Cufflinks

Transcript assembly, differential expression, and differential regulation for RNA-Seq

      
Please Note If you have questions about how to use Cufflinks or would like more information about the software, please email tophat.cufflinks@gmail.com, though we ask you to have a look at the paper and the supplemental methods first, as your question be answered there.

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    New releases and related tools will be announced through the mailing list

Getting Help

    Questions about Cufflinks and Cuffdiff should be posted on our Google Group. Please use tophat.cufflinks@gmail.com for private communications only. Please do not email technical questions to Cufflinks contributors directly.

Releases

Related Tools

  • Monocle: Single-cell RNA-Seq analysis
  • CummeRbund: Visualization of RNA-Seq differential analysis
  • TopHat: Alignment of short RNA-Seq reads
  • Bowtie: Ultrafast short read alignment

Publications

Contributors

Links

Manual


Prerequisites


Cufflinks runs on intel-based computers running Linux or Mac OS X and that have GCC 4.0 or greater installed. You can install pre-compiled binaries or build Cufflinks from the source code. If you wish to build Cufflinks yourself, you will need to install the Boost C++ libraries. See Installing Boost, on the getting started page. You will also need to build and install the SAM tools, but you should take a look at the getting started page for detailed instructions, because the headers and libbam must be accessible to the Cufflinks build scripts.



Run cufflinks from the command line as follows:
Usage: cufflinks [options]* <aligned_reads.(sam/bam)>

The following is a detailed description of the options used to control Cufflinks:


Arguments:
<aligned_reads.(sam/bam)> A file of RNA-Seq read alignments in the SAM format. SAM is a standard short read alignment, that allows aligners to attach custom tags to individual alignments, and Cufflinks requires that the alignments you supply have some of these tags. Please see Input formats for more details.
General Options:
-h/--help Prints the help message and exits
-o/--output-dir <string> Sets the name of the directory in which Cufflinks will write all of its output. The default is "./".
-p/--num-threads <int> Use this many threads to align reads. The default is 1.
-G/--GTF <reference_annotation.(gtf/gff)> Tells Cufflinks to use the supplied reference annotation (a GFF file) to estimate isoform expression. It will not assemble novel transcripts, and the program will ignore alignments not structurally compatible with any reference transcript.
-g/--GTF-guide <reference_annotation.(gtf/gff)> Tells Cufflinks to use the supplied reference annotation (GFF) to guide RABT assembly. Reference transcripts will be tiled with faux-reads to provide additional information in assembly. Output will include all reference transcripts as well as any novel genes and isoforms that are assembled.
-M/--mask-file <mask.(gtf/gff)> Tells Cufflinks to ignore all reads that could have come from transcripts in this GTF file. We recommend including any annotated rRNA, mitochondrial transcripts other abundant transcripts you wish to ignore in your analysis in this file. Due to variable efficiency of mRNA enrichment methods and rRNA depletion kits, masking these transcripts often improves the overall robustness of transcript abundance estimates.
-b/--frag-bias-correct <genome.fa> Providing Cufflinks with a multifasta file via this option instructs it to run our new bias detection and correction algorithm which can significantly improve accuracy of transcript abundance estimates. See How Cufflinks Works for more details.
-u/--multi-read-correct Tells Cufflinks to do an initial estimation procedure to more accurately weight reads mapping to multiple locations in the genome. See How Cufflinks Works for more details.
--library-type See Library Types
--library-norm-method See Library Normalization Methods
Advanced Abundance Estimation Options:
-m/--frag-len-mean <int> This is the expected (mean) fragment length. The default is 200bp.
Note: Cufflinks now learns the fragment length mean for each SAM file, so using this option is no longer recommended with paired-end reads.
-s/--frag-len-std-dev <int> The standard deviation for the distribution on fragment lengths. The default is 80bp.
Note: Cufflinks now learns the fragment length standard deviation for each SAM file, so using this option is no longer recommended with paired-end reads.
-N/--upper-quartile-norm With this option, Cufflinks normalizes by the upper quartile of the number of fragments mapping to individual loci instead of the total number of sequenced fragments. This can improve robustness of differential expression calls for less abundant genes and transcripts.
--total-hits-norm With this option, Cufflinks counts all fragments, including those not compatible with any reference transcript, towards the number of mapped hits used in the FPKM denominator. This option can be combined with -N/--upper-quartile-norm. It is active by default.
--compatible-hits-norm With this option, Cufflinks counts only those fragments compatible with some reference transcript towards the number of mapped hits used in the FPKM denominator. This option can be combined with -N/--upper-quartile-norm. It is inactive by default, and can only be used in combination with --GTF. Use with either RABT or ab initio assembly is not supported
--max-mle-iterations <int> Sets the number of iterations allowed during maximum likelihood estimation of abundances. Default: 5000
--max-bundle-frags <int> Sets the maximum number of fragments a locus may have before being skipped. Skipped loci are listed in skipped.gtf. Default: 1000000
--no-effective-length-correction Cufflinks will not employ its "effective" length normalization to transcript FPKM.
--no-length-correction Cufflinks will not normalize fragment counts by transcript length at all. Use this option when fragment count is independent of the size of the features being quantified (e.g. for small RNA libraries, where no fragmentation takes place, or 3 prime end sequencing, where sampled RNA fragments are all essentially the same length). Experimental option, use with caution.
Advanced Assembly Options:
-L/--label Cufflinks will report transfrags in GTF format, with a prefix given by this option. The default prefix is "CUFF".
-F/--min-isoform-fraction <0.0-1.0> After calculating isoform abundance for a gene, Cufflinks filters out transcripts that it believes are very low abundance, because isoforms expressed at extremely low levels often cannot reliably be assembled, and may even be artifacts of incompletely spliced precursors of processed transcripts. This parameter is also used to filter out introns that have far fewer spliced alignments supporting them. The default is 0.1, or 10% of the most abundant isoform (the major isoform) of the gene.
-j/--pre-mrna-fraction <0.0-1.0> Some RNA-Seq protocols produce a significant amount of reads that originate from incompletely spliced transcripts, and these reads can confound the assembly of fully spliced mRNAs. Cufflinks uses this parameter to filter out alignments that lie within the intronic intervals implied by the spliced alignments. The minimum depth of coverage in the intronic region covered by the alignment is divided by the number of spliced reads, and if the result is lower than this parameter value, the intronic alignments are ignored. The default is 15%.
-I/--max-intron-length <int> The maximum intron length. Cufflinks will not report transcripts with introns longer than this, and will ignore SAM alignments with REF_SKIP CIGAR operations longer than this. The default is 300,000.
-a/--junc-alpha <0.0-1.0> The alpha value for the binomial test used during false positive spliced alignment filtration. Default: 0.001
-A/--small-anchor-fraction <0.0-1.0> Spliced reads with less than this percent of their length on each side of the junction are considered suspicious and are candidates for filtering prior to assembly. Default: 0.09.
--min-frags-per-transfrag <int> Assembled transfrags supported by fewer than this many aligned RNA-Seq fragments are not reported. Default: 10.
--overhang-tolerance <int> The number of bp allowed to enter the intron of a transcript when determining if a read or another transcript is mappable to/compatible with it. The default is 8 bp based on the default bowtie/TopHat parameters.
--max-bundle-length <int> Maximum genomic length allowed for a given bundle. The default is 3,500,000 bp.
--min-intron-length <int> Minimum intron size allowed in genome. The default is 50 bp.
--trim-3-avgcov-thresh <int> Minimum average coverage required to attempt 3' trimming. The default is 10.
--trim-3-dropoff-frac <int> The fraction of average coverage below which to trim the 3' end of an assembled transcript. The default is 0.1.
--max-multiread-fraction <0.0-1.0> The fraction a transfrag's supporting reads that may be multiply mapped to the genome. A transcript composed of more than this fraction will not be reported by the assembler. Default: 0.75 (75% multireads or more is suppressed).
--overlap-radius <int> Transfrags that are separated by less than this distance get merged together, and the gap is filled. Default: 50 (in bp).
Advanced Reference Annotation Based Transcript (RABT) Assembly Options:
These options have an affect only when used in conjuction with -g/--GTF-guide.
--3-overhang-tolerance <int> The number of bp allowed to overhang the 3' end of a reference transcript when determining if an assembled transcript should be merged with it (ie, the assembled transcript is not novel). The default is 600 bp.
--intron-overhang-tolerance <int> The number of bp allowed to enter the intron of a reference transcript when determining if an assembled transcript should be merged with it (ie, the assembled transcript is not novel). The default is 50 bp.
--no-faux-reads This option disables tiling of the reference transcripts with faux reads. Use this if you only want to use sequencing reads in assembly but do not want to output assembled transcripts that lay within reference transcripts. All reference transcripts in the input annotation will also be included in the output.
Advanced Program Behavior Options:
-v/--verbose Print lots of status updates and other diagnostic information.
-q/--quiet Suppress messages other than serious warnings and errors.
--no-update-check Turns off the automatic routine that contacts the Cufflinks server to check for a more recent version.


Cufflinks takes a text file of SAM alignments, or a binary SAM (BAM) file as input. For more details on the SAM format, see the specification. The RNA-Seq read mapper TopHat produces output in this format, and is recommended for use with Cufflinks. However Cufflinks will accept SAM alignments generated by any read mapper. Here's an example of an alignment Cufflinks will accept:


s6.25mer.txt-913508	16	chr1 4482736 255 14M431N11M * 0 0 \
   CAAGATGCTAGGCAAGTCTTGGAAG IIIIIIIIIIIIIIIIIIIIIIIII NM:i:0 XS:A:-
	
Note the use of the custom tag XS. This attribute, which must have a value of "+" or "-", indicates which strand the RNA that produced this read came from. While this tag can be applied to any alignment, including unspliced ones, it must be present for all spliced alignment records (those with a 'N' operation in the CIGAR string).

The SAM file supplied to Cufflinks must be sorted by reference position. If you aligned your reads with TopHat, your alignments will be properly sorted already. If you used another tool, you may want to make sure they are properly sorted as follows:


sort -k 3,3 -k 4,4n hits.sam > hits.sam.sorted


Cufflinks produces three output files:


    1) transcripts.gtf

    This GTF file contains Cufflinks' assembled isoforms. The first 7 columns are standard GTF, and the last column contains attributes, some of which are also standardized ("gene_id", and "transcript_id"). There one GTF record per row, and each record represents either a transcript or an exon within a transcript. The columns are defined as follows:

    Column numberColumn nameExampleDescription
    1seqnamechrXChromosome or contig name
    2sourceCufflinksThe name of the program that generated this file (always 'Cufflinks')
    3featureexonThe type of record (always either "transcript" or "exon".
    4start77696957The leftmost coordinate of this record (where 1 is the leftmost possible coordinate)
    5end77712009The rightmost coordinate of this record, inclusive.
    6score77712009The most abundant isoform for each gene is assigned a score of 1000. Minor isoforms are scored by the ratio (minor FPKM/major FPKM)
    7strand+Cufflinks' guess for which strand the isoform came from. Always one of "+", "-", "."
    7frame.Cufflinks does not predict where the start and stop codons (if any) are located within each transcript, so this field is not used.
    8attributes...See below.
    Each GTF record is decorated with the following attributes:
    AttributeExampleDescription
    gene_idCUFF.1Cufflinks gene id
    transcript_idCUFF.1.1Cufflinks transcript id
    FPKM101.267Isoform-level relative abundance in Fragments Per Kilobase of exon model per Million mapped fragments
    frac0.7647Reserved. Please ignore, as this attribute may be deprecated in the future
    conf_lo0.07Lower bound of the 95% confidence interval of the abundance of this isoform, as a fraction of the isoform abundance. That is, lower bound = FPKM * (1.0 - conf_lo)
    conf_hi0.1102Upper bound of the 95% confidence interval of the abundance of this isoform, as a fraction of the isoform abundance. That is, upper bound = FPKM * (1.0 + conf_lo)
    cov100.765Estimate for the absolute depth of read coverage across the whole transcript
    full_read_supportyesWhen RABT assembly is used, this attribute reports whether or not all introns and internal exons were fully covered by reads from the data.

    2) isoforms.fpkm_tracking

    This file contains the estimated isoform-level expression values in the generic FPKM Tracking Format. Note, however that as there is only one sample, the "q" format is not used.


    3) genes.fpkm_tracking

    This file contains the estimated gene-level expression values in the generic FPKM Tracking Format. Note, however that as there is only one sample, the "q" format is not used.


Running Cuffcompare


Cufflinks includes a program that you can use to help analyze the transfrags you assemble. The program cuffcompare helps you:

  • Compare your assembled transcripts to a reference annotation
  • Track Cufflinks transcripts across multiple experiments (e.g. across a time course)
From the command line, run cuffcompare as follows:

cuffcompare [options]* <cuff1.gtf> [cuff2.gtf] ... [cuffN.gtf]

Cuffcompare Input


Cuffcompare takes Cufflinks' GTF output as input, and optionally can take a "reference" annotation (such as from Ensembl)
Arguments:
<cuff1.gtf> A GTF file produced by cufflinks.
Options:
-h Prints the help message and exits
-o <outprefix> All output files created by Cuffcompare will have this prefix (e.g. <outprefix>.loci, <outprefix>.tracking, etc.). If this option is not provided the default output prefix being used is: "cuffcmp"
-r An optional "reference" annotation GFF file. Each sample is matched against this file, and sample isoforms are tagged as overlapping, matching, or novel where appropriate. See the refmap and tmap output file descriptions below.
-R If -r was specified, this option causes cuffcompare to ignore reference transcripts that are not overlapped by any transcript in one of cuff1.gtf,...,cuffN.gtf. Useful for ignoring annotated transcripts that are not present in your RNA-Seq samples and thus adjusting the "sensitivity" calculation in the accuracy report written in the <outprefix> file
-s <seq_dir> Causes cuffcompare to look into for fasta files with the underlying genomic sequences (one file per contig) against which your reads were aligned for some optional classification functions. For example, Cufflinks transcripts consisting mostly of lower-case bases are classified as repeats. Note that <seq_dir> must contain one fasta file per reference chromosome, and each file must be named after the chromosome, and have a .fa or .fasta extension.
-C Enables the "contained" transcripts to be also written in the <outprefix>.combined.gtffile, with the attribute "contained_in" showing the first container transfrag found. By default, without this option, cuffcompare does not write in that file isoforms that were found to be fully contained/covered (with the same compatible intron structure) by other transfrags in the same locus.
-V Cuffcompare is a little more verbose about what it's doing, printing messages to stderr, and it will also show warning messages about any inconsistencies or potential issues found while reading the given GFF file(s).

Cuffcompare Output


Cuffcompare produces the following output files:


    1) <outprefix>.stats

    Cuffcompare reports various statistics related to the "accuracy" of the transcripts in each sample when compared to the reference annotation data. The typical gene finding measures of "sensitivity" and "specificity" (as defined in Burset, M., Guigó, R. : Evaluation of gene structure prediction programs (1996) Genomics, 34 (3), pp. 353-367. doi: 10.1006/geno.1996.0298) are calculated at various levels (nucleotide, exon, intron, transcript, gene) for each input file and reported in this file. The Sn and Sp columns show specificity and sensitivity values at each level, while the fSn and fSp columns are "fuzzy" variants of these same accuracy calculations, allowing for a very small variation in exon boundaries to still be counted as a "match". (If the -o option was not given the default prefix "cuffcmp" is used and these stats will be printed into a file named cuffcmp.stats in the current directory)

    2) <outprefix>.combined.gtf

    Cuffcompare reports a GTF file containing the "union" of all transfrags in each sample. If a transfrag is present in both samples, it is thus reported once in the combined gtf.

    3) <outprefix>.tracking

    This file matches transcripts up between samples. Each row contains a transcript structure that is present in one or more input GTF files. Because the transcripts will generally have different IDs (unless you assembled your RNA-Seq reads against a reference transcriptome), cuffcompare examines the structure of each the transcripts, matching transcripts that agree on the coordinates and order of all of their introns, as well as strand. Matching transcripts are allowed to differ on the length of the first and last exons, since these lengths will naturally vary from sample to sample due to the random nature of sequencing.

    If you ran cuffcompare with the -r option, the first and second columns contain the closest matching reference transcript to the one described by each row.


    Here's an example of a line from the tracking file:

    TCONS_00000045 XLOC_000023 Tcea|uc007afj.1	j	\
         q1:exp.115|exp.115.0|100|3.061355|0.350242|0.350207 \
         q2:60hr.292|60hr.292.0|100|4.094084|0.000000|0.000000
    		
    In this example, a transcript present in the two input files, called exp.115.0 in the first and 60hr.292.0 in the second, doesn't match any reference transcript exactly, but shares exons with uc007afj.1, an isoform of the gene Tcea, as indicated by the class code j. The first three columns are as follows:
    Column number Column name Example Description
    1 Cufflinks transfrag id TCONS_00000045 A unique internal id for the transfrag
    2 Cufflinks locus id XLOC_000023 A unique internal id for the locus
    3 Reference gene id Tcea The gene_name attribute of the reference GTF record for this transcript, or '-' if no reference transcript overlaps this Cufflinks transcript
    4 Reference transcript id uc007afj.1 The transcript_id attribute of the reference GTF record for this transcript, or '-' if no reference transcript overlaps this Cufflinks transcript
    5 Class code c The type of match between the Cufflinks transcripts in column 6 and the reference transcript. See class codes
    Each of the columns after the fifth have the following format:
    qJ:<gene_id>|<transcript_id>|<FMI>|<FPKM>|<conf_lo>|<conf_hi>|<cov>|<len>

    A transcript need not be present in all samples to be reported in the tracking file. A sample not containing a transcript will have a "-" in its entry in the row for that transcript.

    (The following output files are created for each of the <cuff_in> file given, in the same directories where the <cuff_in> files reside)

    4) <cuff_in>.refmap

    This tab delimited file lists, for each reference transcript, which cufflinks transcripts either fully or partially match it. There is one row per reference transcript, and the columns are as follows:

    Column numberColumn nameExampleDescription
    1Reference gene nameMyogThe gene_name attribute of the reference GTF record for this transcript, if present. Otherwise gene_id is used.
    2Reference transcript iduc007crl.1The transcript_id attribute of the reference GTF record for this transcript
    3Class codecThe type of match between the Cufflinks transcripts in column 4 and the reference transcript. One of either 'c' for partial match, or '=' for full match.
    4Cufflinks matchesCUFF.23567.0,CUFF.24689.0A comma separated list of Cufflinks transcript ids matching the reference transcript

    5) <cuff_in>.tmap

    This tab delimited file lists the most closely matching reference transcript for each Cufflinks transcript. There is one row per Cufflinks transcript, and the columns are as follows:

    Column number Column name Example Description
    1 Reference gene name Myog The gene_name attribute of the reference GTF record for this transcript, if present. Otherwise gene_id is used.
    2 Reference transcript id uc007crl.1 The transcript_id attribute of the reference GTF record for this transcript
    3 Class code c The type of relationship between the Cufflinks transcripts in column 4 and the reference transcript (as described in the Class Codes section below)
    4 Cufflinks gene id CUFF.23567 The Cufflinks internal gene id
    5 Cufflinks transcript id CUFF.23567.0 The Cufflinks internal transcript id
    6 Fraction of major isoform (FMI) 100 The expression of this transcript expressed as a fraction of the major isoform for the gene. Ranges from 1 to 100.
    7 FPKM 1.4567 The expression of this transcript expressed in FPKM
    8 FPKM_conf_lo 0.7778 The lower limit of the 95% FPKM confidence interval
    9 FPKM_conf_hi 1.9776 The upper limit of the 95% FPKM confidence interval
    10 Coverage 3.2687 The estimated average depth of read coverage across the transcript.
    11 Length 1426 The length of the transcript
    12 Major isoform ID CUFF.23567.0 The Cufflinks ID of the gene's major isoform


    Class Codes

    If you ran cuffcompare with the -r option, tracking rows will contain the following values. If you did not use -r, the rows will all contain "-" in their class code column.
    Priority Code Description
    1 = Complete match of intron chain
    2 c Contained
    3 j Potentially novel isoform (fragment): at least one splice junction is shared with a reference transcript
    4 e Single exon transfrag overlapping a reference exon and at least 10 bp of a reference intron, indicating a possible pre-mRNA fragment.
    5 i A transfrag falling entirely within a reference intron
    6 o Generic exonic overlap with a reference transcript
    7 p Possible polymerase run-on fragment (within 2Kbases of a reference transcript)
    8 r Repeat. Currently determined by looking at the soft-masked reference sequence and applied to transcripts where at least 50% of the bases are lower case
    9 u Unknown, intergenic transcript
    10 x Exonic overlap with reference on the opposite strand
    11 s An intron of the transfrag overlaps a reference intron on the opposite strand (likely due to read mapping errors)
    12 . (.tracking file only, indicates multiple classifications)

Merging assemblies with cuffmerge


Cufflinks includes a script called cuffmerge that you can use to merge together several Cufflinks assemblies. It handles also handles running Cuffcompare for you, and automatically filters a number of transfrags that are probably artfifacts. If you have a reference GTF file available, you can provide it to the script in order to gracefully merge novel isoforms and known isoforms and maximize overall assembly quality. The main purpose of this script is to make it easier to make an assembly GTF file suitable for use with Cuffdiff. From the command line, run cuffmerge as follows:

cuffmerge [options]* <assembly_GTF_list.txt>

cuffmerge Input


cuffmerge takes several assembly GTF files from Cufflinks' as input. Input GTF files must be specified in a "manifest" file listing full paths to the files.
Arguments:
<assembly_list.txt> Text file "manifest" with a list (one per line) of GTF files that you'd like to merge together into a single GTF file.
Options:
-h/--help Prints the help message and exits
-o <outprefix> Write the summary stats into the text output file <outprefix>(instead of stdout)
-g/--ref-gtf An optional "reference" annotation GTF. The input assemblies are merged together with the reference GTF and included in the final output.
-p/--num-threads <int> Use this many threads to align reads. The default is 1.
-s/--ref-sequence <seq_dir>/<seq_fasta> This argument should point to the genomic DNA sequences for the reference. If a directory, it should contain one fasta file per contig. If a multifasta file, all contigs should be present. The merge script will pass this option to cuffcompare, which will use the sequences to assist in classifying transfrags and excluding artifacts (e.g. repeats). For example, Cufflinks transcripts consisting mostly of lower-case bases are classified as repeats. Note that <seq_dir> must contain one fasta file per reference chromosome, and each file must be named after the chromosome, and have a .fa or .fasta extension.

cuffmerge Output


cuffmerge produces a GTF file that contains an assembly that merges together the input assemblies.


    <outprefix>/merged.gtf



Running Cuffquant


Run cuffquant from the command line as follows:
Usage: cuffquant [options]* <annotation.(gtf/gff)> <aligned_reads.(sam/bam)>

The following is a detailed description of the options used to control Cuffquant:


Arguments:
<annotation.(gtf/gff)> Tells Cuffquant to use the supplied reference annotation (a GFF file) to estimate isoform expression. The program will ignore alignments not structurally compatible with any reference transcript.
<aligned_reads.(sam/bam)> A file of RNA-Seq read alignments in the SAM format. SAM is a standard short read alignment, that allows aligners to attach custom tags to individual alignments, and Cuffquant requires that the alignments you supply have some of these tags. Please see Input formats for more details.
General Options:
-h/--help Prints the help message and exits
-o/--output-dir <string> Sets the name of the directory in which Cuffquant will write all of its output. The default is "./".
-p/--num-threads <int> Use this many threads to align reads. The default is 1.
-M/--mask-file <mask.(gtf/gff)> Tells Cuffquant to ignore all reads that could have come from transcripts in this GTF file. We recommend including any annotated rRNA, mitochondrial transcripts other abundant transcripts you wish to ignore in your analysis in this file. Due to variable efficiency of mRNA enrichment methods and rRNA depletion kits, masking these transcripts often improves the overall robustness of transcript abundance estimates.
-b/--frag-bias-correct <genome.fa> Providing Cuffquant with a multifasta file via this option instructs it to run bias detection and correction algorithm which can significantly improve accuracy of transcript abundance estimates. See How Cufflinks Works for more details.
-u/--multi-read-correct Tells Cuffquant to do an initial estimation procedure to more accurately weight reads mapping to multiple locations in the genome. See How Cufflinks Works for more details.
--library-type See Library Types
Advanced Abundance Estimation Options:
-m/--frag-len-mean <int> This is the expected (mean) fragment length. The default is 200bp.
Note: Cuffquant learns the fragment length mean for each SAM file, so using this option is no longer recommended with paired-end reads.
-s/--frag-len-std-dev <int> The standard deviation for the distribution on fragment lengths. The default is 80bp.
Note: Cuffquant learns the fragment length standard deviation for each SAM file, so using this option is no longer recommended with paired-end reads.
--max-mle-iterations <int> Sets the number of iterations allowed during maximum likelihood estimation of abundances. Default: 5000
--max-bundle-frags <int> Sets the maximum number of fragments a locus may have before being skipped. Default: 1000000
--no-effective-length-correction Cuffquant will not employ its "effective" length normalization to transcript FPKM.
--no-length-correction Cuffquant will not normalize fragment counts by transcript length at all. Use this option when fragment count is independent of the size of the features being quantified (e.g. for small RNA libraries, where no fragmentation takes place, or 3 prime end sequencing, where sampled RNA fragments are all essentially the same length). Experimental option, use with caution.
Advanced Program Behavior Options:
-v/--verbose Print lots of status updates and other diagnostic information.
-q/--quiet Suppress messages other than serious warnings and errors.
--no-update-check Turns off the automatic routine that contacts the Cufflinks server to check for a more recent version.


Cuffquant produces writes a single output file, abundances.cxb, into the output directory. CXB files are binary files, and can be passed to Cuffnorm or Cuffdiff for further processing.


Running Cuffdiff


Cufflinks includes a program, "Cuffdiff", that you can use to find significant changes in transcript expression, splicing, and promoter use. From the command line, run cuffdiff as follows:

cuffdiff [options]* <transcripts.gtf> <sample1_replicate1.sam[,...,sample1_replicateM.sam]> <sample2_replicate1.sam[,...,sample2_replicateM.sam]>... [sampleN.sam_replicate1.sam[,...,sample2_replicateM.sam]]

Cuffdiff Input


Cuffdiff takes a GTF2/GFF3 file of transcripts as input, along with two or more SAM files containing the fragment alignments for two or more samples. It produces a number of output files that contain test results for changes in expression at the level of transcripts, primary transcripts, and genes. It also tracks changes in the relative abundance of transcripts sharing a common transcription start site, and in the relative abundances of the primary transcripts of each gene. Tracking the former allows one to see changes in splicing, and the latter lets one see changes in relative promoter use within a gene.

If you have more than one replicate for a sample, supply the SAM files for the sample as a single comma-separated list. It is not necessary to have the same number of replicates for each sample.

Note that Cuffdiff can also accepted BAM files (which are binary, compressed SAM files). It can also accept CXB files produced by Cuffquant. Note that mixing SAM and BAM files is supported, but you cannot currently mix CXB and SAM/BAM. If one of the samples is supplied as a CXB file, all of the samples must be supplied as CXB files.

Cuffdiff requires that transcripts in the input GTF be annotated with certain attributes in order to look for changes in primary transcript expression, splicing, coding output, and promoter use. These attributes are:

Attribute Description
tss_id The ID of this transcript's inferred start site. Determines which primary transcript this processed transcript is believed to come from. Cuffcompare appends this attribute to every transcript reported in the .combined.gtf file.
p_id The ID of the coding sequence this transcript contains. This attribute is attached by Cuffcompare to the .combined.gtf records only when it is run with a reference annotation that include CDS records. Further, differential CDS analysis is only performed when all isoforms of a gene have p_id attributes, because neither Cufflinks nor Cuffcompare attempt to assign an open reading frame to transcripts.

    Note: If an arbitrary GTF/GFF3 file is used as input (instead of the .combined.gtf file produced by Cuffcompare), these attributes will not be present, but Cuffcompare can still be used to obtain these attributes with a command like this:

      cuffcompare -s /path/to/genome_seqs.fa -CG -r annotation.gtf annotation.gtf

    The resulting cuffcmp.combined.gtf file created by this command will have the tss_id and p_id attributes added to each record and this file can be used as input for cuffdiff.

Arguments:
<transcripts.(gtf/gff)> A transcript annotation file produced by cufflinks, cuffcompare, or other source.
<sample1.(sam/bam/cxb)> A SAM file of aligned RNA-Seq reads. If more than two are provided, Cuffdiff tests for differential expression and regulation between all pairs of samples.
Options:
-h/--help Prints the help message and exits
-o/--output-dir <string> Sets the name of the directory in which Cuffdiff will write all of its output. The default is "./".
-L/--labels <label1,label2,...,labelN> Specify a label for each sample, which will be included in various output files produced by Cuffdiff.
-p/--num-threads <int> Use this many threads to align reads. The default is 1.
-T/--time-series Instructs Cuffdiff to analyze the provided samples as a time series, rather than testing for differences between all pairs of samples. Samples should be provided in increasing time order at the command line (e.g first time point SAM, second timepoint SAM, etc.)
--total-hits-norm With this option, Cufflinks counts all fragments, including those not compatible with any reference transcript, towards the number of mapped fragments used in the FPKM denominator. This option can be combined with -N/--upper-quartile-norm. It is inactive by default.
--compatible-hits-norm With this option, Cufflinks counts only those fragments compatible with some reference transcript towards the number of mapped fragments used in the FPKM denominator. This option can be combined with -N/--upper-quartile-norm. Using this mode is generally recommended in Cuffdiff to reduce certain types of bias caused by differential amounts of ribosomal reads which can create the impression of falsely differentially expressed genes. It is active by default.
-b/--frag-bias-correct <genome.fa> Providing Cufflinks with the multifasta file your reads were mapped to via this option instructs it to run our bias detection and correction algorithm which can significantly improve accuracy of transcript abundance estimates. See How Cufflinks Works for more details.
-u/--multi-read-correct Tells Cufflinks to do an initial estimation procedure to more accurately weight reads mapping to multiple locations in the genome. See How Cufflinks Works for more details.
-c/--min-alignment-count <int> The minimum number of alignments in a locus for needed to conduct significance testing on changes in that locus observed between samples. If no testing is performed, changes in the locus are deemed not signficant, and the locus' observed changes don't contribute to correction for multiple testing. The default is 10 fragment alignments.
-M/--mask-file <mask.(gtf/gff)> Tells Cuffdiff to ignore all reads that could have come from transcripts in this GTF file. We recommend including any annotated rRNA, mitochondrial transcripts other abundant transcripts you wish to ignore in your analysis in this file. Due to variable efficiency of mRNA enrichment methods and rRNA depletion kits, masking these transcripts often improves the overall robustness of transcript abundance estimates.
--FDR <float> The allowed false discovery rate. The default is 0.05.
--library-type See Library Types
--library-norm-method See Library Normalization Methods
--dispersion-method See Cross-replicate dispersion estimation methods
Advanced Options:
-m/--frag-len-mean <int> This is the expected (mean) fragment length. The default is 200bp.
Note: Cufflinks now learns the fragment length mean for each SAM file, so using this option is no longer recommended with paired-end reads.
-s/--frag-len-std-dev <int> The standard deviation for the distribution on fragment lengths. The default is 80bp.
Note: Cufflinks now learns the fragment length standard deviation for each SAM file, so using this option is no longer recommended with paired-end reads.
--num-importance-samples <int> Deprecated
--max-mle-iterations <int> Sets the number of iterations allowed during maximum likelihood estimation of abundances. Default: 5000
-v/--verbose Print lots of status updates and other diagnostic information.
-q/--quiet Suppress messages other than serious warnings and errors.
--no-update-check Turns off the automatic routine that contacts the Cufflinks server to check for a more recent version.
--poisson-dispersion Use the Poisson fragment dispersion model instead of learning one in each condition.
--emit-count-tables Cuffdiff will output a file for each condition (called <sample>_counts.txt) containing the fragment counts, fragment count variances, and fitted variance model. For internal debugging only. This option will be removed in a future version of Cuffdiff.
-F/--min-isoform-fraction <0.0-1.0> Cuffdiff will round down to zero the abundance of alternative isoforms quantified at below the specified fraction of the major isoforms. This is done after MLE estimation but before MAP estimation to improve robustness of confidence interval generation and differential expression analysis. The default is 1e-5, and we recommend you not alter this parameter.
--max-bundle-frags <int> Sets the maximum number of fragments a locus may have before being skipped. Skipped loci are marked with status HIDATA. Default: 1000000
--max-frag-count-draws <int> Cuffdiff will make this many draws from each transcript's predicted negative binomial random numbder generator. Each draw is a number of fragments that will be probabilistically assigned to the transcripts in the transcriptome. Used to estimate the variance-covariance matrix on assigned fragment counts. Default: 100.
--max-frag-assign-draws <int> For each fragment drawn from a transcript, Cuffdiff will assign it this many times (probabilistically), thus estimating the assignment uncertainty for each transcript. Used to estimate the variance-covariance matrix on assigned fragment counts. Default: 50.
-F/--min-outlier-p <0.0-1.0> DEPRECATED
--min-reps-for-js-test <int> Cuffdiff won't test genes for differential regulation unless the conditions in question have at least this many replicates. Default: 3.
--no-effective-length-correction Cuffdiff will not employ its "effective" length normalization to transcript FPKM.
--no-length-correction Cuffdiff will not normalize fragment counts by transcript length at all. Use this option when fragment count is independent of the size of the features being quantified (e.g. for small RNA libraries, where no fragmentation takes place, or 3 prime end sequencing, where sampled RNA fragments are all essentially the same length). Experimental option, use with caution.

Cuffdiff Output



    1) FPKM tracking files

    Cuffdiff calculates the FPKM of each transcript, primary transcript, and gene in each sample. Primary transcript and gene FPKMs are computed by summing the FPKMs of transcripts in each primary transcript group or gene group. The results are output in FPKM tracking files in the format described here. There are four FPKM tracking files:

    isoforms.fpkm_trackingTranscript FPKMs
    genes.fpkm_trackingGene FPKMs. Tracks the summed FPKM of transcripts sharing each gene_id
    cds.fpkm_trackingCoding sequence FPKMs. Tracks the summed FPKM of transcripts sharing each p_id, independent of tss_id
    tss_groups.fpkm_trackingPrimary transcript FPKMs. Tracks the summed FPKM of transcripts sharing each tss_id

    2) Count tracking files

    Cuffdiff estimates the number of fragments that originated from each transcript, primary transcript, and gene in each sample. Primary transcript and gene counts are computed by summing the counts of transcripts in each primary transcript group or gene group. The results are output in count tracking files in the format described here. There are four Count tracking files:

    isoforms.count_trackingTranscript counts
    genes.count_trackingGene counts. Tracks the summed counts of transcripts sharing each gene_id
    cds.count_trackingCoding sequence counts. Tracks the summed counts of transcripts sharing each p_id, independent of tss_id
    tss_groups.count_trackingPrimary transcript counts. Tracks the summed counts of transcripts sharing each tss_id

    3) Read group tracking files

    Cuffdiff calculates the expression and fragment count for each transcript, primary transcript, and gene in each replicate. The results are output in per-replicate tracking files in the format described here. There are four read group tracking files:

    isoforms.read_group_trackingTranscript read group tracking
    genes.read_group_trackingGene read group tracking. Tracks the summed expression and counts of transcripts sharing each gene_id in each replicate
    cds.read_group_trackingCoding sequence FPKMs. Tracks the summed expression and counts of transcripts sharing each p_id, independent of tss_id in each replicate
    tss_groups.read_group_trackingPrimary transcript FPKMs. Tracks the summed expression and counts of transcripts sharing each tss_id in each replicate

    4) Differential expression tests

    This tab delimited file lists the results of differential expression testing between samples for spliced transcripts, primary transcripts, genes, and coding sequences. For each pair of samples x and y, four files are created

    isoform_exp.diffTranscript differential FPKM.
    gene_exp.diffGene differential FPKM. Tests difference sin the summed FPKM of transcripts sharing each gene_id
    tss_group_exp.diffPrimary transcript differential FPKM. Tests differences in the summed FPKM of transcripts sharing each tss_id
    cds_exp.diffCoding sequence differential FPKM. Tests differences in the summed FPKM of transcripts sharing each p_id independent of tss_id
    Each of the above files has the following format:
    Column numberColumn nameExampleDescription
    1Tested idXLOC_000001A unique identifier describing the transcipt, gene, primary transcript, or CDS being tested
    2geneLypla1The gene_name(s) or gene_id(s) being tested
    3locuschr1:4797771-4835363Genomic coordinates for easy browsing to the genes or transcripts being tested.
    4sample 1LiverLabel (or number if no labels provided) of the first sample being tested
    5sample 2BrainLabel (or number if no labels provided) of the second sample being tested
    6Test statusNOTESTCan be one of OK (test successful), NOTEST (not enough alignments for testing), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents testing.
    7FPKMx8.01089FPKM of the gene in sample x
    8FPKMy8.551545FPKM of the gene in sample y
    9log2(FPKMy/FPKMx)0.06531The (base 2) log of the fold change y/x
    10test stat0.860902The value of the test statistic used to compute significance of the observed change in FPKM
    11p value0.389292The uncorrected p-value of the test statistic
    12q value0.985216The FDR-adjusted p-value of the test statistic
    13significantnoCan be either "yes" or "no", depending on whether p is greater then the FDR after Benjamini-Hochberg correction for multiple-testing

    5) Differential splicing tests - splicing.diff

    This tab delimited file lists, for each primary transcript, the amount of overloading detected among its isoforms, i.e. how much differential splicing exists between isoforms processed from a single primary transcript. Only primary transcripts from which two or more isoforms are spliced are listed in this file.

    Column numberColumn nameExampleDescription
    1Tested idTSS10015A unique identifier describing the primary transcript being tested.
    2gene nameRtknThe gene_name or gene_id that the primary transcript being tested belongs to
    3locuschr6:83087311-83102572Genomic coordinates for easy browsing to the genes or transcripts being tested.
    4sample 1LiverLabel (or number if no labels provided) of the first sample being tested
    5sample 2BrainLabel (or number if no labels provided) of the second sample being tested
    6Test statusOKCan be one of OK (test successful), NOTEST (not enough alignments for testing), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents testing.
    7Reserved0
    8Reserved0
    9√JS(x,y)0.22115The splice overloading of the primary transcript, as measured by the square root of the Jensen-Shannon divergence computed on the relative abundances of the splice variants
    10test stat0.22115The value of the test statistic used to compute significance of the observed overloading, equal to √JS(x,y)
    11p value0.000174982The uncorrected p-value of the test statistic.
    12q value0.985216The FDR-adjusted p-value of the test statistic
    13significantyesCan be either "yes" or "no", depending on whether p is greater then the FDR after Benjamini-Hochberg correction for multiple-testing

    6) Differential coding output - cds.diff

    This tab delimited file lists, for each gene, the amount of overloading detected among its coding sequences, i.e. how much differential CDS output exists between samples. Only genes producing two or more distinct CDS (i.e. multi-protein genes) are listed here.

    Column numberColumn nameExampleDescription
    1Tested idXLOC_000002-[chr1:5073200-5152501]A unique identifier describing the gene being tested.
    2gene nameAtp6v1hThe gene_name or gene_id
    3locuschr1:5073200-5152501Genomic coordinates for easy browsing to the genes or transcripts being tested.
    4sample 1LiverLabel (or number if no labels provided) of the first sample being tested
    5sample 2BrainLabel (or number if no labels provided) of the second sample being tested
    6Test statusOKCan be one of OK (test successful), NOTEST (not enough alignments for testing), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents testing.
    7Reserved0
    8Reserved0
    9√JS(x,y)0.0686517The CDS overloading of the gene, as measured by the square root of the Jensen-Shannon divergence computed on the relative abundances of the coding sequences
    10test stat0.0686517The value of the test statistic used to compute significance of the observed overloading, equal to √JS(x,y)
    11p value0.00546783The uncorrected p-value of the test statistic
    12q value0.985216The FDR-adjusted p-value of the test statistic
    13significantyesCan be either "yes" or "no", depending on whether p is greater then the FDR after Benjamini-Hochberg correction for multiple-testing

    7) Differential promoter use - promoters.diff

    This tab delimited file lists, for each gene, the amount of overloading detected among its primary transcripts, i.e. how much differential promoter use exists between samples. Only genes producing two or more distinct primary transcripts (i.e. multi-promoter genes) are listed here.

    Column numberColumn nameExampleDescription
    1Tested idXLOC_000019A unique identifier describing the gene being tested.
    2gene nameTmem70The gene_name or gene_id
    3locuschr1:16651657-16668357Genomic coordinates for easy browsing to the genes or transcripts being tested.
    4sample 1LiverLabel (or number if no labels provided) of the first sample being tested
    5sample 2BrainLabel (or number if no labels provided) of the second sample being tested
    6Test statusOKCan be one of OK (test successful), NOTEST (not enough alignments for testing), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents testing.
    7Reserved0
    8Reserved0
    9√JS(x,y)0.0124768The promoter overloading of the gene, as measured by the square root of the Jensen-Shannon divergence computed on the relative abundances of the primary transcripts
    10test stat0.0124768The value of the test statistic used to compute significance of the observed overloading, equal to √JS(x,y)
    11p value0.394327The uncorrected p-value of the test statistic
    12q value0.985216The FDR-adjusted p-value of the test statistic
    13significantnoCan be either "yes" or "no", depending on whether p is greater then the FDR after Benjamini-Hochberg correction for multiple-testing

    8) Read group info - read_groups.info

    This tab delimited file lists, for each replicate, key properties used by Cuffdiff during quantification, such as library normalization factors. The read_groups.info file has the following format:

    Column numberColumn nameExampleDescription
    1filemCherry_rep_A/accepted_hits.bamBAM or SAM file containing the data for the read group
    2conditionmCherryCondition to which the read group belongs
    3replicate_num0Replicate number of the read group
    4total_mass4.72517e+06Total number of fragments for the read group
    5norm_mass4.72517e+06Fragment normalization constant used during calculation of FPKMs.
    6internal_scale1.23916Internal scaling factor, used to transform replicates of a single condition onto the "internal" common count scale.
    7external_scale0.96External scaling factor, used to transform counts from different conditions onto an internal common count scale.


    8) Run info - run.info

    This tab delimited file lists various bits of information about a Cuffdiff run to help track what options were provided. For example:

    param   value
    cmd_line        cuffdiff  base_ref.gtf mCherry/accepted_hits.bam R49/accepted_hits.bam 
    version 2.0.0
    SVN_revision    3258
    boost_version   104700
    		

Running Cuffnorm


Cufflinks includes a program, "Cuffnorm", that you can use to generate tables of expression values that are properly normalized for library size. From the command line, run cuffnorm as follows:

cuffnorm [options]* <transcripts.gtf> <sample1_replicate1.sam[,...,sample1_replicateM.sam]> <sample2_replicate1.sam[,...,sample2_replicateM.sam]>... [sampleN.sam_replicate1.sam[,...,sample2_replicateM.sam]]

Cuffnorm Input


Running Cuffnorm is very similar to running Cuffdiff. Cuffnorm takes a GTF2/GFF3 file of transcripts as input, along with two or more SAM, BAM, or CXB files for two or more samples. It produces a number of output files that contain expression levels and normalized fragment counts at the level of transcripts, primary transcripts, and genes. It also tracks changes in the relative abundance of transcripts sharing a common transcription start site, and in the relative abundances of the primary transcripts of each gene. Tracking the former allows one to see changes in splicing, and the latter lets one see changes in relative promoter use within a gene.

If you have more than one replicate for a sample, supply the SAM files for the sample as a single comma-separated list. It is not necessary to have the same number of replicates for each sample.

Note that Cuffnorm can also accepted BAM files (which are binary, compressed SAM files). It can also accept CXB files produced by Cuffquant. Note that mixing SAM and BAM files is supported, but you cannot currently mix CXB and SAM/BAM. If one of the samples is supplied as a CXB file, all of the samples must be supplied as CXB files.

Cuffnorm also requires a GFF/GTF file, conforming to the same specifications as needed for Cuffdiff.

Arguments:
<transcripts.(gtf/gff)> A transcript annotation file produced by cufflinks, cuffcompare, or other source.
<sample1.(sam/bam/cxb)> A SAM file of aligned RNA-Seq reads. If more than two are provided, Cuffdiff tests for differential expression and regulation between all pairs of samples.
Options:
-h/--help Prints the help message and exits
-o/--output-dir <string> Sets the name of the directory in which Cuffdiff will write all of its output. The default is "./".
-L/--labels <label1,label2,...,labelN> Specify a label for each sample, which will be included in various output files produced by Cuffdiff.
-p/--num-threads <int> Use this many threads to align reads. The default is 1.
--total-hits-norm With this option, Cuffquant counts all fragments, including those not compatible with any reference transcript, towards the number of mapped fragments used in the FPKM denominator. It is inactive by default.
--compatible-hits-norm With this option, Cuffnorm counts only those fragments compatible with some reference transcript towards the number of mapped fragments used in the FPKM denominator. It is active by default.
--library-type See Library Types
--library-norm-method See Library Normalization Methods
--output-format See Output format options
Advanced Options:
-v/--verbose Print lots of status updates and other diagnostic information.
-q/--quiet Suppress messages other than serious warnings and errors.
--no-update-check Turns off the automatic routine that contacts the Cufflinks server to check for a more recent version.

Cuffnorm Output


Cuffnorm outputs a set of files containing normalized expression levels for each gene, transcript, TSS group, and CDS group in the experiment. It does not perform differential expression analysis. To assess the significance of changes in expression for genes and transcripts between conditions, use Cuffdiff. Cuffnorm's output files are useful when you have many samples and you simply want to cluster them or plot expression levels of genes important in your study.

By default, Cuffnorm reports expression levels in the "simple-table" tab-delimted text files. The program also reports information about your samples and about the genes, transcripts, TSS groups, and CDS groups as tab delimited text files. Note that these files have a different format than the files used by Cuffdiff. However, you can direct Cuffnorm to report its output in the same format as used by Cuffdiff if you wish. Simply supply the option --output-format cuffdiff at the command line.

Cuffnorm will report both FPKM values and normalized, estimates for the number of fragments that originate from each gene, transcript, TSS group, and CDS group. Note that because these counts are already normalized to account for differences in library size, they should not be used with downstream differential expression tools that require raw counts as input.

To see the details of the simple table format used by Cuffnorm, refer to the simple table expression format, simple table sample attribute format, and simple table feature (e.g. gene) attribute format sections below.

Sample sheets for Cuffdiff and Cuffnorm


Both Cuffdiff and Cuffnorm can be run by specifying a list of SAM, BAM, or CXB files at the command line. For analysis with many such files, specifying them in this way can be cumbersome and error-prone. Both programs also allow you to specify these inputs in a simple, tab-delimited table. Create a file called sample_sheet.txt or another name of your choice, and specify samples as follows, one per line:

Column numberColumn nameExampleDescription
1sample_idC1_R1.samthe path to the SAM/BAM/CXB file for this sample
2group_labelC1The condition label for this sample. Replicates of a condition should share the same label

To run Cuffdiff or Cuffnorm with a sample sheet, create one and then at the command line, provide the --use-sample-sheet option and replace the list of SAM/BAM/CXB files with the name of your sample sheet file, as follows:


cuffdiff --use-sample-sheet <transcripts.gtf> <sample_sheet.txt>

An example sample sheet might look like this:
sample_id	group_label
C1_R1.sam	C1
C1_R2.sam	C1
C2_R1.sam	C2
C2_R2.sam	C2	
		 

Contrast files for Cuffdiff


Cuffdiff, by default, compares each pair of conditions in your experiment. If you have many conditions, this can create a lot of additional work for the program. These extra conditions can cause Cuffdiff's output files to be very large, which can slow down CummeRbund and other downstream analysis software. Often, you are not interested in all pairwise contrasts. Rather, you'd like to compare all conditions to a common control, or only look at matched pairs of samples. You can specify the contrasts Cuffdiff should perform using a contrast file.

Contrast files are simple, tab delimited text files. They should have a single header line as the first line in the file, followed by one line for each contrast you'd like to perform. The files should have two columns, as specified below:

Column numberColumn nameExampleDescription
1condition_ACtrlA condition label. Must match one of the labels specified through -L or in the sample sheet.
1condition_BCtrlA condition label. Must match one of the labels specified through -L or in the sample sheet.

To run Cuffdiff with a contrast file, simply provide the -C <contrasts.txt> option at the command line.
An example table might look like this:

condition_A	condition_B
Ctrl	Mutant_X
Ctrl	Mutant_Y
Ctrl	Mutant_Z		
		 

Output formats


Cufflinks, Cuffdiff, and Cuffnorm all output numerous types of files. They fall into one of the formats described below:



FPKM Tracking Files


FPKM tracking files use a generic format to output estimated expression values. Each FPKM tracking file has the following format:
Column numberColumn nameExampleDescription
1tracking_idTCONS_00000001A unique identifier describing the object (gene, transcript, CDS, primary transcript)
2class_code=The class_code attribute for the object, or "-" if not a transcript, or if class_code isn't present
3nearest_ref_idNM_008866.1The reference transcript to which the class code refers, if any
4gene_idNM_008866The gene_id(s) associated with the object
5gene_short_nameLypla1The gene_short_name(s) associated with the object
6tss_idTSS1The tss_id associated with the object, or "-" if not a transcript/primary transcript, or if tss_id isn't present
7locuschr1:4797771-4835363Genomic coordinates for easy browsing to the object
8length2447The number of base pairs in the transcript, or '-' if not a transcript/primary transcript
9coverage43.4279Estimate for the absolute depth of read coverage across the object
10q0_FPKM8.01089FPKM of the object in sample 0
11q0_FPKM_lo7.03583the lower bound of the 95% confidence interval on the FPKM of the object in sample 0
12q0_FPKM_hi8.98595the upper bound of the 95% confidence interval on the FPKM of the object in sample 0
13q0_statusOKQuantification status for the object in sample 0. Can be one of OK (deconvolution successful), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents deconvolution.
14q1_FPKM8.55155FPKM of the object in sample 1
15q1_FPKM_lo7.77692the lower bound of the 95% confidence interval on the FPKM of the object in sample 0
16q1_FPKM_hi9.32617the upper bound of the 95% confidence interval on the FPKM of the object in sample 1
17q1_status9.32617the upper bound of the 95% confidence interval on the FPKM of the object in sample 1. Can be one of OK (deconvolution successful), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents deconvolution.
3N + 12qN_FPKM7.34115FPKM of the object in sample N
3N + 13qN_FPKM_lo6.33394the lower bound of the 95% confidence interval on the FPKM of the object in sample N
3N + 14qN_FPKM_hi8.34836the upper bound of the 95% confidence interval on the FPKM of the object in sample N
3N + 15qN_statusOKQuantification status for the object in sample N. Can be one of OK (deconvolution successful), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents deconvolution.

Count Tracking Files


Count tracking files use a generic format to output estimated fragment count values. Each Count tracking file has the following format:
Column numberColumn nameExampleDescription
1tracking_idTCONS_00000001A unique identifier describing the object (gene, transcript, CDS, primary transcript)
2q0_count201.334Estimated (externally scaled) number of fragments generated by the object in sample 0
3q0_count_variance5988.24Estimated variance in the number of fragments generated by the object in sample 0
4q0_count_uncertainty_var170.21Estimated variance in the number of fragments generated by the object in sample 0 due to fragment assignment uncertainty.
5q0_count_dispersion_var4905.63Estimated variance in the number of fragments generated by the object in sample 0 due to cross-replicate variability.
6q0_statusOKQuantification status for the object in sample 0. Can be one of OK (deconvolution successful), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents deconvolution.
7q1_count201.334Estimated (externally scaled) number of fragments generated by the object in sample 1
8q1_count_variance5988.24Estimated variance in the number of fragments generated by the object in sample 1
9q1_count_uncertainty_var170.21Estimated variance in the number of fragments generated by the object in sample 1 due to fragment assignment uncertainty.
10q1_count_dispersion_var4905.63Estimated variance in the number of fragments generated by the object in sample 1 due to cross-replicate variability.
11q1_statusOKQuantification status for the object in sample 1. Can be one of OK (deconvolution successful), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents deconvolution.
7qN_count201.334Estimated (externally scaled) number of fragments generated by the object in sample N
4N + 6qN_count_variance7.34115Estimated variance in the number of fragments generated by the object in sample N
4N + 7qN_count_uncertainty_var6.33394Estimated variance in the number of fragments generated by the object in sample N due to fragment assignment uncertainty.
4N + 8qN_count_dispersion_var8.34836Estimated variance in the number of fragments generated by the object in sample N due to cross-replicate variability.
4N + 9qN_statusOKQuantification status for the object in sample N. Can be one of OK (deconvolution successful), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents deconvolution.

Read Group Tracking Files


Read group tracking files record per-replicate expression and count data. Each Count tracking file has the following format:
Column numberColumn nameExampleDescription
1tracking_idTCONS_00000001A unique identifier describing the object (gene, transcript, CDS, primary transcript)
2conditionFibroblastsName of the condition
3replicate1Name of the replicate of the condition
4raw_frags170.21The estimate number of (unscaled) fragments originating from the object in this replicate
5internal_scaled_frags4905.63Estimated number of fragments originating from the object, after transforming to the internal common count scale (for comparison between replicates of this condition.)
6external_scaled_frags99.21Estimated number of fragments originating from the object, after transforming to the external common count scale (for comparison between conditions)
7FPKM201.334FPKM of this object in this replicate
8effective_length5988.24The effective length of the object in this replicate. Currently a reserved, unreported field.
9statusOKQuantification status for the object. Can be one of OK (deconvolution successful), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents deconvolution.

Simple-table expression format


Cuffnorm reports two different types of files with this format: *.fpkm_table files and *.count_table files for each group of features: genes, transcripts, TSS groups, and CDS groups. The files start with a column indicating the feature ID for each row. There is one subsequent column for each sample in the analysis:
Column numberColumn nameExampleDescription
1tracking_idTCONS_00000001A unique identifier describing the object (gene, transcript, CDS, primary transcript)
2conditionX_N=The FPKM value (for *.fpkm_table files) or normalized fragment count (for *.count_table files) for this feature in replicate N of conditionX

Simple-table gene attributes format


Cuffdiff reports metadata for each gene, transcript, TSS group, and CDS group in the following tab delimited format:
Column numberColumn nameExampleDescription
1tracking_idTCONS_00000001A unique identifier describing the object (gene, transcript, CDS, primary transcript)
2class_code=The class_code attribute for the object, or "-" if not a transcript, or if class_code isn't present
3nearest_ref_idNM_008866.1The reference transcript to which the class code refers, if any
4gene_idNM_008866The gene_id(s) associated with the object
5gene_short_nameLypla1The gene_short_name(s) associated with the object
6tss_idTSS1The tss_id associated with the object, or "-" if not a transcript/primary transcript, or if tss_id isn't present
7locuschr1:4797771-4835363Genomic coordinates for easy browsing to the object
8length2447The number of base pairs in the transcript, or '-' if not a transcript/primary transcript

Simple-table sample attributes format


Cuffnorm reports some information about each sample (i.e. each SAM, BAM, or CXB file) in the analysis in the following format:
Column numberColumn nameExampleDescription
1sample_idq1_0A unique identifier describing the sample. Has the format conditionX_N, meaning replicate N of conditionX.
2fileC1_R1.samThe path to the file (SAM/BAM/CXB) attribute for the sample
3total_mass94610The total (un-normalized) number of fragment alignments for this sample
4internal_scale1.0571The scaling factor used to normalize this sample library size.
5external_scale1Reserved

Library Types


In cases where Cufflinks cannot determine the platform and protocol used to generate input reads, you can supply this information manually, which will allow Cufflinks to infer source strand information with certain protocols. The available options are listed below. For paired-end data, we currently only support protocols where reads are point towards each other.

Library TypeExamplesDescription
fr-unstranded (default)Standard IlluminaReads from the left-most end of the fragment (in transcript coordinates) map to the transcript strand, and the right-most end maps to the opposite strand.
fr-firststranddUTP, NSR, NNSRSame as above except we enforce the rule that the right-most end of the fragment (in transcript coordinates) is the first sequenced (or only sequenced for single-end reads). Equivalently, it is assumed that only the strand generated during first strand synthesis is sequenced.
fr-secondstrandDirectional Illumina (Ligation), Standard SOLiDSame as above except we enforce the rule that the left-most end of the fragment (in transcript coordinates) is the first sequenced (or only sequenced for single-end reads). Equivalently, it is assumed that only the strand generated during second strand synthesis is sequenced.

Please contact tophat.cufflinks@gmail.com to request support for a new protocol.


Library Normalization Methods


You can control how library sizes (i.e. sequencing depths) are normalized in Cufflinks and Cuffdiff. Cuffdiff has several methods that require multiple libraries in order to work. Library normalization methods supported by Cufflinks work on one library at a time.

Normalization MethodSupported by CufflinksSupported by CuffdiffDescription
classic-fpkmYesYesLibrary size factor is set to 1 - no scaling applied to FPKM values or fragment counts. (default for Cufflinks)
geometricNoYesFPKMs and fragment counts are scaled via the median of the geometric means of fragment counts across all libraries, as described in Anders and Huber (Genome Biology, 2010). This policy identical to the one used by DESeq. (default for Cuffdiff)
quartileNoYesFPKMs and fragment counts are scaled via the ratio of the 75 quartile fragment counts to the average 75 quartile value across all libraries.

Cross-replicate dispersion estimation methods


Cuffdiff works by modeling the variance in fragment counts across replicates as a function of the mean fragment count across replicates. Strictly speaking, models a quantitity called dispersion - the variance present in a group of samples beyond what is expected from a simple Poisson model of RNA_Seq. You can control how Cuffdiff constructs its model of dispersion in locus fragment counts. Each condition that has replicates can receive its own model, or Cuffdiff can use a global model for all conditions. All of these policies are identical to those used by DESeq (Anders and Huber, Genome Biology, 2010)

Dispersion MethodDescription
pooledEach replicated condition is used to build a model, then these models are averaged to provide a single global model for all conditions in the experiment. (Default)
per-conditionEach replicated condition receives its own model. Only available when all conditions have replicates.
blindAll samples are treated as replicates of a single global "condition" and used to build one model.
poissonThe Poisson model is used, where the variance in fragment count is predicted to equal the mean across replicates. Not recommended.

Which method you choose largely depends on whether you expect variability in each group of samples to be similar. For example, if you are comparing two groups, A and B, where A has low cross-replicate variability and B has high variability, it may be best to choose per-condition. However, if the conditions have similar levels of variability, you might stick with the default, which sometimes provides a more robust model, especially in cases where each group has few replicates. Finally, if you only have a single replicate in each condition, you must use blind, which treats all samples in the experiment as replicates of a single condition. This method works well when you expect the samples to have very few differentially expressed genes. If there are many differentially expressed genes, Cuffdiff will construct an overly conservative model and you may not get any significant calls. In this case, you will need more replicates in your experiment.