Biology subjects

Chen, X. E.

Publications and source records attributed to Chen, X. E..

3 recordsLinked to original sources

bioRxiv2022

Biochemical activity is the default DNA state in eukaryotes

Genomes encode for genes and the regulatory signals that enable those genes to be transcribed, and are continually shaped by evolution. Genomes, including those of human and yeast, encode for numerous regulatory elements and transcripts that have limited evidence of conservation or function. Here, we sought to create a genomic null hypothesis by quantifying the gene regulatory activity of evolutionarily naive DNA, using RNA-seq of evolutionarily distant DNA expressed in yeast and computational predictions of random DNA activity in human cells and tissues. In yeast, we found that >99% of bases in naive DNA expressed as part of one or more transcripts. Naive transcripts are sometimes spliced, and are similar to evolved transcripts in length and expression distribution, indicating that stable expression and/or splicing are insufficient to indicate adaptation. However, naive transcripts do not achieve the extreme high expression levels as achieved by evolved genes, and frequently overlap with antisense transcription, suggesting that selection has shaped the yeast transcriptome to achieve high expression and coherent gene structures. In humans, we found that, while random DNA is predicted to have minimal activity, dinucleotide content-matched randomized DNA is predicted to have much of the regulatory activity of evolved sequences, including active chromatin marks at between half (DNase I and H3K4me3) and 1/16th (H3K27ac and H3K4me1) the rate of evolved DNA, and the repression-associated H3K27me3 at about twice the rate of evolved DNA. Naive human DNA is predicted to be more cell type-specific than evolved DNA and is predicted to generate co-occurring chromatin marks, indicating that these are not reliable indicators of selection. However, extreme high activity is rarely achieved by naive DNA, consistent with these arising via selection. Our results indicate that evolving regulatory activity from naive DNA is comparatively easy in both yeast and humans, and we expect to see many biochemically active and cell type-specific DNA sequences in the absence of selection. Such naive biochemically active sequences have the potential to evolve a function or, if sufficiently detrimental, selection may act to repress them.

genomics↗

bioRxiv2022

GIL: A Python package for designing custom indexing primers

Next-generation sequencing allows samples to be multiplexed by adding a unique DNA index to each sample. Multiplexing greatly reduces the price of sequencing large numbers of samples, yet the minimum cost per sample remains high when using commercially available indexing kits and designing custom indexes is challenging. To address these issues, we created GIL (Generate Indexes for Libraries), a software tool that designs indexing primers for producing multiplexed sequencing libraries. GIL can be customized in numerous ways to meet user specifications, including index length, sequencing modality, color balancing, and compatibility with existing primers, and produces ordering and demultiplexing-ready outputs. GIL is written in Python and is freely available on GitHub at https://github.com/de-Boer-Lab/GIL. It can also be accessed as a web-application implemented in Streamlit at https://dbl-gil.streamlitapp.com.

bioinformatics↗

bioRxiv2021

Modeling the trajectory of SARS-CoV-2 spike protein evolution in continuous latent space using a neural network and Gaussian process

Viral vaccines can lose their efficacy as the genomes of targeted viruses rapidly evolve, resulting in new variants that may evade vaccine-induced immunity. This process is apparent in the emergence of new SARS-CoV-2 variants which have the potential to undermine vaccination efforts and cause further outbreaks. Predictive vaccinology points to a future of pandemic preparedness in which vaccines can be developed preemptively based in part on predictive models of viral evolution. Thus, modeling the trajectory of SARS-CoV-2 spike protein evolution could have value for mRNA vaccine development. Traditionally, in silico sequence evolution has been modeled discretely, while there has been limited investigation into continuous models. Here we present the Viral Predictor for mRNA Evolution (VPRE), an open-source software tool which learns from mutational patterns in viral proteins and models their most statistically likely evolutionary trajectories. We trained a variational autoencoder with real-time and simulated SARS-CoV-2 genome data from Australia to encode discrete spike protein sequences into continuous numerical variables. To simulate evolution along a phylogenetic path, we trained a Gaussian process model with the numerical variables to project spike protein evolution up to five months in advance. Our predictions mapped primarily to a sequence that differed by a single amino acid from the most reported spike protein in Australia within the prediction timeframe, indicating the utility of deep learning and continuous latent spaces for modeling viral protein evolution. VPRE can be readily adapted to investigate and predict the evolution of viruses other than SARS-CoV-2 in temporal, geographic, and lineage-specific pathways.

bioinformatics↗