**Jermdemo Raised to the Law**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Samtools and its BioPerl wrapper Bio::DB:Sam prefer to give read coverage on a depth per base pair basis. This is typically an array of depths, one for every position that has at least one read aligned. OK, works for me. But how can we quickly see which targets (in my case transcripts) have the greatest total weighted read coverage (i.e. sum every base pair of every read that aligned)?

My solution is to take that target-pos-depth information and import a table into R with at least the following columns:

targetName

depth

I added the pos column here to emphasize the base-pair granularity

tx pos depth

1 tx500090 227 1

2 tx500090 228 1

3 tx500090 229 1

4 tx500090 230 1

5 tx500090 231 1

...

66 tx500123 184 1

67 tx500123 185 1

68 tx500123 186 1

69 tx500123 187 2

70 tx500123 188 2

71 tx500123 189 2

In R

myCoverage<-read.table("myCoverage.txt",header=TRUE)

myxTab<-xtabs(depth ~ tx,data=myCoverage)

xtabs will sum up depth-weighted positions by default (i suppose this is what tabulated contigency really means) and return an unsorted list of transcripts and their weighted coverage (total base pair read coverage)

> myxTab[1:100]

tx

tx500090 tx500123 tx500134 tx500155 tx500170 tx500178 tx500203 tx500207

38 92 610 46 176 46 92 130

tx500273 tx500441 tx500481 tx500482 tx500501 tx500507 tx500667 tx500684

76 2390 114 71228 762 222 542 442

tx500945 tx500955 tx501016 tx501120 tx501127 tx501169 tx501190 tx501192

1378 3604 46 46 420 854 130 352

tx501206 tx501226 tx501229 tx501245 tx501270 tx501297 tx501390 tx501405

244 1204 206 15926 214 46 168 46

tx501406 tx501438 tx501504 tx501694 tx501702 tx501877 tx501902 tx502238

38 2572 7768 3274 314 298 84 198

tx502320 tx502364 tx502403 tx502414 tx502462 tx502515 tx502517 tx502519

122 38 588 46 46 38 38 466

tx502610 tx502624 tx502680 tx502841 tx502882 tx503090 tx503192 tx503204

206 38 168 3750 38 122 76 92

tx503416 tx503468 tx503523 tx503536 tx503571 tx503578 tx503623 tx503700

260 38 168 38 46 46 84 38

tx503720 tx503721 tx503722 tx503788 tx503872 tx503892 tx503930 tx503970

97112 38 38 4708 38 38 1290 84

tx503995 tx504107 tx504115 tx504346 tx504353 tx504355 tx504357 tx504398

46 152 206 46 3416 1402 122 290

tx504434 tx504483 tx504523 tx504589 tx504612 tx504711 tx504751 tx504827

290 8728 176 46 46 76 5644 1308

tx504828 tx504834 tx504882 tx504931 tx504952 tx505017 tx505029 tx505078

2336 328 46 34138 1000 1838 46 474

tx505123 tx505146 tx505159 tx505184

38 123344 160 588

this is approximately 10000x faster than using a formula like:

by(myCoverage,myCoverage$tx,function(x){sum(x$depth)}

**leave a comment**for the author, please follow the link and comment on their blog:

**Jermdemo Raised to the Law**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.