scholarly journals Variant calling for cpn60 barcode sequence-based microbiome profiling

2019 ◽  
Author(s):  
Sarah J. Vancuren ◽  
Scott J. Dos Santos ◽  
Janet E. Hill ◽  
Keyword(s):  
De Novo ◽  
Sequence Data ◽  
Variant Calling ◽  
Taxonomic Composition ◽  
Species Level ◽  
Reference Sequence ◽  
Sequence Length ◽  
Sequence Variant ◽  
Operational Taxonomic Units ◽  
Microbiome Profiling

AbstractAmplification and sequencing of conserved genetic barcodes such as the cpn60 gene is a common approach to determining the taxonomic composition of microbiomes. Exact sequence variant calling has been proposed as an alternative to previously established methods for aggregation of sequence reads into operational taxonomic units (OTU). We investigated the utility of variant calling for cpn60 barcode sequences and determined the minimum sequence length required to provide species-level resolution. Sequence data from the 5’ region of the cpn60 barcode amplified from the human vaginal microbiome (n=45), and a mock community were used to compare variant calling to de novo assembly of reads, and mapping to a reference sequence database in terms of number of OTU formed, and overall community composition. Variant calling resulted in microbiome profiles that were consistent in apparent composition to those generated with the other methods but with significant logistical advantages. Variant calling is rapid, achieves high resolution of taxa, and does not require reference sequence data. Our results further demonstrate that 150 bp from the 5’ end of the cpn60 barcode sequence is sufficient to provide species-level resolution of microbiota.

scholarly journals OptiFit: an improved method for fitting amplicon sequences to existing OTUs

2021 ◽  
Author(s):  
Kelly L Sovacool ◽  
Sarah L Westcott ◽  
M Brodie Mumphrey ◽  
Gabrielle A Dotson ◽  
Patrick D. Schloss
Keyword(s):  
Processing Speed ◽  
De Novo ◽  
Sequence Data ◽  
Query Sequence ◽  
Reference Sequence ◽  
Reference Database ◽  
Clustering Methods ◽  
Improved Method ◽  
Operational Taxonomic Units ◽  
External Reference

Assigning amplicon sequences to operational taxonomic units (OTUs) is often an important step in characterizing the composition of microbial communities across large datasets. OptiClust, a de novo OTU clustering method, has been shown to produce higher quality OTU assignments than other methods and at comparable or faster speeds. A notable difference between de novo clustering and database-dependent reference clustering methods is that OTU assignments from de novo methods may change when new sequences are added to a dataset. However, in some cases one may wish to incorporate new samples into a previously clustered dataset without performing clustering again on all sequences, such as when comparing across datasets or deploying machine learning models where OTUs are features. Existing reference-based clustering methods produce consistent OTUs, but they only consider the similarity of each query sequence to a single reference sequence in an OTU, thus resulting in OTU assignments that are significantly worse than those generated by de novo methods. To provide an efficient and robust method to fit amplicon sequence data to existing OTUs, we developed the OptiFit algorithm. Inspired by OptiClust, OptiFit considers the similarity of all pairs of reference and query sequences in an OTU to produce OTUs of the best possible quality. We tested OptiFit using four microbiome datasets with two different strategies: by clustering to an external reference database or by splitting the dataset into a reference and query set and clustering the query sequences to the reference set after clustering it using OptiClust. The result is an improved implementation of closed and open-reference clustering. OptiFit produces OTUs of similar quality as OptiClust and at faster speeds when using the split dataset strategy, although the OTU quality and processing speed depends on the database chosen when using the external database strategy. OptiFit provides a suitable option for users who require consistent OTU assignments at the same quality afforded by de novo clustering methods.

scholarly journals Haplotype-aware variant calling enables high accuracy in nanopore long-reads using deep neural networks

2021 ◽  
Author(s):  
Kishwar Shafin ◽  
Trevor Pesout ◽  
Pi-Chuan Chang ◽  
Maria Nattestad ◽  
Alexey Kolesnikov ◽  
...  
Keyword(s):  
De Novo ◽  
Sequence Data ◽  
Variant Calling ◽  
High Accuracy ◽  
Superior Performance ◽  
Read Length ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Short Read ◽  
Long Read

Long-read sequencing has the potential to transform variant detection by reaching currently difficult-to-map regions and routinely linking together adjacent variations to enable read based phasing. Third-generation nanopore sequence data has demonstrated a long read length, but current interpretation methods for its novel pore-based signal have unique error profiles, making accurate analysis challenging. Here, we introduce a haplotype-aware variant calling pipeline PEPPER-Margin-DeepVariant that produces state-of-the-art variant calling results with nanopore data. We show that our nanopore-based method outperforms the short-read-based single nucleotide variant identification method at the whole genome-scale and produces high-quality single nucleotide variants in segmental duplications and low-mappability regions where short-read based genotyping fails. We show that our pipeline can provide highly-contiguous phase blocks across the genome with nanopore reads, contiguously spanning between 85% to 92% of annotated genes across six samples. We also extend PEPPER-Margin-DeepVariant to PacBio HiFi data, providing an efficient solution with superior performance than the current WhatsHap-DeepVariant standard. Finally, we demonstrate de novo assembly polishing methods that use nanopore and PacBio HiFi reads to produce diploid assemblies with high accuracy (Q35+ nanopore-polished and Q40+ PacBio-HiFi-polished).

scholarly journals Information theoretic alignment free variant calling

2016 ◽  
Vol 2 ◽  
pp. e71
Author(s):  
Justin Bedo ◽  
Benjamin Goudey ◽  
Jeremy Wazny ◽  
Zeyu Zhou
Keyword(s):  
Sequence Data ◽  
Multinomial Distribution ◽  
Variant Calling ◽  
Whole Genome Sequence ◽  
Reference Sequence ◽  
Information Theoretic ◽  
Learning Tasks ◽  
Leibler Divergence ◽  
Suitable Reference ◽  
Mouse Dataset

While traditional methods for calling variants across whole genome sequence data rely on alignment to an appropriate reference sequence, alternative techniques are needed when a suitable reference does not exist. We present a novel alignment and assembly free variant calling method based on information theoretic principles designed to detect variants have strong statistical evidence for their ability to segregate samples in a given dataset. Our method uses the context surrounding a particular nucleotide to define variants. Given a set of reads, we model the probability of observing a given nucleotide conditioned on the surrounding prefix and suffixes of lengthkas a multinomial distribution. We then estimate which of these contexts are stable intra-sample and varying inter-sample using a statistic based on the Kullback–Leibler divergence.The utility of the variant calling method was evaluated through analysis of a pair of bacterial datasets and a mouse dataset. We found that our variants are highly informative for supervised learning tasks with performance similar to standard reference based calls and another reference free method (DiscoSNP++). Comparisons against reference based calls showed our method was able to capture very similar population structure on the bacterial dataset. The algorithm’s focus on discriminatory variants makes it suitable for many common analysis tasks for organisms that are too diverse to be mapped back to a single reference sequence.

scholarly journals DeepTrio: Variant Calling in Families Using Deep Learning

2021 ◽  
Author(s):  
Alexey Kolesnikov ◽  
Sidharth Goel ◽  
Maria Nattestad ◽  
Taedong Yun ◽  
Gunjan Baid ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Sequence Data ◽  
Genetic Diseases ◽  
Variant Calling ◽  
Training Data ◽  
Sequencing Error ◽  
Sequence Information ◽  
Genome Context ◽  
Parental Inheritance

Every human inherits one copy of the genome from their mother and another from their father. Parental inheritance helps us understand the transmission of traits and genetic diseases, which often involve de novo variants and rare recessive alleles. Here we present DeepTrio, which learns to analyze child-mother-father trios from the joint sequence information, without explicit encoding of inheritance priors. DeepTrio learns how to weigh sequencing error, mapping error, and de novo rates and genome context directly from the sequence data. DeepTrio has higher accuracy on both Illumina and PacBio HiFi data when compared to DeepVariant. Improvements are especially pronounced at lower coverages (with 20x DeepTrio roughly equivalent to 30x DeepVariant). As DeepTrio learns directly from data, we also demonstrate extensions to exome calling solely by changing the training data. DeepTrio includes pre-trained models for Illumina WGS, Illumina exome, and PacBio HiFi.

scholarly journals Sequence variation aware genome references and read mapping with the variation graph toolkit

2017 ◽  
Author(s):  
Erik Garrison ◽  
Jouni Sirén ◽  
Adam M. Novak ◽  
Glenn Hickey ◽  
Jordan M. Eizenga ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Large Scale ◽  
De Novo ◽  
Sequence Data ◽  
Variant Calling ◽  
Read Mapping ◽  
Dna Sequence Data ◽  
Suffix Arrays ◽  
Improved Accuracy ◽  
Reference Genomes

AbstractReference genomes guide our interpretation of DNA sequence data. However, conventional linear references are fundamentally limited in that they represent only one version of each locus, whereas the population may contain multiple variants. When the reference represents an individual’s genome poorly, it can impact read mapping and introduce bias. Variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation, including large scale structural variation such as inversions and duplications.1 Equivalent structures are produced by de novo genome assemblers.2,3 Here we present vg, a toolkit of computational methods for creating, manipulating, and utilizing these structures as references at the scale of the human genome. vg provides an efficient approach to mapping reads onto arbitrary variation graphs using generalized compressed suffix arrays,4 with improved accuracy over alignment to a linear reference, creating data structures to support downstream variant calling and genotyping. These capabilities make using variation graphs as reference structures for DNA sequencing practical at the scale of vertebrate genomes, or at the topological complexity of new species assemblies.

scholarly journals Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank

2020 ◽  
Author(s):  
Martin Steinegger ◽  
Steven L Salzberg
Keyword(s):  
Large Scale ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Model Organism ◽  
Taxonomic Composition ◽  
Reference Sequence ◽  
Metagenomic Sequencing ◽  
Protein Database ◽  
Input Size ◽  
Reference Databases

Metagenomic sequencing allows researchers to investigate organisms sampled from their native environments by sequencing their DNA directly, and then quantifying the abundance and taxonomic composition of the organisms thus captured. However, these types of analyses are sensitive to contamination in public databases caused by incorrectly labeled reference sequences. Here we describe Conterminator, an efficient method to detect and remove incorrectly labelled sequences by an exhaustive all-against-all sequence comparison. Our analysis reports contamination in 114,035 sequences and 2767 species in the NCBI Reference Sequence Database (RefSeq), 2,161,746 sequences and 6795 species in the GenBank database, and 14,132 protein sequences in the NR non-redundant protein database. Conterminator uncovers contamination in sequences spanning the whole range from draft genomes to “complete” model organism genomes. Our method, which scales linearly with input size, was able to process 3.3 terabytes of genomic sequence data in 12 days on a single 32-core compute node. We believe that Conterminator can become an important tool to ensure the quality of reference databases with particular importance for downstream metagenomic analyses. Source code (GPLv3): https://github.com/martin-steinegger/conterminator

scholarly journals Easy and Accurate Reconstruction of Whole HIV Genomes from Short-Read Sequence Data

2016 ◽  
Author(s):  
Chris Wymant ◽  
François Blanquart ◽  
Astrid Gall ◽  
Margreet Bakker ◽  
Daniela Bezemer ◽  
...  
Keyword(s):  
De Novo ◽  
Sequence Data ◽  
Consensus Sequence ◽  
Heterogeneous Data ◽  
Reference Sequence ◽  
Data Sets ◽  
Illumina Platform ◽  
Short Read ◽  
Variant Information ◽  
Short Read Sequence

AbstractNext-generation sequencing has yet to be widely adopted for HIV. The difficulty of accurately reconstructing the consensus sequence of a quasispecies from reads (short fragments of DNA) in the presence of rapid between- and within-host evolution may have presented a barrier. In particular, mapping (aligning) reads to a reference sequence leads to biased loss of information; this bias can distort epidemiological and evolutionary conclusions.De novoassembly avoids this bias by effectively aligning the reads to themselves, producing a set of sequences called contigs. However contigs provide only a partial summary of the reads, misassembly may result in their having an incorrect structure, and no information is available at parts of the genome where contigs could not be assembled. To address these problems we developed the toolshiverto preprocess reads for quality and contamination, then map them to a reference tailored to the sample using corrected contigs supplemented with existing reference sequences. Run with two commands per sample, it can easily be used for large heterogeneous data sets. We useshiverto reconstruct the consensus sequence and minority variant information from paired-end short-read data produced with the Illumina platform, for 65 existing publicly available samples and 50 new samples. We show the systematic superiority of mapping toshiver’s constructed reference over mapping the same reads to the standard reference HXB2: an average of 29 bases per sample are called differently, of which 98.5% are supported by higher coverage. We also provide a practical guide to working with imperfect contigs.

scholarly journals A reference bacterial genome dataset generated on the MinION™ portable single-molecule nanopore sequencer

2014 ◽  
Author(s):  
Josh Quick ◽  
Aaron Quinlan ◽  
Nicholas Loman
Keyword(s):  
Single Molecule ◽  
De Novo ◽  
Sequence Data ◽  
Bacterial Genome ◽  
Model Organism ◽  
Variant Calling ◽  
Laptop Computer ◽  
Early Access ◽  
Dna Strands ◽  
K 12

Background: The MinION™ is a new, portable single-molecule sequencer developed by Oxford Nanopore Technologies. It measures four inches in length and is powered from the USB 3.0 port of a laptop computer. By measuring the change in current produced when DNA strands translocate through and interact with a charged protein nanopore the device is able to deduce the underlying nucleotide sequence. Findings: We present a read dataset from whole-genome shotgun sequencing of the model organism Escherichia coli K-12 substr. MG1655 generated on a MinION™ device during the early-access MinION Access Program (MAP). Sequencing runs of the MinION™ are presented, one generated using R7 chemistry (released in July 2014) and one using R7.3 (released in September 2014). Conclusions: Base-called sequence data are provided to demonstrate the nature of data produced by the MinION™ platform and to encourage the development of customised methods for alignment, consensus and variant calling, de novo assembly and scaffolding. FAST5 files containing event data within the HDF5 container format are provided to assist with the development of improved base-calling methods. Datasets are provided through the GigaDB database at http://gigadb.org/dataset/100102

scholarly journals A two-tiered model for simulating the ecological and evolutionary dynamics of rapidly evolving viruses, with an application to influenza

2010 ◽  
Vol 7 (50) ◽  
pp. 1257-1274 ◽  
Author(s):  
Katia Koelle ◽  
Priya Khatri ◽  
Meredith Kamradt ◽  
Thomas B. Kepler
Keyword(s):  
Influenza A ◽  
Evolutionary Dynamics ◽  
De Novo ◽  
Sequence Data ◽  
Nucleotide Composition ◽  
Sequence Length ◽  
Influenza B ◽  
Model Parameters ◽  
De Novo Mutations ◽  
Estimation Of Model Parameters

Understanding the epidemiological and evolutionary dynamics of rapidly evolving pathogens is one of the most challenging problems facing disease ecologists today. To date, many mathematical and individual-based models have provided key insights into the factors that may regulate these dynamics. However, in many of these models, abstractions have been made to the simulated sequences that limit an effective interface with empirical data. This is especially the case for rapidly evolving viruses in which de novo mutations result in antigenically novel variants. With this focus, we present a simple two-tiered ‘phylodynamic’ model whose purpose is to simulate, along with case data, sequence data that will allow for a more quantitative interface with observed sequence data. The model differs from previous approaches in that it separates the simulation of the epidemiological dynamics (tier 1) from the molecular evolution of the virus's dominant antigenic protein (tier 2). This separation of phenotypic dynamics from genetic dynamics results in a modular model that is computationally simpler and allows sequences to be simulated with specifications such as sequence length, nucleotide composition and molecular constraints. To illustrate its use, we apply the model to influenza A (H3N2) dynamics in humans, influenza B dynamics in humans and influenza A (H3N8) dynamics in equine hosts. In all three of these illustrative examples, we show that the model can simulate sequences that are quantitatively similar in pattern to those empirically observed. Future work should focus on statistical estimation of model parameters for these examples as well as the possibility of applying this model, or variants thereof, to other host–virus systems.

scholarly journals Information theoretic alignment free variant calling

2016 ◽  
Author(s):  
Justin Bedo ◽  
Benjamin Goudey ◽  
Jeremy Wazny ◽  
Zeyu Zhou
Keyword(s):  
Sequence Data ◽  
Multinomial Distribution ◽  
Variant Calling ◽  
Whole Genome Sequence ◽  
Reference Sequence ◽  
Information Theoretic ◽  
Learning Tasks ◽  
Leibler Divergence ◽  
Suitable Reference ◽  
Mouse Dataset

While traditional methods for calling variants across whole genome sequence data rely on alignment to an appropriate reference sequence, alternative techniques are needed when a suitable reference does not exist. We present a novel alignment and assembly free variant calling method based on information theoretic principles designed to detect variants have strong statistical evidence for their ability to segregate samples in a given dataset. Our method uses the context surrounding a particular nucleotide to define variants. Given a set of reads, we model the probability of observing a given nucleotide conditioned on the surrounding prefix and suffixes of length k as a multinomial distribution. We then estimate which of these contexts are stable intra-sample and varying inter-sample using a statistic based on the Kullback–Leibler divergence. The utility of the variant calling method was evaluated through analysis of a pair of bacterial datasets and a mouse dataset. We found that our variants are highly informative for supervised learning tasks with performance similar to standard reference based calls and another reference free method (DiscoSNP++). Comparisons against reference based calls showed our method was able to capture very similar population structure on the bacterial dataset. The algorithm’s focus on discriminatory variants makes it suitable for many common analysis tasks for organisms that are too diverse to be mapped back to a single reference sequence.

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