Biology subjects

Delong, A.

Publications and source records attributed to Delong, A..

5 recordsLinked to original sources

bioRxiv2025

Tomato pollen tube integrity is critical for pollen function at high temperature and is controlled by RALF signaling.

Reproductive success at high temperature (HT) depends on the ability of pollen to germinate a pollen tube that delivers sperm for double fertilization. We recently found that Solanum lycopersicum (tomato) cultivars capable of setting fruit at HT produce pollen tubes that continue to grow under heat stress. Our present goal is to define the molecular basis of thermotolerant pollen tube growth using quantitative live imaging, transcriptomic and proteomic profiling, and genetic analysis. We found that pollen tube integrity - the ability to maintain an intact tip during germination and elongation - was the pollen performance parameter that distinguished thermotolerant cultivars. We identified the components of the Rapid Alkalinization Factor (RALF) pollen tube integrity signal transduction pathway in tomato and found that RALF peptides repress germination at control temperature and that signaling is antagonized by HT. Loss of a single RALF peptide changes pollen tube cell wall pectin distribution and enhances pollen tube integrity at HT in thermotolerant Heinz, revealing that RALF signaling regulates thermotolerant pollen tube growth and establishing a molecular framework for improving reproductive heat-resilience in crop plants.

plant biology↗

bioRxiv2020

Transcriptome-Wide Off-Target Effects of Steric-Blocking Oligonucleotides

Steric-blocking oligonucleotides (SBOs) are short, single-stranded nucleic acids designed to modulate gene expression by binding to mRNA and blocking access from cellular machinery such as splicing factors. SBOs have the potential to bind to near-complementary sites in the transcriptome, causing off-target effects. In this study, we used RNA-seq to evaluate the off-target differential splicing events of 81 SBOs and differential expression events of 46 SBOs. Our results suggest that differential splicing events are predominantly hybridization-driven, while differential expression events are more common and driven by other mechanisms. We further evaluated the performance of in silico screens for off-target events, and found an edit distance cutoff of three to result in a sensitivity of 14% and false discovery rate of 99%. A machine learning model incorporating splicing predictions substantially improved the ability to prioritize low edit distance hits, increasing sensitivity from 4% to 26% at a fixed FDR. Despite these large improvements in performance, the approach does not detect the majority of events at a false discovery rate below 99%. Our results suggest that in silico methods are currently of limited use for predicting the off-target effects of SBOs.

molecular biology↗

bioRxiv2018

cDeepbind: A context sensitive deep learning model of RNA-protein binding

MotivationDetermining RNA binding protein(RBP) binding specificity is crucial for understanding many cellular processes and genetic disorders. RBP binding is known to be affected by both the sequence and structure of RNAs. Deep learning can be used to learn generalizable representations of raw data and has improved state of the art in several fields such as image classification, speech recognition and even genomics. Previous work on RBP binding has either used shallow models that combine sequence and structure or deep models that use only the sequence. Here we combine both abilities by augmenting and refining the original Deepbind architecture to capture structural information and obtain significantly better performance.\n\nResultsWe propose two deep architectures, one a lightweight convolutional network for transcriptome wide inference and another a Long Short-Term Memory(LSTM) network that is suitable for small batches of data. We incorporate computationally predicted secondary structure features as input to our models and show its effectiveness in boosting prediction performance. Our models achieved significantly higher correlations on held out in-vitro test data compared to previous approaches, and generalise well to in-vivo CLIP-SEQ data achieving higher median AUCs than other approaches. We analysed the output from our model for VTS1 and CPO and provided intuition into its working. Our models confirmed known secondary structure preferences for some proteins as well as found new ones where secondary structure might play a role. We also demonstrated the strengths of our model compared to other approaches such as the ability to combine information from long distances along the input.\n\nAvailabilitySoftware and models are available at https://github.com/shreshthgandhi/cDeepbind\n\nContactljlee@psi.toronto.edu, frey@psi.toronto.edu

genomics↗

bioRxiv2018

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Genetic variation has long been known to alter transcription factor binding sites, resulting in sometimes major phenotypic consequences. While the performance for current binding site predictors is well characterised, little is known on how these models perform at predicting impact of variants. We collected and curated over 132,000 potential allele-specific binding (ASB) ChIP-seq variants across 101 transcription factors (TFs). We then assessed the accuracy of TF binding models from five different methods on these high-confidence measurements, finding that deep learning methods were best performing yet still have room for improvement. Importantly, machine learning methods were consistently better than the venerable position weight matrix (PWM). Finally, predictions for certain TFs were consistently poor, and our investigation supports efforts to use features beyond sequence, such as methylation, DNA shape, and post-translational modifications. We submit that ASB data is a valuable benchmark for variant impact on TF binding.

bioinformatics↗

bioRxiv2017

Inference Of The Human Polyadenylation Code

Processing of transcripts at the 3-end involves cleavage at a polyadenylation site followed by the addition of a poly(A)-tail. By selecting which polyadenylation site is cleaved, alternative polyadenylation enables genes to produce transcript isoforms with different 3-ends. To facilitate the identification and treatment of disease-causing mutations that affect polyadenylation and to understand the underlying regulatory processes, a computational model that can accurately predict polyadenylation patterns based on genomic features is desirable. Previous works have focused on identifying candidate polyadenylation sites and classifying sites which may be tissue-specific. What is lacking is a predictive model of the underlying mechanism of site selection, competition, and processing efficiency in a tissue-specific manner. We develop a deep learning model that trains on 3-end sequencing data and predicts tissue-specific site selection among competing polyadenylation sites in the 3 untranslated region of the human genome.\n\nTwo neural network architectures are evaluated: one built on hand-engineered features, and another that directly learns from the genomic sequence. The hand-engineered features include polyadenylation signals, cis-regulatory elements, n-mer counts, nucleosome occupancy, and RNA-binding protein motifs. The direct-from-sequence model is inferred without prior knowledge on polyadenylation, based on a convolutional neural network trained with genomic sequences surrounding each polyadenylation site as input. Both models are trained using the TensorFlow library.\n\nThe proposed polyadenylation code can predict site selection among competing polyadenylation sites in different tissues. Importantly, it does so without relying on evolutionary conservation. The model can distinguish pathogenic from benign variants that appear near annotated polyadenylation sites in ClinVar and inspect the genome to find candidate polyadenylation sites. We also provide an analysis on how different features affect the models performance.

bioinformatics↗