Soft­wares

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.

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.

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: 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

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

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

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

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

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

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