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martine beaufils — Fri, 09 Apr 2021 07:01:35 -0000

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martine beaufils — Fri, 09 Apr 2021 07:00:32 -0000

hi,
somebody can explain me, how use the software
thanks

Home modified by Mick Watson

Mick Watson — Wed, 30 Mar 2016 12:50:25 -0000

--- v21
+++ v22
@@ -1,160 +1,3 @@
 ###poRe: an R package for the visualization and analysis of nanopore sequencing data

-####Installation
-
-poRe is written for R version 3.1.1 or above, which is available from [CRAN](http://cran.r-project.org/).  
-
-There are only two dependencies: Bioconductors rdh5 and bit64.  To install these, from within R, type:
-
--source("http://www.bioconductor.org/biocLite.R")
-biocLite("rhdf5")
-install.packages("bit64")
-
-
-You will then need to install poRe.  For Windows, download the zip file.  For Linux, download the tar.gz file.  
-
-In Windows, open R and choose Packages -> Install package(s) from local zip file.  Navigate to the downloaded zip file.
-
-In Linux, from the command line, run: R CMD INSTALL poRe_0.2.tar.gz
-
-That's it!
-
-####Data
-
-poRe has two datasets built in, a set of summary information about a MinION run and a set of events from a 43Kb MinION read.  These can be accessed via:
-
--library(poRe)
-data(MINION)
-MINION
-
-data(MINIONEVENTS)
-MINIONEVENTS
-
-
-These data are a summary of an early MinION run carried out at Edinburgh Genomics under the MinION Access Programme.  The pore used was R6 and the genome sequenced was lambda phage.
-
-####Tutorial
-
-poRe has been tested on MinION data produced at Edinburgh Genomics.  The very nature of the MinION Access Programme makes it difficult to share runs.  Therefore, we suggest that you **make a copy** of your MinION data and run poRe on the copy, until you are satisfied that it is working.
-
-**Loading the library**
-
-Be sure to load the library before attempting to run any of poRe's commands from within R:
-
--library(poRe)
-
-
-**Organising data**
-
-The MinION outputs data from all runs to a single directory.  Should you wish to copy the fast5 files to a destination directory, and organise them into run folders, use the copy.runs() function.
-
--copy.runs(dir="C:/minion/reads/downloads/", dest="C:/MinION_Runs")
-
-
-The function will extract the unique run_id from each fast5 file (e.g. cffe450b92096c83547bd98ec7767b0137145400) and the analysis and version (e.g. metrichor_0.7.1) and copy the fast5 file to subfolders within the destination directory (e.g. cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1)
-
-Whilst the run_id is not very user friendly, it does represent the unique identifier that the MinION assigns to your run.  We have chosen to use it here as it avoids potential errors.
-
-**Extracting Fastq**
-
-Once the data have been organised into run folders, for each run folder, we can extract the fastq.  Each read can have a template strand, a complement strand or a 2D strand (a combination of both the template and complement).  These are extracted with extract.run.fastq()
-
--extract.run.fastq(dir="C:/MinION_Runs/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1")
-
-
-By default, this will create a folder underneath dir called "extracted", and will extract single fastq files for each read.
-
-Fasta files may be extracted with the function extract.run.fasta
-
--extract.run.fasta(dir="C:/MinION_Runs/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1")
-
-
-**Gather metadata about a run**
-
-Embedded within each fast5 is a whole load of metadata about each read.  We can extract this for every read in a run with read.fast5.info()
-
--finfo <- read.fast5.info(dir="C:/MinION_Runs/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1")
-
-
-finfo is a data frame with a row for each fast5 file and 24 columns of metadata.  We can extract interesting summary information with run.summary.stats():
-
--stats <- run.summary.stats(finfo)
-print(stats)
-
-
-Here we will see min, max and mean read lengths plus the number of reads
-
-**Read length histograms**
-
-One of the key pieces of information for any variable-length run is the distribution of read lengths obtained.  We can visualise this using the plot.length.histogram() function.  It takes as input finfo, the metadata retrieved by read.fast5.info():
-
--plot.length.histogram(finfo)
-
-
-This plots three histograms to the current device, one each for template, complement and 2D reads
-
-**Cumulative yield plot**
-
-We can also plot the yield of each run over time, measured from when the read analysis starts:
-
--yield <- plot.cumulative.yield(finfo)
-
-
-Not only does this plot yield data to the current device, it also returns the data as a data frame so that users can explore using whichever visualisation they wish
-
-**Channel statistics**
-
-At present each MinION flowcell has 512 channels, each of which produces reads.  We can plot the number of reads per channel, and the yield per channel, like this:
-
--cr <- plot.channel.reads(finfo)
-cy <- plot.channel.yield(finfo)
-
-
-Again, these plot to the current device, but they also return the data as data frames so the user can create any other plots they wish.
-
-**Events**
-
-The raw data from the MinION is actually data about the electronic signal measured as a DNA molecule passes through the nanopore.  This is accessible for each read through the get.events() function:
-
--f5 <- "C:/MinION_Runs/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1/sce_bio_c03807_Lambda_8_BN_04_20140616_1718_1_ch78_file30_strand.fast5"
-
-ev <- get.events(f5)
-
-
-Each set of events is a data frame with multiple rows and columns.  The function extracts events for both the template and complement, and therefore ev is a list() with two elements:
-
--ev$template
-ev$complement
-
-
-**Squiggles**
-
-Events can be plotted using the plot.squiggle() function.  As each read has many 1000s of events, plotting them all is not ususally informative.  Instead, plot.squiggle() allows you to define a time period, in seconds from the beginning of the events, to plot.  Use the minseconds and maxseconds arguments to define this:
-
--plot.squiggle(ev$template, minseconds=5, maxseconds=16)
-
-
-####Screenshot
-
-![poRe screenshot] (https://sourceforge.net/p/rpore/wiki/Home/attachment/screenshot.jpg)
-
-####Support
-
-If you have any queries, please send an e-mail to [Mick Watson](mailto:mick.watson@roslin.ed.ac.uk)
-
-[[members limit=20]]
-[[download_button]]
+Please refer to [github for up-to-date documentation](https://github.com/mw55309/poRe_docs)

Home modified by Mick Watson

Mick Watson — Tue, 14 Oct 2014 11:34:06 -0000

--- v20
+++ v21
@@ -80,7 +80,7 @@
 Embedded within each fast5 is a whole load of metadata about each read.  We can extract this for every read in a run with read.fast5.info()

-finfo <- read.fast5.info(dir="C:/poRe/fast5/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1")
+finfo <- read.fast5.info(dir="C:/MinION_Runs/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1")

finfo is a data frame with a row for each fast5 file and 24 columns of metadata. We can extract interesting summary information with run.summary.stats(): @@ -128,7 +128,7 @@ The raw data from the MinION is actually data about the electronic signal measured as a DNA molecule passes through the nanopore. This is accessible for each read through the get.events() function:

-f5 <- "C:/poRe/fast5/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1/sce_bio_c03807_Lambda_8_BN_04_20140616_1718_1_ch78_file30_strand.fast5"
+f5 <- "C:/MinION_Runs/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1/sce_bio_c03807_Lambda_8_BN_04_20140616_1718_1_ch78_file30_strand.fast5"

 ev <- get.events(f5)

Home modified by Mick Watson

Mick Watson — Fri, 22 Aug 2014 15:02:55 -0000

--- v19
+++ v20
@@ -68,6 +68,12 @@

By default, this will create a folder underneath dir called "extracted", and will extract single fastq files for each read.
+
+Fasta files may be extracted with the function extract.run.fasta
+
+



+extract.run.fasta(dir="C:/MinION_Runs/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1")

+

Gather metadata about a run

Home modified by Mick Watson

Mick Watson — Fri, 22 Aug 2014 14:35:40 -0000

--- v18
+++ v19
@@ -33,6 +33,7 @@
 MINIONEVENTS

+These data are a summary of an early MinION run carried out at Edinburgh Genomics under the MinION Access Programme. The pore used was R6 and the genome sequenced was lambda phage.

####Tutorial

Home modified by Mick Watson

Mick Watson — Thu, 21 Aug 2014 15:05:34 -0000

--- v17
+++ v18
@@ -25,6 +25,7 @@
 poRe has two datasets built in, a set of summary information about a MinION run and a set of events from a 43Kb MinION read.  These can be accessed via:

+library(poRe)
 data(MINION)
 MINION

@@ -36,6 +37,14 @@
 ####Tutorial

 poRe has been tested on MinION data produced at Edinburgh Genomics.  The very nature of the MinION Access Programme makes it difficult to share runs.  Therefore, we suggest that you **make a copy** of your MinION data and run poRe on the copy, until you are satisfied that it is working.
+
+**Loading the library**
+
+Be sure to load the library before attempting to run any of poRe's commands from within R:
+
+

+library(poRe)
+

**Organising data**

Home modified by Mick Watson

Mick Watson — Thu, 31 Jul 2014 06:42:49 -0000

--- v16
+++ v17
@@ -19,6 +19,19 @@
 In Linux, from the command line, run: R CMD INSTALL poRe_0.2.tar.gz

 That's it!
+
+####Data
+
+poRe has two datasets built in, a set of summary information about a MinION run and a set of events from a 43Kb MinION read.  These can be accessed via:
+
+

+data(MINION)
+MINION
+
+data(MINIONEVENTS)
+MINIONEVENTS
+

+ ####Tutorial

Home modified by Mick Watson

Mick Watson — Wed, 30 Jul 2014 15:18:39 -0000

--- v15
+++ v16
@@ -28,7 +28,9 @@

 The MinION outputs data from all runs to a single directory.  Should you wish to copy the fast5 files to a destination directory, and organise them into run folders, use the copy.runs() function.

+

 copy.runs(dir="C:/minion/reads/downloads/", dest="C:/MinION_Runs")
+

The function will extract the unique run_id from each fast5 file (e.g. cffe450b92096c83547bd98ec7767b0137145400) and the analysis and version (e.g. metrichor_0.7.1) and copy the fast5 file to subfolders within the destination directory (e.g. cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1) @@ -38,7 +40,9 @@ Once the data have been organised into run folders, for each run folder, we can extract the fastq. Each read can have a template strand, a complement strand or a 2D strand (a combination of both the template and complement). These are extracted with extract.run.fastq() +

 extract.run.fastq(dir="C:/MinION_Runs/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1")
+

By default, this will create a folder underneath dir called "extracted", and will extract single fastq files for each read. @@ -46,12 +50,16 @@ Embedded within each fast5 is a whole load of metadata about each read. We can extract this for every read in a run with read.fast5.info() +

 finfo <- read.fast5.info(dir="C:/poRe/fast5/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1")
+

finfo is a data frame with a row for each fast5 file and 24 columns of metadata. We can extract interesting summary information with run.summary.stats(): +

 stats <- run.summary.stats(finfo)
 print(stats)
+

Here we will see min, max and mean read lengths plus the number of reads @@ -59,7 +67,9 @@ One of the key pieces of information for any variable-length run is the distribution of read lengths obtained. We can visualise this using the plot.length.histogram() function. It takes as input finfo, the metadata retrieved by read.fast5.info(): +

 plot.length.histogram(finfo)
+

This plots three histograms to the current device, one each for template, complement and 2D reads @@ -67,7 +77,9 @@ We can also plot the yield of each run over time, measured from when the read analysis starts: +

 yield <- plot.cumulative.yield(finfo)
+

Not only does this plot yield data to the current device, it also returns the data as a data frame so that users can explore using whichever visualisation they wish @@ -75,8 +87,10 @@ At present each MinION flowcell has 512 channels, each of which produces reads. We can plot the number of reads per channel, and the yield per channel, like this: +

 cr <- plot.channel.reads(finfo)
 cy <- plot.channel.yield(finfo)
+

Again, these plot to the current device, but they also return the data as data frames so the user can create any other plots they wish. @@ -84,20 +98,26 @@ The raw data from the MinION is actually data about the electronic signal measured as a DNA molecule passes through the nanopore. This is accessible for each read through the get.events() function: +

 f5 <- "C:/poRe/fast5/cffe450b92096c83547bd98ec7767b0137145400/metrichor_0.7.1/sce_bio_c03807_Lambda_8_BN_04_20140616_1718_1_ch78_file30_strand.fast5"

 ev <- get.events(f5)
+

Each set of events is a data frame with multiple rows and columns. The function extracts events for both the template and complement, and therefore ev is a list() with two elements:



 ev$template

 ev$complement

+

Squiggles

Events can be plotted using the plot.squiggle() function. As each read has many 1000s of events, plotting them all is not ususally informative. Instead, plot.squiggle() allows you to define a time period, in seconds from the beginning of the events, to plot. Use the minseconds and maxseconds arguments to define this:



 plot.squiggle(ev$template, minseconds=5, maxseconds=16)

+

####Screenshot

Home modified by Mick Watson

Mick Watson — Wed, 30 Jul 2014 15:17:09 -0000

--- v14
+++ v15
@@ -6,9 +6,11 @@

 There are only two dependencies: Bioconductors rdh5 and bit64.  To install these, from within R, type:

+

 source("http://www.bioconductor.org/biocLite.R")
 biocLite("rhdf5")
 install.packages("bit64")
+

You will then need to install poRe. For Windows, download the zip file. For Linux, download the tar.gz file.