CufflinksOptions

Option set for cufflinks

Description

A CufflinksOptions object contains options for the cufflinks function, which assembles a transcriptome from aligned reads [1].

Creation

Description

example

cufflinksOpt = CufflinksOptions creates a CufflinksOptions object with the default property values.

CufflinksOptions requires the Cufflinks Support Package for Bioinformatics Toolbox™. If the support package is not installed, then the function provides a download link.

Note

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

cufflinksOpt = CufflinksOptions(Name,Value) sets the object properties using one or more name-value pair arguments. Enclose each property name in quotes. For example, cufflinksOpt = CufflinksOptions('TrimCoverageThreshold',5) specifies the minimum average coverage for 3' end trimming.

cufflinksOpt = CufflinksOptions(S) specifies optional parameters using a string or character vector S.

Input Arguments

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Cufflinks options, specified as a character vector or string. S must be in the Cufflinks option syntax (prefixed by one or two dashes).

Example: '--trim-3-avgcov-thresh 5'

Properties

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Flag to normalize fragment counts to fragments per kilobase per million mapped reads (FPKM), specified as true or false.

Example: false

Data Types: logical

Additional commands, specified as a string or character vector. The commands must be in the original syntax (prefixed by one or two dashes). Use this option to apply undocumented flags and flags without corresponding MATLAB properties. When the function converts the original flags to MATLAB properties, it stores any unrecognized flags in this option.

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

Data Types: char | string

Flag to include reference transcripts in the assembled output as faux-reads during RABT (advanced reference annotation based transcript) assembly, specified as true or false.

Note

The function only performs the RABT assembly if you specify GTFGuide. Otherwise, FauxReadTiling, regardless of being true or false, has no effect on the assembled transcript.

Example: false

Data Types: logical

Name of the FASTA file with reference transcripts to detect bias in fragment counts, specified as a string or character vector. Library preparation can introduce sequence-specific bias into RNA-Seq experiments. Providing reference transcripts improves the accuracy of the transcript abundance estimates.

Example: "bias.fasta"

Data Types: char | string

Expected mean fragment length, specified as a positive integer. The default value is 200 base pairs. The function can learn the fragment length mean for each SAM file. Using this option is not recommended for paired-end reads.

Example: 100

Data Types: double

Expected standard deviation for the fragment length distribution, specified as a positive scalar. The default value is 80 base pairs. The function can learn the fragment length standard deviation for each SAM file. Using this option is not recommended for paired-end reads.

Example: 70

Data Types: double

Name of a GTF file to guide the RABT assembly, specified as a string or character vector.

Example: 'tr.gtf'

Data Types: char | string

Flag to include all the object properties with the corresponding default values when converting to the original options syntax, specified as true or false. You can convert the properties to the original syntax prefixed by one or two dashes (such as '-d 100 -e 80') by using getCommand. The default value false means that when you call getCommand(optionsObject), it converts only the specified properties. If the value is true, getCommand converts all available properties, with default values for unspecified properties, to the original syntax.

Example: true

Data Types: logical

Alpha value in the binomial test to filter false-positive alignments, specified as a scalar between 0 and 1.

Example: 0.005

Data Types: double

Flag to correct by the transcript length, specified as true or false. Set this value to false only when the fragment count is independent of the feature size, such as for small RNA libraries with no fragmentation and for 3' end sequencing, where all fragments have the same length.

Example: false

Data Types: logical

Name of the GTF or GFF file containing transcripts to ignore during analysis, specified as a string or character vector. Some examples of transcripts to ignore include annotated rRNA transcripts, mitochondrial transcripts, and other abundant transcripts. Ignoring these transcripts improves the robustness of the abundance estimates.

Example: 'excludes.gtf'

Data Types: char | string

Maximum number of fragments to include for each locus before skipping new fragments, specified as a positive integer. Skipped fragments are marked with the status HIDATA in the file skipped.gtf.

Example: 400000

Data Types: double

Maximum genomic length in base pairs for a bundle, specified as a positive integer.

Example: 3400000

Data Types: double

Maximum number of aligned reads to include for each fragment before skipping new reads, specified as a positive integer. Inf, the default value, sets no limit on the maximum number of aligned reads.

Example: 1000

Data Types: double

Maximum number of bases in an intron to report, specified as a positive integer. cufflinks also ignores SAM alignments with REF_SKIP CIGAR operations longer than this property value.

Example: 350000

Data Types: double

Maximum number of iterations for the maximum likelihood estimation of abundances, specified as a positive integer.

Example: 4000

Data Types: double

Minimum number of aligned RNA-Seq fragments to report on an assembled transfrag, specified as a positive integer.

Example: 15

Data Types: double

Minimum number of base pairs for an intron in the genome, specified as a positive integer.

Example: 50

Data Types: double

Cuffoff value to report the abundance of a particular isoform as a fraction of the most abundant isoform (major isoform), specified as a scalar between 0 and 1. The function filters out transcripts with abundances below the specified value because isoforms expressed at low levels often cannot be assembled reliably. The default value is 0.1, or 10%, of the major isoform of the gene.

Example: 0.20

Data Types: double

Flag to improve abundance estimation for reads mapped to multiple genomic positions using the rescue method, specified as true or false. If the value is false, the function divides multimapped reads uniformly to all mapped positions. If the value is true, the function uses additional information, including gene abundance estimation, inferred fragment length, and fragment bias, to improve transcript abundance estimation.

The rescue method is described in [2].

Example: true

Data Types: logical

Flag to use only fragments compatible with a reference transcript to calculate FPKM values, specified as true or false.

Example: true

Data Types: logical

Flag to include all fragments to calculate FPKM values, specified as true or false. If the value is true, the function includes all fragments, including fragments without a compatible reference.

Example: true

Data Types: logical

Number of fragment assignments to perform on each transcript, specified as a positive integer. For each fragment drawn from a transcript, the function performs the specified number of assignments probabilistically to determine the transcript assignment uncertainty and to estimate the variance-covariance matrix for the assigned fragment counts.

Example: 40

Data Types: double

Number of draws from the negative binomial random number generator for each transcript, specified as a positive integer. Each draw is a number of fragments that the function probabilistically assigns to transcripts in the transcriptome to determine the assignment uncertainty and to estimate the variance-covariance matrix for assigned fragment counts.

Example: 90

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: 4

Data Types: double

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

Example: "./AnalysisResults/"

Data Types: char | string

Number of base pairs of overlap with an intron that the function allows when determining if the read is compatible with another transcript, specified as a positive integer.

Example: 5

Data Types: double

Distance between transfrags, specified as a positive integer. If the distance is below the specified value, the function merges the transfrags. The default value is 50 base pairs.

Example: 40

Data Types: double

Threshold to include alignments in intronic intervals in the assembly, specified as a scalar between 0 and 1. The function ignores the intronic alignments if the minimum depth of coverage divided by the number of spliced reads is below the specified value. Use this property to filter reads originating from incompletely spliced transcripts.

Example: 0.10

Data Types: double

Number of base pairs from a read allowed to overlap with a transcript intron when determining if a read is mappable to another transcript during the RABT assembly, specified as a positive integer. The default value is 8.

Note

The function only performs the RABT assembly if you specify GTFGuide. Otherwise, RABTOverhangTolerance has no effect on the assembled transcript.

Example: 10

Data Types: double

Number of base pairs allowed to overhang the 3' end of each reference transcript during the RABT assembly, specified as a positive integer. The function uses this property when deciding if an assembled transcript is novel or should be merged with the reference.

Note

The function only performs the RABT assembly if you specify GTFGuide. Otherwise, RABTOverhangTolerance3 has no effect on the assembled transcript.

Example: 500

Data Types: double

Name of a GTF or GFF file containing reference annotation used to estimate isoform expression, specified as a string or character vector. If you provide a ReferenceGTF file, the function does not assemble any novel transcripts and ignores any alignments incompatible with the reference transcripts.

Example: 'isoest.gtf'

Data Types: char | string

Seed for the random number generator, specified as a nonnegative integer. Setting a seed value ensures the reproducibility of the analysis results.

Example: 10

Data Types: double

Minimum percentage of alignment on each side of a splice junction, specified as a scalar between 0 and 1. The function filters alignments with a percentage smaller than this property value prior to assembly.

Example: 0.1

Data Types: double

Prefix for the reported transfrags in the output GTF file, specified as a string or character vector. This option must be a string or character vector with a non-zero length.

Example: "tfrags"

Data Types: char | string

Minimum average coverage for 3' trimming, specified as a positive integer.

Example: 8

Data Types: double

Minimum percentage of average coverage for trimming the 3' end of assembled transcripts, specified as a scalar between 0 and 1.

Example: 0.15

Data Types: double

This property is read-only.

Supported version of the original cufflinks software, returned as a string.

Example: "2.2.1"

Data Types: string

Object Functions

getCommandTranslate object properties to original options syntax
getOptionsTableReturn table with all properties and equivalent options in original syntax

Examples

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Create a CufflinksOptions object with default values.

opt = CufflinksOptions;

Create an object using name-value pairs.

opt2 = CufflinksOptions('TranscriptPrefix',"MATLAB",'NumThreads',4)

Create an object by using the original cufflinks syntax.

opt3 = CufflinksOptions('--label MATLAB --num-threads 4')

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.

References

[1] Trapnell, C., B. Williams, G. Pertea, A. Mortazavi, G. Kwan, J. van Baren, S. Salzberg, B. Wold, and L. Pachter. 2010. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology. 28:511–515.

[2] Mortazavi, A., B. Williams, K. McCue, L. Schaeffer, and B. Wold. 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods. 5:621-628.

Introduced in R2019a