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Brooks, A. N.

Publications and source records attributed to Brooks, A. N..

3 recordsLinked to original sources

Full-length transcript characterization of SF3B1 mutation in chronic lymphocytic leukemia reveals downregulation of retained introns

SF3B1 is one of the most frequently mutated genes in chronic lymphocytic leukemia (CLL) and is associated with poor patient prognosis. While alternative splicing patterns caused by mutations in SF3B1 have been identified with short-read RNA sequencing, a critical barrier in understanding the functional consequences of these splicing changes is that we lack the full transcript context in which these changes are occurring. Using nanopore sequencing technology, we have resequenced full-length cDNA from CLL samples with and without the hotspot SF3B1 K700E mutation, and a normal B cell. We have developed a workflow called FLAIR (Full-Length Alternative Isoform analysis of RNA), leveraging the full-length transcript sequencing data that nanopore affords. We report results from nanopore sequencing that are concordant with known SF3B1 biology from short read sequencing as well as altered intron retention events more confidently observed using long reads. Splicing analysis of nanopore reads between the SF3B1WT and SF3B1K700E samples identifies alternative upstream 3 splice sites associated with SF3B1K700E. We also find downregulation of intron retention events in SF3B1K700E relative to SF3B1WT and no difference between CLL SF3B1MT and B cell, suggesting an aberrant intron retention landscape in CLL samples lacking SF3B1 mutation. With full-length isoforms, we are able to better estimate the abundance of RNA transcripts that are productive and will likely be translated versus those that are unproductive. Validation from short-read data also reveals a strong branch point sequence in these downregulated intron retention events, consistent with previously reported branch points associated with mutated SF3B1. As nanopore sequencing has yet to become a routine tool for characterization of the transcriptome, our work demonstrates the potential utility of nanopore sequencing for cancer and splicing research.

genomics

The UCSC Xena Platform for cancer genomics data visualization and interpretation

UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.

cancer biology

A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.\n\nHIGHLIGHTSO_LIA new gene expression profiling method, L1000, dramatically lowers cost\nC_LIO_LIThe Connectivity Map database now includes 1.3 million publicly accessible L1000 perturbational profiles\nC_LIO_LIThis expanded Connectivity Map facilitates discovery of small molecule mechanism of action and functional annotation of genetic variants\nC_LIO_LIThe work establishes feasibility and utility of a truly comprehensive Connectivity Map\nC_LI

genomics