Contents

1 Introduction

Welcome to the introduction of data management with ORFik experiment. This vignette will walk you through how to work with large amounts of sequencing data effectively in ORFik. ORFik is an R package containing various functions for analysis of RiboSeq, RNASeq, RCP-seq, TCP-seq, Chip-seq and Cage data, we advice you to read ORFikOverview vignette, before starting this one.

1.1 Motivation

NGS libraries are becoming more and more numerous. As a bioinformatician / biologist you often work on multi-library experiments, like 6 libraries of RNA-seq and 6 Ribo-seq libraries, split on 3 conditions with 2 replicates each. Then make some plots or statistics. A lot of things can go wrong when you scale up from just 1 library to many, or even to multiple experiments.

Another problem is also that annotations like gff and fasta files combined with the NGS data, must be separately loaded. Making it possible to use wrong annotation for the NGS data.

So to summarize, the ORFik experiment API abstracts what could be done with 1 NGS library and a corresponding organism annotation to the level of multiple libraries and the comparison between them, standardizing ploting, comparisons, loading libraries and many much more.

1.2 What is an ORFik experiment?

It is an object that simplify and error correct your NGS workflow, creating a single R object that stores and controls all results relevant to a specific experiment. It contains following important parts:

  • filepaths and info for each library in the experiment (for multiple files formats: bam, bed, wig, ofst, ..)
  • genome annotation files of the experiment (fasta genome, index, gtf, txdb)
  • organism name (for automatic GO, sequence analysis..)
  • description and author information (list.experiments(), show all experiments you have made with ORFik, easy to find and load them later)
  • API: ORFik supports a rich API for using the experiment,like outputLibs(experiment, type = “wig”) will load all libraries converted to wig format into R, loadTxdb(experiment) will load the txdb (gtf) of experiment, transcriptWindow() will automatically plot metacoverage of all libraries in the experiment, countTable(experiment) will load count tables, etc..)
  • Safety: It is also a safety in that it verifies your experiments contain no duplicate, empty or non-accessible files. Making it almost impossible to load the wrong data. In addition it has other safety checks; comparing chromosome naming of libraries and annotation, making sure there is no mixing of chr1 vs 1 as name for chromosome 1 etc.

2 creating an ORFik experiment

Let’s say we have a human experiment, containing annotation files (.gtf and .fasta genome) + Next generation sequencing libraries (NGS-data); RNA-seq, ribo-seq and CAGE.

An example of how to make the experiment will now be shown:

First load ORFik

library(ORFik)

In a normal experiment, you would usually have only bam files from alignment of your experiment to start with (and split this into 3 experiments, 1 for RNA-seq, 1 for Ribo-seq and 1 for CAGE), but to simplify this for you to replicate we use the ORFik example data.

2.1 A minimal experiment

The minimal amount of information you need to make an ORFik experiment is:

    1. A folder with NGS data
    1. A path to transcriptome annotation (gtf->slow or txdb->faster)
    1. A path to genome (fasta)
    1. A name for the experiment
# 1. Pick directory (normally a folder with your aligned bam files)
NGS.dir <- system.file("extdata/Homo_sapiens_sample", "", package = "ORFik")
# 2. .gff/.gtf location
txdb <- system.file("extdata/Homo_sapiens_sample", "Homo_sapiens_dummy.gtf.db", package = "ORFik")
# 3. fasta genome location
fasta <- system.file("extdata/Homo_sapiens_sample", "Homo_sapiens_dummy.fasta", package = "ORFik")
# 4. Pick an experiment name
exper.name <- "ORFik_example_human"


list.files(NGS.dir)
##  [1] "CAGE_Mutant_rep1.ofst"        "CAGE_Mutant_rep2.ofst"       
##  [3] "CAGE_WT_rep1.ofst"            "CAGE_WT_rep2.ofst"           
##  [5] "Homo_sapiens_dummy.fasta"     "Homo_sapiens_dummy.fasta.fai"
##  [7] "Homo_sapiens_dummy.gtf"       "Homo_sapiens_dummy.gtf.db"   
##  [9] "PAS_Mutant_rep1.ofst"         "PAS_Mutant_rep2.ofst"        
## [11] "PAS_WT_rep1.ofst"             "PAS_WT_rep2.ofst"            
## [13] "QC_STATS"                     "RFP_Mutant_rep1.ofst"        
## [15] "RFP_Mutant_rep2.ofst"         "RFP_WT_rep1.ofst"            
## [17] "RFP_WT_rep2.ofst"             "RNA_Mutant_rep1.ofst"        
## [19] "RNA_Mutant_rep2.ofst"         "RNA_WT_rep1.ofst"            
## [21] "RNA_WT_rep2.ofst"

Experiments are created by all accepted files from a folder (file extension given by type argument, default: bam, bed, wig, ofst), so remember to keep your experiment folder clean of other NGS libraries of these types not related to the experiment.

# This experiment is intentionally malformed, so we first make only a template:
template <- create.experiment(dir = NGS.dir,  # directory of the NGS files for the experiment
                              exper.name,     # Experiment name
                              txdb = txdb,    # gtf / gff / gff.db annotation
                              fa = fasta,     # Fasta genome
                              organism = "Homo sapiens", # Scientific naming
                              saveDir = NULL, # Create template instead of ready experiment
                              )
# The experiment contains 3 main parts:
# 1. Annotation, organism, general info:
data.frame(template)[1:3, ]
##      X1
## 1  name
## 2   gff
## 3 fasta
##                                                                                                X2
## 1                                                                             ORFik_example_human
## 2 /tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/Homo_sapiens_dummy.gtf.db
## 3  /tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/Homo_sapiens_dummy.fasta
##   X3 X4       X5           X6
## 1                            
## 2       organism Homo sapiens
## 3
# 2. NGS data set-up info:
data.frame(template)[4:8, 1:5]
##        X1    X2  X3        X4       X5
## 4 libtype stage rep condition fraction
## 5    CAGE         1    Mutant         
## 6    CAGE         2    Mutant         
## 7    CAGE         1        WT         
## 8    CAGE         2        WT
# 3. NGS File paths:
data.frame(template)[4:8, 6]
## [1] "filepath"                                                                                   
## [2] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/CAGE_Mutant_rep1.ofst"
## [3] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/CAGE_Mutant_rep2.ofst"
## [4] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/CAGE_WT_rep1.ofst"    
## [5] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/CAGE_WT_rep2.ofst"

You see from the template, it excludes files with .bai or .fai, .rdata etc, and only using data of NGS libraries, defined by argument (type).

You can also see it tries to guess library types, stages, replicates, condition etc. It will also try to auto-detect paired end bam files. ## Fixing or updating an experiment Since every NGS file in a experiment must be a unique set of information columns ( there can not be 2 RNA-seq libraries from wildtype that are replicate1 etc), the create.experiment function will intentionally abort if it can not distinguish all the libraries in some way. (Example: It might find 2 files that are categorized as RNA-seq replicate 1, but the condtion: Wild type vs crispr mutant was not auto-detected), so the files would be non-unique.

To fix the things it did not find (a condition not specified, etc), there are 3 ways:

  • Specify the values manually in create.experiment. Example: condition = rep(c(“WT”, “Mutant”), each = 3)
  • Open the file in Excel / Libre office and edit
  • Edit the template (only applies if object was not directly saved, like in this vignette)

Let’s update the template to have correct tissue-fraction in one of the samples.

template$X5[5:6] <- "heart_valve" # <- fix non unique row (tissue fraction is heart valve)

df <- read.experiment(template)# read experiment from template

Normally you read experiments saved to disc, if you made only a template, save it by doing:

save.experiment(df, file = "path/to/save/experiment.csv")

You can then load the experiment whenever you need it.

2.2 ORFik example experiment

ORFik comes with a example experiment, you can load with:

ORFik.template.experiment()

3 The experiment object

To see the object, just show it like this:

df
## experiment: ORFik_example_human with 4 library types and 16 runs 
##     libtype rep condition    fraction
##  1:    CAGE   1    Mutant heart_valve
##  2:    CAGE   2    Mutant heart_valve
##  3:    CAGE   1        WT            
##  4:    CAGE   2        WT            
##  5:     PAS   1    Mutant            
##  6:     PAS   2    Mutant            
##  7:     PAS   1        WT            
##  8:     PAS   2        WT            
##  9:     RFP   1    Mutant            
## 10:     RFP   2    Mutant            
## 11:     RFP   1        WT            
## 12:     RFP   2        WT            
## 13:     RNA   1    Mutant            
## 14:     RNA   2    Mutant            
## 15:     RNA   1        WT            
## 16:     RNA   2        WT

You see here that file paths are hidden, you can access them like this:

filepath(df, type = "default")
##  [1] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/CAGE_Mutant_rep1.ofst"
##  [2] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/CAGE_Mutant_rep2.ofst"
##  [3] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/CAGE_WT_rep1.ofst"    
##  [4] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/CAGE_WT_rep2.ofst"    
##  [5] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/PAS_Mutant_rep1.ofst" 
##  [6] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/PAS_Mutant_rep2.ofst" 
##  [7] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/PAS_WT_rep1.ofst"     
##  [8] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/PAS_WT_rep2.ofst"     
##  [9] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/RFP_Mutant_rep1.ofst" 
## [10] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/RFP_Mutant_rep2.ofst" 
## [11] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/RFP_WT_rep1.ofst"     
## [12] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/RFP_WT_rep2.ofst"     
## [13] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/RNA_Mutant_rep1.ofst" 
## [14] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/RNA_Mutant_rep2.ofst" 
## [15] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/RNA_WT_rep1.ofst"     
## [16] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/RNA_WT_rep2.ofst"

If you have varying version of libraries, like p-shifted, bam, simplified wig files, you can get file paths to different version with this function, like this:

filepath(df[df$libtype == "RFP", ], type = "pshifted")[2] # RFP = Ribo-seq, Default location for pshifted reads
## [1] "/tmp/RtmptY8XD7/Rinst1eaee02284122e/ORFik/extdata/Homo_sapiens_sample/RFP_Mutant_rep2.ofst"

3.1 Loading data from experiment

3.1.1 Loading NGS data to environment

There are 3 ways to load NGS data, the first one is to load data into an environment. By default all libraries are loaded into .GlobalEnv (global environment), you can check what environment it is output to, by running:

envExp(df) #This will be the environment
## <environment: R_GlobalEnv>

The library names are decided by the columns in experiment, to see what the names will be, do:

bamVarName(df) #This will be the names:
##  [1] "CAGE_Mutant_fheart_valve_r1" "CAGE_Mutant_fheart_valve_r2"
##  [3] "CAGE_WT_r1"                  "CAGE_WT_r2"                 
##  [5] "PAS_Mutant_r1"               "PAS_Mutant_r2"              
##  [7] "PAS_WT_r1"                   "PAS_WT_r2"                  
##  [9] "RFP_Mutant_r1"               "RFP_Mutant_r2"              
## [11] "RFP_WT_r1"                   "RFP_WT_r2"                  
## [13] "RNA_Mutant_r1"               "RNA_Mutant_r2"              
## [15] "RNA_WT_r1"                   "RNA_WT_r2"

Now let’s auto-load the libraries to the global environment

outputLibs(df) # With default output.mode = "envir".
## Outputting libraries from: ORFik_example_human

To remove the outputted libraries:

# remove.experiments(df)

3.1.2 Loading NGS data as list

The second way gives you a list, where the elements are the NGS libraries. There are also two ways of loading the list:

outputLibs(df, output.mode = "envirlist")[1:2] # Save NGS to envir, then return as list
## $CAGE_Mutant_fheart_valve_r1
## GRanges object with 102 ranges and 2 metadata columns:
##         seqnames    ranges strand |     score      size
##            <Rle> <IRanges>  <Rle> | <numeric> <numeric>
##     [1]     chr1       306      + |        25         1
##     [2]     chr1       308      + |         3         1
##     [3]     chr1       309      + |         8         1
##     [4]     chr1       310      + |         2         1
##     [5]     chr1       313      + |         1         1
##     ...      ...       ...    ... .       ...       ...
##    [98]     chr6       333      + |         1         1
##    [99]     chr6       339      + |         1         1
##   [100]     chr6       340      + |         1         1
##   [101]     chr6       355      + |         1         1
##   [102]     chr6       363      + |         1         1
##   -------
##   seqinfo: 6 sequences from an unspecified genome; no seqlengths
## 
## $CAGE_Mutant_fheart_valve_r2
## GRanges object with 106 ranges and 2 metadata columns:
##         seqnames    ranges strand |     score      size
##            <Rle> <IRanges>  <Rle> | <numeric> <numeric>
##     [1]     chr1       306      + |        44         1
##     [2]     chr1       307      + |        11         1
##     [3]     chr1       309      + |         1         1
##     [4]     chr1       310      + |         2         1
##     [5]     chr1       312      + |         3         1
##     ...      ...       ...    ... .       ...       ...
##   [102]     chr6       326      + |         1         1
##   [103]     chr6       327      + |         1         1
##   [104]     chr6       329      + |         2         1
##   [105]     chr6       334      + |         2         1
##   [106]     chr6       336      + |         1         1
##   -------
##   seqinfo: 6 sequences from an unspecified genome; no seqlengths
# Check envir, if it exist, list them and return, if not, only return list
outputLibs(df, output.mode = "list")[1:2]
## $CAGE_Mutant_fheart_valve_r1
## GRanges object with 102 ranges and 2 metadata columns:
##         seqnames    ranges strand |     score      size
##            <Rle> <IRanges>  <Rle> | <numeric> <numeric>
##     [1]     chr1       306      + |        25         1
##     [2]     chr1       308      + |         3         1
##     [3]     chr1       309      + |         8         1
##     [4]     chr1       310      + |         2         1
##     [5]     chr1       313      + |         1         1
##     ...      ...       ...    ... .       ...       ...
##    [98]     chr6       333      + |         1         1
##    [99]     chr6       339      + |         1         1
##   [100]     chr6       340      + |         1         1
##   [101]     chr6       355      + |         1         1
##   [102]     chr6       363      + |         1         1
##   -------
##   seqinfo: 6 sequences from an unspecified genome; no seqlengths
## 
## $CAGE_Mutant_fheart_valve_r2
## GRanges object with 106 ranges and 2 metadata columns:
##         seqnames    ranges strand |     score      size
##            <Rle> <IRanges>  <Rle> | <numeric> <numeric>
##     [1]     chr1       306      + |        44         1
##     [2]     chr1       307      + |        11         1
##     [3]     chr1       309      + |         1         1
##     [4]     chr1       310      + |         2         1
##     [5]     chr1       312      + |         3         1
##     ...      ...       ...    ... .       ...       ...
##   [102]     chr6       326      + |         1         1
##   [103]     chr6       327      + |         1         1
##   [104]     chr6       329      + |         2         1
##   [105]     chr6       334      + |         2         1
##   [106]     chr6       336      + |         1         1
##   -------
##   seqinfo: 6 sequences from an unspecified genome; no seqlengths

3.1.3 Loading NGS data by fimport

The third way is to load manually, more secure, but also more cumbersome.

files <- filepath(df, type = "default")
CAGE_loaded_manually <- fimport(files[1])

If you use the auto-loading to environment and you have multiple experiments, it might be a chance of non-unique naming, 2 experiments might have a library called cage. To be sure names are unique, we add the experiment name in the variable name:

df@expInVarName <- TRUE
bamVarName(df) #This will be the names:
##  [1] "ORFik_example_human_CAGE_Mutant_fheart_valve_r1"
##  [2] "ORFik_example_human_CAGE_Mutant_fheart_valve_r2"
##  [3] "ORFik_example_human_CAGE_WT_r1"                 
##  [4] "ORFik_example_human_CAGE_WT_r2"                 
##  [5] "ORFik_example_human_PAS_Mutant_r1"              
##  [6] "ORFik_example_human_PAS_Mutant_r2"              
##  [7] "ORFik_example_human_PAS_WT_r1"                  
##  [8] "ORFik_example_human_PAS_WT_r2"                  
##  [9] "ORFik_example_human_RFP_Mutant_r1"              
## [10] "ORFik_example_human_RFP_Mutant_r2"              
## [11] "ORFik_example_human_RFP_WT_r1"                  
## [12] "ORFik_example_human_RFP_WT_r2"                  
## [13] "ORFik_example_human_RNA_Mutant_r1"              
## [14] "ORFik_example_human_RNA_Mutant_r2"              
## [15] "ORFik_example_human_RNA_WT_r1"                  
## [16] "ORFik_example_human_RNA_WT_r2"

You see here that the experiment name, “ORFik” is in the variable name If you are only working on one experiment, you do not need to include the name, since there is no possibility of duplicate naming (the experiment class validates all names are unique).

Since we want NGS data names without “ORFik”, let’s remove the loaded libraries and load them again.

df@expInVarName <- FALSE
remove.experiments(df)
## Removed loaded libraries from experiment:ORFik_example_human
outputLibs(df) 
## Outputting libraries from: ORFik_example_human

3.1.4 Loading Annotation and specific regions

There is also many function to load specific parts of the annotation:

txdb <- loadTxdb(df) # transcript annotation
## Loading required package: GenomicFeatures
## Loading required package: AnnotationDbi

Let’s say we want to load all leaders, cds and 3’ UTRs that are longer than 30. With ORFik experiment this is easy:

txNames <- filterTranscripts(txdb, minFiveUTR = 30, minCDS = 30, minThreeUTR = 30)
loadRegions(txdb, parts = c("leaders", "cds", "trailers"), names.keep = txNames)

The regions are now loaded into .GlobalEnv, only keeping transcripts from txNames.

3.2 Plotting with ORFik experiments

ORFik supports a myriad of plots for experiments. Lets make a plot with coverage over mrna, seperated by 5’ UTR, CDS and 3’ UTR in one of the ribo-seq libraries from the experiment

transcriptWindow(leaders, cds, trailers, df[9,])
## Outputting libraries from: ORFik_example_human
## [[1]]

## 
## [[2]]

4 P-site shifting experiment

If your experiment consists of Ribo-seq, you want to do p-site shifting.

shiftFootprintsByExperiment(df[df$libtype == "RFP",])

P-shifted ribo-seq will automaticly be stored as .ofst (ORFik serialized for R) and .wig (track files for IGV/UCSC) files in a ./pshifted folder, relative to original libraries.

To validate p-shifting, use shiftPlots. Here is an example from Bazzini et al. 2014 I made.

df.baz <- read.experiment("zf_bazzini14_RFP") # <- this exp. does not exist for you
shiftPlots(df.baz, title = "Ribo-seq, zebrafish, Bazzini et al. 2014", type = "heatmap")