SCRAM - Fast and simple small RNA read alignment

SCRAM (Small Complementary RNA Mapper) is a small RNA-focused index-free aligner that rapidly maps small RNA reads exactly to reference sequences, with an integrated visualization package generating interactive and publication-quality plots.

Details on installation and usage can be found here.

To use the Docker image, see this Gist.

Workflow

scram CLI options

Profile alignment of 1 set of read files (likely biological replicates) to one or more reference sequences

./scram profile

Align reads of length l from 1 read file set to all sequences in a reference file

For example:

scram profile -r ref.fa -1 seq1a.fa,seq1b.fa,seq1c.fa -l 21,22,24 -o testAlign

Usage: scram profile [flags]

Required Flags:

-r, --alignTo : Path/to/FASTA reference file

-1, --fastxSet1 : Comma-separated path/to/read file set 1. GZIPped files must have .gz file extension

-l, --length : Comma-separated read (sRNA) lengths to align

-o, --outFilePrefix : Path/to/outfile prefix (_len.csv will be appended)

Optional Flags:

-t, --readFileType : Read file type: cfa (collapsed FASTA), fa (FASTA), fq (FASTQ), clean (BGI clean.fa). (default "cfa")

--adapter : 3' adapter sequence to trim - FASTA & FASTQ only (default "nil")

--maxLen : Maximum read length to include for RPMR normalization (default 32)

--minCount : Minimum read count for alignment and to include for RPMR normalization (default 1)

--minLen : Minimum read length to include for RPMR normalization (default 18)

--noSplit : Do not split alignment count for each read by the number of times it aligns

Compare alignment of 2 sets of read files (likely biological replicates) to multiple reference sequences

./scram compare

Compare normalised alignment counts and standard errors for 2 read file sets

For example:

scram compare -r ref.fa -1 seq1a.fa,seq1b.fa,seq1c.fa -2 seq2a.fa,seq2b.fa,seq2c.fa -l 21,22,24 -o testAlign

Usage: scram compare [flags]

Additional Required Flags:

-2, --fastxSet2 : Comma-separated path/to/read file set 2. GZIPped files must have .gz file extension

scram_plot.py CLI options

Profile plot

%run scram_plot.py profile

Required Flags:

-a, --alignment : sRNA alignment file prefix used by SCRAM profile (i.e. exclude _21.csv, _22.csv, _24.csv)

-l, --length : Comma-separated list of sRNA lengths to plot. SCRAM alignment files must be available for each sRNA length

-s, --search : Full header or substring of header. Without flag, all headers will be plotted

-cutoff : Min. alignment RPMR from the most abundant profile (if multi) to generate plot

-ylim : +/- y axis limit

-win : Smoothing window size (default=auto)

-pub : Remove all labels from profiles for editing for publication

-png : Export plot/s as 300 dpi .png file/s

-bin_reads : For plotting large profiles (i.e. chromosomes). Assigns reads 10,000 bins prior to smoothing. X-axis shows bin, not reference position

Compare plot

%run scram_plot.py compare

Required Flags:

-a, --alignment : sRNA alignment file prefix used by SCRAM compare (i.e. exclude _21.csv, _22.csv, _24.csv)

-l, --length : Comma-separated list of sRNA lengths to plot. SCRAM alignment files must be available for each sRNA length

Optional Flags

-plot_type : Bokeh plot type to display (log, log_error or all)

-xlab : x label - corresponds to -s1 treatment in SCRAM arguments. Used to generate .png file name

-ylab : y label - corresponds to -s2 treatment in SCRAM arguments. Used to generate .png file name

-html : If not using Jupyter Notebook, output interactive plot to browser as save to .html

-pub : Remove all labels from profiles for editing for publication

-png : Export plot/s as 300 dpi .png file/s

Worked Example

• For a detailed worked example of scram's capabilities, see the following link (which uses the scram_docker image):

Jupyter notebook on nbviewer

Installation

1. Use the scram_docker image

• The scram aligner and scram_plot.py plotting script are installed, along with Jupyter notebook, on the minimal Miniconda base.

• You'll need docker installed. Ensure your project drive is shared and you've got a decent about of RAM (i.e. 8 GB+) available.

  1. Navigate to your project base directory. Your host project files (i.e. collapsed FASTA read and FASTA reference files in sub-directories) will be mounted.

     Bash shell

     docker run -it --rm  -v `pwd`:/work -p 8888:8888 sfletcher/scram_docker
     

     Windows PowerShell

     docker run -it --rm  -v ${PWD}:/work -p 8888:8888 sfletcher/scram_docker
     

  2. Copy generated link with token into your browser.

  3. From a Jupyter notebook file, the scram aligner can be invoked by:

     !scram
     

     And the scram_plot.py script by:

     %run /scram_plot/scram_plot.py
     

2a. Download scram binary:

• Pre-compiled binaries are can be found at (*nix binaries may need to be made executable with chmod +x /path/to/binary):

  Mac OSX (64bit)

  Linux (64 bit)

  Windows (64 bit)

• Execute with the full binary name (e.g. scram_osx) rather than scram

2b. Or build from source:

• Go(lang) 1.8+ is required

  1. Install via go get

     go get github.com/sfletc/scram github.com/sfletc/scramPkg github.com/spf13/cobra github.com/spf13/viper github.com/montanaflynn/stats
     

  2. Navigate to scram directory containing main.go (e.g. GOPATH/src/github.com/sfletc/scram/)

     go install

  3. scram will be in the GOPATH/bin directory

3. Install the scram_plot package and dependencies:

• Python 3.5+ is required

  git clone https://github.com/sfletc/scram_plot.git

  cd scram_plot

  python setup.py install