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All free R scripts available on this page can only run with the R platform already installed on your system (Windows, Linux, and MacOS). Please visit CRAN to get more information about how to install R.
Alpha diversity
Rare biosphere
Description: In Gobet et al. 2012 ISMEJ, the definition of two kinds of singleton OTU was introduced. They correspond to:
- SSOabs: These are OTU that occur with only one sequence in the whole denoised dataset ("abs" stands for absolute singletons). These are the ultimate rare OTUs.
- SSOrel: These are OTU that occur as only one sequence in at least one sample, and are not absolute singletons ("rel" stands for relative singletons). Therefore the total sum of sequences for an SSOrel across all samples of the dataset is greater than one. OTU can be either SSOabs or SSOrel in a dataset, but not both.
See attached R script to calculate SSOabs and SSOrel in your data.
Examples of applications:
- Calculate SSOabs and determine how their proportion per sample varies according to space or the environment.
- Calculate SSOrel and determine if and how changes from rare (samples where abundance is 1) to not rare (>>1) are random or related to changes in spatial, temporal or environmental parameters.
- Detect whether the sub-table consiting of all SSOrel follows the same pattern as for the rest of the community.
Description: In Gobet et al. 2012 ISMEJ, the definition of two kinds of singleton OTU was introduced. They correspond to:
- SSOabs: These are OTU that occur with only one sequence in the whole denoised dataset ("abs" stands for absolute singletons). These are the ultimate rare OTUs.
- SSOrel: These are OTU that occur as only one sequence in at least one sample, and are not absolute singletons ("rel" stands for relative singletons). Therefore the total sum of sequences for an SSOrel across all samples of the dataset is greater than one. OTU can be either SSOabs or SSOrel in a dataset, but not both.
See attached R script to calculate SSOabs and SSOrel in your data.
Examples of applications:
- Calculate SSOabs and determine how their proportion per sample varies according to space or the environment.
- Calculate SSOrel and determine if and how changes from rare (samples where abundance is 1) to not rare (>>1) are random or related to changes in spatial, temporal or environmental parameters.
- Detect whether the sub-table consiting of all SSOrel follows the same pattern as for the rest of the community.
Alpha diversity of next generation sequencing data sets
developed by Christiane Hassenrück
Description: To account for library size bias, this function repeatedly subsamples a Sample-by-OTU table created by next generation sequencing to a specified number of sequences per sample. It will output the value of several alpha diversity indices for each subsampling run. Indices included in the function: observed richness, Chao1, ACE, Shannon, inverse Simpson, percentage of absolute and relative singletons and absolute doubletons per sample.
Last updated: 05 May 2015
developed by Christiane Hassenrück
Description: To account for library size bias, this function repeatedly subsamples a Sample-by-OTU table created by next generation sequencing to a specified number of sequences per sample. It will output the value of several alpha diversity indices for each subsampling run. Indices included in the function: observed richness, Chao1, ACE, Shannon, inverse Simpson, percentage of absolute and relative singletons and absolute doubletons per sample.
Last updated: 05 May 2015
Parsing taxonomic paths
developed by Christiane Hassenrück
Description: Function to allocate correct taxonomic ranks to the individual levels of a taxonomic path based on the SILVA taxonomy. The function requires a taxonomic mapping file that can be downloaded from the SILVA archives. Version 2 (v2) of the function is much faster, but was only designed for the taxonomc of bacteria and archaea.
The zipped folder includes the R scripts and the taxonomic mapping file for the Silva release 123 and 119 (archaea and bacteria).
Last updated: 27 July 2015
Description: Function to allocate correct taxonomic ranks to the individual levels of a taxonomic path based on the SILVA taxonomy. The function requires a taxonomic mapping file that can be downloaded from the SILVA archives. Version 2 (v2) of the function is much faster, but was only designed for the taxonomc of bacteria and archaea.
The zipped folder includes the R scripts and the taxonomic mapping file for the Silva release 123 and 119 (archaea and bacteria).
Last updated: 27 July 2015
Automated Ribosomal Intergenic Spacer Analysis (ARISA)
Automatic binner
developed by Alban Ramette
Description: The script performs an automatic binning of fragments into OTUs from GeneMapper output text files (samples, peak sizes, peak areas) and identifies the best combination of the window size (WS) and of the shift (Sh) value. This enables an optimal determination of the best binning strategy for a dataset without a priori knowing the ideal WS value. A compromise between high resolution (low WS) and high similarity among samples (high WS) must be found based on the output of the script. Note that the script may take a long time to run, especially if the number of samples being compared is high.
The zipped folder includes a manual, examples and the R script.
Last updated: 20 October 2008
developed by Alban Ramette
Description: The script performs an automatic binning of fragments into OTUs from GeneMapper output text files (samples, peak sizes, peak areas) and identifies the best combination of the window size (WS) and of the shift (Sh) value. This enables an optimal determination of the best binning strategy for a dataset without a priori knowing the ideal WS value. A compromise between high resolution (low WS) and high similarity among samples (high WS) must be found based on the output of the script. Note that the script may take a long time to run, especially if the number of samples being compared is high.
The zipped folder includes a manual, examples and the R script.
Last updated: 20 October 2008
Interactive binner
developed by Alban Ramette
Description: A shifting window size binning strategy is implemented as it offers the possibility to optimally aligned electrophoretic profiles and to deal with different window starting positions. The binning frame that offers the highest similarity among samples is identified out of all binning frames starting at a given position. The distance between two consecutive binning frames is defined as the Shift (Sh) value. The current implementation of the window shifting algorithm enables a user-defined choice of WS and Sh values to calculate the best binning frame for a given data set. The script then reports the best frame among all calculated as the one that maximizes sample similarities.
The zipped folder includes a manual, examples and the R script.
Last updated: 22 April 2009
developed by Alban Ramette
Description: A shifting window size binning strategy is implemented as it offers the possibility to optimally aligned electrophoretic profiles and to deal with different window starting positions. The binning frame that offers the highest similarity among samples is identified out of all binning frames starting at a given position. The distance between two consecutive binning frames is defined as the Shift (Sh) value. The current implementation of the window shifting algorithm enables a user-defined choice of WS and Sh values to calculate the best binning frame for a given data set. The script then reports the best frame among all calculated as the one that maximizes sample similarities.
The zipped folder includes a manual, examples and the R script.
Last updated: 22 April 2009
Replicate merger
developed by Alban Ramette
Function to merge the results of PCR replicates from ARISA into one OTU profile per sample.
Last updated: 04 May 2013
developed by Alban Ramette
Function to merge the results of PCR replicates from ARISA into one OTU profile per sample.
Last updated: 04 May 2013
Quality control
developed by Christiane Hassenrück and Alban Ramette
Description: Three functions to check the similarity of the OTU profile of replicate ARISA PCRs. RepDist calculates the maximum or minimum distance between replicate PCRs. FindFailedPCR selects replicate PCRs that are more dissimilar than a certain cut-off from all other replicate PCRs of a specific sample. Plotrepvariation plots the number of OTUs of each PCR replicate per sample. In conjunction these functions can be used as indication which replicate PCRs or samples should be excluded from further analysis.
Last updated: 12 August 2014
developed by Christiane Hassenrück and Alban Ramette
Description: Three functions to check the similarity of the OTU profile of replicate ARISA PCRs. RepDist calculates the maximum or minimum distance between replicate PCRs. FindFailedPCR selects replicate PCRs that are more dissimilar than a certain cut-off from all other replicate PCRs of a specific sample. Plotrepvariation plots the number of OTUs of each PCR replicate per sample. In conjunction these functions can be used as indication which replicate PCRs or samples should be excluded from further analysis.
Last updated: 12 August 2014
Quantitative fingerprinting
developed by Alban Ramette
Description: The script calculates the abundances of identified operational taxonomic units (OTUs) when using the qfingerprinting strategy. The input data consist of a binned table of rows of replicated dilutions by OTUs. The abundance of each OTU is then estimated according to the consensus and continuity rules.
The zipped folder includes a manual, examples and the R script.
Last updated: 20 October 2008
developed by Alban Ramette
Description: The script calculates the abundances of identified operational taxonomic units (OTUs) when using the qfingerprinting strategy. The input data consist of a binned table of rows of replicated dilutions by OTUs. The abundance of each OTU is then estimated according to the consensus and continuity rules.
The zipped folder includes a manual, examples and the R script.
Last updated: 20 October 2008
Multivariate Cutoff Level Analysis (MultiCoLA)
Description: Multivariate Cutoff Level Analysis (MultiCoLA) - A strategy to systematically assess the impact of rarity definition on large community datasets and on their further ecological interpretations. This method was published in Gobet, Quince and Ramette (2010) Nucleic Acids Research.
The zipped folder includes a manual, examples and the R scripts.
version: 1.4.
Last updated 21 September 2012
The zipped folder includes a manual, examples and the R scripts.
version: 1.4.
Last updated 21 September 2012
Other applications and external links
- GUide to STasitical Analysis in Microbial Ecology (GUSTA ME)
- The R package otu2ot for implementing the entropy decomposition of nucleotide variation in sequence data. Ramette and Buttigieg (2014) Frontiers in Microbiology
- Latest updates and additional R and bash scripts developed in the Deep-Sea Ecology and Technology groups are available on github - either in our group repository or on the github websites of the developers (Christiane Hassenrück)