Searching journal content for articles similar to Garcia-Alonso et al. 29 (8): 1363.

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  1. Review: A systematic guide for identifying transcription factors that directly regulate the expression of a gene of interest
    • Andrew D. Bates,
    • Dawid Grzela,
    • Maciej Studzian,
    • Louise Brennan,
    • Moli Williams,
    • Conor Fawcett,
    • Beth Hammond,
    • Manreen Grewal,
    • Marcin Ratajewski,
    • Lukasz Pulaski,
    • and Urszula L. McClurg
    Genome Res. March 2026 36: 433-459; Published in Advance February 17, 2026, doi:10.1101/gr.281154.125
    ...of Oncobiology and Epigenetics, University of Lodz, 90-237 Lodz, Poland ↵6 These authors contributed equally to this work. Corresponding authors: urszula.mcclurg@liverpool.ac.uk, mratajewski@cbm.pan.pl, lpulaski@uni.lodz.plAbstractTranscriptional regulation lies at the heart of cellular identity and function...
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  2. Corrigendum: Corrigendum: Benchmark and integration of resources for the estimation of human transcription factor activities
    • Luz Garcia-Alonso,
    • Christian H. Holland,
    • Mahmoud M. Ibrahim,
    • Denes Turei,
    • and Julio Saez-Rodriguez
    Genome Res. April 2021 31: 745; doi:10.1101/gr.275408.121
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  3. Method: Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets
    • Alexander Morin,
    • Eric Ching-Pan Chu,
    • Aman Sharma,
    • Alex Adrian-Hamazaki,
    • and Paul Pavlidis
    Genome Res. May 2023 33: 763-778; Published in Advance June 12, 2023, doi:10.1101/gr.277273.122
    ...resource were leukemia cell lines, the benchmark may be biased toward cell type–specific effects compared with the cell type–agnostic aggregations. This may also explain why mouse NEUROD1 had the least performant aggregations: The highest AUPRCs belonged to individual genomic experiments (and their rank...
  4. Method: Evidence of widespread, independent sequence signature for transcription factor cobinding
    • Manqi Zhou,
    • Hongyang Li,
    • Xueqing Wang,
    • and Yuanfang Guan
    Genome Res. February 2021 31: 265-278; Published in Advance December 10, 2020, doi:10.1101/gr.267310.120
    ...Roadmap Projects have provided invaluable resources of the in vivo biochemical signatures, including chromatin accessibility and TF binding. Many exciting computational approaches have been developed to predict transcription factor binding sites (TFBSs) and to study what controls the binding patterns...
  5. Method: Comprehensive determination of transcription start sites derived from all RNA polymerases using ReCappable-seq
    • Bo Yan,
    • George Tzertzinis,
    • Ira Schildkraut,
    • and Laurence Ettwiller
    Genome Res. January 2022 32: 162-174; Published in Advance November 23, 2021, doi:10.1101/gr.275784.121
    ...: ettwiller@neb.comAbstractDetermination of eukaryotic transcription start sites (TSSs) has been based on methods that require the cap structure at the 5′ end of transcripts derived from Pol II RNA polymerase. Consequently, these methods do not reveal TSSs derived from the other RNA polymerases that also play...
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  6. Method: A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes
    • Shang Gao,
    • Yang Dai,
    • and Jalees Rehman
    Genome Res. July 2021 31: 1296-1311; Published in Advance June 30, 2021, doi:10.1101/gr.265595.120
    ...transcription factor target genes as the input data for BITFAMTo benchmark the performance of BITFAM using biological validation of a real-world scRNA-seq data, we used a scRNA-seq data set of CRISPRi studies in which 50 TFs were targeted for CRISPR-mediated deletion or knockdown (Genga et al. 2019). This data...
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  7. Method: Fast decoding cell type–specific transcription factor binding landscape at single-nucleotide resolution
    • Hongyang Li and
    • Yuanfang Guan
    Genome Res. April 2021 31: 721-731; Published in Advance March 19, 2021, doi:10.1101/gr.269613.120
    ...Hongyang Li and Yuanfang Guan Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA Corresponding author: gyuanfan@umich.eduAbstractDecoding the cell type–specific transcription factor (TF) binding landscape at single-nucleotide resolution...
  8. Method: Anchor: trans-cell type prediction of transcription factor binding sites
    • Hongyang Li,
    • Daniel Quang,
    • and Yuanfang Guan
    Genome Res. February 2019 29: 281-292; Published in Advance December 19, 2018, doi:10.1101/gr.237156.118
    ...Hongyang Li, Daniel Quang and Yuanfang Guan Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA Corresponding author: gyuanfan@umich.eduAbstractThe ENCyclopedia of DNA Elements (ENCODE) consortium has generated transcription factor (TF...
  9. Method: Read-level genotyping of short tandem repeats using long reads and single-nucleotide variation with STRkit
    • David R. Lougheed,
    • Tomi Pastinen,
    • and Guillaume Bourque
    Genome Res. March 2026 36: 578-588; Published in Advance January 15, 2026, doi:10.1101/gr.280766.125
    ...that STRkit has unique strengths versus other methods: It can use data from both major LRS technologies (Pacific Biosciences HiFi [PacBio] and Oxford Nanopore Technologies [ONT]) to output both allele- and read-level copy number and sequence; it performs best in benchmarking with F1 scores of 0.9631 and 0...
  10. Research: Epigenetic characterization of pseudogenes across human tissues
    • Yunzhe Jiang,
    • Beatrice Borsari,
    • and Mark Gerstein
    Genome Res. April 15, 2026 gr.280768.125; Published in Advance April 15, 2026, doi:10.1101/gr.280768.125
    ...). To this end, we defined promoters as ±1,000 base pair (bp) windows centered on the transcription start sites (TSSs) of all GENCODE v29 transcripts (Table 1) and annotated their activity based on the presence of active and repressive epigenetic features. The resulting resource provides a uniform...
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