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http://users.mccammon.ucsd.edu/~bgrant/bio3d/user_guide/user_guide.html#example Some Beginner Examples
library(bio3d) # load the bio3d package lbio3d() # list the functions within the package
## See the help pages of individual functions for full documentation and worked examples. help(read.pdb) # type “q” to exit help page and return to the R prompt example(read.pdb)
## Read a PDB […] Background: Identifying disease genes from human genome is an important but challenging task in biomedical research. Machine learning methods can be applied to discover new disease genes based on the known ones. Existing machine learning methods typically use the known disease genes as the positive training set P and the unknown genes as the […] Motivation: During the last years NGS sequencing has become a key technology for many applications in the biomedical sciences. Throughput continues to increase and new protocols provide longer reads than currently available. In almost all applications, read mapping is a first step. Hence, it is crucial to have algorithms and implementations that perform fast, […] Motivation: It is well recognized that the effects of drugs are far beyond targeting individual proteins, but rather influencing the complex interactions among many relevant biological pathways. Genome-wide expression profiling before and after drug treatment has become a powerful approach for capturing a global snapshot of cellular response to drugs, as well as to […] Motivation: The sequence alignment/map (SAM) and the binary alignment/map (BAM) formats have become the standard method of representation of nucleotide sequence alignments for next-generation sequencing data. SAM/BAM files usually contain information from tens to hundreds of millions of reads. Often, the sequencing technology, protocol, and/or the selected mapping algorithm introduce some unwanted biases in […] Summary: There is a growing need in the research community to apply the Molecular Evolutionary Genetics Analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. We now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an […] Summary: HSPIR is a concerted database of six major Heat ShockProteins (HSPs) namely Hsp70, Hsp40, Hsp60, Hsp90, Hsp100 and sHsp (small HSP). The HSPs are essential for the survival of all living organisms which protects the conformations of proteins upon exposure to various stress conditions. They are highly conserved group of proteins involved in […]
> gtsdata_test=read.table(“gtsdata.txt”, header=T) > gtsenv=read.table(“gtsenv.txt”, header=T) > gtsdata_data_t<-t(gtsdata_data) > decorana(gtsdata_data_t) Call: decorana(veg = gtsdata_data_t) Detrended correspondence analysis with 26 segments. Rescaling of axes with 4 iterations. DCA1 DCA2 DCA3 DCA4 Eigenvalues 0.8634 0.4834 0.23788 0 Decorana values 0.8721 0.3793 0.07223 0 Axis lengths 5.3292 2.1115 1.80907 0 > gts.ca=cca(gtsdata_data_t) > gts.ca Call: cca(X = […] a.str <- matrix(c(‘1′,’2′,’3′,’5′,NA,’6′) + ,c(2,3),dimnames = list(c(‘g1′,’g2′),c(‘t1′,’t2′,’t3′))) a.str # t1 t2 t3 # g1 “1” “3” NA # g2 “2” “5” “6” a.num <- apply(a.str, c(1,2), as.numeric) a.num # t1 t2 t3 # g1 1 3 NA # g2 2 5 6 Note: 第一行，第一列位置要为空！！ |
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