Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences

ABSTRACT: Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences

PROVIDER: PRJNA299151 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR2723893_1.fastq.gz	Fastqsanger.gz
	SRR2723893_2.fastq.gz	Fastqsanger.gz
	SRR2723895_1.fastq.gz	Fastqsanger.gz
	SRR2723895_2.fastq.gz	Fastqsanger.gz
	SRR2723896_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 8

Similar Datasets

Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences

Project description:Most human transcripts are alternatively spliced, and many disease-causing mutations affect RNA splicing. Towards better modeling the sequence determinants of alternative splicing, we measured the splicing patterns of nearly 2 million (M) synthetic mini-genes, which include degenerate subsequences totaling to nearly 100M bases of variation. The massive size of these training data allowed us to improve upon current models of splicing as well as to gain new mechanistic insights. Our results show that a vast majority of hexamer sequence motifs measurably influence splice site selection when positioned within alternative exons, with multiple motifs acting additively rather than cooperatively. Intriguingly, motifs that enhance (suppress) exon inclusion in alternative 5’ splicing also enhance (suppress) exon inclusion in alternative 3’ or cassette exon splicing, suggesting a universal mechanism for alternative exon recognition. Finally, our empirically trained models are highly predictive of the effects of naturally occurring variants on alternative splicing in vivo.

2015-10-21 | GSE74070 | GEO

Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

Project description:Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

| PRJNA1115911 | ENA

Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

Project description:More than 50 human diseases are characterized by the deposition of specific protein aggregates in the form of insoluble amyloid fibrils. However, only a very small number of proteins are known to form amyloids with high propensity, limiting our ability to understand, predict and engineer amyloid aggregation from sequence. Here we use a massively parallel assay to quantify the amyloid nucleation propensity of >100,000 random 20 amino acid sequences. Approximately 5% of assayed random sequences nucleate the formation of aggregates, generating a very large and diverse training dataset from which to train models to predict amyloid nucleation. We use this dataset to train CANYA, a convolution-attention hybrid neural network that predicts the propensity of any primary sequence to form amyloids. CANYA outperforms previous predictors of protein aggregation on additional random sequences and out-of-sample datasets including human disease-causing amyloids, with very stable performance across diverse prediction tasks. We adapt and extend recent advances in interpretability of genomic neural networks to elucidate CANYA’s decision-making process and learned grammar and to provide mechanistic insights into amyloid formation. Our results demonstrate the power of massive experimental random sequence-space exploration and provide an interpretable and robust neural network model for understanding, predicting and designing amyloid-forming proteins.

2024-07-17 | GSE268261 | GEO

Random bioactive sequences

Project description:Random sequences are an abundant source of bioactive RNAs or peptides

| PRJEB19640 | ENA

Learning the regulatory grammar of yeast 5' untranslated regions from a large library of random sequences

Project description:Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the translational efficiency of the 5’ untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide- long random 5’ UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on translation of Kozak sequence composition, upstream open reading frames (uORFs) and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the translational efficiency of both a held-out set of the random 5’ UTRs as well as native S. cerevisiae 5’ UTRs. The model additionally was used to computationally evolve highly translating 5’ UTRs. We confirmed experimentally that the great majority of the evolved sequences lead to higher translation rates than the starting sequences, demonstrating the predictive power of this model. The sequences were assayed in the context of a constant promoter and downstream coding sequence. Cell growth was used as a proxy for protein expression. Growth was measured by determining the ratio of sequence counts before and after growth selection.

2017-09-27 | GSE104252 | GEO

Sequence determinants of intron-mediated enhancement learned from thousands of random introns

Project description:Spliceosomal introns are a ubiquitous feature of eukaryotic genes, whose presence often boosts the expression of their host gene, a phenomenon known as intron-mediated enhancement (IME). IME has been noted across diverse genes and organisms, but remains mysterious in many respects. For example, how does intron sequence affect the magnitude of IME? In this study, we performed a massively parallel reporter assay (MPRA) to assess the effect of varying intron sequence on gene expression in a high-throughput manner, using tens of thousands of synthetic introns with natural splice sites and randomized internal sequence. We observe that most random introns splice efficiently and enhance gene expression as well as or better than fully natural introns. Nearly all introns stimulate gene expression ~eight-fold above an intronless control, at both mRNA and protein levels, suggesting that the primary mechanism acts to increase mRNA levels. IME strength is positively associated with splicing efficiency and with the intronic content of poly-uridine stretches, which we confirm using reporter experiments. In sum, this work elucidates sequence determinants of IME from tens of thousands of random introns.

2024-10-28 | GSE278584 | GEO

Learning the regulatory grammar of yeast 5' untranslated regions from a large library of random sequences

Project description:Learning the regulatory grammar of yeast 5' untranslated regions from a large library of random sequences

| PRJNA412167 | ENA

Sequence determinants of intron-mediated enhancement learned from thousands of random introns

Project description:Sequence determinants of intron-mediated enhancement learned from thousands of random introns

| PRJNA1168030 | ENA

Sequence determinants of human gene regulatory elements, STARR-seq random enhancer experiments

Project description:Sequence determinants of human gene regulatory elements, STARR-seq random enhancer experiments

| PRJNA746787 | ENA

Sequence determinants of human gene regulatory elements, STARR-seq random promoter enhancer experiments

Project description:Sequence determinants of human gene regulatory elements, STARR-seq random promoter enhancer experiments