Biology subjects

Farouni, R.

Publications and source records attributed to Farouni, R..

8 recordsLinked to original sources

bioRxiv2021

Transcriptional Reprogramming of Skeletal Muscle Stem Cells by the Niche Environment

Adult stem cells are indispensable for tissue regeneration. Tissue-specific stem cells reside in a specialized location called their niche, where they are in constant cross talk with neighboring niche cells and circulatory signals from their environment. Aging has a detrimental effect on the number and the regenerative function of various stem cells. However, whether the loss of stem cell function is a cause or consequence of their aging niche is unclear. Using skeletal muscle stem cells (MuSCs) as a model, we decouple cell-intrinsic from niche-mediated extrinsic effects of aging on their transcriptome. By combining in vivo MuSC heterochronic transplantation models and computational methods, we show that on a genome-wide scale, age-related altered genes fall into two distinct categories regarding their response to the niche environment. Genes that are inelastic in their response to the niche exhibit altered chromatin accessibility and are associated with differentially methylated regions (DMRs) between young and aged cells. On the other hand, genes that are restorable by niche exposure exhibit altered transcriptome but show no change in chromatin accessibility or DMRs. Taken together, our data suggest that the niche environment plays a decisive role in controlling the transcriptional activity of MuSCs, and exposure to a young niche can reverse approximately half of all age-associated changes that are not epigenetically encoded. The muscle niche therefore serves as an important therapeutic venue to mitigate the negative consequence of aging on tissue regeneration.

genetics↗

bioRxiv2019

Legacy Data Confounds Genomics Studies

Recent reports have identified differences in the mutational spectra across human populations. While some of these reports have been replicated in other cohorts, most have been reported only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative population stratification within the Japanese population, we identified a previously unreported batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population stratification. Because the 1kGP data is used extensively, we find that the batch effects also lead to incorrect imputation by leading imputation servers and a small number of suspicious GWAS associations. Lower-quality data from the early phases of the 1kGP thus continues to contaminate modern studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data.

bioinformatics↗

bioRxiv2019

Statistical modeling, estimation, and remediation of sample index hopping in multiplexed droplet-based single-cell RNA-seq data

We introduce a probabilistic model for estimation of sample index-hopping rate in multiplexed droplet-based single-cell RNA sequencing data and for inference of the true sample of origin of the hopped reads. Across the datasets we analyzed, we estimate the sample index hopping probability to range between 0.003-0.009, a small number that counter-intuitively gives rise to a large fraction of phantom molecules - as high as 85% in a given sample. We demonstrate that our model-based approach can correct for this artifact by accurately purging the majority of phantom molecules from the data. Code and reproducible analysis notebooks are available at https://github.com/csglab/phantom_purge.\n\nStructureSection 1 provides a concise summary of the paper. Section 2 provides a brief historical and technical overview of the phenomenon of sample index hopping and an explanation of related concepts. The three sections that follow describe the statistical modeling approach and correspond to the following three goals. (1) Building a generative model that probabilistically describes the phenomenon of sample index hopping of multiplexed sample reads (Section 3). (2) Estimating the index hopping rate from empirical experimental data (Section 4). (3) Correcting for the effects of sample index hopping through a principled probabilistic procedure that reassigns reads to their true sample of origin and discards predicted phantom molecules by optimally minimizing the false positive rate (Section 5). Next, Section 6 details the results of the analyses performed on empirical and experimental validation datasets. The Supplementary Notes consists of three sections: (1) Mathematical Derivations, (2) Overview of Computational Workflow, (3) Methods Limitations.

bioinformatics↗

bioRxiv2018

Analysis and comparison of genome editing using CRISPResso2

Genome editing technologies are rapidly evolving, and analysis of deep sequencing data from target or off-target regions is necessary for measuring editing efficiency and evaluating safety. However, no software exists to analyze base editors, perform allele-specific quantification or that incorporates biologically-informed and scalable alignment approaches. Here, we present CRISPResso2 to fill this gap and illustrate its functionality by experimentally measuring and analyzing the editing properties of six genome editing agents.

bioinformatics↗

bioRxiv2018

CRISPR-SURF: Discovering regulatory elements by deconvolution of CRISPR tiling screen data

Tiling screens using CRISPR-Cas technologies provide a powerful approach to map regulatory elements to phenotypes of interest, but computational methods that effectively model these experimental approaches for different CRISPR technologies are not readily available. Here we present CRISPR-SURF, a deconvolution framework to identify functional regulatory regions in the genome from data generated by CRISPR-Cas nuclease, CRISPR interference (CRISPRi), or CRISPR activation (CRISPRa) tiling screens. We validated CRISPR-SURF on previously published and new data, identifying both experimentally validated and new potential regulatory elements. With CRISPR tiling screens now being increasingly used to elucidate the regulatory architecture of the non-coding genome, CRISPRSURF provides a generalizable and accessible solution for the discovery of regulatory elements.

bioinformatics↗

bioRxiv2018

AmpUMI: Design and analysis of unique molecular identifiers for deep amplicon sequencing

MotivationUnique molecular identifiers (UMIs) are added to DNA fragments before PCR amplification to discriminate between alleles arising from the same genomic locus and sequencing reads produced by PCR amplification. While computational methods have been developed to take into account UMI information in genome-wide and single-cell sequencing studies, they are not designed for modern amplicon based sequencing experiments, especially in cases of high allelic diversity. Importantly, no guidelines are provided for the design of optimal UMI length for amplicon-based sequencing experiments.\n\nResultsBased on the total number of DNA fragments and the distribution of allele frequencies, we present a model for the determination of the minimum UMI length required to prevent UMI collisions and reduce allelic distortion. We also introduce a user-friendly software tool called AmpUMI to assist in the design and the analysis of UMI-based amplicon sequencing studies. AmpUMI provides quality control metrics on frequency and quality of UMIs, and trims and deduplicates amplicon sequences with user specified parameters for use in downstream analysis. AmpUMI is open-source and freely available at http://github.com/pinellolab/AmpUMI.\n\nContactIpinello@mgh.harvard.edu

bioinformatics↗

bioRxiv2017

Haystack: systematic analysis of the variation of epigenetic states and cell-type specific regulatory elements

MotivationWith the increasing amount of genomic and epigenomic data in the public domain, a pressing challenge is how to integrate these data to investigate the role of epigenetic mechanisms in regulating gene expression and maintenance of cell-identity. To this end, we have implemented a computational pipeline to systematically study epigenetic variability and uncover regulatory DNA sequences that play a role in gene regulation.\n\nResultsHaystack is a bioinformatics pipeline to characterize hotspots of epigenetic variability across different cell-types as well as cell-type specific cis-regulatory elements along with their corresponding transcription factors. Our approach is generally applicable to any epigenetic mark and provides an important tool to investigate cell-type identity and the mechanisms underlying epigenetic switches during development. Additionally, we make available a set of precomputed tracks for a number of epigenetic marks across several cell types. These precomputed results may be used as an independent resource for functional annotation of the human genome.\n\nAvailabilityThe Haystack pipeline is implemented as an open-source, multiplatform, Python package called haystack_bio available at https://github.com/pinellolab/haystack_bio.\n\nContactlpinello@mgh.harvard.edu, gcyuan@jimmy.harvard.edu

bioinformatics↗

bioRxiv2016

ALTRE: workflow for defining ALTered Regulatory Elements using chromatin accessibility data

SummaryRegulatory elements regulate gene transcription, and their location and accessibility is cell-type specific, particularly for enhancers. Mapping and comparing chromatin accessibility between different cell types may identify mechanisms involved in cellular development and disease progression. To streamline and simplify differential analysis of regulatory elements genome-wide using chromatin accessibility data, such as DNase-seq, ATAC-seq, we developed ALTRE (ALTered Regulatory Elements), an R package and associated R Shiny web app. ALTRE makes such analysis accessible to a wide range of users - from novice to practiced computational biologists.\n\nAvailabilityhttps://github.com/Mathelab/ALTRE\n\nContactewy.mathe@osumc.edu

bioinformatics↗