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install with bioconda


Scallop is an accurate reference-based transcript assembler. Scallop features its high accuracy in assembling multi-exon transcripts as well as lowly expressed transcripts. Scallop achieves this improvement through a novel algorithm that can be proved preserving all phasing paths from reads and paired-end reads, while also achieves both transcripts parsimony and coverage deviation minimization.

Scallop paper has been published at Nature Biotechnology. The datasets and scripts used in this paper to compare the performance of Scallop and other assemblers are available at scalloptest.

Please also checkout the podcast about Scallop (thanks Roman Cheplyaka for the interview). It is available at both the bioinformatics chat and iTunes.


Latest release of Scallop is v0.10.5, including binary (for both linux and mac) and source code.

Below we list the systems that have been tested for whether the Scallop binary can run or not.

Operation System Version Code Name Scallop
Debian 9 Stretch linux
Ubuntu 14.04 Trusty Tahr linux
Ubuntu 16.04 Xenial Xerus linux
CentOS 6.9 N/A
CentOS 7 linux
Fedora 24 linux
Mac OS 10.10 Yosemite mac
Mac OS 10.11 El Capitan mac
Mac OS 10.12 Sierra mac


Scallop is, and will continue to be, freely and actively supported on a best-effort basis.

If you need industrial-grade technical support, please consider the options at


Download the source code of Scallop from here. Scallop uses additional libraries of Boost and htslib (NOTE: from v0.10.4 the dependence on Clp is optional). If they have not been installed in your system, you first need to download and install them. You might also need to export the runtime library path to certain environmental variable (for example, LD_LIBRARY_PATH, for most linux distributions). After install these dependencies, you then compile the source code of Scallop. If some of the above dependencies are not installed to the default system directories (for example, /usr/local, for most linux distributions), their corresponding installing paths should be specified to configure of Scallop.

Download Boost

If Boost has not been downloaded/installed, download Boost (license) from ( Uncompress it somewhere (compiling and installing are not necessary).

Install htslib

If htslib has not been installed, download htslib (license) from ( with version 1.5 or higher. Note that htslib relies on zlib. So if zlib has not been installed in your system, you need to install zlib first. To do so, download zlib (license) at ( Use the following commands to install zlib:

make install

After installing zlib, use the following commands to build htslib:

./configure --disable-bz2 --disable-lzma --disable-gcs --disable-s3 --enable-libcurl=no
make install

The default installation location of htslib is /usr/lib. If you would install it to a different location, replace the above configure line with the following (by adding --prefix=/path/to/your/htslib to the end):

./configure --disable-bz2 --disable-lzma --disable-gcs --disable-s3 --enable-libcurl=no --prefix=/path/to/your/htslib

In this case, you also need to export the runtime library path (note that there is an additional lib following the installation path):

export LD_LIBRARY_PATH=/path/to/your/htslib/lib:$LD_LIBRARY_PATH

Install Clp (optional since v0.10.4)

NOTE: Clp will be used to solve the linear programming instances created when decomposing unsplitable vertices. An alternative algorithm is provided in Scallop from version v0.10.4 (and hence since then the installation of Clp becomes optional). Our testing shows that these two algorithms give very similar results.

If Clp has not been installed in your system, download Clp (license) from ( Use the following to install Clp

./configure --disable-bzlib --disable-zlib
make install

The default installation of Clp is the current directory, rather than /usr/lib. If you would install it to a different location, replace the above configure line with the following (by adding --prefix=/path/to/your/Clp to the end):

./configure --disable-bzlib --disable-zlib --prefix=/path/to/your/Clp

You need to export the runtime library path (note that there is an additional lib following the installation path):

export LD_LIBRARY_PATH=/path/to/your/Clp/lib:$LD_LIBRARY_PATH

Compile Scallop

Use the following to compile Scallop (without Clp; therefore the alternative algorithm for decomposing unsplitable vertices will be used; available for versions newer than v0.10.4):

./configure --with-htslib=/path/to/your/htslib --with-boost=/path/to/your/boost

Use the following to compile Scallop (with Clp; therefore an linear programming formulation will be used to decompose unsplitable vertices):

./configure --with-htslib=/path/to/your/htslib --with-boost=/path/to/your/boost --enable-useclp --with-clp=/path/to/your/Clp

If some of the dependencies are installed in the default system directory (for example, /usr/lib), then the corresponding --with- option might not be necessary. The executable file scallop will appear at src/scallop.


The usage of scallop is:

./scallop -i <input.bam> -o <output.gtf> [options]

The input.bam is the read alignment file generated by some RNA-seq aligner, (for example, TopHat2, STAR, or HISAT2). Make sure that it is sorted; otherwise run samtools to sort it:

samtools sort input.bam > input.sort.bam

The reconstructed transcripts shall be written as gtf format into output.gtf.

Scallop support the following parameters. Please refer to the additional explanation below the table.

Parameters Default Value Description
--help print usage of Scallop and exit
--version print version of Scallop and exit
--preview show the inferred library_type and exit
--verbose 1 chosen from {0, 1, 2}
--library_type empty chosen from {empty, unstranded, first, second}
--min_transcript_coverage 1 the minimum coverage required to output a multi-exon transcript
--min_single_exon_coverage 20 the minimum coverage required to output a single-exon transcript
--min_transcript_length_base 150 the minimum base length of a transcript
--min_transcript_length_increase 50 the minimum increased length of a transcript with each additional exon
--min_mapping_quality 1 ignore reads with mapping quality less than this value
--max_num_cigar 7 ignore reads with CIGAR size larger than this value
--min_bundle_gap 50 the minimum distances required to start a new bundle
--min_num_hits_in_bundle 20 the minimum number of reads required in a bundle
--min_flank_length 3 the minimum match length required in each side for a spliced read
--min_splice_bundary_hits 1 the minimum number of spliced reads required to support a junction
  1. For --verbose, 0: quiet; 1: one line for each splice graph; 2: details of graph decomposition.

  2. --library_type is highly recommended to provide. The unstranded, first, and second correspond to fr-unstranded, fr-firststrand, and fr-secondstrand used in standard Illumina sequencing libraries. If none of them is given, i.e., it is empty by default, then Scallop will try to infer the library_type by itself (see --preview). Notice that such inference is based on the XS tag stored in the input bam file. If the input bam file do not contain XS tag, then it is essential to provide the library_type to Scallop. You can try --preview to see the inferred library_type.

  3. --min_transcript_coverage is used to filter lowly expressed transcripts: Scallop will filter out transcripts whose (predicted) raw counts (number of moleculars) is less than this number.

  4. --min_transcript_length_base and --min_transcript_length_increase is combined to filter short transcripts: the minimum length of a transcript is given by --min_transcript_length_base + --min_transcript_length_increase * num-of-exons-in-this-transcript. Transcripts that are less than this number will be filtered out.

Quantification by Combining Scallop and Salmon

We recommend users to perform RNA-seq quantification using the combination of Scallop and Salmon. This pipeline involves the following steps:

Step 1: Align the reads to a reference genome (for example, with TopHat2, STAR, or HISAT2) to obtain the (sorted) reads alignment file sort.bam.

Step 2: Assemble the expressed transcripts with Scallop:

scallop -i sort.bam -o scallop.gtf

The assembled transcripts will be written to scallop.gtf.

Step 3: Use gffcompare to evaluate the assembled transcripts using a reference annotation:

gffcompare -o gffall -r reference.gtf scallop.gtf

where reference.gtf is the reference annotation file (for example, ensembl annotation). This command will generate a file defining which transcripts in scallop.gtf can be found in the reference.gtf.

Step 4: Union the assembled transcripts with the reference transcriptome. Specifically, First, use our tool gtfcuff to fetch the transcripts that are only in scallop.gtf:

gtfcuff puniq gffall.scallop.gtf.tmap scallop.gtf reference.gtf unique.gtf

The uniquely expressed transcripts (i.e., those are in scallop.gtf but not in reference.gtf) will be written to unique.gtf. Second, extract the cDNA sequences of the transcripts in unique.gtf from a reference genome using tool gffread:

gffread unique.gtf -g genome -w unique.fa

where genome is the reference genome, for example ensembl reference genome. The cDNA sequences of the uniquely assembled transcripts (i.e., those in unique.gtf) will be written to unique.fa. Finally, merge unique.fa and the reference transcriptome to obtained the extended transcriptome:

cat unique.fa reference.fa > union.fa

where reference.fa is the reference transcriptome (i.e., the cDNA sequences of the transcripts in reference.gtf), for example, ensembl cDNA sequences. The extended transcriptome will be written to union.fa.

Step 5: Run Salmon to quantify with respect to the above extended transcriptome. First, create Salmon index:

salmon index -t union.fa -i salmon.index -p 4

After that we can quantify:

salmon quant -i salmon.index -1 fastq-file1 -2 fastq-file2 -p 4

The main quantification file will appear as salmon.quant/quant.sf. Please refer to Salmon documentation for more advanced usage.

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