Biology subjects

Nasko, D. J.

Publications and source records attributed to Nasko, D. J..

6 recordsLinked to original sources

bioRxiv2024

Highly accurate metagenome-assembled genomes from human gut microbiota using long-read assembly, binning, and consolidation methods

Long-read metagenomic sequencing is a powerful approach for cataloging the microbial diversity present in complex microbiomes, including the human gut microbiome. We performed a deep-sequencing experiment using PacBio HiFi reads to obtain metagenome-assembled genomes (MAGs) from a pooled human gut microbiome. We performed long-read metagenome assembly using two methods (hifiasm-meta, metMDBG), used improved bioinformatic and proximity ligation binning strategies to cluster contigs and identify MAGs, and developed a novel framework to compare and consolidate MAGs (pb-MAG-mirror). We found proximity ligation binning yielded more MAGs than bioinformatic binning, but our novel comparison framework resulted in higher MAG yields than either binning strategy individually. In total, from 255 Gbp of total HiFi data we produced 595 total MAGs (including 175 high-quality MAGs) using hifiasm-meta, and 547 total MAGs (including 277 high-quality MAGs) with metaMDBG. Hifiasm-meta assembled almost twice as many strain-level MAGs as metaMDBG (246 vs. 156), but both assembly methods produced up to five strains for a species. Approximately 85% of the MAGs were assigned to known species, but we recovered >35 high-quality MAGs that represent uncultured diversity. Based on strict similarity scores, we found 125 MAGs were unequivocally shared across the assembly methods at the strain level, representing [~]22% of the total MAGs recovered per method. Finally, we detected more total viral sequences in the metaMDBG assembly versus the hifiasm-meta assembly ([~]6,700 vs. [~]4,500). Overall, we find the use of HiFi sequencing, improved metagenome assembly methods, and complementary binning strategies is highly effective for rapidly cataloging microbial genomes in complex microbiomes.

microbiology↗

bioRxiv2021

SeqScreen: Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning

The COVID-19 pandemic has emphasized the importance of detecting known and emerging pathogens from clinical and environmental samples. However, robust characterization of pathogenic sequences remains an open challenge. To this end, we developed SeqScreen, which can accurately characterize short nucleotide sequences using taxonomic and functional labels, and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed pathogen characterization and is available for download at: www.gitlab.com/treangenlab/seqscreen

bioinformatics↗

bioRxiv2021

PASV: Automatic protein partitioning and validation using conserved residues

BackgroundIncreasingly, researchers use protein-coding genes from targeted PCR amplification or direct metagenomic sequencing in community and population ecology. Analysis of protein-coding genes presents different challenges from those encountered in traditional SSU rRNA studies. Most protein-coding sequences are annotated based on homology to other computationally-annotated sequences, which can lead to inaccurate annotations. Therefore, the results of sensitive homology searches must be validated to remove false-positives and assess functionality. Multiple lines of in silico evidence can be gathered by examining conserved domains and residues identified through biochemical investigations. However, manually validating sequences in this way can be time consuming and error prone, especially in large environmental studies. ResultsAn automated pipeline for protein active site validation (PASV) was developed to improve validation and partitioning accuracy for protein-coding sequences, combining multiple sequence alignment with expert domain knowledge. PASV was tested using commonly misannotated proteins: ribonucleotide reductase (RNR), alternative oxidase (AOX), and plastid terminal oxidase (PTOX). PASV partitioned 9,906 putative Class I alpha and Class II RNR sequences from bycatch in a global viral metagenomic investigation with >99% true positive and true negative rates. PASV predicted the class of 2,579 RNR sequences in >98% agreement with manual annotations. PASV correctly partitioned all 336 tested AOX and PTOX sequences. ConclusionsPASV provides an automated and accurate way to address post-homology search validation and partitioning of protein-coding marker genes. Source code is released under the MIT license and is found with documentation and usage examples on GitHub at https://github.com/mooreryan/pasv.

bioinformatics↗

bioRxiv2018

CRISPR spacers indicate preferential matching of specific virioplankton genes

Viral infection exerts selection pressure on marine microbes as viral-induced cell lysis causes 20 to 50% of cell mortality resulting in fluxes of biomass into oceanic dissolved organic matter. Archaeal and bacterial populations can defend against viral infection using the CRISPR-Cas system which relies on specific matching between a spacer sequence and a viral gene. If a CRISPR spacer match to any gene within a viral genome is equally effective in preventing lysis, then no viral genes should be preferentially matched by CRISPR spacers. However, if there are differences in effectiveness then certain viral genes may demonstrate a greater frequency of CRISPR spacer matches. Indeed, homology search analyses of bacterioplankton CRISPR spacer sequences against virioplankton sequences revealed preferential matching of replication proteins, nucleic acid binding proteins, and viral structural proteins. Positive selection pressure for effective viral defense is one parsimonious explanation for these observations. CRISPR spacers from virioplankton metagenomes preferentially matched methyltransferase and phage integrase genes within virioplankton sequences. These viriolankton CRISPR spacers may assist infected host cells in defending against competing phage. Analyses also revealed that half of the spacer-matched viral genes were unknown and that some genes matched several spacers and some spacers matched multiple genes, a many-to-many relationship. Thus, CRISPR spacer matching may be an evolutionary algorithm, agnostically identifying those genes under stringent selection pressure for sustaining viral infection and lysis. Investigating this subset of viral genes could reveal those genetic mechanisms essential to viral-host interactions and provide new technologies for optimizing CRISPR defense in beneficial microbes.

microbiology↗

bioRxiv2018

Fast and sensitive protein sequence homology searches using hierarchical cluster BLAST

The throughput of DNA sequencing continues to increase, allowing researchers to analyze genomes of interest at greater depths. An unintended consequence of this data deluge is the increased cost of analyzing these datasets. As a result, genome and metagenome annotation pipelines are left with a few options: (i) search against smaller reference databases, (ii) use faster, but less sensitive, algorithms to assess sequence similarities, or (iii) invest in computing hardware specifically designed to improve BLAST searches such as GPGPU systems and/or large CPU-rich clusters.\n\nWe present a pipeline that improves the speed of amino acid sequence homology searches with a minimal decrease in sensitivity and specificity by searching against hierarchical clusters. Briefly, the pipeline requires two homology searches: the first search is against a clustered version of the database and the second is against sequences belonging to clusters with a hit from the first search. We tested this method using two assembled viral metagenomes and three databases (Swiss-Prot, Metagenomes Online, and UniRef100). Hierarchical cluster homology searching proved to be 12-times faster than BLASTp and produced alignments that were nearly identical to BLASTp (precision=0.99; recall=0.97). This approach is ideal when searching large collections of sequences against large databases.

bioinformatics↗

bioRxiv2018

RefSeq database growth influences the accuracy of k-mer-based species identification

Accurate species-level taxonomic classification and profiling of complex microbial communities remains a challenge due to homologous regions shared among closely related species and a sparse representation of non-human associated microbes in the database. Although the database undoubtedly has a strong influence on the sensitivity of taxonomic classifiers and profilers, to date, no study has carefully explored this topic on historical RefSeq releases and explored its impact on accuracy. In this study, we examined the influence of the database, over time, on k-mer based sequence classification and profiling. We present three major findings: (i) database growth over time resulted in more classified reads, but fewer species-level classifications and more species-level misclassifications; (ii) Bayesian re-estimation of abundance helped to recover species-level classifications when the exact target strain was present; and (iii) Bayesian reestimation struggled when the database lacked the target strain, resulting in a notable decrease in accuracy. In summary, our findings suggest that the growth of RefSeq over time has strongly influenced the accuracy of k-mer based classification and profiling methods, resulting in different classification results depending on the particular database used. These results suggest a need for new algorithms specially adapted for large genome collections and better measures of classification uncertainty.

bioinformatics↗