JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D174 - D182

Published: Nov. 14, 2023

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing provided orthogonal support 72 from previous release's UNVALIDATED collection. Altogether, 2024 update provides 20% increase in release. A trimming algorithm enhanced by removing low information content flanking base pairs, which were likely uninformative (within capacity of PFM models) TFBS predictions modelling TF-DNA interactions. This includes metadata, featuring refined classification plant TFs' structural domains. The collections prompt updates to genomic tracks predicted TF binding sites (TFBSs) 8 organisms, human mouse available as native UCSC Genome browser. All data are through web interface programmatically its API Bioconductor pyJASPAR packages. Finally, extraction tool enables users retrieve TFBSs intersecting their regions interest.

Language: Английский

---

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Nature Methods, Journal Year: 2023, Volume and Issue: 20(9), P. 1355 - 1367

Published: July 13, 2023

Abstract Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven regulatory networks (GRNs). Here we present a method for the inference GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) links these target genes. To improve both recall precision TF identification, curated clustered motif collection more than 30,000 motifs. We benchmarked on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, melanoma states Drosophila retinal development. Next, exploit predictions study conserved TFs, GRNs between mouse types cerebral cortex. Finally, use dynamics regulation differentiation trajectories effect perturbations state. is available at scenicplus.readthedocs.io .

Language: Английский

---

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

Seminars in Cancer Biology, Journal Year: 2022, Volume and Issue: 88, P. 187 - 200

Published: Dec. 31, 2022

Language: Английский

---

Deep learning: systematic review, models, challenges, and research directions

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(31), P. 23103 - 23124

Published: Sept. 7, 2023

Abstract The current development in deep learning is witnessing an exponential transition into automation applications. This can provide a promising framework for higher performance and lower complexity. ongoing undergoes several rapid changes, resulting the processing of data by studies, while it may lead to time-consuming costly models. Thus, address these challenges, studies have been conducted investigate techniques; however, they mostly focused on specific approaches, such as supervised learning. In addition, did not comprehensively other techniques, unsupervised reinforcement techniques. Moreover, majority neglect discuss some main methodologies learning, transfer federated online Therefore, motivated limitations existing this study summarizes techniques supervised, unsupervised, reinforcement, hybrid learning-based addition each category, brief description categories their models provided. Some critical topics namely, transfer, federated, models, are explored discussed detail. Finally, challenges future directions outlined wider outlooks researchers.

Language: Английский

---

Machine Learning-Guided Protein Engineering

ACS Catalysis, Journal Year: 2023, Volume and Issue: 13(21), P. 13863 - 13895

Published: Oct. 13, 2023

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid the discovery annotation of enzymes, as well suggesting beneficial mutations for improving known targets. The field protein is gathering steam, driven by recent success stories notable other areas. It already encompasses ambitious tasks such understanding predicting structure function, catalytic efficiency, enantioselectivity, dynamics, stability, solubility, aggregation, more. Nonetheless, still evolving, with many challenges overcome questions address. In this Perspective, we provide an overview ongoing trends domain, highlight case studies, examine current limitations learning-based We emphasize crucial importance thorough validation emerging models before their use rational design. present our opinions on fundamental problems outline potential directions future research.

Language: Английский

---

Emotion recognition in EEG signals using deep learning methods: A review

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 165, P. 107450 - 107450

Published: Sept. 9, 2023

Language: Английский

---

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings

Nature Genetics, Journal Year: 2023, Volume and Issue: 55(12), P. 2060 - 2064

Published: Nov. 30, 2023

Language: Английский

---

Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence

Water Research, Journal Year: 2024, Volume and Issue: 257, P. 121679 - 121679

Published: April 26, 2024

Language: Английский

---

Sequence basis of transcription initiation in the human genome

Science, Journal Year: 2024, Volume and Issue: 384(6694)

Published: April 25, 2024

Transcription initiation is a process that essential to ensuring the proper function of any gene, yet we still lack unified understanding sequence patterns and rules explain most transcription start sites in human genome. By predicting at base-pair resolution from sequences with deep learning-inspired explainable model called Puffin, show small set simple can promoters. We identify key contribute promoter activity, each activating distinct position-specific effects. Furthermore, basis bidirectional promoters, links between gene expression variation across cell types, explore conservation determinants mammalian species.

Language: Английский

---

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Science, Journal Year: 2025, Volume and Issue: 387(6735)

Published: Jan. 2, 2025

Combinations of transcription factors govern the identity cell types, which is reflected by genomic enhancer codes. We used deep learning to characterize these codes and devised three metrics compare types in telencephalon across amniotes. To this end, we generated single-cell multiome spatially resolved transcriptomics data chicken telencephalon. Enhancer orthologous nonneuronal γ-aminobutyric acid–mediated (GABAergic) show a high degree similarity amniotes, whereas excitatory neurons mammalian neocortex avian pallium exhibit varying degrees similarity. mesopallial are most similar those deep-layer neurons. With study, present generally applicable approaches on basis regulatory sequences.

Language: Английский

---