An open source and fully customized pipeline that:
- Maps all small non-coding RNAs to customized and comprehensive reference sequences
- Is modularized and iterative
- Run on an HPCC cluster (default) or on a computer/server
- Performs quality control on the data
- Provides a detailed summary of mapping:
- FastQC plots for every iteration
- MultiQC plots for every iteration
- Summary plots and statistics of smallRNA distribution and abundance
- Provides raw and normalized (RPKM) counts
- Detailed logs of every iteration and steps
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gjsrmap: SmallRNA mapping and analysis pipeline schematic:
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Pre-processing of the sequences:
- This is the iteration 0 in the above schematic diagram
- Preprocessing of the sequences to avoid multi mapping of the reads
- Build custom reference sequence indexes
- Removal of low quality reads
- Removal of 3' adapter sequences and size reduction of the reads
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Iterative mapping of processed and filtered reads:
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Iteration 1: Map reads between 16 to 33 bp to custom reference sequences of mature microRNAs and piRNAs
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Iteration 2: Map reads greater than 32 bp to custom reference sequences of other small non-coding RNAs. These are:
| Other small non-coding RNAs | Description | |-----------------------------|------------------------------------------------------| | rRNA | Ribosomal RNA | | scRNA | Small cytoplasmic RNA | | snRNA | Small nuclear RNA | | snoRNA | Small nucleolar RNA | | premiRNA | microRNA precursors | | osncRNA | Other small noncoding RNA | | - tRNA | - Transfer RNA | | - Mt-tRNA | - Transfer RNA located in the mitochondrial genome | | - misc_RNA | - Miscellaneous other RNA | -
Iteration 3: Map the unmapped reads from iteration 1 and 2 to the species reference genome
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Count the reads and distribute them to individual smallRNA classes
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Generate QC, mapping and summary report
- Sequence quality information
- Bar plot of library sizes
- Small non-coding RNA reads distrubution
- Profile of expressed small non-coding RNAs (miRNAs in the above figure). Plots are also generated for other classes as well
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bedtoolsbowtiebowtie2cutadaptfastqcmatplotlibmultiqcnumpysamtoolsscipy
- Run wrapper with following options:
SPC=${1} # Species: hsa or mmu or some other species
IFD=${2} # Input Fastq Dir: input/fastq/test
ORD=${3} # Output Results Dir: output/test
BWD=${4} # path/to/bowtie/indexes
QUE=${5:-"fat"} # mpi, fat, mpi-short, fat-short, mpi-long, fat-long
SPK=${6:-""} # exiseq_spikein_dna_unique.fa or spike_rna1_unique.fa
threePadapter=${7:-"TGGAATTCTCGGGTGCCAAGG"} # trueseq adapter
JID=${8:-"$(echo $HOME)/gjsrmap"} # Job dir
NCL=${9:-"input/annotation/rna_classes"} # ncrna folder containing ncrna class fasta
- Example command:
bash 06_run_ncRNA_mapping_usage.sh <above mentioned arguments>