Quick Start: 3 Steps

TIMEOR accepts 2 input types: (1) raw .fastq files and SraRunTable (e.g. here) or a (2) RNA-seq time-series read count matrix (e.g. here) and metadata file (e.g. here).

  1. Visit https://timeor.brown.edu.
  2. For (1) in ‘Example Data’ (side-bar) under ‘Load raw data’ click the ‘SraRunTable & .fastq files’ button. This will guide you through the ‘Set Input and Defaults, Process Raw Data’ tab demo. Follow pop-ups and fill in grey boxes. See Run TIMEOR below for walk-through.
  3. Next, for (2) in ‘Example Data’ (side-bar) under ‘Load count matrix’ click the ‘Metadata & read count file’ button. This will guide you through the rest of the full method demo. Follow pop-ups and fill in grey boxes. See Run TIMEOR below for full application walk-through.

Website

Computational Biology Core at Brown Univeristy and DRSC/TRiP Functional Genomics Resources at Harvard Medical School Partnership

TIMEOR is available online at https://timeor.brown.edu.

Run TIMEOR

Two ways to input data:

  1. Import SraRunTable from GEO* where TIMEOR will process raw data through retrieving .fastq files, quality control, alignment, and read count matrix creation. Read first tab of TIMEOR (Getting Started) for information about this input specification. Read this section for information about how to process these data in TIMEOR. We strongly encourage users to upload a read count matrix, or process raw .fastq data via TIMEOR’s interface locally using Docker (see 4 steps here).

  2. Import metadata file** and count matrix *** (skipping raw data retrieval, quality control, alignment, and read count matrix creation) and proceeding straight to normalization and correction. Read first tab of TIMEOR (Getting Started) for information about this input specification. Read this section for information about how to process these data in TIMEOR.

Then simply follow the prompts. Fill out the grey boxes to begin interacting with each stage and tab.

Input file types:

  NOTE: see first tab of TIMEOR called Getting Started for specifications.
  • * SraRunTable from GEO requires at least these columns (which will be reordered to produce the metadata file).
    • treatment, time, Run, replicate, batch
      • treatment: one word describing experiment
      • time: numerical values e.g. (0, 20, 40)
      • replicate: one integer description of replicate (e.g. 1, 2, 3) (could have same information as batch)
      • batch: one integer description of batch (e.g. 1, 2, 3)
  • ** metadata file requires at least these columns.

    • ID, condition, time, batch

      • ID: a unique identifier (ID) for the user (e.g. case1min_rep1)
      • condition: one word description (e.g. case, control)
      • time: numerical values e.g. (0, 20, 40)
      • batch: one integer description of batch (e.g. 1, 2, 3)

    • An example might be:

        ID    batch condition   time
  simT0.1 1   control   0
  simT0.2 2   control   0
  simT0.3 3   control   0
  simT1.1 1   case      1
  simT1.2 2   case      1
  simT1.3 3   case      1
  simT2.1 1   case      2
  simT2.2 2   case      2
  simT2.3 3   case      2
  simT3.1 1   case      3
  simT3.2 2   case      3
  simT3.3 3   case      3
  • *** count matrix requires Ensembl or Flybase unique gene identifiers, and columns should be the IDs from metadata file, and in the same order as metadata file.

NOTE Importantly, we assume that replicate batches are sampled with each batch sampled at each time point. That means batch 1 across 4 timepoints would have corresponding replicate 1 at time point 1, replicate 1 at time point 2, replicate 1 at time point 3 and replicate 1 at time point 4. This process continues for all batches. This structure is adopted to work with all three differential expression methods. Moreover, it is a common structure to control for non-biological variation in a time-series experiment. For example, say RNA-seq was performed on a cell line after insulin stimulation, on 10 consecutive time points, every 20 minutes, with three biological replicates (such as in our manuscript). This means that non-biological factors could be considered when determining temporal differential expression by being able to compare three biological replicates!

Getting Started with TIMEOR

Please read through this first page of information to help guide you through TIMEOR.

 

 

 

 

Importantly, before beginning the analysis, TIMEOR requires the user to give data details to set the adaptive default methods used downstream. The ‘Getting Started’ Tab above under section ‘Suggestions for How to Answer Six ’Determine Adaptive Default Methods’ Questions’ (here) provides suggestions for how to answer these.

NOTE: We stronly recommend keeping ‘Yes’ as the answer for Question 5 to compare multiple methods, as this highlights valuable TIMEOR functionality.

 

 

Run TIMEOR from Raw Data: Starting from .fastq Time-Series RNA-seq

This tutorial uses a subset of real data used in the TIMEOR publication to take the user through TIMEOR’s “Process Raw Data” tab. You will first see this pop-up. Please read. There are 4 steps.

NOTE: When possible we advise the user to start from a read count matrix, as our file size limit is 10GB. For larger datasets, the user can use TIMEOR locally (still through web interface) via Docker, by following the 4 steps below, and here.

  1. In the far-left side-bar click on “Example Data” and then under “Load raw data” click on “SraRunTable & raw .fastq files”. This will load the answers to the adaptive default questions (left) and also load input details (right).

 

 

  1. Follow the pop-up prompt to explore the default settings to questions 1-6 to set the adaptive default parameters, and then click the “Run” button to begin retrieving the raw data (SRR8843738 and SRR8843750), performing quality control, and aligning the reads using HISAT2 and Bowtie2.

     

     

 

 

  1. Once the data have been retrieved and quality control has finished. You can view a summary under the “Quality Control” panel on the right. Interactive results can be downloaded and viewed from MultiQC.

 

 

 

 

  

  1. You can explore the alignment results between both methods in the “Alignment Quality” panel. Note that HISAT2 is splice-site aware). You can choose the method and then click “Generate count matrix” to have TIMEOR generate the read count matrix for the next tab “Load Count Matrix”. You will see a green check mark when finished. Follow pop-up. You have completed this section of the tutorial for loading raw time-series RNA-seq data.

 

 

 

 

Run TIMEOR Using Simulated Data: Starting from Read Count Matrix

This tutorial uses simulaated data and takes the user through TIMEOR’s full functionality beginning from a read count matrix (genes x sample/time). NOTE: figures with two panels are the same page, just split. There are 20 steps.

The user can begin this tutorial before or after following “Run TIMEOR from Raw Data: Starting from .fastq Time-Series RNA-seq”.

  1. In the far-left side-bar click on “Example Data” and then under “Load simulated data” click on “Metadata & read count file”.

  2. Follow the pop-up to explore results on the Pre-processing tabs “Process Raw Data” and “Process Count Matrix”.

 

 

 

 

 

 

  1. On the Normalize and Correct Data tab, choose from normalization and correction methods and click “Run” to view result. Visit the ‘Method and Question Choice Assistance’ section of the ‘Getting Started’ tab (here) for suggestions on how to choose the appropriate normalization and correlation settings. You have completed pre-processing. Follow pop-up.

 

 

 

 

  1. Proceed to Primary Analysis and click “Run”.

  2. At the bottom right you will see a pop-up to click “Render Venn Diagram” in the top right to compare differential expression results between three methods (ImpulseDE2, Next maSigPro, and DESeq2) and choose which method results to proceed with. You will then see a pop-up saying that you have completed Primary Analysis. Feel free to move on with TIMEOR’s default parameters, or explore Primary Analysis options (see next step).

 

 

 

 

  1. See 3-way Venn diagram in white box. You can compare a previous study list of genes with the three differential expression results by first downloading prev_study.txt. Then upload this file using the Browse button. See 4-way Venn diagram in blue box.

 

 

  1. Examine differential expression method results in the bottom row. Toggle under “Display Desired Differential Expression Method Results” between ImpulseDE2, Next maSigPro, and DESeq2 on the left, and the interactive clustermap with automated clustering will display the differentially expressed gene trajectories for the chosen method. TIMEOR provides an automatic clustering option (shown) which takes the mode between three unsupervised clustering methods (partition around medoids, Silhouette, and Calinski criterion) to automatically return the number of gene trajectory clusters to the user. PDF available on download to see clustering plots. You can then also toggle “Cluster Gene Expression Trajectories” to choose the number of clusters desired. For this demonstration, we chose “ImpulseDE2” differentially expressed gene output. We also chose “automatic” clustering of gene trajectories. On new data the user can choose these two parameters. NOTE: ImpulseDE2 is chosen because it has the largest differential expressed gene overlap with the previous study and other methods.

  2. Click ‘Fix cluster number’ to solidify the number of clusters. This button fetches the genomic sequences of each gene to perform motif enrichment per gene trajectory cluster for tab ‘Factor Binding’ in the Secondary Analysis stage.

 

 

  1. As said in pop-up, proceed to Secondary Analysis tab in side-bar.

  2. Clusters are labeled in ascending order from 1 for top-most cluster. Under Gene Expression Trajectory Clusters choose cluster 1, 2, or 3 in the dropdown. On the right under “Chosen Cluster Gene Set” you will see the genes in that cluster. Genes are the same color as the gene trajectory cluster to which they belong.

  3. Once you have chosen which genes set to test for enrichment, click the “Analyze” toggle to “ON”.

  4. Wait to view any enriched gene ontology (GO) terms (Molecular Function, Biological Process, or Cellular Component), pathway, network, and/or motif analysis. NOTE you may download the interactive motif results for viewing.

  5. Toggle the “Analyze” button to “OFF” to choose another gene set, and repeat steps 9-12. NOTE if no images show simply toggle ‘OFF’ then ‘ON’ again to view any results. If no enrichment is found, that box is left blank.

 

 

  1. Proceed to the “Factor Binding” tab to view the observed (that is, differentially expressed genes in data) and top predicted transcription factors in each gene cluster (under “Observed and Top Predicted Transcription Factors”). At least 40% of the transcription factor prediction methods must agree on their top predicted transcription factors, otherwise cell is left blank. Row names are gene expression trajectory clusters. NOTE for this demonstration the threshold is reduced.

 

 

  1. In that same table on the far right you will see ENCODE IDs indicating published ChIP-seq data for the predicted transcription factors. For this tutorial, can either see an example provided with “pho”. You may also either download these read-depth normalized .bigWig files here (ENCFF467OWR, ENCFF609FCZ, ENCFF346CDA) or follow the prompts (step 16) in the grey box under “Upload .bigWig Files”. Any .bigWig files from protein-DNA data are accepted.

  2. If you are interested, click on the “+” under “See each method’s predicted transcription factors:” to see the ranked lists of transcription factors and motifs by method. Blanks indicate an enriched motif is not assigned to a transcription factor region (to see motifs click ‘Download interactive cluster motif result’). Search for a method (e.g. transfac in blue box), enrichment score, etc. Row names are top 1 - 4 transcription factors.

 

 

  1. Under “Average Profiles Across Each Gene Expression Trajectory Cluster”, in the second box type “stat92e”, upload ENCFF467OWR.bigWig, and click “Go”. In the third box type “CG7786”, upload ENCFF346CDA.bigWig, and click “Go”. You will see 3 average profile distributions in each plot and a heatmap with 3 partitions (one for gene trajectory each cluster). NOTE each color of the distribution matches the corresponding gene trajectory cluster.

 

 

  1. Proceed to the last tab to view the temporal relations between transcription factors. On the first row you are reminded of the observed and predicted transcription factors to bind each gene cluster.

     

     

  2. Specifically, zooming into the second row to the left you will see “Transcription Factor Network” of both obiserved and predicted transcription factors. On the right (”Temporal Relations Between Observed and Predicted Transcription Factors") you will see a table highlighting the temporal relations between transcription factors. TIMEOR identified the transcription factor (TF) gene regulatory network (GRN). These temporal relationships are identified and represented in the legend (far-right). Blue arrow/highlight: predicted TF to observed TF, experimentally determined interaction. Pink arrow/highlight: observed TF to observed TF, experimentally determined interaction. Yellow arrow/highlight: observed TF to observed TF, predicted interaction. Green arrow/highlight: predicted TF to observed TF, predicted interaction. Network displayed in table format in app to enhance flexibility of GRN visualization.

 

 

  1. On the third row (“Network Customization: move and add desired genes to describe temporal relation”) the user can use this information to create a customized network to temporally relate all transcription factors and other genes. Do so by clicking “Search” and then “Multiple proteins”.

 

 

  1. Your results folder AND a log file can both be downloaded on the far-left side-bar under “Download Results Folder” and “Download Log File”. As TIMEOR integrates ~21 methods, the log enables users to see runtime output and messages from TIMEOR and other methods. NOTE The original simulated data and results can be downloaded directly here.

 

 

Local Installation

To run TIMEOR outside of website (recommended for preprocessing from raw .fastq files), users may use Docker and Docker Hub. First, the TIMEOR repository must be cloned (https://github.com/ashleymaeconard/TIMEOR.git). To use Docker, it must be installed (version 20.10.0 recommended).

Docker Hub and Docker:

  1. Download contents of organism genome folder (/genomes_info/) into desired location (e.g. /Users/USERNAME/Desktop/test_folder/genomes_info/) to mount later.
  2. Make sure contents of /genome_info/ are readable.
    • For example if using Drosophila melanogaster, in a console type chmod -R 777 /Users/USERNAME/Desktop/test_folder/genomes_info/dme/.
  3. Run TIMEOR via Docker
    • On command line type
      • $ docker pull ashleymaeconard/timeor:latest
      • $ docker images
      • $ docker run -v /Users/USERNAME/Desktop/test_folder/:/srv/ -p 3838:3838 <IMAGE_ID>
  4. Open TIMEOR Application is available by typing:
    • Shiny server will be running on port 3838. Thus, in a browser visit localhost:3838.

Or, build Docker image

NOTE: This could take a while. Please follow these commands:

  1. $ cd /PATH/TO/TIMEOR/
  2. Build Docker image in TIMEOR directory:
    • $ docker build -t timeor_env .
  3. Follow instructions 3 and 4 above.
  4. In another command line window
    • $ docker container ls
    • $ docker exec -it <CONTAINER_NAME> /bin/bash/
  5. Now you have a console within Docker to run commands.

Details

Real Data Subset in Tutorial

The original temporal RNA-seq data analyzed in our paper comes from Zirin et al., 2019). In this tutorial SRR8843750 and SRR8843738 are analyzed to demonstrate the “Process Raw Data” tab in which raw RNA-seq data are retrieved, quality checked, aligned (with HISAT2 and Bowtie2), and converted to a read count matrix. The real data subset folder (which TIMEOR automatically generates) can be downloaded here.

Simulated Data in Tutorial

The original simulated data folder can be downloaded here.

Secondary Analysis: Factor Binding

To get the top 4 TFs a 25% concensus threshold was used, with a normalized enrichement score threshold of 3.

Command used: Rscript get_top_tfs.r /PATH/TO/simulated_results/ dme 3 4 25 /PATH/TO/TIMEOR/

The following bigWig files were collected:

  • ENCFF467OWR (read-depth normalized signal between both replicates) within dataset ENCSR240ADR for Stat92E

  • ENCFF609FCZ (read-depth normalized signal between both replicates) within dataset ENCSR681YMA for pho

  • ENCFF346CDA (read-depth normalized signal between all three replicates) within dataset ENCSR776AVR for CG7786

Real Data in Publication

The results presented in TIMEOR’s publication can be downloaded in TIMEOR’s automatically generated folders here.