Not really a bioinformatics question, more suitable for SO on which it has been cross-posted. Anyway look at R aggregate() function, something along the line of:

aggregate(data[,-c(1,2)], by = list(data$V1), function(x) {x})

test.txt is from OP data.

$ datamash -s -g 1 collapse 3 < test.txt  | sed 's/,/ /g'

TMCS09g1008676  0.000234939 1.38379E-05 0.00331883
TMCS09g1008677  0.0481578
TMCS09g1008678  0.0350491
TMCS09g1008679  0.0335639
TMCS09g1008680  0.0167087
TMCS09g1008681  0.00301089
TMCS09g1008682  0.00519838 0.0399833 0.0122184
TMCS09g1008683  0.00202427 0.00199513 0.0350491 0.00331883 0.0399833

Using dplyr, tidyr, group by gene, then spread:

# example data
df1 <- read.table(text ="
TMCS09g1008676  fleshy  0.000234939
TMCS09g1008676  fleshy  1.38379E-05
TMCS09g1008676  fleshy  0.00331883
TMCS09g1008677  fleshy  0.0481578
TMCS09g1008678  fleshy  0.0350491
TMCS09g1008679  fleshy  0.0335639
TMCS09g1008680  fleshy  0.0167087
TMCS09g1008681  fleshy  0.00301089
TMCS09g1008682  fleshy  0.00519838
TMCS09g1008682  fleshy  0.0399833
TMCS09g1008682  fleshy  0.0122184
TMCS09g1008683  fleshy  0.00202427
TMCS09g1008683  fleshy  0.00199513
TMCS09g1008683  fleshy  0.0350491
TMCS09g1008683  fleshy  0.00331883
TMCS09g1008683  fleshy  0.0399833", stringsAsFactors = FALSE)


library(dplyr)
library(tidyr)

res <- df1[, -2] %>% 
  group_by(V1) %>% 
  mutate(rn = row_number()) %>% 
  spread(key = "rn", value = "V3") %>% 
  data.frame()


res
#               V1          X1          X2         X3         X4        X5
# 1 TMCS09g1008676 0.000234939 1.38379e-05 0.00331883         NA        NA
# 2 TMCS09g1008677 0.048157800          NA         NA         NA        NA
# 3 TMCS09g1008678 0.035049100          NA         NA         NA        NA
# 4 TMCS09g1008679 0.033563900          NA         NA         NA        NA
# 5 TMCS09g1008680 0.016708700          NA         NA         NA        NA
# 6 TMCS09g1008681 0.003010890          NA         NA         NA        NA
# 7 TMCS09g1008682 0.005198380 3.99833e-02 0.01221840         NA        NA
# 8 TMCS09g1008683 0.002024270 1.99513e-03 0.03504910 0.00331883 0.0399833

AWK solution

First extract only required columns:

cut -f1,3 YOUR_FILE.txt > YOUR_NEW_FILE.txt

Group your repeated entries: Assuming your file is TAB-delimited and using following command you can group repeated entries from second column by ',' (comma). You can change the separator as you wish.

awk 'BEGIN{FS="\t"}{ if( !seen[$1]++ ) order[++oidx] = $1; stuff[$1] = stuff[$1] $2 " " } END { for( i = 1; i <= oidx; i++ ) print order[i]"\t"stuff[order[i]] }' YOUR_NEW_FILE.txt > YOUR_FINAL_OUTPUT.txt