Biology subjects

Brooks, T. G.

Publications and source records attributed to Brooks, T. G..

7 recordsLinked to original sources

bioRxiv2026

A Benchmark of Unsupervised Circadian Phase Inference Methods for Bulk Transcriptomic Data

Circadian phase can be inferred from gene expression when sample collection times are unavailable, but the available methods have not been systematically compared. We compared ten unsupervised phase inference methods and two clock-gene baselines on 25 datasets with reference times (16 real datasets from five studies and 9 simulations). The primary measure was the aligned circular mean absolute error (MAE), with a permutation test for significance, circular correlations and gene-level reconstruction of rhythmicity, acrophase and amplitude were secondary outcomes. CHIRAL had the lowest mean MAE (1.80 h, 95% confidence interval 1.21-2.97 h from resampling study families). The two clock-gene baselines (2.15 and 2.20 h) were next, followed by TEMPO, CYCLOPS and CYCLOPS 2.0 (2.49-2.80 h), and the confidence intervals of these six overlapped substantially. The other six methods had mean errors of 3.91-4.86 h, compared with 5.08-5.23 h for permuted phases. Accuracy depended on the dataset and on the strength of clock-gene rhythms, and, for several methods, on the random seed and the adjustment for batch or covariates. When the inferred phases were used to analyse gene rhythms in 22 datasets, the four leading methods identified rhythmic genes with a median ROC AUC of 0.64-0.70 and recovered gene acrophases with a median error of 0.18-0.64 h. In two mouse datasets without collection times, agreement with external reference acrophases differed between methods and tissues.

bioinformatics↗

bioRxiv2025

Role of IL10 signaling in the circadian control of host response to Influenza infection

We have previously demonstrated that the circadian clock regulates the host response to influenza A virus (IAV) infection, conferring a time-of-day-specific protection -infection at dawn resulted in a threefold increase in survival and reduced immunopathology compared to infection at dusk. While IL10 is well-known for its immunoregulatory function, its role in IAV remains unclear, with studies reporting both protective and detrimental effects. Given the diurnal rhythmicity of IL-10 receptor (Il10ra) expression in the lung, we investigated the contribution of IL-10 signaling to time-of-day-specific IAV protection. We found that blocking IL10 signaling abrogated the time of day protection, leading to increased immunopathology characterized by enhanced lymphocyte infiltration and global immune activation (transcriptomic analysis). Interestingly, while later, IL-10R blockade also eliminated the time-of-day difference in IAV outcomes, it improved the outcome of dusk-infected mice. Furthermore, natural killer (NK) cell depletion suppressed IL-10 levels in bronchoalveolar lavage, suggesting a role for NK cells in regulating IL-10 signaling. In conclusion, incorporating the circadian context has not only clarified the IL-10 role in IAV infection but also underscored the pivotal influence of circadian regulation on immune responses.

immunology↗

bioRxiv2025

Generating Correlated Data for Omics Simulation

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each samples and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to the inconvenience and computational hurdles of doing so. To alleviate this, we describe in detail three approaches for quickly generating omics-scale data with correlated measures which mimic real data sets. These approaches all are based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

bioinformatics↗

bioRxiv2025

Sources of non-uniform coverage in short-read RNA-Seq data

The origin of several normal cellular functions and related abnormalities can be traced back to RNA splicing. As such, RNA splicing is currently the focus of a vast array of studies. To quantify the transcriptome, short-read RNA-Seq remains the standard assay. The primary technical artifact of RNA-Seq library prep, which severely interferes with analysis, is extreme non-uniformity in coverage across transcripts. This non-uniformity is present in both bulk and single-cell RNA-Seq and is observed even when the sample contains only full-length transcripts. This issue dramatically affects the accuracy of isoform-level quantification of multi-isoform genes. Understanding the sources of this non-uniformity is critical to developing improved protocols and analysis methods. Here, we explore eight potential sources of non-uniformity. We demonstrate that it cannot be explained by one factor alone. We performed targeted experiments to investigate the effect of fragment length, PCR ramp rate, and ribosomal depletion. We assessed existing data sets with varying sample quality, PCR cycle number, reverse transcriptase, and technical or biological replicates. We found evidence that interference of reverse transcription by secondary structure is unlikely to be the major contributing factor, that rRNA pull-down methods do not cause non-uniformity, that PCR ramp rate does not substantially impact non-uniformity, and that shorter fragments do not reduce non-uniformity. All these findings contradict prior publications or recommendations.

bioinformatics↗

bioRxiv2023

BEERS2: RNA-Seq simulation through high fidelity in silico modeling

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking, and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully-length mRNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM, or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in PCR amplification, barcode read errors, and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

bioinformatics↗

bioRxiv2022

Meta-analysis of diurnal transcriptomics reveals strong patterns of concordance and discordance in mouse liver

The accumulation of public transcriptomic timeseries data enables robust meta-analyses that were not possible until recently. To assess the consistency of biological rhythms across studies, 43 public mouse liver tissue timeseries totaling 805 RNA-seq samples were obtained and analyzed. Only the control groups of each study were included, in order to create comparable data. Technical factors in RNA-seq library preparation were the largest contributors to transcriptome-level differences, beyond biological or experiment-specific factors such as lighting conditions. Core clock genes were remarkably consistent in phase across all studies, while phase distributions of other periodic genes were generally less consistent. Overlap of genes identified as rhythmic across studies was generally low, up to around 50% between some of the highest sample count studies. Distributions of phases of significant genes were remarkably inconsistent across studies, but genes consistently identified as rhythmic clustered near ZT0 and ZT12 in acrophase. Data was integrated across studies in a JIVE analysis, which showed that the top two components of joint within-study variation are determined by time of day. A shape-invariant model with random effects was fit to the genes to identify the underlying shape of the rhythms, consistent across all studies. This revealed the extent of asymmetric and multimodal genes.

genomics↗

bioRxiv2019

Comparative evaluation of full-length isoform quantification from RNA-Seq

Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short. Here we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systematic comparative analyses of isoform quantification accuracy and its impact on differential expression analysis. Genome, transcriptome and pseudo alignment-based methods are included; and a simple approach is included as a baseline control. Salmon, kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform dramatically better than the simple approach. We determine the structural parameters with the greatest impact on quantification accuracy to be length and sequence compression complexity and not so much the number of isoforms. The effect of incomplete annotation on performance is also investigated. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification and isoform level DE should still be employed selectively.

bioinformatics↗