Software

Population genetics

DEMEtics

R package to evaluate the genetic differentiation between populations based on G_ST and D values

The degree of genetic differentiation between populations is often measured by the fixation index G_ST (Nei, 1973). Jost (2008) showed that G_ST values do not reflect genetic differentiation and suggested calculating D-values instead. To verify this far reaching statement, we programmed this R package and compared G_ST versus D values of two artificial populations with known genetic divergence. The insights of this approach are published in Gerlach et al. (2010). DEMEtics allows to calculate the fixation index G_ST and the differentiation index D pairwise between or averaged over several populations according to the formulas given in Jost (2008). P-values, stating the significance of differentiation, and 95 percent confidence intervals can be estimated using bootstrap resamplings. To cite the package 'DEMEtics' in publications use:

Gerlach G., Jueterbock A., Kraemer P., Deppermann J. and Harmand P. 2010 Calculations of population differentiation based on G_ST and D: forget G_ST but not all of statistics! Molecular Ecology 19, p. 3845-3852. Link to publication

References:

Jost, L. 2008 G_ST and its relatives do not measure differentiation. Molecular Ecology 17, 18, p. 4015-4026.

Nei, M. 1973 Analysis of gene diversity in subdivided populations. Proceedings of the National Academy of Sciences of the United States of America 70, 12, p. 3321-3323.

Niche Modeling

Maxent Variable Selection

R package to select the best set of relevant environmental variables along with the optimal regularization multiplier for Maxent Niche Modeling

Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011; Warren et al., 2014). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974) , and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997). Users of this package should be familiar with Maxent niche modelling.

Future Ocean Rasters

Global ocean rasters for future conditions of sea surface temperature, surface air temperature, and salinity (predicted under three CO₂ emission scenarios) can be downloaded from the Bio-ORACLE database. These GIS rasters (ASCII format) were used to predict the future distribution of seaweed meadows in the North Atlantic under projected climate change ( see publication).

Next Generation Sequencing

FilterFasta.pl

download here

Perl script to remove sequences with a certain sequence ID from a fasta file. Two input files are required: (1) a fasta file, and (2) a text file of sequence IDs (one ID per line). A sequence ID refers to the text string in the fasta file that follows the '>' sign and is not interrupted by a white space. For example, the correct sequence IDs for the following three fasta sequence identifiers

>crab_bovin ALPHA CRYSTALLIN B CHAIN (ALPHA(B)-CRYSTALLIN)
>gi|129295|sp|P01013|OVAX_CHICK GENE X PROTEIN (OVALBUMIN-RELATED)
>HSBGPG Human gene for bone gla protein (BGP)

would be:

crab_bovin
gi|129295|sp|P01013|OVAX_CHICK
HSBGPG

The perl script, the fasta file, and the sequence ID file should all be saved in the same folder. The two required arguments are the file names of the fasta file and the sequence ID file. To run the script from a terminal window type the command:

perl FilterFasta.pl FastaFile_name.fasta SequenceID_filename.txt

The outfiltered sequences will be stored in a file named 'outfiltered.fasta', the remaining sequences will be stored in a file named 'remaining.fasta'.