The current version of sRNA-Seq data analyzer contain a series of python based modules that automate the analysis process. I highly recommend reading through this step-by-step manual carefully before you start analyzing your data.
The sRNA-seq data analyzer requires following tools to be installed (see here) for data analysis.
- FastQC : Raw sequence quality assessment
- Cutadapt : Adaptor trimming
- Bowtie : Mapping reads to a given genome
- ShortStack : Predict/identify novel/known small RNAs
- SAMtools : Sorting, filtering and indexing of mapped reads
- deepTools : Generate bigwig files for IGV visualization
- featureCounts : Count miRNAs
- MultiQC : Summarize logs
We thank developers of these valueble tools!
All analyzed data will be saved onto the home directory where you deposited the scripts directory. The pipeline first use FastQC to assess the quality of raw input files. Conventional illumina adaptors, random UMIs and low quality reads are removed using Cutadapt. Based on the user defined species, corresponding genome and miRBase miRNA annotation files will be downloaded. Then, Bowtie genome indices will be generated. Next, quality-trimmed sequences are mapped to the reference genome using Bowtie genome aligner. The analysis scheme use SAMtools to coordinate sort, remove unmapped sequences and index alignment output files. The sorted alignment file and the index will be used by deepTools to generate bigwig files for IGV visualization. Next subread package featureCounts will be called to quantify, by default, the number of reads aligning to miRNAs. In addition, the pipeline uses ShortStack to independently align trimmed small RNA-seq data and annotate small RNA-producing genes. Finally, the pipeline integrates MultiQC to generate summary files in an interactive manner.
Now that you know the general outline of the analysis process, go through the step-by-step user guide given here to analyze your small RNA-seq data.