Efficient querying of genomic reference databases with <i>gget</i>

Luebbert, Laura; Pachter, Lior

doi:10.1093/bioinformatics/btac836

Cited by 22 publications

(22 citation statements)

References 36 publications

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“…During the generation of the reference index with ‘kb ref’, the D-list option may be used to mask host genomic and/or transcriptomic sequences, as further discussed in this manuscript. Here, human genomic sequences fetched from Ensembl using gget 45 are masked using the D-list. The reference index only needs to be generated once, and precomputed PalmDB reference indices for human and mouse hosts are available here: https://tinyurl.com/aaxyy8v8.…”

Section: Resultsmentioning

confidence: 99%

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Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression

Luebbert,

Sullivan,

Carilli

et al. 2023

Preprint

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More than 300,000 mammalian virus species are estimated to cause disease in humans. They inhabit human tissues such as the lungs, blood, and brain and often remain undetected. Efficient and accurate detection of viral infection is vital to understanding its impact on human health and to make accurate predictions to limit adverse effects, such as future epidemics. The increasing use of high-throughput sequencing methods in research, agriculture, and healthcare provides an opportunity for the cost-effective surveillance of viral diversity and investigation of virus-disease correlation. However, existing methods for identifying viruses in sequencing data rely on and are limited to reference genomes or cannot retain single-cell resolution through cell barcode tracking. We introduce a method that accurately and rapidly detects viral sequences in bulk and single-cell transcriptomics data based on highly conserved amino acid domains, which enables the detection of RNA viruses covering up to 1012virus species. The analysis of viral presence and host gene expression in parallel at single-cell resolution allows for the characterization of host viromes and the identification of viral tropism and host responses. We applied our method to identify novel viruses in rhesus macaque PBMC data that display cell type specificity and whose presence correlates with altered host gene expression.

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Section: Resultsmentioning

confidence: 99%

“…Cluster 'Undefined 1' was omitted because it only contained 12 cells. Gene names and descriptions for Ensembl IDs without annotations were obtained using gget 45 .…”

Section: Macaque Cell Clustering and Cell Type Assignmentmentioning

confidence: 99%

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression

Luebbert,

Sullivan,

Carilli

et al. 2023

Preprint

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“…While ffq facilitates downloading of data from numerous genomic databases, the results retrieved are only useful to the extent that the metadata uploaded is meaningful and complete. Meaningful and complete user-generated data underlies the curation of genomic references essential for comparative genomic data analysis ( Luebbert and Pachter, 2023 ). Unfortunately, there is little to no standardization of user-uploaded sequencing metadata ( Rajesh et al , 2021 ; Wang et al , 2019 ), and metadata descriptions can become exceedingly complex for current multiplexed experiments, where different assays with distinct data types are combined.…”

Section: Discussionmentioning

confidence: 99%

Metadata retrieval from sequence databases with ffq

et al. 2023

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Motivation Several genomic databases host data and metadata for an ever-growing collection of sequence datasets. While these databases have a shared hierarchical structure, there are no tools specifically designed to leverage it for metadata extraction. Results We present a command-line tool, called ffq, for querying user-generated data and metadata from sequence databases. Given an accession or a paper’s DOI, ffq efficiently fetches metadata and links to raw data in JSON format. ffq’s modularity and simplicity makes it extensible to any genomic database exposing its data for programmatic access. Availability and implementation ffq is free and open source, and the code can be found here: https://github.com/pachterlab/ffq. Supplementary information Supplementary data are available at Bioinformatics online.

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“…All datasets analyzed in this paper from previous publications are publicly available. The CellxGene blood atlas dataset were downloaded from CellxGene census (https://chanzuckerberg.github.io/cellxgene-census/) via gget [7]. The drug perturbation dataset was downloaded from Kaggle (https://www.kaggle.com/ competitions/open-problems-single-cell-perturbations).…”

Section: Data Availabilitymentioning

confidence: 99%

Scaling deep identifiable models enables zero-shot characterization of single-cell biological states

Dong,

Agrawal,

Fan

et al. 2023

Preprint

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With the emerging single-cell RNA-seq datasets at atlas levels, the potential of a universal model built on existing atlas that can extrapolate to new data remains unclear. A fundamental yet challenging problem for such a model is to identify the underlying biological and batch variations in a zero-shot manner, which is crucial for characterizing scRNA-seq datasets with new biological states. In this work, we present scShift, a mechanistic model that learns batch and biological patterns from atlas-level scRNA-seq data as well as perturbation scRNA-seq data. scShift models genes as functions of latent biological processes, with sparse shifts induced by batch effects and biological perturbations, leveraging recent advances of causal representation learning. Through benchmarking in holdout real datasets, we show scShift reveals unified cell type representations as well as underlying biological variations for query data in zero-shot manners, outperforming widely-used atlas integration, batch correction, and perturbation modeling approaches. scShift enables mapping of gene expression profiles to perturbation labels, and predicts meaningful targets for exhausted T cells as well as a list of diseases in the CellxGene blood atlas.

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Efficient querying of genomic reference databases with gget

Cited by 22 publications

References 36 publications

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression

Metadata retrieval from sequence databases with ffq

Scaling deep identifiable models enables zero-shot characterization of single-cell biological states

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