Reconstructing and counting genomic fragments through tagmentation-based haploid phasing.
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ABSTRACT:
SUBMITTER: Leong PPT
PROVIDER: S-EPMC8460729 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Metagenomics has transformed our understanding of the microbial world, allowing researchers to bypass the need to isolate and culture individual taxa and to directly characterize both the taxonomic and gene compositions of environmental samples. However, associating the genes found in a metagenomic sample with the specific taxa of origin remains a critical challenge. Existing binning methods, based on nucleotide composition or alignment to reference genomes allow only a coarse-grained classification and rely heavily on the availability of sequenced genomes from closely related taxa. Here, we introduce a novel computational framework, integrating variation in gene abundances across multiple samples with taxonomic abundance data to deconvolve metagenomic samples into taxa-specific gene profiles and to reconstruct the genomic content of community members. This assembly-free method is not bounded by various factors limiting previously described methods of metagenomic binning or metagenomic assembly and represents a fundamentally different approach to metagenomic-based genome reconstruction. An implementation of this framework is available at http://elbo.gs.washington.edu/software.html. We first describe the mathematical foundations of our framework and discuss considerations for implementing its various components. We demonstrate the ability of this framework to accurately deconvolve a set of metagenomic samples and to recover the gene content of individual taxa using synthetic metagenomic samples. We specifically characterize determinants of prediction accuracy and examine the impact of annotation errors on the reconstructed genomes. We finally apply metagenomic deconvolution to samples from the Human Microbiome Project, successfully reconstructing genus-level genomic content of various microbial genera, based solely on variation in gene count. These reconstructed genera are shown to correctly capture genus-specific properties. With the accumulation of metagenomic data, this deconvolution framework provides an essential tool for characterizing microbial taxa never before seen, laying the foundation for addressing fundamental questions concerning the taxa comprising diverse microbial communities.
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Project description:Macromolecular structures can be solved by molecular replacement provided that suitable search models are available. Models from distant homologues may deviate too much from the target structure to succeed, notwithstanding an overall similar fold or even their featuring areas of very close geometry. Successful methods to make the most of such templates usually rely on the degree of conservation to select and improve search models. ARCIMBOLDO_SHREDDER uses fragments derived from distant homologues in a brute-force approach driven by the experimental data, instead of by sequence similarity. The new algorithms implemented in ARCIMBOLDO_SHREDDER are described in detail, illustrating its characteristic aspects in the solution of new and test structures. In an advance from the previously published algorithm, which was based on omitting or extracting contiguous polypeptide spans, model generation now uses three-dimensional volumes respecting structural units. The optimal fragment size is estimated from the expected log-likelihood gain (LLG) values computed assuming that a substructure can be found with a level of accuracy near that required for successful extension of the structure, typically below 0.6?Å root-mean-square deviation (r.m.s.d.) from the target. Better sampling is attempted through model trimming or decomposition into rigid groups and optimization through Phaser's gyre refinement. Also, after model translation, packing filtering and refinement, models are either disassembled into predetermined rigid groups and refined (gimble refinement) or Phaser's LLG-guided pruning is used to trim the model of residues that are not contributing signal to the LLG at the target r.m.s.d. value. Phase combination among consistent partial solutions is performed in reciprocal space with ALIXE. Finally, density modification and main-chain autotracing in SHELXE serve to expand to the full structure and identify successful solutions. The performance on test data and the solution of new structures are described.
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Project description:Through X-ray crystallographic fragment screening, 4-bromopyrazole was discovered to be a 'magic bullet' that is capable of binding at many of the ligand 'hot spots' found in HIV-1 reverse transcriptase (RT). The binding locations can be in pockets that are 'hidden' in the unliganded crystal form, allowing rapid identification of these sites for in silico screening. In addition to hot-spot identification, this ubiquitous yet specific binding provides an avenue for X-ray crystallographic phase determination, which can be a significant bottleneck in the determination of the structures of novel proteins. The anomalous signal from 4-bromopyrazole or 4-iodopyrazole was sufficient to determine the structures of three proteins (HIV-1 RT, influenza A endonuclease and proteinase K) by single-wavelength anomalous dispersion (SAD) from single crystals. Both compounds are inexpensive, readily available, safe and very soluble in DMSO or water, allowing efficient soaking into crystals.
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Project description:Fragment-based molecular-replacement methods can solve a macromolecular structure quasi-ab initio. ARCIMBOLDO, using a common secondary-structure or tertiary-structure template or a library of folds, locates these with Phaser and reveals the rest of the structure by density modification and autotracing in SHELXE. The latter stage is challenging when dealing with diffraction data at lower resolution, low solvent content, high ?-sheet composition or situations in which the initial fragments represent a low fraction of the total scattering or where their accuracy is low. SEQUENCE SLIDER aims to overcome these complications by extending the initial polyalanine fragment with side chains in a multisolution framework. Its use is illustrated on test cases and previously unknown structures. The selection and order of fragments to be extended follows the decrease in log-likelihood gain (LLG) calculated with Phaser upon the omission of each single fragment. When the starting substructure is derived from a remote homolog, sequence assignment to fragments is restricted by the original alignment. Otherwise, the secondary-structure prediction is matched to that found in fragments and traces. Sequence hypotheses are trialled in a brute-force approach through side-chain building and refinement. Scoring the refined models through their LLG in Phaser may allow discrimination of the correct sequence or filter the best partial structures for further density modification and autotracing. The default limits for the number of models to pursue are hardware dependent. In its most economic implementation, suitable for a single laptop, the main-chain trace is extended as polyserine rather than trialling models with different sequence assignments, which requires a grid or multicore machine. SEQUENCE SLIDER has been instrumental in solving two novel structures: that of MltC from 2.7?Å resolution data and that of a pneumococcal lipoprotein with 638 residues and 35% solvent content.
| S-EPMC7057211 | biostudies-literature
Project description:BackgroundPreviously developed TaME-seq method for deep sequencing of HPV, allowed simultaneous identification of the human papillomavirus (HPV) DNA consensus sequence, low-frequency variable sites, and chromosomal integration events. The method has been successfully validated and applied to the study of five carcinogenic high-risk (HR) HPV types (HPV16, 18, 31, 33, and 45). Here, we present TaME-seq2 with an updated laboratory workflow and bioinformatics pipeline. The HR-HPV type repertoire was expanded with HPV51, 52, and 59. As a proof-of-concept, TaME-seq2 was applied on SARS-CoV-2 positive samples showing the method's flexibility to a broader range of viruses, both DNA and RNA.ResultsCompared to TaME-seq version 1, the bioinformatics pipeline of TaME-seq2 is approximately 40× faster. In total, 23 HPV-positive samples and seven SARS-CoV-2 clinical samples passed the threshold of 300× mean depth and were submitted to further analysis. The mean number of variable sites per 1 kb was ~ 1.5× higher in SARS-CoV-2 than in HPV-positive samples. Reproducibility and repeatability of the method were tested on a subset of samples. A viral integration breakpoint followed by a partial genomic deletion was found in within-run replicates of HPV59-positive sample. Identified viral consensus sequence in two separate runs was > 99.9% identical between replicates, differing by a couple of nucleotides identified in only one of the replicates. Conversely, the number of identical minor nucleotide variants (MNVs) differed greatly between replicates, probably caused by PCR-introduced bias. The total number of detected MNVs, calculated gene variability and mutational signature analysis, were unaffected by the sequencing run.ConclusionTaME-seq2 proved well suited for consensus sequence identification, and the detection of low-frequency viral genome variation and viral-chromosomal integrations. The repertoire of TaME-seq2 now encompasses seven HR-HPV types. Our goal is to further include all HR-HPV types in the TaME-seq2 repertoire. Moreover, with a minor modification of previously developed primers, the same method was successfully applied for the analysis of SARS-CoV-2 positive samples, implying the ease of adapting TaME-seq2 to other viruses.
| S-EPMC9993372 | biostudies-literature
Project description:We have adapted transposase-based in vitro shotgun library construction ("tagmentation") for whole-genome bisulfite sequencing. This method, Tn5mC-seq, enables a >100-fold reduction in starting material relative to conventional protocols, such that we generate highly complex bisulfite sequencing libraries from as little as 10 ng of input DNA, and ample useful sequences from 1 ng of input DNA. We demonstrate Tn5mC-seq by sequencing the methylome of a human lymphoblastoid cell line to ?8.6× high-quality coverage of each strand.
| S-EPMC3371708 | biostudies-literature
Project description:Despite the known relevance of genomic structural variants to pathogen behavior, cancer, development, and evolution, certain repeat based structural variants may evade detection by existing high-throughput techniques. Here, we present ruler arrays, a technique to detect genomic structural variants including insertions and deletions (indels), duplications, and translocations. A ruler array exploits DNA polymerase's processivity to detect physical distances between defined genomic sequences regardless of the intervening sequence. The method combines a sample preparation protocol, tiling genomic microarrays, and a new computational analysis. The analysis of ruler array data from two genomic samples enables the identification of structural variation between the samples. In an empirical test between two closely related haploid strains of yeast ruler arrays detected 78% of the structural variants larger than 100 bp.
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Project description:BACKGROUND:Haplotype data contains more information than genotype data and provides possibilities such as imputing low frequency variants, inferring points of recombination, detecting recurrent mutations, mapping linkage disequilibrium (LD), studying selection signatures, estimating IBD probabilities, etc. In addition, haplotype structure is used to assess genetic diversity and expected accuracy in genomic selection programs. Nevertheless, the quality and efficiency of phasing has rarely been a subject of thorough study but was assessed mainly as a by-product in imputation quality studies. Moreover, phasing studies based on data of a poultry population are non-existent. The aim of this study was to evaluate the phasing quality of FImpute and Beagle, two of the most used phasing software. RESULTS:We simulated ten replicated samples of a layer population comprising 888 individuals from a real SNP dataset of 580?k and a pedigree of 12 generations. Chromosomes analyzed were 1, 7 and 20. We measured the percentage of SNPs that were phased equally between true and phased haplotypes (Eqp), proportion of individuals completely correctly phased, number of incorrectly phased SNPs or Breakpoints (Bkp) and the length of inverted haplotype segments. Results were obtained for three different groups of individuals, with no parents or offspring genotyped in the dataset, with only one parent, and with both parents, respectively. The phasing was performed with Beagle (v3.3 and v4.1) and FImpute v2.2 (with and without pedigree). Eqp values ranged from 88 to 100%, with the best results from haplotypes phased with Beagle v4.1 and FImpute with pedigree information and at least one parent genotyped. FImpute haplotypes showed a higher number of Bkp than Beagle. As a consequence, switched haplotype segments were longer for Beagle than for FImpute. CONCLUSION:We concluded that for the dataset applied in this study Beagle v4.1 or FImpute with pedigree information and at least one parent genotyped in the data set were the best alternatives for obtaining high quality phased haplotypes.
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Project description:It is well known that patterns of nucleotide composition vary within and among genomes, although the reasons why these variations exist are not completely understood. Between-genome compositional variation has been exploited to assign environmental shotgun sequences to their most likely originating genomes, whereas within-genome variation has been used to identify recently acquired genetic material such as pathogenicity islands. Recent sequence assignment techniques have achieved high levels of accuracy on artificial data sets, but the relative difficulty of distinguishing lineages with varying degrees of relatedness, and different types of genomic sequence, has not been examined in depth. We investigated the compositional differences in a set of 774 sequenced microbial genomes, finding rapid divergence among closely related genomes, but also convergence of compositional patterns among genomes with similar habitats. Support vector machines were then used to distinguish all pairs of genomes based on genome fragments 500 nucleotides in length. The nearly 300,000 accuracy scores obtained from these trials were used to construct general models of distinguishability versus taxonomic and compositional indices of genomic divergence. Unusual genome pairs were evident from their large residuals relative to the fitted model, and we identified several factors including genome reduction, putative lateral genetic transfer, and habitat convergence that influence the distinguishability of genomes. The positional, compositional, and functional context of a fragment within a genome has a strong influence on its likelihood of correct classification, but in a way that depends on the taxonomic and ecological similarity of the comparator genome.
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Project description:Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.
| S-EPMC4773135 | biostudies-literature