cuffmerge

Merge RNA-seq assemblies into a master transcriptome

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Syntax

mergedGTF = cuffmerge(gtfFiles)
mergedGTF = cuffmerge(gtfFiles,opt)
mergedGTF = cuffmerge(gtfFiles,Name,Value)

Description

example

mergedGTF = cuffmerge(gtfFiles) merges assembled transcriptome from two or more GTF files [1]. Merging GTF files is a required step to perform the downstream differential analysis with cuffdiff.

cuffmerge requires Python® 2 installed in your system.

cuffmerge requires the Cufflinks Support Package for the Bioinformatics Toolbox™. If the support package is not installed, then the function provides a download link. For details, see Bioinformatics Toolbox Software Support Packages.

Note

cuffmerge is supported on the Mac and UNIX® platforms only.

mergedGTF = cuffmerge(gtfFiles,opt) uses additional options specified by opt.

mergedGTF = cuffmerge(gtfFiles,Name,Value) uses additional options specified by one or more name-value pair arguments. For example, cuffmerge(["Myco_1_1.transcripts.gtf","Myco_1_2.transcripts.gtf"],'NumThreads',5) specifies to use five parallel threads.

Examples

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Create a CufflinksOptions object to define cufflinks options, such as the number of parallel threads and the output directory to store the results.

cflOpt = CufflinksOptions;
cflOpt.NumThreads = 8;
cflOpt.OutputDirectory = "./cufflinksOut";

The SAM files provided for this example contain aligned reads for Mycoplasma pneumoniae from two samples with three replicates each. The reads are simulated 100bp-reads for two genes (gyrA and gyrB) located next to each other on the genome. All the reads are sorted by reference position, as required by cufflinks.

sams = ["Myco_1_1.sam","Myco_1_2.sam","Myco_1_3.sam",...
        "Myco_2_1.sam", "Myco_2_2.sam", "Myco_2_3.sam"];

Assemble the transcriptome from the aligned reads.

[gtfs,isofpkm,genes,skipped] = cufflinks(sams,cflOpt);

gtfs is a list of GTF files that contain assembled isoforms.

Compare the assembled isoforms using cuffcompare.

stats = cuffcompare(gtfs);

Merge the assembled transcripts using cuffmerge.

mergedGTF = cuffmerge(gtfs,'OutputDirectory','./cuffMergeOutput');

mergedGTF reports only one transcript. This is because the two genes of interest are located next to each other, and cuffmerge cannot distinguish two distinct genes. To guide cuffmerge, use a reference GTF (gyrAB.gtf) containing information about these two genes. If the file is not located in the same directory that you run cuffmerge from, you must also specify the file path.

gyrAB = which('gyrAB.gtf');
mergedGTF2 = cuffmerge(gtfs,'OutputDirectory','./cuffMergeOutput2',...
			'ReferenceGTF',gyrAB);

Calculate abundances (expression levels) from aligned reads for each sample.

abundances1 = cuffquant(mergedGTF2,["Myco_1_1.sam","Myco_1_2.sam","Myco_1_3.sam"],...
                        'OutputDirectory','./cuffquantOutput1');
abundances2 = cuffquant(mergedGTF2,["Myco_2_1.sam", "Myco_2_2.sam", "Myco_2_3.sam"],...
                        'OutputDirectory','./cuffquantOutput2');

Assess the significance of changes in expression for genes and transcripts between conditions by performing the differential testing using cuffdiff. The cuffdiff function operates in two distinct steps: the function first estimates abundances from aligned reads, and then performs the statistical analysis. In some cases (for example, distributing computing load across multiple workers), performing the two steps separately is desirable. After performing the first step with cuffquant, you can then use the binary CXB output file as an input to cuffdiff to perform statistical analysis. Because cuffdiff returns several files, specify the output directory is recommended.

isoformDiff = cuffdiff(mergedGTF2,[abundances1,abundances2],...
                      'OutputDirectory','./cuffdiffOutput');

Display a table containing the differential expression test results for the two genes gyrB and gyrA.

readtable(isoformDiff,'FileType','text')
ans =

  2×14 table

        test_id            gene_id        gene              locus             sample_1    sample_2    status     value_1       value_2      log2_fold_change_    test_stat    p_value    q_value    significant
    ________________    _____________    ______    _______________________    ________    ________    ______    __________    __________    _________________    _________    _______    _______    ___________

    'TCONS_00000001'    'XLOC_000001'    'gyrB'    'NC_000912.1:2868-7340'      'q1'        'q2'       'OK'     1.0913e+05    4.2228e+05          1.9522           7.8886      5e-05      5e-05        'yes'   
    'TCONS_00000002'    'XLOC_000001'    'gyrA'    'NC_000912.1:2868-7340'      'q1'        'q2'       'OK'     3.5158e+05    1.1546e+05         -1.6064          -7.3811      5e-05      5e-05        'yes'

You can use cuffnorm to generate normalized expression tables for further analyses. cuffnorm results are useful when you have many samples and you want to cluster them or plot expression levels for genes that are important in your study. Note that you cannot perform differential expression analysis using cuffnorm.

Specify a cell array, where each element is a string vector containing file names for a single sample with replicates.

alignmentFiles = {["Myco_1_1.sam","Myco_1_2.sam","Myco_1_3.sam"],...
                  ["Myco_2_1.sam", "Myco_2_2.sam", "Myco_2_3.sam"]}
isoformNorm = cuffnorm(mergedGTF2, alignmentFiles,...
                      'OutputDirectory', './cuffnormOutput');

Display a table containing the normalized expression levels for each transcript.

readtable(isoformNorm,'FileType','text')
ans =

  2×7 table

      tracking_id          q1_0          q1_2          q1_1          q2_1          q2_0          q2_2   
    ________________    __________    __________    __________    __________    __________    __________

    'TCONS_00000001'    1.0913e+05         78628    1.2132e+05    4.3639e+05    4.2228e+05    4.2814e+05
    'TCONS_00000002'    3.5158e+05    3.7458e+05    3.4238e+05    1.0483e+05    1.1546e+05    1.1105e+05

Column names starting with q have the format: conditionX_N, indicating that the column contains values for replicate N of conditionX.

Input Arguments

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Names of GTF files, specified as a string vector or cell array of character vectors.

Example: ["Myco_1_1.transcripts.gtf", "Myco_1_2.transcripts.gtf"]

Data Types: string | cell

cuffgffread options, specified as a CuffMergeOptions object, string, or character vector. The string or character vector must be in the original cuffmerge option syntax (prefixed by one or two dashes) [1].

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: cuffmerge(["Myco_1_1.transcripts.gtf","Myco_1_2.transcripts.gtf"],'NumThreads',5)

The commands must be in the native syntax (prefixed by one or two dashes). Use this option to apply undocumented flags and flags without corresponding MATLAB properties.

Example: 'ExtraCommand','--library-type fr-secondstrand'

Data Types: char | string

The original (native) syntax is prefixed by one or two dashes. By default, the function converts only the specified options. If the value is true, the software converts all available options, with default values for unspecified options, to the original syntax.

Note

If you set IncludeAll to true, the software translates all available properties, with default values for unspecified properties. The only exception is that when the default value of a property is NaN, Inf, [], '', or "", then the software does not translate the corresponding property.

Example: 'IncludeAll',true

Data Types: logical

Minimum abundance of an isoform to be included in the merged assembly, specified as a scalar between 0 and 1. This value is expressed as a percentage of the most abundant (major) isoform.

Example: 'MinIsoformFraction',0.4

Data Types: double

Number of parallel threads to use, specified as a positive integer. Threads are run on separate processors or cores. Increasing the number of threads generally improves the runtime significantly, but increases the memory footprint.

Example: 'NumThreads',4

Data Types: double

Directory to store analysis results, specified as a string or character vector.

Example: 'OutputDirectory',"./AnalysisResults/"

Data Types: char | string

Name of an optional reference annotation GTF file to be included in the combined assembly, specified as a string or character vector.

Example: 'ReferenceGTF',"ref.gtf"

Data Types: char | string

Name of a directory or FASTA file containing genomic DNA sequences for the reference, specified as a string or character vector.

  • If you specify a directory, it must contain one FASTA file per contig. In other words, the directory must contain one FASTA file per reference chromosome, and each file must be named after the chromosome and have a .fa or .fasta extension.

  • If you specify a FASTA file, it must contain all the reference sequences.

The function uses the provided sequences to improve transfrag classification and exclude artifacts.

Example: 'ReferenceSequence',"allrefs.fasta"

Data Types: char | string

Output Arguments

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Name of the output GTF file containing the merged transcriptome, returned as a string.

The output string also includes the directory information defined by OutputDirectory. By default, the function

  • Creates the merged_asm subfolder in the current directory and saves the output file (merged.gtf) in that folder.

  • Creates a subfolder named logs inside merged_asm folder and saves a log file.

If you set OutputDirectory to "/local/tmp/", mergedGTF becomes "/local/tmp/merged.gtf". The function also creates the logs folder inside the specified output directory.

References

[1] Trapnell, Cole, Brian A Williams, Geo Pertea, Ali Mortazavi, Gordon Kwan, Marijke J van Baren, Steven L Salzberg, Barbara J Wold, and Lior Pachter. “Transcript Assembly and Quantification by RNA-Seq Reveals Unannotated Transcripts and Isoform Switching during Cell Differentiation.” Nature Biotechnology 28, no. 5 (May 2010): 511–15.

See Also

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Introduced in R2019a

Bioinformatics Toolbox Documentation

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