Biology subjects

Pipes, L.

Publications and source records attributed to Pipes, L..

12 recordsLinked to original sources

bioRxiv2026

Error correction of SARS-CoV-2 genomic sequences using a phylogenetic prior

Genotype calling of sequencing data has improved over recent years thanks to technological and statistical breakthroughs. One such advance is the use of imputation-based genotyping methods in human genetics. For haploid and mostly non-recombining organisms, a natural equivalent is to leverage phylogenetic signal for imputation-based genotype calling. SARS-CoV-2 is one such case, and the use of improved genotyping methods in this species is particularly pertinent given the well-documented systematic problems with read alignment and assembly in rapidly evolving regions of its genome, motivating the need for statistically principled methods for genotype correction that complement existing community efforts to curate sequencing data. We present a statistical framework to combine phylogenetic prior probabilities with the information from aligned reads, and demonstrate its utility in correcting consensus calls made on short-read, viral sequencing data. We derive phylogenetically-informed posterior probabilities based on genome placement in a reference phylogeny, and read likelihoods calculated from machine-reported base quality scores. We further modify the prior at each site by estimating a maximum-likelihood scaling parameter jointly with reads at all other sites. We find that the phylogenetically-informed base calling method stays highly accurate across simulated depth and error conditions. In real datasets, the method vastly improves call rate while still maintaining similar accuracy as reference-based assembly methods. In problematic genomic regions, the phylogenetically-informed method confers both accuracy and call rate gains. Accuracy and call rate do not stratify with sequencing center and primer scheme used, suggesting broad applicability across sequencing technologies and potentially diverging sequences. Our results in real and simulated data demonstrate that phylogenetically-informed genotyping correction results in more complete sequences, achieving a higher call rate without compromising accuracy. In cases of low mean depth, sparse coverage, or difficult-to-sequence regions that may still retain a low call rate, the method improves the accuracy of genotype calls.

bioinformatics↗

bioRxiv2026

Molecular Clock Dating of Ancient Environmental DNA Reveals Damage Beyond Deamination

Ancient environmental DNA (aeDNA) from permafrost, lake, cave, and marine sediments provides a rich source of genetic data that captures broad perspectives of past biodiversity. Accurate dating is crucial for discovering ecologically relevant patterns from aeDNA, and molecular clock dating would allow for sample ages to be estimated from the recovered genetic material itself instead of the geological components. However, the fragmented and damaged nature of short-read ancient DNA (aDNA) from multiple taxonomic sources poses significant challenges and has limited this dating approach for aeDNA. Here we developed ratePlacer, a phylogeny-based method for analyzing aeDNA that can combine information from many short reads in a sample while accounting for DNA damage to provide maximum likelihood estimates of sample ages. Simulations demonstrate that ratePlacer accurately dates samples even under the fragmented, damaged conditions characteristic of aeDNA and outperforms Bayesian tip-dating approaches for taxonomically mixed samples commonly found in aeDNA. Yet age estimates from re-dating Kap Kobenhavn varied across taxa, highlighting the difficulty of molecular clock dating in aeDNA. This dating also revealed elevated G [->] T and C [->] A mismatches consistent with oxidative damage. These patterns reveal aDNA damage beyond deamination and that remains understudied, suggesting that aeDNA should be carefully evaluated in genomic and evolutionary analyses. The new dating method, ratePlacer, extends molecular clock dating of aDNA from single-specimen to pooled environmental DNA data, where traditional methods struggle.

bioinformatics↗

bioRxiv2025

Developing and Benchmarking One Health Genomic Surveillance Tools for Influenza A Virus in Wastewater

Influenza A viruses (IAV) remain a persistent One Health threat, and whole-genome sequencing from wastewater offers a promising surveillance tool. However, IAV is at low abundance in wastewater, making it difficult to sequence. We benchmarked four targeted enrichment methods suited for whole-genome sequencing including custom and off-the-shelf amplicon and probe-based methods. Our custom HA tiled-amplicon panel was sensitive, fast, and cost-effective, making it suitable for monitoring low-abundance seasonal variants of known subtypes. However, its reliance on conserved and intact primer-binding sites limited primer design to fewer subtypes. A previously published universal amplicon method targeted all IAV subtypes, but it performed poorly in wastewater due to its reliance on intact genome segments. Probe-capture methods were resilient to RNA degradation and mismatches, potentially enabling broader surveillance and detection of emerging strains. However, probes were costly, labor-intensive, and less sensitive than tiled-amplicon. When testing compatibility of sequencing methods with upstream virus concentration and extraction methods, ultrafiltration-based virus concentration outperformed large-volume direct extraction with all four sequencing methods. This set of benchmarking comparisons and custom panels provides needed information for the translation of IAV genomic sequencing into a routine component of wastewater surveillance.

microbiology↗

bioRxiv2023

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic "novel" lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances, and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1% frequency, results were more reliable above a 5% threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of noise or bias in wastewater sequencing data and to appreciate the commonalities and differences across methods.

bioinformatics↗

bioRxiv2022

A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets

Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern Next-Generation Sequencing data. We here present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.

genomics↗

bioRxiv2021

AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees

MotivationClustering is a fundamental task in the analysis of nucleotide sequences. Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster divergent sequences. Traditional clustering methods have mostly focused on optimizing high speed clustering of highly similar sequences. We develop a phylogenetic clustering method which infers ancestral sequences for a set of initial clusters and then uses a greedy algorithm to cluster sequences. ResultsWe describe a clustering program AncestralClust, which is developed for clustering divergent sequences. We compare this method with other state-of-the-art clustering methods using datasets of homologous sequences from different species. We show that, in divergent datasets, AncestralClust has higher accuracy and more even cluster sizes than current popular methods. Availability and implementationAncestralClust is an Open Source program available at https://github.com/lpipes/ancestralclust. Contactlpipes@berkeley.edu Supplementary informationSupplementary figures and table are available online.

bioinformatics↗

bioRxiv2020

Assessing uncertainty in the rooting of the SARS-CoV-2 phylogeny

The rooting of the SARS-CoV-2 phylogeny is important for understanding the origin and early spread of the virus. Previously published phylogenies have used different rootings that do not always provide consistent results. We investigate several different strategies for rooting the SARS-CoV-2 tree and provide measures of statistical uncertainty for all methods. We show that methods based on the molecular clock tend to place the root in the B clade, while methods based on outgroup rooting tend to place the root in the A clade. The results from the two approaches are statistically incompatible, possibly as a consequence of deviations from a molecular clock or excess back-mutations. We also show that none of the methods provide strong statistical support for the placement of the root in any particular edge of the tree. Our results suggest that inferences on the origin and early spread of SARS-CoV-2 based on rooted trees should be interpreted with caution.

evolutionary biology↗

bioRxiv2020

Synonymous mutations and the molecular evolution of SARS-Cov-2 origins

Human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is most closely related, by average genetic distance, to two coronaviruses isolated from bats, RaTG13 and RmYN02. However, there is a segment of high amino acid similarity between human SARS-CoV-2 and a pangolin isolated strain, GD410721, in the receptor binding domain (RBD) of the spike protein, a pattern that can be caused by either recombination or by convergent amino acid evolution driven by natural selection. We perform a detailed analysis of the synonymous divergence, which is less likely to be affected by selection than amino acid divergence, between human SARS-CoV-2 and related strains. We show that the synonymous divergence between the bat derived viruses and SARS-CoV-2 is larger than between GD410721 and SARS-CoV-2 in the RBD, providing strong additional support for the recombination hypothesis. However, the synonymous divergence between pangolin strain and SARS-CoV-2 is also relatively high, which is not consistent with a recent recombination between them, instead it suggests a recombination into RaTG13. We also find a 14-fold increase in the dN/dS ratio from the lineage leading to SARS-CoV-2 to the strains of the current pandemic, suggesting that the vast majority of non-synonymous mutations currently segregating within the human strains have a negative impact on viral fitness. Finally, we estimate that the time to the most recent common ancestor of SARS-CoV-2 and RaTG13 or RmYN02 based on synonymous divergence, is 51.71 years (95% C.I., 28.11-75.31) and 37.02 years (95% C.I., 18.19-55.85), respectively.

evolutionary biology↗

bioRxiv2019

The California Environmental DNA \"CALeDNA\" Program

Global change is leading to habitat shifts that threaten species persistence throughout Californias unique ecosystems. Baseline biodiversity data provide opportunities for ecosystems to be managed for community complexity and connectivity. In 2017, the University of California Conservation Genomics Consortium launched the California Environmental DNA (CALeDNA) program, a community science initiative monitoring Californias biodiversity through environmental DNA (eDNA)--DNA shed from organisms through fur, mucus, spores, pollen, etc. Community scientists collect soil and sediment samples, then researchers analyze the eDNA in the samples and share results with the public. The results are catalogues of thousands of organisms per sample, ranging from microbes to mammals. The CALeDNA website presents biodiversity inventories in a platform designed for the public and researchers alike, as well as user-friendly analysis tools and educational modules. Here, we present CALeDNA as a scalable community science framework that can harmonize with future biodiversity research and education initiatives.

scientific communication and education↗

bioRxiv2018

Anacapa Toolkit: an environmental DNA toolkit for processing multilocus metabarcode datasets

1. Environmental DNA (eDNA) metabarcoding is a promising method to monitor species and community diversity that is rapid, affordable, and non-invasive. Longstanding needs of the eDNA community are modular informatics tools, comprehensive and customizable reference databases, flexibility across high-throughput sequencing platforms, fast multilocus metabarcode processing, and accurate taxonomic assignment. As bioinformatics tools continue to improve, addressing each of these demands within a single bioinformatics toolkit is becoming a reality.\n\n2. We present the modular metabarcode sequence toolkit Anacapa (https://github.com/limey-bean/Anacapa/), which addresses the above needs, allowing users to build comprehensive reference databases and assign taxonomy to raw multilocus metabarcode sequence data A novel aspect of Anacapa is our database building module, Creating Reference libraries Using eXisting tools (CRUX), which generates comprehensive reference databases for specific user-defined metabarcode loci. The Quality Control and Dereplication module sorts and processes multiple metabarcode loci and processes merged, unmerged and unpaired reads maximizing recovered diversity. Followed by amplicon sequence variants (ASVs) detection using DADA2. The Anacapa Classifier module aligns these ASVs to CRUX-generated reference databases using Bowtie2. Taxonomy is assigned to ASVs with confidence scores using a Bayesian Lowest Common Ancestor (BLCA) method. The Anacapa Toolkit also includes an R package, ranacapa, for automated results exploration through standard biodiversity statistical analysis.\n\n3. We performed a series of benchmarking tests to verify that the Anacapa Toolkit generates comprehensive reference databases that capture wide taxonomic diversity and that it can assign high-quality taxonomy to both MiSeq-length and Hi-Seq length sequence data. We demonstrate the value of the Anacapa Toolkit to assigning taxonomy to eDNA sequences from seawater samples from southern California including capability of this tool kit to process multilocus metabarcoding data.\n\n4. The Anacapa Toolkit broadens the exploration of eDNA and assists in biodiversity assessment and management by generating metabarcode specific databases, processing multilocus data, retaining all read types, and expanding non-traditional eDNA targets. Anacapa software and source code are open and available in a virtual container to ease installation.

bioinformatics↗

bioRxiv2017

Genomic responses to selection for tame/aggressive behaviors in the silver fox (Vulpes vulpes)

Animal domestications have led to a shared spectrum of striking behavioral and morphological changes. To recapitulate this process, silver foxes have been selectively bred for tame and aggressive behaviors for over 50 generations at the Institute for Cytology and Genetics in Novosibirsk, Russia. To understand the genetic basis and molecular mechanisms underlying the phenotypic changes, we profiled gene expression level and coding SNP allele frequencies in two brain tissues from 12 aggressive and 12 tame foxes. Expression analysis revealed 146 genes in prefrontal cortex and 33 genes in basal forebrain that were differentially expressed (5% FDR). These candidates include genes in key pathways known to be critical to neurological processing, including the serotonin and glutamate receptor pathways. In addition, 295 of the 31,000 exonic SNPs show significant allele frequency differences between tame and aggressive population (1% FDR), including genes with a role in neural crest cell fate determination.

evolutionary biology↗

bioRxiv2017

What are the most influencing factors in reconstructing a reliable transcriptome assembly?

Reconstructing the genome and transcriptome for a new or extant species are essential steps in expanding our understanding of the organisms active RNA landscape and gene regulatory dynamics, as well as for developing therapeutic targets to fight disease. The advancement of sequencing technologies has paved the way to generate high-quality draft transcriptomes. With many possible approaches available to accomplish this task, there is a need for a closer investigation of the factors that influence the quality of the results. We carried out an extensive survey of variety of elements that are important in transcriptome assembly. We utilized the human RNA-Seq data from the Sequencing Quality Control Consortium (SEQC) as a well-characterized and comprehensive resource with an available, well-studied human reference genome. Our results indicate that the quality of the library construction significantly impacts the quality of the assembly. Higher coverage of the genome is not as important as the quality of the input RNA-Seq data. Thus, once a certain coverage is attained, the quality of the assembly is mainly dependent on the base-calling accuracy of the input sequencing reads; and it is important to avoid saturating the assembler with extra coverage.

bioinformatics↗