ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'targ...
Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. Ho...
Characterization of individual cell types is fundamental to the study of multicellular samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task....
N 6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological processes, such as splicing, RNA degradation and RNA-protein interaction. We report here a prediction fram...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2019
BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNA that regulate gene expression through post-transcriptional silencing. Differential expression observed in miRNAs, combined with advancements in deep learning (DL), have the potential to improve...
MOTIVATION: During the last decade, improvements in high-throughput sequencing have generated a wealth of genomic data. Functionally interpreting these sequences and finding the biological signals that are hallmarks of gene function and regulation is...
Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several types of studies require long and accurate reads. In such cases researchers often combine both technologies and t...
MOTIVATION: Long non-coding RNAs (lncRNAs) are important regulatory elements in biological processes. LncRNAs share similar sequence characteristics with messenger RNAs, but they play completely different roles, thus providing novel insights for biol...
Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning t...