Authors : Marc Friedlaender and Sebastian Mackowiak.
Date:     12/08/2009

This is miRDeep2 developed by Marc Friedlaender and Sebastian Mackowiak.
miRDeep2 discovers active known or novel miRNAs from deep sequencing data (Solexa/Illumina, 454, ...).


Requirements:
------------
Linux system, 2GB Ram, enough disk space dependent on your deep sequencing data

# Testing version
MacOSX with Xcode and gcc compiler installed. (This can be obtained from the appstore,
if there are any issues with installing it please look for help online).

To compile the Vienna package it maybe necessary to have GNU-grep installed since
the MacOSX grep is BSD based and sometimes not accepted by the installer.
To get a GNU grep you could for example install homebrew by typing
##
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
##
(the link could be out of date, in that case look up online what to do)

After that typing
##
brew tap homebrew/dupes; brew install grep
##
will install gnu grep as 'ggrep' in /usr/local/bin/
##
 


Installation:
------------
1. with the provided install.pl script type:

                perl install.pl


2. without the install mirdeep script follow the instructions given in Sample Installation


2.1 Sample Installation
First download all necessary packages listed here

a     bowtie short read aligner (http://bowtie-bio.sourceforge.net/index.shtml)
b     Vienna package with RNAfold (http://www.tbi.univie.ac.at/~ivo/RNA/)
c.i   SQUID library (http://selab.janelia.org/software.html) goto Squid and download it
c.ii  randfold (http://bioinformatics.psb.ugent.be/software/details/Randfold)
d     Perl package PDF::API2 (http://search.cpan.org/search?query=PDF%3A%3AAPI2&mode=all)


Sample installation tutorial when packages are downloaded

2           attach the miRDeep2 executable path to your PATH
            (echo 'export PATH=$PATH:your_path_to_mirdeep2/src' >> ~/.bashrc)



2.a.1)      unzip bowtie-0.11.3-bin-linux-x86_64.zip
2.a.2)      put the bowtie directory into your PATH variable e.g
            echo 'export PATH=$PATH:your_path_tobowtie' >> ~/.bashrc 


2.b.1)      tar xvvzf ViennaRNA-1.8.4.tar.gz
2.b.2)      cd to the Vienna dir
2.b.3)      type   ./configure --prefix=your_path_to_Vienna/install_dir
            make
            make install

2.b.4)      add Vienna binaries to your PATH variable e.g.
           (echo 'export PATH=$PATH:your_path_to_Vienna/install_dir/bin' >> ~/.bashrc) 


2.c.i.1)   tar xxvzf squid-1.9g.tar.gz
2.c.ii.1)  tar xvvzf randfold-2.0.tar.gz
2.c.ii.2)  cd randfold2.0
2.c.ii.3)  edit Makefile e.g. emacs Makefile
           change line with INCLUDE=-I. to           
           INCLUDE=-I. -Iyour_path_to_squid-1.9g -Lyour_path_to_squid-1.9g
      e.g. INCLUDE=-I. -I/home/Pattern/squid-1.9g/ -L/home/Pattern/squid-1.9g/   

2.c.ii.4)  make
2.c.ii.5)  add randfold to your PATH variable e.g.
           (echo 'export PATH=$PATH:your_path_to_randfold' >> ~/.bashrc) 


     
2.d.1)     tar xvvzf PDF-API2-0.73.tar.gz
2.d.2)     cd to you PDF_API2 directory     
2.d.3)     then type in 
           perl Makefile.PL PREFIX=your_path_to_miRDeep2 LIB=your_path_to_miRDeep2/lib
           make
           make test
           make install


2.e.4)     add your library to the PERL5LIB e.g.
           (echo 'export PERL5LIB=PERL5LIB:your_path_to_miRDeep2/lib/perl5/site_perl/5.x/' >> ~/.bashrc)
           (To figure out your perl version type in 'perl -v' and then change 5.x to your perl version)   


2.f)      start a new shell session to apply changes to environment variables

to test if everything is installed properly type in 
1) bowtie
2) RNAfold -h
3) randfold
4) make_html.pl

you should not get any error messages otherwise something is not correctly installed




Install Paths:
-------------
Everything that is download by the installer will be in a directory called your_path_to_mirdeep2/essentials


Script Reference:
----------------

miRDeep2 analyses can be performed using the three scripts miRDeep2.pl, mapper.pl and quantifier.pl.


name:
miRDeep2.pl

description:
Wrapper function for the miRDeep2.pl program package. The script runs all necessary scripts of the miRDeep2 package
to perform a microRNA detection deep sequencing data anlysis.

input:
A fasta file with deep sequencing reads, a fasta file of the corresponding genome, a file of mapped reads to the genome in miRDeep2 
arf format, an optional fasta file with known miRNAs of the analysing species and an option fasta file of known miRNAs of related 
species

output:
A spreadsheet and an html file with an overview of all detected miRNAs in the deep sequencing input data.


options:
-a int        minimum read stack height that triggers analysis. Using this option disables
              automatic estimation of the optimal value.
-b int        minimum score cut-off for predicted novel miRNAs to be displayed in the overview
              table. This score cut-off is by default 0.

-c            disable randfold analysis

-t species    species being analyzed - this is used to link to the appropriate UCSC browser

-u            output list of UCSC browser species that are supported and exit

-v            remove directory with temporary files

-q file       miRBase.mrd file from quantifier module to show miRBase miRNAs in data that were not scored by miRDeep2


Examples:

The miRDeep2 module identifies known and novel miRNAs in deep sequencing data. The output of the
mapper module can be directly plugged into the miRDeep2 module.


Example use 1:

The user wishes to identify miRNAs in mouse deep sequencing data, using default options. The 
miRBase_mmu_v14.fa file contains all miRBase mature mouse miRNAs, while the miRBase_rno_v14.fa
file contains all the miRBase mature rat miRNAs. The '2>' will pipe all progress output to the
report.log file.

miRDeep2.pl reads_collapsed.fa genome.fa reads_collapsed_vs_genome.arf miRBase_mmu_v14.fa miRBase_rno_v14.fa precursors_ref_this_species.fa -t Mouse 2>report.log

This command will generate a directory with pdfs showing the structures, read signatures and
score breakdowns of novel and known miRNAs in the data, an html webpage that links to all
results generated (result.html), a copy of the novel and known miRNAs contained in the webpage
but in text format which allows easy parsing (result.csv), a copy of the performance survey
contained in the webpage but in text format (survey.csv) and a copy of the miRNA read signatures
contained in the pdfs but in text format (output.mrd).



Example use 2:

The user wishes to identify miRNAs in deep sequencing data from an animal with no related species
in miRBase: 

miRDeep2.pl reads_collapsed.fa genome.fa reads_collapsed_vs_genome.arf none none none 2>report.log

This command will generate the same type of files as example use 1 above. Note that there it will
in practice always improve miRDeep2 performance if miRNAs from some related species is input, even
if it is not closely related.


###############################################################################################################################


name:
mapper.pl

description:
Processes reads and/or maps them to the reference genome. 

input:
Default input is a file in fasta, seq.txt or qseq.txt format. More input can be given depending
on the options used.

output:
The output depends on the options used (see below). Either a fasta file with processed reads or
an arf file with with mapped reads, or both, are output.

options:
Read input file:
-a              input file is seq.txt format
-b              input file is qseq.txt format
-c              input file is fasta format

Preprocessing/mapping:
-h              parse to fasta format
-i              convert rna to dna alphabet (to map against genome)
-j              remove all entries that have a sequence that contains letters other than
                a,c,g,t,u,n,A,C,G,T,U,N
-k seq          clip 3' adapter sequence
-l int          discard reads shorter than int nts
-m              collapse reads
-p genome       map to genome (must be indexed by bowtie-build). The 'genome' string must be the
                prefix of the bowtie index. For instance, if the first indexed file is called
                'h_sapiens_37_asm.1.ebwt' then the prefix is 'h_sapiens_37_asm'.
-q              map with one mismatch in the seed (mapping takes longer)

Output files:
-s file         print processed reads to this file
-t file         print read mappings to this file 

Other:
-u              do not remove directory with temporary files
-v              outputs progress report

Examples:

The mapper module is designed as a tool to process deep sequencing reads and/or map them to the
reference genome. The module works in sequence space, and can process or map data that is in
sequence fasta format. A number of the functions of the mapper module are implemented specifically
with Solexa/Illumina data in mind. For example on how to post-process mappings in color space, see
example use 5:



Example use 1:

The user wishes to parse a file in qseq.txt format to fasta format, convert from RNA to DNA alphabet,
remove entries with non-canonical letters (letters other than a,c,g,t,u,n,A,C,G,T,U,N), clip adapters,
discard reads shorter than 18 nts and collapse the reads:
 
mapper.pl reads_qseq.txt -b -h -i -j -k TCGTATGCCGTCTTCTGCTTGT -l 18 -m -s reads_collapsed.fa



Example use 2:

The user wishes to map a fasta file against the reference genome. The genome has already been
indexed by bowtie-build. The first of the indexed files is named genome.1.ebwt:

mapper.pl reads_collapsed.fa -c -p genome -t reads_collapsed_vs_genome.arf



Example use 3:

The user wishes to process the reads as in example use 1 and map the reads as in example use 2
in a single step, while observing the progress:

mapper.pl reads_qseq.txt -b -h -i -j -k TCGTATGCCGTCTTCTGCTTGT -l 18 -m -p genome -s reads_collapsed.fa -t reads_collapsed_vs_genome.arf -v



Example use 4:

The user wishes to parse a GEO file to fasta format and process it as in example use 1. The GEO file
is in tabular format, with the first column showing the sequence and the second column showing the
read counts:

geo2fasta.pl GSM.txt > reads.fa

mapper.pl reads.fa -c -h -i -j -k TCGTATGCCGTCTTCTGCTTGT -l 18 -m -s reads_collapsed.fa 



Example use 5:

The user has already removed 3' adapters in color space and has mapped the reads against the genome
using the BWA tool. The BWA output file is named reads_vs_genome.sam. Notice that the BWA output
contains extra fields that are not required for SAM format. Our converter requires these fields and
thus may not work with all types of SAM files. The user wishes to generate 'reads_collapsed.fa'
and 'reads_vs_genome.arf' to input to miRDeep2:

bwa_sam_converter.pl reads_vs_genome.sam reads.fa reads_vs_genome.arf

mapper.pl reads.fa -c -i -j -l 18 -m -s reads_collapsed.fa

############################################################################################################################### 



name:
quantifier.pl

description:
The module maps the deep sequencing reads to predefined miRNA precursors and determines by that the expression of the corresponding miRNAs.
First, the predefined mature miRNA sequences are mapped to the predefined precursors. Optionally, predefined star sequences can be mapped 
to the precursors too. By that the mature and star sequence in the precursors are determined.
Second, the deep sequencing reads are mapped to the precursors. The number of reads falling into an interval 2nt upstream and 5nt downstream 
of the mature/star sequence is determined.

input:
A fasta file with precursor sequences, a fasta file with mature miRNA sequences, a fasta file with deep sequencing reads and optionally a fasta
file with star sequences and the 3 letter code of the species of interest 

output:
A 2 column table file called miRNA_expressed.csv with miRNA identifiers and its read count, a file called miRNA_not_expressed.csv with all
miRNAs having 0 read counts, a signature file called miRBase.mrd, a file called expression.html that gives an overview of all miRNAs the input data
and a directory called pdfs that contains for each miRNA a pdf file showing its signature and structure. 

options:
-t  list all values allowed for the species parameter that have an entry at UCSC 

example usage:
quantifier.pl precursors.fa mature.fa reads.fa star.fa/none species/none timestamp/none pdf



#####################################################################################################################################



name:
make_html.pl

description:
It creates a file called result.html that gives an overview of miRDeep2 detected miRNAs (known and novel ones).
The html file lists up each detected miRNA and provides among others information on its miRDeep2 score, reads mapped to its mature, 
loop and star sequence, the mature, star and consensus precursor sequences themselfes and provides links to BLAST, BLAT, 
mirbase for miRBase miRNAs and to a pdf file that shows the signature and structure. 


input: 
A miRDeep2 output.mrd file, a miRDeep2 survey.csv file


output:
A result.html file with an entry for each provisional miRNA that contains information about its assigned Id, miRDeep2 score, 
estimated probability that the miRNA candidate is a true positive, rfam alert, total read count, mature read count, loop read count, 
star read count, significant randfold p-value, miRBase miRNA, example miRBase miRNA with the same seed, BLAT, BLAST, 
consensus mature sequence, consensus star sequence and consensus precursor sequence. Furthermore, the miRBase miRNAs existent in the input data 
but not scored by miRDeep2 are listed.
A directory called pdfs that contains for each provisional miRNA ID a pdf with its signature and structure.
A file called result.csv (when option -c is used) that contains the same entrys as the html file. 


options:
-v int    only output hairpins with score above int
-c        also create overview in excel format.
-k file   supply file with known miRNAs
-s file   supply survey file if score cutoff is used to get information about how big is the confidence of resulting reads
-f file   miRDeep2 output mrd file

-e        report complete survey file
-g        report survey for current score cutoff
-w        project_folder        automatically used when running webinterface, otherwise don't use it
-r file   Rfam file to check for already reported small RNA sequences

-q file   miRBase.mrd file produced by quantifier module
-x file   signature.arf file with mapped reads to precursors
-t org    specify the organism from which your sequencing data was obtained
-u        print all available UCSC input organisms
-d        do not generate pdfs
-y        timestamp
-z        switch is automatically used when script is called by quantifier.pl
-o        print reads in pdf signature sorted by their 3 letter code in front of their identifier

example usage:
perl make_html.pl -f miRDeep_outfile -s survey.csv -c -e -y 123456789

notes:
-



#####################################################################################################################################



name:
clip_adapters.pl

description:
Removes 3' end adaptors from deep sequenced small RNAs. The script searches for occurrences of the
six first nucleotides of the adapter in the read sequence, starting after position 18 in the read
sequence (so the shortest clipped read will be 18 nts). If no matches to the first six nts of the
adapter are identified in a read, the 3' end of the read is searched for shorter matches to the 5
to 1 first nts of the adapter.

input:
A fasta file with the deep sequencing reads and the adapter sequence (both in RNA or DNA alphabet).

output:
A fasta file with the clipped reads. Fasta ids are retained. If no matches to the adapter prefixes
are identified in a given read, the unclipped read is output.

options:
-

example usage:
clip_adapters.pl reads.fa TCGTATGCCGTCTTCTGCTTGT > reads_clipped.fa

notes:
It is possible to clip adapters using more sophisticated methods. Users are encouraged to test other
methods with the miRDeep2 modules.



#####################################################################################################################################



name:
collapse_reads.pl

description:
Collapses reads in the fasta file to ensure that each sequence only occurs once. To indicate how
many times reads the sequence represents, a suffix is added to each fasta identifier. E.g. a
sequence that represents ten reads in the data will have the '_x10' suffix added to the identifier.

input:
A fasta file, either in standard format or in the collapsed suffix format.

output:
A fasta file in the collapsed suffix format.

options:
-a    outputs progress

example usage:
collapse_reads.pl reads.fa > reads_collapsed

notes:
Since the script reads all fasta entries into a hash using the sequence as key, it can potentially
use more than 3 GB memory when collapsing very big datasets, >50 million reads. In this case, the
user can partition the reads (for instance based on the 5' nucleotide), collapse separately and
concatenate.



#####################################################################################################################################



name:
excise_precursors_iterative.pl

description:
This script is a wrapper for excise_precursors.pl, which it calls one or more times, incrementing
the height of the read stack required for initiating excision until the number of excised precursors
falls below a given threshold.

input:
The reference genome in fasta format, the mapped reads in .arf format, a filename that the excised
precursors will be written to and the maximal number of precursors that should be reported.

output:
The excised precursors in fasta format.

options:
-a           Output progress to screen

example usage:
excise_precursors_iterative.pl genome.fa reads_vs_genome.arf potential_precursors.fa 50000 -a



#####################################################################################################################################



name:
excise_precursors.pl

description:
Excises precursors from the genome using the mapped reads as guidelines.

input:
The reference genome in fasta format and the mapped reads in .arf format.

output:
The excised precursors in fasta format.

options:
-a integer   Only excise if the highest local read stack is 'integer' reads high (default 2).
-b           Output progress to screen

example usage:
excise_precursors.pl genome.arf reads_vs_genome.arf -b



#####################################################################################################################################



name:
fastaparse.pl

description:
Performs simple filtering of entries in a fasta file.

input:
A fasta file

ouput:
A filtered fasta file

options:
-a int    only output entries where the sequence is minimum int nts long
-b        remove all entries that have a sequence that contains letters other than
          a,c,g,t,u,n,A,C,G,T,U,N.
-s        output progress

example usage:
fastaparse.pl reads.fa -a 18 -s > reads_no_short.fa



#####################################################################################################################################



name:
fastaselect.pl

description:
This script only prints out the fasta entries that match an id in the id file.

input:
A fastafile and a file with ids, one id per line.

output:
A fastafile containing the fasta entries that match an id.

options:
-a       only prints out entries that has an id that is not present in the id file.

example usage:
fastaselect.pl reads.fa reads_select.ids > reads_select.fa



#####################################################################################################################################



name:
find_read_count.pl

description:
Scans a file searching for the suffixes that are generated by collapse_reads.pl (e.g. '_x10').
It sums up the integer values in the suffixes and outputs the sum. If a given id occurs
multiple times in the file, it will multi-count the integer value of the id. It will also only
count the first integer occurrence in a given line.

input:
Any file containing the suffixes that are generated by collapse_reads.pl. This will typically
be a fasta file or a list of ids.

output:
The sum of integer values (the total read count).

options:
-

example usage:
find_read_count.pl reads_collapsed.fa



#####################################################################################################################################



name:
geo2fasta.pl

description:
Parses GSM format files into fasta format.

input:
GSM files in tabular format. The first column should be sequences and the second column the
number of times the sequence occurs in the data.

output:
A fasta file, one sequence per line (the sequences are expanded).

options:
-

example usage:
geo2fasta.pl GSM.txt > reads.fa



#####################################################################################################################################



name:
illumina_to_fasta.pl

description:
Parses seq.txt or qseq.txt output from the Solexa/Illumina platform to fasta format.

input:
A seq.txt or qseq.txt file. By default seq.txt.

output:
A fasta file, one entry for each line of seq.txt. The entries are named 'seq' plus a running
number that is incremented by one for each entry. Any '.'characters in the seq.txt file is
substituted with a 'N'.

options:
-a    format is qseq.txt

example usage:
illumina_to_fasta.pl s_1.qseq.txt -a > reads.fa



#####################################################################################################################################



name:
miRDeep2_core_algorithm.pl

description:
For each potential miRNA precursor input, the miRDeep2 core algorithm either discards it or 
assigns it a log-odds score that reflects the probability that the precursor is a genuine miRNA.

input:
Default input is an arf file with the read signatures and an RNAfold output file with the structures
of the potential miRNA precursors.

output:
A .mrd file with all potential miRNA precursors that are scored.

options:
-h          print this usage
-s          fasta file with reference mature miRNAs from one or more related species
-t          print filtered
-u          limited output (only ids)
-v          cut-off (default 1)
-x          sensitive option for Sanger sequences
-y          file with randfold p-values
-z          consider Drosha processing

example usage:
miRDeep2_core_algorithm.pl signature.arf potential_precursors.str -s miRBase_related_species.fa -y potential_precursors.rand > output.mrd

notes:
The -z option has not been thoroughly tested.



#####################################################################################################################################



name:
parse_mappings.pl

description:
Performs simple filtering of entries in an .arf file.

input:
Default input is an .arf file.

output:
A filtered .arf file.

options:
-a int     Discard mappings of edit distance higher than this
-b int     Discard mappings of read queries shorter than this
-c int     Discard mappings of read queries longer than this
-d file    Discard read queries not in this file
-e file    Discard read queries in this file
-f file    Discard reference dbs not in this file
-g file    Discard reference dbs in this file
-h         Discard remaining suboptimal mappings
-i int     Discard remaining suboptimal mappings and discard any reads that have more remaining mappings
           than this
-j         Remove any unmatched nts in the very 3' end
-k         Output progress to standard output

example usage:
parse_mappings.pl reads_vs_genome.arf -a 0 -b 18 -c 25 -i 5 > reads_vs_genome_parsed.arf



#####################################################################################################################################



name:
perform_controls.pl

description:
Performs a designated number of rounds of permuted controls (for details, see Friedlaender et al., Nature
Biotechnology, 2008).

input:
The permutation controls estimate the number of false positives produced by a miRDeep2_core_algorithm.pl run.
The input to perform_controls.pl should be a file containing the exact command line used to initiate the 
miRDeep2_core_algorithm.pl run, the structure file input to miRDeep2_core_algorithm.pl and the desired
rounds of controls.

output:
A file in .mrd format. The output of each control run is separated by a line 'permutation integer'. The mean
number of entries output by the control runs gives an estimate of the false positives produced. The further
contents (besides the number of entries) of the .mrd output by perform_controls.pl is not biologically
meaningful.

options:
-a   Output progress to screen

example usage:
perform_controls.pl line potential_precursors.str 100 > output_controls.mrd



#####################################################################################################################################



name:
permute_structure.pl

description:
In a file output by RNAfold, each entry can be partitioned into an 'id' part and an 'other' part, consisting
of the dot-bracket structure, sequence, mfe etc. This scripts reads all 'id' parts into a hash and pairs
them with 'other' parts from random entries. This is used by the perform_controls.pl script.

input:
An RNAfold output file.

output:
An RNAfold output file with ids moved to random entries.

options:
-

example usage:
permute_structure.pl potential_precursors.str > potential_precursors_permuted.str



#####################################################################################################################################



name:
prepare_signature.pl

description:
Prepares the signature file to be input to the miRDeep2_core_algorithm.pl script.

input:
A fasta file with deep sequencing reads and a fasta file with precursors.

output:
A signature file in .arf format.

options:
-a file     Fasta file with the sequences of known mature miRNAs for the species. These sequences will not
            influence the miRDeep scoring, but will subsequently make it easy to estimate sensitivity of the run.
-b          Output progress to screen

example usage:
prepare_signature.pl reads_collapsed.fa potential_precursors.fa -a  miRBase_this_species.fa > signature.arf



#####################################################################################################################################



name:
rna2dna.pl

description:
Substitutes 'u's and 'U's to 'T's. This is useful since bowtie does not match 'U's to 'T's.

input:
A fasta file.

output:
A substituted fasta file.

options:
-

example usage:
rna2dna.pl reads_RNA_alphabet.fa > reads_DNA_alphabet.fa



#####################################################################################################################################



name:
select_for_randfold.pl

description:
This script identifies potential precursors whose structure is basically consistent with Dicer recognition.
Since running randfold is time-consuming, it is practical only to estimate p-values for those potential
precursors that actually fold into hairpin structures.

input:
An arf file with the read signatures and an RNAfold output file with the structures of the potential miRNA
precursors.

output:
A list of ids, separated by newlines.

options:
-

example usage:
select_for_randfold.pl signature.arf potential_precursors.str > potential_precursors_for_randfold.ids



#####################################################################################################################################



name:
survey.pl

description:
Surveys miRDeep2 performance at score cut-offs from -10 to 10.

input:
Default input is a .mrd file output by the miRDeep2_core_algorithm.pl script.

output:
A .csv file with performace statistics.

options:
-a file       file outputted by controls
-b file       mature miRNA fasta reference file for the species
-c file       signature file
-d int        read stack height necessary for triggering excision

example usage:
survey.pl output.mrd -a output_controls.mrd -b miRBase_this_species.fa -c signature.arf -d 2 > survey.csv



#####################################################################################################################################



name:
convert_bowtie_output.pl 

description:
It converts a bowtie 'bwt' mapping file to a mirdeep 'arf' file.

input:
A file in 'bwt' format.

output:
A file in mirdeep 'arf' format.

options:
-

notes:
-



#####################################################################################################################################


 
name:
bwa_sam_converter.pl 

description:
It converts a bwa 'sam' mapping file to a mirdeep 'arf' file.

input:
A bwa created file in 'sam' format.

output:
A file in mirdeep 'arf' format.

options:
-

notes:
-



#####################################################################################################################################



name:
samFLAGinfo.pl 

description:
It gives information about the bwa FLAG in a bwa created mapping file in 'sam' format. 

input:
A FLAG number created by bwa.

output:
Information about the alignment created by bwa.

options:
-

notes:
-



#####################################################################################################################################



name:
clip_adapters.pl

description:
Removes 3' end adaptors from deep sequenced small RNAs. The script searches for occurrences of the
six first nucleotides of the adapter in the read sequence, starting after position 18 in the read
sequence (so the shortest clipped read will be 18 nts). If no matches to the first six nts of the
adapter are identified in a read, the 3' end of the read is searched for shorter matches to the 5
to 1 first nts of the adapter.

input:
A fasta file with the deep sequencing reads and the adapter sequence (both in RNA or DNA alphabet).

output:
A fasta file with the clipped reads. Fasta ids are retained. If no matches to the adapter prefixes
are identified in a given read, the unclipped read is output.

options:
-

example usage:
clip_adapters.pl reads.fa TCGTATGCCGTCTTCTGCTTGT > reads_clipped.fa

notes:
It is possible to clip adapters using more sophisticated methods. Users are encouraged to test other
methods with the miRDeep2 modules.



#####################################################################################################################################



name:
sanity_check_genome.pl 

description:
It checks the supplied genome fasta file for its correctness. Identifier lines are not allowed to contain whitespaces and must be unique.
Sequence lines are not allowed to contain characters others than [A|C|G|T|N|a|c|g|t|n].

input:
A genome file in fasta format

output:
-

options:
-

notes:
-



#####################################################################################################################################



name:
sanity_check_mapping_file.pl 

description:
It checks the supplied mapping file for its correctness. Each line in the file must be in the arf format. 


input:
A mapping file in arf format

output:
-

options:
-

notes:
-



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name:
sanity_check_mature_ref.pl 

description:
It checks the supplied mature_miRNA fasta file for its correctness. Identifier lines are not allowed to contain whitespaces and must be unique.
Sequence lines are not allowed to contain characters others than [A|C|G|T|N|a|c|g|t|n].

input:
A mature miRNA file in fasta format

output:
-

options:
-

notes:
-



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name:
sanity_check_reads_ready.pl 

description:
It checks the supplied reads file for its correctness. Each identifier line must have the format of 
'>name_uniqueNumber_xnumber' e.g. >xyz_1_x20. See also file format_descriptions.txt for more detailed informations.



input:
A mapping file in arf format

output:
-

options:
-

notes:
-
