Biology subjects

Panchy, N. L.

Publications and source records attributed to Panchy, N. L..

6 recordsLinked to original sources

bioRxiv2021

High throughput measurement of Arabidopsis thaliana fitness traits using transfer learning

O_LIRevealing the contributions of genes to plant phenotype is frequently challenging because the effects of loss of gene function may be subtle or be masked by genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. C_LIO_LIAn image segmentation-based (ImageJ) and a Faster Region Based Convolutional Neural Network (R-CNN) approach were used for measuring two Arabidopsis fitness traits: seed and fruit counts. C_LIO_LIAlthough straightforward to use, ImageJ was error-prone (correlation between true and predicted seed counts, r2=0.849) because seeds touching each other were undercounted. In contrast, Faster R-CNN yielded near perfect seed counts (r2=0.9996) and highly accurate fruit counts (r2=0.980). C_LIO_LIBy examining seed counts, we were able to reveal fitness effects for genes that were previously reported to have no or condition-specific loss-of-function phenotypes. C_LIO_LIOur study provides models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits in the study of gene functions. C_LI

bioinformatics↗

bioRxiv2021

Interpretable, scalable, and transferrable functional projection of large-scale transcriptome data using constrained matrix decomposition

Large-scale transcriptome data, such as single-cell RNA-sequencing data, have provided unprecedented resources for studying biological processes at the systems level. Numerous dimensionality reduction methods have been developed to visualize and analyze these transcriptome data. In addition, several existing methods allow inference of functional variations among samples using gene sets with known biological functions. However, it remains challenging to analyze transcriptomes with reduced dimensions that are interpretable in terms of dimensions directionalities, transferrable to new data, and directly expose the contribution of individual genes. In this study, we used gene set non-negative principal component analysis (gsPCA) and non-negative matrix factorization (gsNMF) to analyze large-scale transcriptome datasets. We found that these methods provide low-dimensional information about the progression of biological processes in a quantitative manner, and their performances are comparable to existing functional variation analysis methods in terms of distinguishing multiple cell states and samples from multiple conditions. Remarkably, upon training with a subset of data, these methods allow predictions of locations in the functional space using data from experimental conditions that are not exposed to the models. Specifically, our models predicted the extent of progression and reversion for cells in the epithelial-mesenchymal transition (EMT) continuum. These methods revealed conserved EMT program among multiple types of single cells and tumor samples. Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.

bioinformatics↗

bioRxiv2018

Predicting cell-cycle expressed genes identifies canonical and non-canonical regulators of time-specific expression in Saccharomyces cerevisiae

The collection all TFs, target genes and their interactions in an organism form a gene regulatory network (GRN), which underly complex patterns of transcription even in unicellular species. However, identifying which interactions regulate expression in a specific temporal context remains a challenging task. With multiple experimental and computational approaches to characterize GRNs, we predicted general and phase-specific cell-cycle expression in Saccharomyces cerevisiae using four regulatory data sets: chromatin immunoprecipitation (ChIP), TF deletion data (Deletion), protein binding microarrays (PBMs), and position weight matrices (PWMs). Our results indicate that the source of regulatory interaction information significantly impacts our ability to predict cell-cycle expression where the best model was constructed by combining selected TF features from ChIP and Deletion data as well as TF-TF interaction features in the form of feed-forward loops. The TFs that were the best predictors of cell-cycle expression were enriched for known cell-cycle regulators but also include regulators not implicated in cell-cycle regulation previously. In addition, ChIP and Deletion datasets led to the identification different subsets of TFs important for predicting cell-cycle expression. Finally, analysis of important TF-TF interaction features suggests that the GRN regulating cell cycle expression is highly interconnected and clustered around four groups of genes, two of which represent known cell-cycle regulatory complexes, while the other two contain TFs that are not known cell-cycle regulators (Ste12-Tex1 and Rap1-Hap1-Msn4), but are nonetheless important to regulating the timing of expression. Thus, not only do our models accurately reflect what is known about the regulation of the S. cerevisiae cell cycle, they can be used to discover regulatory factors which play a role in controlling expression during the cell cycle as well as other contexts with discrete temporal patterns of expression.

genetics↗

bioRxiv2018

Factors influencing gene family size variation among related species in a plant family

Gene duplication and loss contribute to gene content differences as well as phenotypic divergence across species. However, the extent to which gene content varies among closely related plant species and the factors responsible for such variation remain unclear. Here, we used the Solanaceae family as a model to investigate differences in gene family size and the likely factors contributing to these differences. We found that genes in highly variable families have high turnover rate and tend to be involved in processes that have diverged between Solanaceae species, whereas genes in low-variability families tend to have housekeeping roles. In addition, genes in high-and low-variability gene families tend to be duplicated by tandem and whole genome duplication, respectively. This finding together with the observation that genes duplicated by different mechanisms experience different selection pressures suggests that duplication mechanism impacts gene family turnover. We explored using pseudogene number as a proxy for gene loss but discovered that a substantial number of pseudogenes are actually products of pseudogene duplication, contrary to the expectation that most plant pseudogenes are remnants of once-functional duplicates. Our findings reveal complex relationships between variation in gene family size, gene functions, duplication mechanism, and evolutionary rate. The patterns of lineage-specific gene family expansion within the Solanaceae provide the foundation for a better understanding of the genetic basis underlying phenotypic diversity in this economically important family.

evolutionary biology↗

bioRxiv2017

Defining functional intergenic transcribed regions based on heterogeneous features of phenotype genes and pseudogenes

With advances in transcript profiling, the presence of transcriptional activities in intergenic regions has been well established in multiple model systems. However, whether intergenic expression reflects transcriptional noise or the activity of novel genes remains unclear. We identified intergenic transcribed regions (ITRs) in 15 diverse flowering plant species and found that the amount of intergenic expression correlates with genome size, a pattern that could be expected if intergenic expression is largely non-functional. To further assess the functionality of ITRs, we first built machine learning classifiers using Arabidopsis thaliana as a model that can accurately distinguish functional sequences (phenotype genes) and non-functional ones (pseudogenes and random unexpressed intergenic regions) by integrating 93 biochemical, evolutionary, and sequence-structure features. Next, by applying the models to ITRs, we found that 2,453 (21%) had features significantly similar to phenotype genes and thus were likely parts of functional genes, while an additional 17% resembled benchmark RNA genes. However, [~]60% of ITRs were more similar to nonfunctional sequences and should be considered transcriptional noise unless falsified with experiments. The predictive framework establish here provides not only a comprehensive look at how functional, genic sequences are distinct from likely non-functional ones, but also a new way to differentiate novel genes from genomic regions with noisy transcriptional activities.

genomics↗

bioRxiv2017

Asymmetric evolution of the transcription profiles and cis-regulatory sites contributes to the retention of transcription factor duplicates

Transcription factors (TFs) play a key role in regulating plant development and response to environmental stimuli. While most genes revert to single copy after a duplication event, transcription factors are retained at a significantly higher rate. However, it is unclear why TF duplicates have higher rates of retention relative to other genes. In this study, we compared three types of features (expression, sequence, and conservation) of retained TFs following whole genome duplication (WGD) events to genes with other functions, using Arabidopsis thaliana as a model. We found that gene function groups with higher maximum expression but lower mean expression tended to have higher duplicate retention rate post WGD, though TFs in particular are retained more often than would be expected based on the features examined. Conversely, expression of individual genes was not associated with duplication, but sequence conservation was. Furthermore, we found that the evolution of TF expression patterns and cis-regulatory cites favors the partitioning of ancestral states among the resulting duplicates. In particular, we found that one duplicate retains the majority of ancestral expression and cis-regulatory sites, while the \"non-ancestral\" duplicate was enriched for novel regulatory sites. To investigate how this pattern of partitioning pattern evolved, we modeled the retention of ancestral states in duplicate pairs using a system of differential equations. Our findings indicate that duplicate pairs evolve to a partitioned state more often than away from it, which in combination with accumulation of new regulatory sites in non-ancestral duplicates, suggest that selection favors partitioning via neofunctionalization.\n\nAuthor SummaryGene expression is controlled by regulatory proteins known as transcription factors. These factors control how an organism develops and responds to its environment. The evolution of transcription factor functions also contributes to the emergence of new species and crop domestication. In plants, new transcription factors mainly arise due to polyploidy, multiplication of the genome. Although most duplicated copies are lost following a genome duplication event, transcription factors are exceptional because they are often kept. Furthermore, we found that transcription factor duplicates that tend to diverge in how they are expressed and regulated in an unusual way where one copy mirrors the original, pre-duplication functional states of the ancestral gene, while the other loses the ancestral status and instead accumulates novel regulatory sites. Our results suggest these duplicate transcription factors may have been kept because one copy preserve ancestral function while the other has evolved new ones.

evolutionary biology↗