importantly, the use of Comet for peptide spectral matching allowed for a fully featured search, including analysis of post-translational modifications, with well-known and extensively validated scoring. These data could then be used to trigger subsequent scans in an adaptive and flexible manner. In this work we tested the utility of this adaptive data acquisition platform to improve the efficiency and accuracy of multiplexed quantitative experiments. We found that RTS enabled a 2-fold increase in mass spectrometric data acquisition efficiency. Orbiter's RTS quantified more than 8000 proteins across 10 proteomes in half the time of an SPS-MS3 analysis (18 h for RTS, 36 h for SPS-MS3).
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Project description:Aspartate family amino acids (AFAAs) have important commercial values due to their wide spectrum of applications. Most if not all AFAAs are produced under aerobic conditions which is energy-intensive. To establish a cost-effective anaerobic process for production of AFAAs, it holds great promise to develop a new pathway enabling the conversion of oxoloacetate into aspartate through direct amination which is catalyzed by aspartate dehydrogenase (AspDH). Compared with the well studied aspartate aminotransferase and aspartate ammonia-lyase, only a few AspDHs are characterized till date, and failure to reproduce the high activity of AspDH from Rastonia eutropha documented in the literature encouraged us to screen and characterize novel AspDHs from different origins. Interestingly, the AspDHs from Klebsiella pneumoniae 34618 (KpnAspDH) and Delftia sp. Cs1-4 (DelAspDH) showed successful soluble expression. KpnAspDH and DelAspDH containing C-terminal hexa-histidine tags were purified and characterized for their catalytic properties. Notably, in addition to its high reductive amination activity, DelAspDH exhibited considerable stability as compared to the other source of AspDHs. This work thus provides novel enzyme resource for engineering strains capable of producing AFAAs under anaerobic conditions.
| S-EPMC5554190 | biostudies-other