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miRDeep2
READMEAbout
Authors: Sebastian Mackowiak & Marc Friedländer
This is miRDeep2 developed by Sebastian Mackowiak & Marc Friedländer. miRDeep2 discovers active known or novel miRNAs from deep sequencing data (Solexa/Illumina, 454, …).
(minor edits to
README,TUTORIAL,CHANGELOG, andFAQ, convertion to Markdown, trailing whitespace removal & CI setup by Marcel Schilling)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 may be 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
(the link could be out of date, in that case look up online what to do)
After that typing
will install GNU grep as
ggrepin/usr/local/bin/Installation
Option 1: with the provided
install.plscriptType
Option 2. without the install mirdeep script
Follow the instructions given below
Dependencies
First download all necessary packages listed here
Manual installation
When packages are downloaded
unzip bowtie-0.11.3-bin-linux-x86_64.zipput the bowtie directory into your
PATHvariable, e.g.tar xvvzf ViennaRNA-1.8.4.tar.gzcdto the Vienna dirtype
PATHvariable, e.g.tar xxvzf squid-1.9g.tar.gztar xvvzf randfold-2.0.tar.gzcd randfold2.0edit Makefile, e.g.
emacs Makefile:change line with
INCLUDE=-I.toINCLUDE=-I. -I<your_path_to_squid-1.9g> -L<your_path_to_squid-1.9g>, e.g.INCLUDE=-I. -I/home/Pattern/squid-1.9g/ -L/home/Pattern/squid-1.9g/makeadd randfold to your
PATHvariable, e.g.tar xvvzf PDF-API2-0.73.tar.gzcdto your PDF_API2 directorythen type in
PERL5LIB, e.g.cdto your mirdeep2 directory (the one containinginstall.pl)touch install_successfulstart a new shell session to apply the changes to environment variables
Test installation
To test if everything is installed properly type in
bowtieRNAfold -hrandfoldmake_html.plYou 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>/essentialsScript Reference
miRDeep2 analyses can be performed using the three scripts
miRDeep2.pl,mapper.plandquantifier.pl.miRDeep2.plDescription
Wrapper function for the
miRDeep2.plprogram package. The script runs all necessary scripts of the miRDeep2 package to perform a microRNA detection deep sequencing data anlysis.Input
Output
with an overview of all detected miRNAs in the deep sequencing input data.
Options
‑a <int>‑b <int>‑c‑t <species>‑u‑v‑q <file>miRBase.mrdfile from quantifier module to show miRBase miRNAs in data that were not scored by miRDeep2Examples:
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.fafile contains all miRBase mature mouse miRNAs, while themiRBase_rno_v14.fafile contains all the miRBase mature rat miRNAs. The2>will pipe all progress output to thereport.logfile.This command will generate
result.html),result.csv),survey.csv), andoutput.mrd).Example use 2
The user wishes to identify miRNAs in deep sequencing data from an animal with no related species in miRBase:
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.
mapper.plDescription
Processes reads and/or maps them to the reference genome.
Input
Default input is
seq.txtorqseq.txtformat.More input can be given depending on the options used.
Output
The output depends on the options used (see below).
Either
are output.
Options
Read input file
‑aseq.txtformat‑bqseq.txtformat‑cPreprocessing/mapping
‑h‑i‑ja,c,g,t, u,n,A,C,G,T,U, orN.‑k <seq>‑l <int><int>nts‑m‑p <genome>bowtie-build). Thegenomestring must be the prefix of the bowtie index. For instance, if the first indexed file is calledh_sapiens_37_asm.1.ebwtthen the prefix ish_sapiens_37_asm.‑qOutput files
‑s file‑t fileOther
‑u‑vExamples
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.txtformat to FASTA format, convert from RNA to DNA alphabet, remove entries with non-canonical letters (letters other thana,c,g,t,u,n,A,C,G,T,U, orN), clip adapters, discard reads shorter than 18 nts and collapse the reads: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 namedgenome.1.ebwt: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:
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:
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 generatereads_collapsed.faandreads_vs_genome.arfto input to miRDeep2:quantifier.plDescription
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 2 nt upstream and 5 nt downstream of the mature/star sequence is determined.
Input
Output
miRNA_expressed.csvwith miRNA identifiers and its read count,miRNA_not_expressed.csvwith all miRNAs having 0 read counts,miRBase.mrd,expression.htmlthat gives an overview of all miRNAs the input data, andpdfsthat contains for each miRNA a PDF file showing its signature and structure.Options
Example usage
make_html.plDescription
It creates a file called
result.htmlthat 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 themselves and provides links to BLAST, BLAT, mirBase for miRBase miRNAs and to a PDF file that shows the signature and structure.Input
Output
result.htmlfile 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.pdfsthat contains for each provisional miRNA ID a PDF with its signature and structure.result.csv(when option-cis used) that contains the same entries as the HTML file.Options
‑v <int><int>‑c‑k <file>‑s <file>‑f <file>‑e‑g‑w <project_folder>‑r <file>‑q <file>miRBase.mrdfile produced by quantifier module‑x <file>signature.arffile with mapped reads to precursors‑t <org>‑u‑d‑y‑zquantifier.pl‑oExample usage
clip_adapters.plDescription
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
Output
FASTA IDs are retained. If no matches to the adapter prefixes are identified in a given read, the unclipped read is output.
Example usage
Notes
It is possible to clip adapters using more sophisticated methods. Users are encouraged to test other methods with the miRDeep2 modules.
collapse_reads.plDescription
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
_x10suffix added to the identifier.Input
Output
Options
‑aExample usage
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.
excise_precursors_iterative.plDescription
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
.arfformat,Output
The excised precursors in FASTA format.
Options
‑aExample usage
excise_precursors.plDescription
Excises precursors from the genome using the mapped reads as guidelines.
Input
.arfformat.Output
Options
‑a <integer><integer>reads high (default 2).‑bExample usage
fastaparse.plDescription
Performs simple filtering of entries in a FASTA file.
Input
Ouput
Options
‑a <int>‑ba,c,g,t,u,n,A,C,G,T,U, orN.‑sExample usage
fastaselect.plDescription
This script only prints out the FASTA entries that match an ID in the ID file.
Input
Output
Options
‑aExample usage
find_read_count.plDescription
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
collapse_reads.pl.This will typically be a FASTA file or a list of IDs.
Output
Example usage
geo2fasta.plDescription
Parses GSM format files into FASTA format.
Input
The first column should be sequences and the second column the number of times the sequence occurs in the data.
Output
Example usage
illumina_to_fasta.plDescription
Parses
seq.txtorqseq.txtoutput from the Solexa/Illumina platform to FASTA format.Input
seq.txtorqseq.txtfile.By default
seq.txt.Output
seq.txt.The entries are named
seqplus a running number that is incremented by one for each entry. Any.characters in theseq.txtfile is substituted with anN.Options
‑aqseq.txtExample usage
miRDeep2_core_algorithm.plDescription
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
Output
Options
‑h‑s‑t‑u‑v‑x‑y‑zExample usage
Notes
The
-zoption has not been thoroughly tested.parse_mappings.plDescription
Performs simple filtering of entries in an
.arffile.Input
Default input is
.arffile.Output
.arffile.Options
‑a <int>‑b <int>‑c <int>‑d <file>‑e <file>‑f <file>‑g <file>‑h‑i <int>‑j‑kExample usage
perform_controls.plDescription
Performs a designated number of rounds of permuted controls (for details, see Friedländer et al., Nature Biotechnology, 2008).
Input
The permutation controls estimate the number of false positives produced by a
miRDeep2_core_algorithm.plrun. The input toperform_controls.plshould bemiRDeep2_core_algorithm.plrun,miRDeep2_core_algorithm.pl, andOutput
.mrdformat.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.mrdoutput byperform_controls.plis not biologically meaningful.Options
‑aExample usage
permute_structure.plDescription
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.plscript.Input
Output
Example usage
prepare_signature.plDescription
Prepares the signature file to be input to the
miRDeep2_core_algorithm.plscript.Input
Output
.arfformat.Options
‑a <file>‑bExample usage
rna2dna.plDescription
Substitutes
us andUs toTs. This is useful sincebowtiedoes not matchUs toTs.Input
Output
Example usage
select_for_randfold.plDescription
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
Output
Example usage
survey.plDescription
Surveys miRDeep2 performance at score cut-offs from -10 to 10.
Input
Default input is
.mrdfile output by themiRDeep2_core_algorithm.plscript.Output
Options
‑a <file>‑b <file>‑c <file>‑d <int>Example usage
convert_bowtie_output.plDescription
It converts a
bowtiebwtmapping file to amirdeeparffile.Input
bwtformat.Output
mirdeeparfformat.bwa_sam_converter.plDescription
It converts a
bwasammapping file to amirdeeparffile.Input
bwacreated file insamformat.Output
mirdeeparfformat.samFLAGinfo.plDescription
It gives information about the
bwaFLAG in abwacreated mapping file insamformat.Input
bwa.Output
bwa.clip_adapters.plDescription
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
Output
FASTA IDs are retained. If no matches to the adapter prefixes are identified in a given read, the unclipped read is output.
Example usage
Notes
It is possible to clip adapters using more sophisticated methods. Users are encouraged to test other methods with the miRDeep2 modules.
sanity_check_genome.plDescription
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, orn.Input
sanity_check_mapping_file.plDescription
It checks the supplied mapping file for its correctness. Each line in the file must be in the ARF format.
Input
sanity_check_mature_ref.plDescription
It checks the supplied
mature_miRNAFASTA 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 thanA,C,G,T,N,a,c,g,t, orn.Input
sanity_check_reads_ready.plDescription
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 fileformat_descriptions.txt` for more detailed informations.Input
extract_miRNAs.plDescription
Extracts mature and precursor sequences from miRBase fasta files for species of interest.
Input
Output
Example usage