Biology subjects

Anshul Kundaje

Publications and source records attributed to Anshul Kundaje.

3 recordsLinked to original sources

bioRxiv2016

Initiation of mtDNA transcription is followed by pausing, and diverge across human cell types and during evolution

Mitochondrial DNA (mtDNA) genes are long known to be co-transcribed in polycistrones, yet it remains impossible to study nascent mtDNA transcripts quantitatively in vivo using existing tools. To this end we used deep sequencing (GRO-seq and PRO-seq) and analyzed nascent mtDNA-encoded RNA transcripts in diverse human cell lines and metazoan organisms. Surprisingly, accurate detection of human mtDNA transcription initiation sites (TIS) in the heavy and light strands revealed a novel conserved transcription pausing site near the light strand TIS, upstream to the transcription-replication transition region. This pausing site correlated with the presence of a bacterial pausing sequence motif, yet the transcription pausing index varied quantitatively among the cell lines. Analysis of non-human organisms enabled de novo mtDNA sequence assembly, as well as detection of previously unknown mtDNA TIS, pausing, and transcription termination sites with unprecedented accuracy. Whereas mammals (chimpanzee, rhesus macaque, rat, and mouse) showed a human-like mtDNA transcription pattern, the invertebrate pattern (Drosophila and C. elegans) profoundly diverged. Our approach paves the path towards in vivo, quantitative, reference sequence-free analysis of mtDNA transcription in all eukaryotes.

Genomics↗

bioRxiv2016

Denoising genome-wide histone ChIP-seq with convolutional neural networks

MotivationChromatin immunoprecipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChIP-seq data is affected by a myriad of experimental parameters such as the amount of input DNA, antibody specificity, ChIP enrichment, and sequencing depth. Making accurate inferences from chromatin profiling experiments that involve diverse experimental parameters is challenging.\n\nResultsWe introduce a convolutional denoising algorithm, Coda, that uses convolutional neural networks to learn a mapping from suboptimal to high-quality histone ChIP-seq data. This overcomes various sources of noise and variability, substantially enhancing and recovering signal when applied to low-quality chromatin profiling datasets across individuals, cell types, and species. Our method has the potential to improve data quality at reduced costs. More broadly, this approach - using a high-dimensional discriminative model to encode a generative noise process - is generally applicable to other biological domains where it is easy to generate noisy data but difficult to analytically characterize the noise or underlying data distribution.\n\nAvailabilityhttps://github.com/kundajelab/coda\n\nContactakundaje@stanford.edu

Bioinformatics↗

bioRxiv2015

Impact of the X chromosome and sex on regulatory variation

The X chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. We have conducted an analysis of the impact of both sex and the X chromosome on patterns of gene expression identified through transcriptome sequencing of whole blood from 922 individuals. We identified that genes on the X chromosome are more likely to have sex-specific expression compared to the autosomal genes. Furthermore, we identified a depletion of regulatory variants on the X chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X chromosome. To resolve the molecular mechanisms underlying such effects, we generated and connected sex-specific chromatin accessibility to sex-specific expression and regulatory variation. As sex-specific regulatory variants can inform sex differences in genetic disease prevalence, we have integrated our data with genome-wide association study data for multiple immune traits and to identify traits with significant sex biases. Together, our study provides genome-wide insight into how the X chromosome and sex shape human gene regulation and disease.

Genomics↗