Biology subjects

Yuan, G.-H.

Publications and source records attributed to Yuan, G.-H..

4 recordsLinked to original sources

bioRxiv2024

Deep generative model for protein subcellular localization prediction

Protein sequence determines not only its structure but also its subcellular localization. Although a series of artificial intelligence models have been reported to predict protein subcellular localization, most of them provide only textual outputs. Here, we present deepGPS, a deep generative model for protein subcellular localization prediction. After trained with both protein primary sequences and protein subcellular localization fluorescence images, deepGPS shows the ability to predict cytoplasmic and nuclear localizations by reporting both textual labels and generative images as outputs. In addition, deepGPS shows potential to be further extended for other types of subcellular localization prediction, even with limited input data volumes for training. Finally, an openGPS website (https://bits.fudan.edu.cn/opengps) is constructed to provide a public and convenient platform for protein subcellular localization prediction with the scientific community.

bioinformatics↗

bioRxiv2022

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed by computational detection tools. During the last decade, a plethora of such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools were used and detected over 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were empirically validated using three orthogonal methods. Generally, tool-specific precision values are high and similar (median of 98.8%, 96.3%, and 95.5% for qPCR, RNase R, and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant tool differentiators. Furthermore, we demonstrate the complementarity of tools through the increase in detection sensitivity by considering the union of highly-precise tool combinations while keeping the number of false discoveries low. Finally, based on the benchmarking results, recommendations are put forward for circRNA detection and validation.

genomics↗

bioRxiv2022

RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization

Different RNAs have distinct subcellular localizations. However, nucleotide features that determine these distinct distributions of lncRNAs and mRNAs have yet to be fully addressed. Here, we develop RNAlight, a machine learning model based on LightGBM, to identify nucleotide k-mers contributing to the subcellular localizations of mRNAs and lncRNAs. With the Tree SHAP algorithm, RNAlight extracts nucleotide features for cytoplasmic or nuclear localization of RNAs, indicating the sequence basis for distinct RNA subcellular localizations. By assembling k-mers to sequence features and subsequently mapping to known RBP-associated motifs, different types of sequence features and their associated RBPs were additionally uncovered for lncRNAs and mRNAs with distinct subcellular localizations. Finally, we extended RNAlight to precisely predict the subcellular localizations of other types of RNAs, including snRNAs, snoRNAs and different circular RNA transcripts, suggesting the generality of using RNAlight for RNA subcellular localization prediction. Key pointsO_LIA machine learning model, RNAlight, is developed to efficiently and sensitively predict subcellular localizations of mRNAs and lncRNAs. C_LIO_LIWith embedded Tree SHAP algorithm, RNAlight further reveals distinct key sequence features and their associated RBPs for subcellular localizations of mRNAs or lncRNAs. C_LIO_LIRNAlight is successfully extended for the subcellular localization prediction of additional types of noncoding RNAs that were not used for model development, such as circular RNAs, suggesting its generality in RNA subcellular localization prediction. C_LIO_LIRNAlight is freely available at https://github.com/YangLab/RNAlight. C_LI

bioinformatics↗

bioRxiv2021

SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells

Single-cell RNA-seq (scRNA-seq) profiles gene expression with a resolution that empowers depiction of cell atlas in complex systems. Here, we developed a stepwise computational pipeline SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3 tag-based scRNA-seq. SCAPTURE detects PASs de novo in single cells with high sensitivity and accuracy, enabling detection of previously unannotated PASs. Quantified alternative PAS transcripts refine cell identities, enriching information extracted from scRNA-seq.

bioinformatics↗