Stable RNA processing product analyzer
Stable RNA processing product analyzer
Tool to predict, quantify and characterize stable RNA processing products from RNA-seq data.
Starpa workflow is divided into multiple consecutive tasks which can be executed separately, as a freely chosen successive subsets or all tasks at once in sequential order. This adds flexibility to the tool to use as an input RNA-seq data in various state of processing. For example Starpa can handle raw data in FastQ format, but also trimmed reads (FastQ format) or aligned reads in SAM format.
Both paired-end (PE) and single-end (SE) sequencing reads are accepted as an input.
In addition, the tool is highly configurable and can handle multiple libraries in parallel manner (multiprocessing).
Tasks are following:
Cutadapt is used to trim low quality 3’ end of the reads followed by adapter removal from 3’ end of the reads.
In case of SE, the reads where 3’ adapter was not trimmed are excluded. This ensures that 3’ end of the read is stable RNA processing products is estimated with higher confidence.
Bowtie2 is used to align reads to the genome. All matches to the genome are recorded.
From aligned reads the unmapped and discordantly mapped reads are discarded. In addition, only the reads belonging to best stratum (class of alignment score) are retained while alignments with lower alignments score are excluded.
Alignments with too many mismatches and reads with too many genomic alignments are discarded. All other reads get NH tag (if not present) describing the number of reported alignments. Sequence and quality fields of secondary alignments are filled with sequence and quality data. In the end the PE reads are converted to pseudo SE reads to ease subsequent analysis steps.
Flaimapper2 is used to predict stable RNA processing products. To ensure prediction of all processing products which share start or end positions, the reads are fractionated according to their length. Subsequently, Flaimmper2 is run on each fraction of reads separately and the predicted processing products are filtered by the read count (estimation by Flaimapper-2) exceeding threshold set. The filtered predicted processing products are quantified more precisely via bedtools intersect.
Quantified processing products are filtered once again by the read counts (bedtools intersect) exceeding threshold and by relative coverage (average coverage of reads assigned to processing products divided by average coverage of all reads aligned to the positions of processing products). Next, the processing products from all libraries analysed are combined (identifying unique species) and clustered.
Clustering is two step process:
- clustering by overlap.
As the prediction of processing products by Flaimapper-2 is probabilistic, the predicted ends of the processing products in different libraries might slightly vary, as also the true ends. Therefore, the predicted processing products which do largely overlap and have some bases (adjustable) not overlapping are clustered and representative processing products for clusters are selected.
- clustering by sequence
As a majority of genomes contain repeating regions (repeat regions, rRNA operons, some tRNA genes etc) reads can be mapped to multiple positions resulting multiple processing products consisting from the same or similar set of reads. To reduce the number of identical processing products they are clustered by sequence identity via CDI-HIT-EST. Still the genomic matches of particular reads can be in genomic regions with different surrounding sequence/context (eg. different genes) therefore clustering solely based on sequence identity can result loss of information. To avoid it the predicted processing products which cluster by sequence identity has to be supported by the clustering (again via CDI-HIT-EST) of the contigs they overlap with and representative processing product for the clusters are selected.
In addition, the contigs are identified and wig formatted files (containing coverage data of individual libraries) are created.
Representative processing products will be quantified using bedtools intersect in every library. Additional characteristics will be gathered (relative coverage, coverage at single position level, consensus sequence, quality of consensus sequence, genomic sequence, uniqueness). Quantification data is also converted to read per million of mapped reads (RPM), RPM of biotype and RPM of biotype groups.
pip install --user starpa
Starpa is depending on following tools which have to be installed in your system:
Python3 requires following packages which will be installed (if missing) during the installation of starpa:
pyfaidx, docopt, schema
Starpa is compatible with UNIX like operating systems.
- Colorspace reads are not supported.
- Both paired-end (PE) and single-end (SE) reads are supported.
Usage of starpa is as follows:
Usage: starpa [-hv] starpa -s <start_task> -e <end_task> -c <parameter_file> -i <input> -o <output> Arguments: <start_task> task to start with <end_task> tast to end with <config_file> configuration file <input> input folder <output> output folder Options: -v, --version -h, --help -s <start_task>, --start=<start_task> -e <end_task>, --end=<end_task> -c <config_file>, --config=<config_file> -i <input_folder>, --input=<input_folder> -o <output_folder>, --output=<output_folder>
Starpa work-flow is divided into multiple consecutive tasks which can be executed:
- as a freely chosen successive subsets
- all at once in sequential order
Tasks in sequential order:
trim, align, sam_sort, pseudoSE, identify, cluster, quantify
Configuration file is used to set various parameters which allow to adjust the performance of the work-flow according to the user needs and input data. The description of each parameter is given in the file itself.
Configuration file states also the location of following files:
adapter files - adapter sequencies in fasta format
genome file - genome sequence in fasta format
annotation file - in GFF or GFF3 format.
“library_file” - describing libraries to be analysed.
- “library_file” is a tabular file containing:
- the name of the libraries
- conditions they are derived from and
- identifier of replicate
(note that all three columns are separated by tab)
#Library number Sample Replicate library1 LB OD 0.4 I library2 LB OD 0.4 II
While running a single or multiple tasks, the input folder has to contain specific data required for the first task. For the following task the preceding tasks will prepare proper data.
Each task has different requirements for the input data:
Output folder will contain parameter folder:
parameters/ eg. config.txt - copy of configuration file arguments.txt - command line arguments eg. libraries.txt - copy of library file eg. parameters.dev-2-100-2.txt - copy of Flaimapper-2 parameter file
Each task creates a subfolder with its name containing specific output of the task.
trim_info/ XXX_triminfo.log - log of task XXX_triminfo.error - collected errors during trimming PE: discard/ XXX_1_short.fq - forward reads discared while being too short after trimming XXX_2_short.fq - reverse reads discared while being too short after trimming XXX_trim_1.fq - trimmed forward reads XXX_trim_2.fq - trimmed reverse reads SE: discard/ XXX_short.fq - reads discarded while being too short after trimming XXX_untrimmed.fq - reads discarded while having no adapter trimmed XXX_trim.fq - trimmed reads
align_info/ XXX_aligninfo.log - log of task XXX.sam - aligned reads
sort_info/ XXX_sortinfo.log - log of task XXX_unmapped.sam - unmapped reads XXX_sort.sam - processed reads
pseudoSE_info/ XXX_pseudoSEinfo.log - log of task mismatched/ XXX_pseudoSE_mismatch.sam - reads discarded while having too many mismatches too_many_matches/ XXX_pseudoSE_multimatch.sam - reads discarded while haveing too many genomic matches XXX_pseudoSE.sam - processed reads If oligoA allowed: oligoA/ XXX-oligoA-mm_pseudoSE.sam - reads with 3' oligoA (non-genome encoded) which would have otherwise discarded XXX-oligoA-pseudoSE.sam - reads with 3' oligoA (non-genome encoded)
flaimapper/ flaimapper_info/ XXX/ XXX_strand_Y_flaimapper.information - log of flaimapper flaimapper_temp/ XXX/ XXX_strand_Y_flaimapper.tab - flaimapper predicitons bam/ XXX_strand.bam - strand-wise sorted reads from input XXX_strand.bam.bai - index of of bam file XXX_strand.sam - NOT NEEDED identify_info/ XXX_strand_identifyinfo.log - log of task XXX_strand_pp.BED - NOT NEEDED XXX_strand_pp_counted.BED - predicted processing products with quantification
cd_hit_est/ pp_cd_hit_est.info - log of sequence identity based clustering of combined and overlap clustered predicted processing products via CD-HIT-EST pp_combined.cdhit - genomic sequence of combined and overlap clustered predicted processing products pp_combined.cdhit.clstr - clusters of combined and overlap clustered predicted processing products created via CD-HIT-EST contigs/ XXX_contigs.BED - list of contigs identified XXX/ contig_name.fasta - sequences of all reads belonging to the corresponding contigs contig_name.sam - all reads belonging to the corresponding contigs contigs_meta/ combined_contigs_meta.BED - combined contigs to be used to create metacontigs from all libraries XXX_contigs_meta.BED - list of contigs to be used to created metacontigs metacontig_cd_hit_est.info - log of sequence identity based clustering of metacontigs via CD-HIT-EST metacontigs.cdhit - genomic sequence of metacontigs metacontigs.cdhit.clstr - clusters of metacontigs created via CD-HIT-EST metacontigs.BED - list of metacontigs in bed format pp_to_metacontig.BED - combined and overlap clustered predicted processing product match with metacontigs in BED-like format mpileup/ XXX_strand_mpileup.info - log of bedtools mpileup wig/ XXX_strand.wig - strand specific absolute read coverage XXX_strand_RPM.wig - strand specific relative read coverage as read per million mapped reads (RPM) pp_clusterinfo.log - log of task pp_unique.library_info - combined predicted processing products and the origins of libraries pp_combined.BED - representatives of combined and overlap clustered predicted processing products in BED format pp_combined.cluster - overlap clusters of combined predicted processing products pp_combined.library_info - representatives of combined and overlap clustered predicted processing products and the origins of libraries pp_metacontig.BED - representatives of predicted processing products from pp_combined.BED clustered by sequence identity supported by metacontig clustering in BED format pp_metacontig.cluster - sequence identity clusters of predicted processing products from pp_combined.BED supported by metacontig clustering
libraries/ - data in library wise XXX.biotype_annotation.statistics - read alignement statistics by annotation biotypes XXX.gene_annotation.statistics - read alignement statistics by genes pp_metacontig_XXX_counted.BED - absolute quantification of predicted processing products in BED format collected.annotation2.statistics - combined alignement statistics by annotation biotypes pp_metacontig_biotype.BED - predicted processing products with biotype in BED-like format pp_metacontig_biotype_match.BED - predicted processing products match with genes in BED-like format pp_metacontig_counts_total.tsv - absolute quantification of predicted processing products in BED format pp_metacontig_counts_RPM.tsv - relative quantification of predicted processing products as read per million mapped reads (RPM) in BED format pp_metacontig_counts_biotype_RPM.tsv - relative quantification of predicted processing products as RPM of biotype in BED format pp_metacontig_counts_groupped_biotype_RPM.tsv - relative quantification of predicted processing products as RPM of biotype groups in BED format pp_metacontig_cons_qual.tsv - quality of consensus sequence of predicted processing products expressed as frequency of the most abundant base in a given position pp_metacontig_cons_seq.tsv - consensus sequence of predicted processing products pp_metacontig_coverage.tsv - coverage of reads assigned to predicted processing products at single position level pp_metacontig_genomic_seq.tsv - genomic sequence of predicted processing products pp_metacontig_rel_cov.tsv - relative coverage of predicted processing products pp_metacontig_uniqness.tsv - mean number of genomic genomic matches of reads assigned to the predicted processing products
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