Biology subjects

Spielman, S. J.

Publications and source records attributed to Spielman, S. J..

6 recordsLinked to original sources

bioRxiv2024

The Single-cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors

The Single-cell Pediatric Cancer Atlas (ScPCA) Portal (https://scpca.alexslemonade.org/) is a data resource for uniformly processed single-cell and single-nuclei RNA sequencing (RNA-seq) data and de-identified metadata from pediatric tumor samples. Originally comprised of data from 10 projects funded by Alexs Lemonade Stand Foundation (ALSF), the Portal currently contains summarized gene expression data for over 700 samples across 55 cancer types from ALSF-funded and community-contributed datasets. Downloads include gene expression data as SinglecellExperiment or AnnData objects containing raw and normalized counts, PCA and UMAP coordinates, and automated cell type annotations, along with summary reports. Some samples have additional data from bulk RNA-seq, spatial transcriptomics, and/or feature barcoding (e.g., CITE-seq and cell hashing) included in the download. All data on the Portal were uniformly processed using scpca-nf, an efficient and open-source Nextflow workflow that uses alevin-fry to quantify gene expression. Comprehensive documentation, including descriptions of file contents and a guide to getting started, is available at https://scpca.readthedocs.io.

bioinformatics↗

bioRxiv2022

OpenPBTA: An Open Pediatric Brain Tumor Atlas

SummaryPediatric brain and spinal cancer are the leading disease-related cause of death in children, thus we urgently need curative therapeutic strategies for these tumors. To accelerate such discoveries, the Childrens Brain Tumor Network and Pacific Pediatric Neuro-Oncology Consortium created a systematic process for tumor biobanking, model generation, and sequencing with immediate access to harmonized data. We leverage these data to create OpenPBTA, an open collaborative project which establishes over 40 scalable analysis modules to genomically characterize 1,074 pediatric brain tumors. Transcriptomic classification reveals that TP53 loss is a significant marker for poor overall survival in ependymomas and H3 K28-altered diffuse midline gliomas and further identifies universal TP53 dysregulation in mismatch repair-deficient hypermutant high-grade gliomas. OpenPBTA is a foundational analysis platform actively being applied to other pediatric cancers and inform molecular tumor board decision-making, making it an invaluable resource to the pediatric oncology community. In BriefThe OpenPBTA is a global, collaborative open-science initiative which brought together researchers and clinicians to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Shapiro, et. al create over 40 open-source, scalable modules to perform cancer genomics analyses and provide a richly-annotated somatic dataset across 58 brain tumor histologies. The OpenPBTA framework can be used as a model for large-scale data integration to inform basic research, therapeutic target identification, and clinical translation. HighlightsOpenPBTA collaborative analyses establish resource for 1,074 pediatric brain tumors NGS-based WHO-aligned integrated diagnoses generated for 641 of 1,074 tumors RNA-Seq analysis infers medulloblastoma subtypes, TP53 status, and telomerase activity OpenPBTA will accelerate therapeutic translation of genomic insights

genomics↗

bioRxiv2021

Relative model selection can be sensitive to multiple sequence alignment uncertainty

BackgroundMultiple sequence alignments (MSAs) represent the fundamental unit of data inputted to most comparative sequence analyses. In phylogenetic analyses in particular, errors in MSA construction have the potential to induce further errors in downstream analyses such as phylogenetic reconstruction itself, ancestral state reconstruction, and divergence time estimation. In addition to providing phylogenetic methods with an MSA to analyze, researchers must also specify a suitable evolutionary model for the given analysis. Most commonly, researchers apply relative model selection to select a model from candidate set and then provide both the MSA and the selected model as input to subsequent analyses. While the influence of MSA errors has been explored for most stages of phylogenetics pipelines, the potential effects of MSA uncertainty on the relative model selection procedure itself have not been explored. ResultsWe assessed the consistency of relative model selection when presented with multiple perturbed versions of a given MSA. We find that while relative model selection is mostly robust to MSA uncertainty, in a substantial proportion of circumstances, relative model selection identifies distinct best-fitting models from different MSAs created from the same set of sequences. We find that this issue is more pervasive for nucleotide data compared to amino-acid data. However, we also find that it is challenging to predict whether relative model selection will be robust or sensitive to uncertainty in a given MSA. ConclusionsWe find that that MSA uncertainty can affect virtually all steps of phylogenetic analysis pipelines to a greater extent than has previously been recognized, including relative model selection.

evolutionary biology↗

bioRxiv2018

Relative evolutionary rates in proteins are largely insensitive to the substitution model

The relative evolutionary rates at individual sites in proteins are informative measures of conservation or adaptation. Often used as evolutionarily-aware conservation scores, relative rates reveal key functional or strongly-selected residues. Estimating rates in a phylogenetic context requires specifying a protein substitution model, which is typically a phenomenological model trained on a large empirical dataset. A strong emphasis has traditionally been placed on selecting the \"best-fit\" model, with the implicit understanding that suboptimal or otherwise ill-fitting models can potentially bias inferences. However, the pervasiveness and degree of such bias has not been systematically examined. We investigated how model choice impacts site-wise relative rates from a large set of empirical protein alignments. We compared models designed for use on any general protein, models designed for specific domains of life, and the simple equal-rates Jukes Cantor-style model (JC). As expected, information theoretic measures showed overwhelming evidence that some models fit the data decidedly better than others. By contrast, estimates of site-specific evolutionary rates were impressively insensitive to the substitution model used, revealing an unexpected degree of robustness to potential model misspecification. A deeper examination of the fewer than 5% of sites for which model inferences differed in a meaningful way showed that the JC model can uniquely identify rapidly-evolving sites that models with empirically-derived exchangeabilities fail to detect. We conclude that relative protein rates appear robust to the applied substitution model, and any sensible model of protein evolution, regardless of its fit to the data, should produce broadly consistent evolutionary rates.

evolutionary biology↗

bioRxiv2018

Viral gain-of-function experiments uncover residues under diversifying selection in nature

Viral gain-of-function mutations are commonly observed in the laboratory; however, it is unknown whether those mutations also evolve in nature. We identify two key residues in the host recognition protein of bacteriophage {lambda} that are necessary to exploit a new receptor; both residues repeatedly evolved among homologs from environmental samples. Our results provide evidence for widespread host-shift evolution in nature and a proof of concept for integrating experiments with genomic epidemiology.

evolutionary biology↗

bioRxiv2017

Relative evolutionary rate inference in HyPhy with LEISR

We introduce LEISR (Likehood Estimation of Individual Site Rates, pronounced \"laser\"), a convenient implementation in HyPhy to infer relative evolutionary rates from protein and nucleotide data. LEISR is based on the popular Rate4Site (1) approach for inferring relative site-wise evolutionary rates from protein data. We extend the original method by allowing for rate inference on both protein and nucleotide data, and we further overcome data-size limitations in Rate4Site. LEISR is available in HyPhy starting with version 2.3.7.

bioinformatics↗