hom.Rn.inpHOMSA        package:hom.Rn.inp.db        R Documentation

_M_a_p _b_e_t_w_e_e_n _I_D_s _f_o_r _g_e_n_e_s _i_n _o_n_e _o_r_g_a_n_i_s_m _t_o _t_h_e_i_r _p_r_e_d_i_c_t_e_d
_p_a_r_a_l_o_g_s _i_n _a_n_o_t_h_e_r

_D_e_s_c_r_i_p_t_i_o_n:

     A map of this type is an R object that provides mappings between
     identifiers for genes in the package organism and their predicted
     paralogs in the map that the organism is named after.  So for
     example, if the inparanoid package is the human package, then the
     hom.Rn.inpRATNO map would provide mappings between human and rat.

_D_e_t_a_i_l_s:

     Mappings between gene identifiers and their paralogs as predicted
     by the Inparanoid algorithm.  The map filters out paralogs that
     have an Inparanoid score less than 100

     Mappings are normally given from the ID of the organism in the
     package to the IDs of the organism listed in the map name.

     Reversal can be made of ANY map by using the function revmap (see
     examples below).

     Names for these maps are done in the "INPARANOID style" which
     means that they are normally the 1st three letters of the genus
     followed by the 1st two letters of the species.  For example: "Mus
     musculus" becomes "MUSMU", "Homo sapiens" becomes "HOMSA",
     "Monodelphis domestica" becomes "MONDO" etc. This means that for
     most of these organisms it will be possible to easily guess the
     abbreviations used.  An exception may occur in the future if a new
     model organism has a very similar genus and species name to an
     existing one.

_R_e_f_e_r_e_n_c_e_s:

     <URL: http://inparanoid.sbc.su.se/download/current/sqltables>

_E_x_a_m_p_l_e_s:

             x <- hom.Rn.inpAPIME
             # Get honeybee IDs that are paralogous to the pkg IDs
             mapped_IDs <- mappedkeys(x)
             # Convert to a list
             xx <- as.list(x[mapped_IDs])
             if(length(xx) > 0) {
               # Get the paralogs for the first five genes
               xx[1:5]
               # Get the first one
               xx[[1]]
             }

             #Now for the reverse map (honeybee IDs back to pkg paralog)
             x <- revmap(hom.Rn.inpAPIME)
             mapped_IDs <- mappedkeys(x)
             # Convert to a list
             xx <- as.list(x[mapped_IDs])
             if(length(xx) > 0) {
               # Get the paralogs for the first five IDs
               xx[1:5]
               # Get the first one
               xx[[1]]
             }         

         ## Not run: 
             #For the most common organisms, we try to ensure that you can
             #map back to an Entrez Gene ID by providing you with necessary
             #maps in the related organism based annotation packages.  The
             #following example shows how to get from an Entrez Gene ID for
             #Human to Entrez Gene IDs for Mouse even though inparanoid does
             #not map to Entrez Gene IDs for either of these species.

             #You will have to include the appropriate packages for
             #humans:
             library("org.Hs.eg.db")
             #and for mouse:
             library("org.Mm.eg.db")
             #And of course you will need the inparanoid package:
             library("hom.Hs.inp.db")

             #Start with some Human Entrez Gene IDs
             humanEGIds <- c("4488","4487")

             #Inparanoid uses ensembl protein IDs so start with
             #those. Notice that there will be many protein IDs returned for
             #a typical gene since there are many possible translations.
             humanProtIds <- mget(humanEGIds,org.Hs.egENSEMBLPROT)

             #Map the IDs that we can from inparanoid. Notice that by design,
             #inparanoid only represents each gene product with a single
             #translation product. Therefore your list could slim down a lot
             #during this step.  Also, if the thing you are trying to match
             #up at this step has less than 100% seed status, you will not
             #find it in this step.
             rawMouseProtIds <- mget(unlist(humanProtIds),hom.Hs.inpMUSMU,ifnotfound=NA)
             #This also means that we need to clean up the NAs from our result
             mouseProtIds <- rawMouseProtIds[!is.na(rawMouseProtIds)]

             #Then use the mouse organism based packages to convert these IDs
             #back to an Entrez Gene ID again (this time for mouse). 
             mouseEGIds <- mget(unlist(mouseProtIds),org.Mm.egMGI2EG,ifnotfound=NA)

             #Now go ahead and have a look at the output
             mouseEGIds
         ## End(Not run)

