Biology subjects

Ni, P.

Publications and source records attributed to Ni, P..

27 records · Page 2Linked to original sources

bioRxiv2021

Prevalent uses of exons as regulatory sequences are possible and inevitable

BackgroundIt has long been known that exons can be used as cis-regulatory sequences such as enhancers. However, the prevalence of such dual-use of exons and how they evolve remain elusive. Our recently predicted highly accurate, large sets of cis-regulatory module candidates (CRMCs) and non-CRMCs in the human genome positioned us to address these questions. ResultsWe found that exonic transcription factor binding sites (TFBSs) occupied at least a third of the total exon lengths, and 96.7% of genes had exonic TFBSs. Both A/T and C/G in exonic TFBSs are more likely under evolutionary constraints than those in non-CRMC exons. Interestingly, exonic TFBSs in codons tend to encode loops rather than more critical helices and strands in protein structures, while exonic TFBSs in untranslated regions (UTRs) tend to avoid positions where known UTR-related functions are located. Moreover, active exonic TFBSs tend to be in close physical proximity to distal promoters whose immediately downstream genes have elevated transcription levels, suggesting that they might be involved in transcriptional regulation of target genes. It is highly possible that less critical positions in an exon that is physically close to a promoter can evolve into a TFBS when no non-exonic sequences are physically available to the promoter. ConclusionsExonic TFBSs might be more prevalent than originally thought and are likely in dual-use. Possible detrimental effects caused by such dual-use can be reduced by using less critical exonic positions. We proposed a parsimonious model to explain how a stretch of codons evolve into a TFBS.

evolutionary biology↗

bioRxiv2021

PCRMS: a database of predicted cis-regulatory modules and constituent transcription factor binding sites in genomes

More accurate and more complete predictions of cis-regulatory modules (CRMs) and constituent transcriptional factor (TF) binding sites (TFBSs) in genomes can facilitate characterizing functions of regulatory sequences. Here, we developed a database PCRMS (https://cci-bioinfo.uncc.edu) that stores highly accurate and unprecedentedly complete maps of predicted CRMs and TFBSs in the human and mouse genomes. The web interface allows the user to browse CRMs and TFBSs in an organism, find the closest CRMs to a gene, search CRMs around a gene, and find all TFBSs of a TF. PCRMS can be a useful resource for the research community to characterize regulatory genomes.

bioinformatics↗

bioRxiv2021

Accurate prediction of functional states of cis-regulatory modules reveals the universal epigenetic code in mammals

We proposed a two-step approach for predicting active cis-regulatory modules (CRMs) in a cell/tissue type. We first predict a map of CRM loci in the genome using all available transcription factor binding data in the organism, and then predict functional states of all the putative CRMs in any cell/tissue type using few epigenetic marks. We have recently developed a pipeline dePCRM2 for the first step, and now presented machine-learning methods for the second step. Our approach substantially outperforms existing methods. Our results suggest common epigenetic rules for defining functional states of CRMs in various cell/tissue types in humans and mice.

bioinformatics↗

bioRxiv2021

Genome-wide Detection of Cytosine Methylations in Plant from Nanopore sequencing data using Deep Learning

Methylation states of DNA bases can be detected from native Nanopore reads directly. At present, there are many computational methods that can detect 5mCs in CpG contexts accurately by Nanopore sequencing. However, there is currently a lack of methods to detect 5mCs in non-CpG contexts. In this study, we propose a computational pipeline which can detect 5mC sites in both CpG and non-CpG contexts of plant genomes by using Nanopore sequencing. And we sequenced two model plants Arabidopsis thaliana (A. thaliana) and Oryza sativa (O. sativa) by using Nanopore sequencing and bisulfite sequencing. The results of our proposed pipeline in the two plants achieved high correlations with bisulfite sequencing: above 0.98, 0.96, 0.85 for CpG, CHG, and CHH (H indicates A, C or T) motif, respectively. Our proposed pipeline also achieved high performance on Brassica nigra (B. nigra). Experiments also showed that our proposed pipeline can achieve high performance even with low coverage of reads. Moreover, by using Nanopore sequencing, our proposed pipeline is capable of profiling methylation of more cytosines than bisulfite sequencing.

bioinformatics↗

bioRxiv2020

A map of cis-regulatory modules and constituent transcription factor binding sites in 77.5% regions of the human genome

Annotating all cis-regulatory modules (CRMs) and transcription factor (TF) binding sites(TFBSs) in genomes remains challenging. We tackled the task by integrating putative TFBSs motifs found in available 6,092 datasets covering 77.47% of the human genome. This approach enabled us to partition the covered genome regions into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, like known enhancers, the predicted 1,404,973 CRMCs are under strong evolutionary constraints, suggesting that they might be cis-regulator. In contrast, the non-CRMCs are largely selectively neutral, suggesting that they might not be cis-regulatory. Our method substantially outperforms three state-of-the-art methods (GeneHancers, EnhancerAtlas and ENCODE phase 3) for recalling VISTA enhancers and ClinVar variants, as well as by measurements of evolutionary constraints. We estimated that the human genome might encode about 1.46 million CRMs and 67 million TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought.

bioinformatics↗

bioRxiv2019

Noninvasive prenatal diagnosis for Hemophilia A by haplotype-based approach using cell-free fetal DNA in maternal plasma

ObjectivesThis study was used to demonstrate the feasibility of haplotype-based noninvasive prenatal diagnosis for Hemophilia A. MethodsTwo families affected by Hemophilia A participated in our study. Maternal haplotypes associated with pathogenic mutation was built using targeted region genotypes of mother and the proband. Combined with maternal pathogenic haplotype, a Hidden Markov Model was constructed to deduce fetal haplotype using high-coverage targeted sequencing of maternal plasma. The presence of pathogenic haplotype in male indicated affected fetus, in female indicated carrier. Prenatal diagnosis was confirmed with amniocentesis by long distance PCR. ResultsThe haplotype-based noninvasive prenatal diagnosis was successfully performed in Hemophilia A. One fetus was identified to be normal, another fetus was identified as carrier, and the results were confirmed by amniocentesis . ConclusionOur research demonstrated the feasibility of noninvasive prenatal diagnosis for Hemophilia A by haplotype-based approach.

molecular biology↗

bioRxiv2018

Deciphering epigenomic code for cell differentiation using deep learning

Epigenomic markers, such as histone modifications, play important roles in cell fate determination and type maintenance during cell differentiation. Although genomic sequence plays a crucial role in establishing the unique epigenome in each cell type produced during cell differentiation, little is known about the sequence determinants that lead to the unique epigenomes of the cells. Here, using a dataset of six histone markers measured in four human CD4+ T cell types produced at different stages of T cell development, we showed that two types of highly accurate deep convolutional neural networks (CNNs) constructed for each cell type and for each histone marker are a powerful strategy to uncover the sequence determinants of the various histone modification patterns in difference cell types. We found that sequence motifs learned by the CNN models are highly similar to known binding motifs of transcription factors known to play important roles in CD4+ T cell differentiation. Our results suggest that both the unique histone modification patterns in each cell type and the different patterns of the same histone marker in different cell types are determined by a set of motifs with unique combinations. Interestingly, the level of shared few motifs learned in the different cell models reflect the lineage relationships of the cells, while the level of few shared motifs learned in different histone marker models reflect their functional relationships. Furthermore, using these models, we can predict the importance of the learned motifs and their interactions in determining specific histone marker patterns in the cell types.

bioinformatics↗

bioRxiv2018

Ultra-fast and accurate motif finding in large ChIP-seq datasets reveals transcription factor binding patterns

The availability of a large volume of chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets for various transcription factors (TF) has provided an unprecedented opportunity to identify all functional TF binding motifs clustered in the enhancers in genomes. However, the progress has been largely hindered by the lack of a highly efficient and accurate tool that is fast enough to find not only the target motifs, but also cooperative motifs contained in very large ChIP-seq datasets with a binding peak length of typical enhancers ([~] 1,000 bp). To circumvent this hurdle, we herein present an ultra-fast and highly accurate motif-finding algorithm, ProSampler, with automatic motif length detection. ProSampler first identifies significant k-mers in the dataset and combines highly similar significant k-mers to form preliminary motifs. ProSampler then merges preliminary motifs with subtle similarity using a novel graph-based Gibbs sampler to find core motifs. Finally, ProSampler extends the core motifs by applying a two-proportion z-test to the flanking positions to identify motifs longer than k. As the number of preliminary motifs is much smaller than that of k-mers in a dataset, we greatly reduce the search space of the Gibbs sampler compared with conventional ones. By storing flanking sequences in a hash table, we avoid extensive IO and the necessity of examining all lengths of motifs in an interval. When evaluated on both synthetic and real ChIP-seq datasets, ProSampler runs orders of magnitude faster than the fastest existing tools while more accurately discovering primary motifs as well as cooperative motifs than do the best existing tools. Using ProSampler, we revealed previously unknown complex motif occurrence patterns in large ChIP-seq datasets, thereby providing insights into the mechanisms of cooperative TF binding for gene transcriptional regulation. Therefore, by allowing fast and accurate mining of the entire ChIP-seq datasets, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes.

bioinformatics↗

bioRxiv2018

DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning

The Oxford Nanopore sequencing enables to directly detect methylation sites in DNA from reads without extra laboratory techniques. In this study, we develop DeepSignal, a deep learning method to detect DNA methylated sites from Nanopore sequencing reads. DeepSignal construct features from both raw electrical signals and signal sequences in Nanopore reads. Testing on Nanopore reads of pUC19, E. coli and human, we show that DeepSignal can achieve both higher read level and genome level accuracy on detecting 6mA and 5mC methylation comparing to previous HMM based methods. Moreover, DeepSignal achieves similar performance cross different methylation bases and different methylation motifs. Furthermore, DeepSignal can detect 5mC and 6mA methylation states of genome sites with above 90% genome level accuracy under just 5X coverage using controlled methylation data.

bioinformatics↗