Sequence Information for ORF

CDS Sequence

Amino Acid Sequence

Coordinates

Coexpression Info for ORF

Gene set enrichment
Coexpression Network

Gene Set Enrichment Analysis

Coexpression

Filter by ORF classification:

Coexpression threshold:

No coexpression info found

Source

This website accompanies 'Massively Integrated Coexpression Analysis Reveals Transcriptional Regulation, Evolution and Cellular Implications of the Noncanonical Translatome' paper.

All relevant supplementary data can be found at figshare.

To calculate coexpression at the translatome scale in S. cerevisiae, we integrated a vast dataset of RNA-seq samples, applied center log ratio (clr) transformation, and used the proportionality metric ρ for quantification. We addressed data sparsity and reliability by discarding observations with low raw counts and setting a minimum sample threshold, resulting in an 11,630 by 11,630 coexpression matrix. Additionally, we normalized the coexpression data using spatial quantile normalization (SpQN) to correct for expression level biases, and created a network representation by considering only the top 0.2% of ρ values between all ORF pairs. We investigated canonical ORFs (cORFs), those that have been studied in depth and mostly shown to be involved in a variety of biological processes, and noncanonical ORFs (nORFs) that are usually excluded from genome annotations because of their short length, lack of evolutionary conservation, and perceived irrelevance to cellular physiology. We studied 5,803 cORFs and 5,827 nORFs.

The list of RNA-Seq data (SRA sample accessions) used is listed in Supplementary Data 1

To determine the transcriptional associations of nORFs with specific cellular processes, we performed gene set enrichment analyses (GSEA) on the coexpression partners of each cORF and nORF. In this method, we took an ordered list of genes, sorted by their coexpression level, and assessed whether higher-ranked genes were preferentially annotated with specific Gene Ontology (GO) terms. This approach allowed us to identify potential functional associations of nORFs and cORFs with various cellular processes based on the patterns of coexpression.

See methods of our paper for details.

Navigation

Main Navigation: At the top, you'll find a navigation bar titled 'ORF Information App.'

This contains two main tabs: 'Search' and 'About.'

Using the Search Function

Accessing the Search Tab: Click on the 'Search' tab to begin exploring ORF data.

Entering ORF Name: In the sidebar, there's a text input field labeled 'Enter your ORF name here.' Type the name of the ORF you're interested in (e.g., orf14870, YBR196C, PGI1).

Selecting Result Type: Below the text input, choose the type of results you want to view: 'Coexpression' or 'Sequence.'

Submit Your Query: After entering the ORF name and selecting the result type, click the 'Submit' button to proceed.

Viewing Results

Sequence Information: If you chose 'Sequence' in the result type:

A section will appear displaying the sequence information of the ORF, including CDS (coding sequence), amino acid sequence, and genomic coordinates. You can copy sequences to clipboard for further use.

Coexpression Information: If 'Coexpression' is selected:

The app will display coexpression information related to the ORF. This includes Gene Set Enrichment analysis (GSEA) (corresponds to Supplementary Data 5 in Rich et al.) and a visualization of the Coexpression Network (corresponds to Supplementary Data 4 in Rich et al.), which can be used to study the ORF's relationship with other genes and its potential cellular roles.
GSEA results are displayed in a table, which can be sorted by clicking on the column headers. The table contains the following columns:

GO ID: The ID of gene ontology term tested
TERM: The name of gene ontology term
padj Benjamini-hochberg adjusted p-values
NES The normalized enrichment score.

We used R package fgsea to perform GSEA. Algortihm details can be reached at Korotkevich et al. 2021.

Gene Set Enrichment Analysis Example

Coexpression Data

You can use sliders and filters to refine the coexpression network and thresholds.

Developed at the University of Pittsburgh by

Omer Acar
April Rich
Anne-Ruxandra Carvunis

Thanks to the following R packages and their developers

For more information

Massively Integrated Coexpression Analysis Reveals Transcriptional Regulation, Evolution and Cellular Implications of the Noncanonical Translatome.
Rich A, Acar O, Carvunis AR
bioRxiv 2023.03.16.533058; doi: https://doi.org/10.1101/2023.03.16.533058