In one paragraphArticle in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
35 authors.
Ilya E VorontsovVavilov Institute of General Genetics, Russian Academy of Sciences, 119991, Moscow, Russia.ORCID 0000-0001-8888-0804 Ivan KozinFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119991, Moscow, Russia.ORCID 0009-0009-4603-8550 Sergey AbramovVavilov Institute of General Genetics, Russian Academy of Sciences, 119991, Moscow, Russia.ORCID 0000-0002-3334-5334 Alexandr BoytsovVavilov Institute of General Genetics, Russian Academy of Sciences, 119991, Moscow, Russia.ORCID 0000-0002-2712-8368 Mihai AlbuDonnelly Centre and Department of Molecular Genetics, Toronto, ON M5S 3E1, Canada.
Giovanna AmbrosiniÉcole Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland.
Katerina FaltejskovaInstitute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, 160 00 Praha 6, Czech Republic.ORCID 0000-0001-7390-8077 Antoni J GralakLaboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland.ORCID 0009-0007-1086-6165 Semyon KolmykovDepartment of Computational Biology, Sirius University of Science and Technology, 354340, Sirius, Krasnodar region, Russia.ORCID 0000-0002-9259-0495 Judith F Kribelbauer-SwietekLaboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland.ORCID 0000-0002-8072-7773 Vladimir NozdrinLife Improvement by Future Technologies (LIFT) Center, 121205, Moscow, Russia.
Zain M PatelDonnelly Centre and Department of Molecular Genetics, Toronto, ON M5S 3E1, Canada.
Dmitry PenzarVavilov Institute of General Genetics, Russian Academy of Sciences, 119991, Moscow, Russia.ORCID 0000-0001-7960-9385 Marie-Luise PlescherInstitute of Computer Science, Martin Luther University Halle-Wittenberg, 06099, Halle, Germany.
Sara E PourDonnelly Centre and Department of Molecular Genetics, Toronto, ON M5S 3E1, Canada.
Arsenii ZinkevichFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119991, Moscow, Russia.ORCID 0000-0001-9450-4629 Bart DeplanckeLaboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland.ORCID 0000-0001-9935-843X Oriol FornesDepartment of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC V5Z 4H4, Canada.ORCID 0000-0002-5969-3054 Jan GrauInstitute of Computer Science, Martin Luther University Halle-Wittenberg, 06099, Halle, Germany.ORCID 0000-0003-2081-6405 Ivo GrosseInstitute of Computer Science, Martin Luther University Halle-Wittenberg, 06099, Halle, Germany.ORCID 0000-0001-5318-4825 Fedor A KolpakovDepartment of Computational Biology, Sirius University of Science and Technology, 354340, Sirius, Krasnodar region, Russia.ORCID 0000-0002-0396-0256 Codebook/GRECO-BIT Consortium
Vsevolod J MakeevVavilov Institute of General Genetics, Russian Academy of Sciences, 119991, Moscow, Russia.ORCID 0000-0001-9405-9748 Ivan V KulakovskiyVavilov Institute of General Genetics, Russian Academy of Sciences, 119991, Moscow, Russia.ORCID 0000-0002-6554-8128 Funding
X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4MTissue Repository CoreP30AR070549 · NIAMS · CINCINNATI CHILDRENS HOSP MED CTR · PI Leah Claire Kottyan · 2016 to 2026
$7.7MGene regulatory network modeling of disease-associated DNA methylation perturbationsR01AI173314 · NIAID · CINCINNATI CHILDRENS HOSP MED CTR · PI Minji Byun, Emily Miraldi · 2023 to 2026
$3.1MPost-transcriptional Regulatory NetworksR01HG013328 · NHGRI · SLOAN-KETTERING INST CAN RESEARCH · PI Quaid Morris · 2023 to 2026
$2.6MTranscription Factor Genetics in LupusR01AR073228 · NIAMS · CINCINNATI CHILDRENS HOSP MED CTR · PI KOTTYAN, LEAH CLAIRE, WAGGONER, STEPHEN N. · 2019 to 2023
$2.2MCisBP and CisBP-RNA: web resources for protein-DNA and protein-RNA binding modelsU24HG013078 · NHGRI · CINCINNATI CHILDRENS HOSP MED CTR · PI Matthew Tyson Weirauch · 2024 to 2026
$1.6MMeasuring and describing nucleosome remodeler sequence preferencesR21HG012258 · NHGRI · UNIVERSITY OF TORONTO · PI HUGHES, TIMOTHY · 2022 to 2022
$267kNCI NIH HHS P30 CA008748NHGRI NIH HHS R01 HG013328NHGRI NIH HHS R21 HG012258NHGRI NIH HHS U24 HG013078NIAID NIH HHS R01 AI173314NIAMS NIH HHS P30 AR070549NIAMS NIH HHS R01 AR073228
6 · The paper itselfAbstract
A DNA sequence pattern, or "motif", is an essential representation of DNA-binding specificity of a transcription factor (TF). Any particular motif model has potential flaws due to shortcomings of the underlying experimental data and computational motif discovery algorithm. As a part of the Codebook/GRECO-BIT initiative, here we evaluated at large scale the cross-platform recognition performance of positional weight matrices (PWMs), which remain popular motif models in many practical applications. We applied ten different DNA motif discovery tools to generate PWMs from the "Codebook" data comprised of 4,237 experiments from five different platforms profiling the DNA-binding specificity of 394 human proteins, focusing on understudied transcription factors of different structural families. For many of the proteins, there was no prior knowledge of a genuine motif. By benchmarking-supported human curation, we constructed an approved subset of experiments comprising about 30% of all experiments and 50% of tested TFs which displayed consistent motifs across platforms and replicates. We present the Codebook Motif Explorer (https://mex.autosome.org), a detailed online catalog of DNA motifs, including the top-ranked PWMs, and the underlying source and benchmarking data. We demonstrate that in the case of high-quality experimental data, most of the popular motif discovery tools detect valid motifs and generate PWMs, which perform well both on genomic and synthetic data. Yet, for each of the algorithms, there were problematic combinations of proteins and platforms, and the basic motif properties such as nucleotide composition and information content offered little help in detecting such pitfalls. By combining multiple PMWs in decision trees, we demonstrate how our setup can be readily adapted to train and test binding specificity models more complex than PWMs. Overall, our study provides a rich motif catalog as a solid baseline for advanced models and highlights the power of the multi-platform multi-tool approach for reliable mapping of DNA binding specificities.
Indexed as
benchmarkingChIP-SeqDNA binding specificityDNA motif discoveryGHT-SELEXHT-SELEXPBMposition weight matricesPSSMPWMSMiLE-SeqTFBStranscription factor binding sitestranscription factors
Identifiers
PMID39605530
PMCPMC11601219
What OpenQuestion holds
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LicenceCC BY-NC-ND
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