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Joshua D. Welch

Researcher at University of Michigan

Publications -  62
Citations -  3819

Joshua D. Welch is an academic researcher from University of Michigan. The author has contributed to research in topics: Biology & Gene. The author has an hindex of 20, co-authored 50 publications receiving 2081 citations. Previous affiliations of Joshua D. Welch include Broad Institute & University of North Carolina at Chapel Hill.

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TUT7 catalyzes the uridylation of the 3′ end for rapid degradation of histone mRNA

TL;DR: Using high-throughput sequencing of histone mRNAs and degradation intermediates, it is found that knockdown of TUT7 reduces both the uridylation at the 3' end as well as uridolation of the major degradation intermediate in the stem.
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Iterative single-cell multi-omic integration using online learning.

TL;DR: Online integrative non-negative matrix factorization (iNMF) as discussed by the authors is an algorithm for integrating large, diverse and continually arriving single-cell datasets, which scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet.
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Pseudogenes transcribed in breast invasive carcinoma show subtype-specific expression and ceRNA potential.

TL;DR: This work developed a novel bioinformatic method for measuring pseudogene transcription from RNA-seq data and identified a set of 440 pseudogenes that are transcribed in breast cancer tissue, which increases the catalog of putative pseudogene ceRNAs and suggests that pseudogen transcription in cancer may play a larger role than previously appreciated.
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The word landscape of the non-coding segments of the Arabidopsis thaliana genome.

TL;DR: These studies provide a detailed view of the word composition of several segments of the non-coding portion of the Arabidopsis genome, where each segment contains a unique word-based signature.
Posted ContentDOI

Integrative inference of brain cell similarities and differences from single-cell genomics

TL;DR: LIGER, an algorithm that delineates shared and dataset-specific features of cell identity, allowing flexible modeling of highly heterogeneous single-cell datasets, is developed and demonstrated its broad utility by applying it to four diverse and challenging analyses of human and mouse brain cells.