Mirdeep2 Manual For Mirna Expression
MiRDeep2 tutorialExtended miRDeep2 tutorial with step by step instructions. It will cover the mapper.plfor preprocessing and mapping, the miRDeep2.pl for de-novo prediction.DisclaimerThis tutorial comes with no warranty and demands common sense of the reader. I am notresponsible for any damage that happens to your computer by using this tutorial.For comments or questions just create an 'issue' here.Apparently you will need a github account for that.PrefaceThis is a step by step guide for a full small RNA sequencing data analysis using themiRDeep2 package and its patched files. The first part will describe the general workflowto do de-novo miRNA predictions based on a small RNAseq data seq and the second part willfocus on expression analysis with the quantification module.Installation instructionsIf you haven't installed them yet you can obtain the main package fromand the patched files fromby clicking on 'Clone or download' and then on 'Download Zip'.Extract the zipped files and then open a command line window.If you have git installed you can obtain the packages also directly from the command lineby typing. Cd drmirdeep.github.io-master and continue with the tutorial.The data and what else you will needUsually you will have gotten a small RNA sequencing data file from a collaborator thatwants you to analyze the data file. Before you can start with any kind of analysis youshould either already know the small RNA sequencing adapter that was used for thesequencing of the sample or ask your collaborator to sent it to you.
If you don't clipthe adapter then the majority of the reads having an adapterare likely not to be aligned to anywhere.Once you know the adapter sequence you should do a simple check to see how manyof your sequences contain the adapter. This you can do by typing.
Grep -c TGGAATTC examplesmallrnafile.fastqwhere 'TGGAATTC' are the first 8 nucleotides of the adapter that has been usedfor this sample. Replace it with your own sequencing adapter. MicroRNAs have a mean lengthof 22 nucleotides in animals so if you have sequenced one of those it will likely have thesequencing adapter attached to it. If the resulting number of sequences with an adapteris around 70% of the number of your input sequences the data set can be considered asreasonably good. Note: In case that only adapters have been sequenced predominantly you willalso get a high number which is obviously not good.
If you only get 10% of sequences withan adpater then likely something went wrong during thesequnecing library preparation or your sample doesn't contain too many small RNAs.For novel miRNA prediction we need to map the reads against a reference databasewhich has to be indexed by bowtie 1. For this we take a reference database file,lets call it refdb.fa (This can be a genome file or simply a filewith scaffolds) and build a bowtie index by typing. Bowtie-build refdb.fa refdb.faThe first argument is here the actual file to index, the second argument is theprefix for the bowtie index files.
You can name it differently but for ease ofuse I use the same name as my reference database file. Depending on the inputfile size it can take several hours (for the human genome for example) to beindexed. However, the bowtie website has already some prebuild index filesfor download. If you decide to download index files you will also need to downloadthe fasta file with which the index was build. Otherwise the results in the miRDeep2prediction will be not reliable.Data preprocessing for novel miRNA predictionSince the miRDeep2 package was designed as a complete solution for miRNA prediction andquantification it also contains data preprocessing routines that will also clip thesequencing adapter. The main function of the mapping module is the mappingof the preprocessed reads file to reference database. The reference database is typicallyan annotated genome sequence but can also be simply a scaffold assembly if no genome isavailable.
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Mirdeep2 Manual For Mirna Expression In Word
The scaffolds itself however should be at least 200 nucleotides long so thata sane miRNA precursor plus some flanking region fits into it. Apart from clippingadapters the module does sanity checks on your sequencing reads and also collapse readsequences to reduce the file size which will save computing time. Mapper.pl examplesmallrnafile.fastq -e -h -i -j -k TGGAATTC -l 18-m -p refdb.fa -s readscollapsed.fa -t readsvsrefdb.arf -v -o 4What does this command do?
The first argument needs to be your sequencing file.Typically, this will be a fastq file. The format of the fastq file is designated byspecifying option '-e'. If your file is in fasta format already you specify option '-c'instead. If your reads file is not in fasta format you need to specify option '-h' whichadvises the mapper module to parse your file to fasta format. Option -i will convert RNAto DNA and option '-j' will remove sequences that contain characters other than ACGTN.Now comes the actual adapter clipping which is only done if a adapter sequenceis given by option -k. Only the first 6 nucleotides of this sequence will be used tosearch for an exact match in the sequencing reads.
Option '-m' will collapse the readsto remove redundancy and decrease the file size. A sequnecing read seen 10 times in yourraw file will occur only once in the collapsed file and have a x10 in its identifier.After that the reads will be mapped to the given refence genome which index filewas specified by option '-p'. Option -s indicates the preprocessed read file name whichis output by the mapper module and option -t is the file name of the read mappings to thereference database ('refdb.fa') in miRDeep2's arf format.
Mirdeep2 Manual For Mirna Expression 1
A mapping file in arf formatcan be easily obtained from a standard bowtie 1 output file (This is NOT in 'sam' formatbut a proprietary bowtie text file format) by typing. Convertbowtieoutput.pl readsvsrefdb.bwt readsvsrefdb.arfHowever, if you used the mapping module then the mapped output file is already inarf format.Identification of known and novel miRNAsFor predicting novel miRNAs the miRDeep2 module from the package is called with acollapsed reads file and a reference genome file in fasta format. For betterprediction results reference files of miRNAs and related miRNAs should be given sincemiRDeep2 considers predicted miRNAs with conserved seeds in other speciesmore reliable that miRNAs with non-conserved seeds.