Windows XP SP3 Ultimate Edition VALR

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valr provides tools to read and manipulate genome intervals and signals
, similar to the BEDtools suite. valr enables analysis in the R/RStudio environment, leveraging modern R tools in the tidyverse for a terse, expressive syntax. Compute-intensive algorithms are implemented in Rcpp/C++, and many methods take advantage of the speed and grouping capability provided by dplyr.


The functions in valr have similar names to their BEDtools counterparts, and so will be familiar to users coming from the BEDtools suite. Unlike other tools that wrap BEDtools and write temporary files to disk, valr tools run natively in memory. Similar to pybedtools, valr has a terse syntax:


valr includes helpful glyphs to illustrate the results of specific operations, similar to those found in the BEDtools documentation. For example, bed_glyph() illustrates the result of intersecting x and y intervals with bed_intersect():


valr can be used in RMarkdown documents to generate reproducible work-flows for data processing. Because computations in valr are fast, it can be for exploratory analysis with RMarkdown, and for interactive analysis using shiny.


Functions in valr have similar names to their BEDtools counterparts, andso will be familiar to users coming from the BEDtools suite. Unlikeother tools that wrap BEDtools and write temporary files to disk, valrtools run natively in memory. Similar topybedtools, valrhas a terse syntax:



Example of combining valr tools.
 The functions invalr have similar names to theirBEDtools counterparts, and so will be familiar to users of theBEDtools suite. Also, similar to pybedtools10, a python wrapper forBEDtools,valr has a terse syntax. For example, shown below is a demonstration of how to find all intergenic SNPs within 1 kilobase of genes usingvalr. The BED files used in the following examples are described in the Data Availability section.



Visual documentation.
 By conducting interval arithmetic entirely in R,valr is also an effective teaching tool for introducing interval analysis to early-stage analysts without requiring familiarity with both command-line tools and R. To aid in demonstrating the interval operations available invalr, we developed thebed_glyph() tool which produces plots demonstrating the input and output of operations invalr in a manner similar to those found in theBEDtools documentation. Shown below is the code required to produce glyphs displaying the results of intersectingx andy intervals withbed_intersect(), and the result of mergingx intervals withbed_merge() (Figure 1).



Grouping data.
 Thegroup_by function indplyr can be used to execute functions on subsets of single and multipledata_frames. Functions invalr leverage grouping to enable a variety of comparisons. For example, intervals can be grouped bystrand to perform comparisons among intervals on the same strand.


Then, we generate 1 bp intervals to represent transcription start sites (TSSs). We focus on+ strand genes, but- genes are easily accommodated by filtering them and usingbed_makewindows() withreversed window numbers.


(
A
) Timings were calculated by performing 10 repetitions of indicated functions on data frames preloaded in R containing 1 million random 1 kilobase x/y intervals generated usingbed_random(). (
B
) Timings for executing functions inBEDtools v2.25.0 or equivalent functions invalr using the same interval sets as in (
A
) written to files. AllBEDtools function outputs were written to/dev/null/, and were timed using GNUtime. Timings forvalr functions in (
B
) include times for reading files usingread_bed() functions and were timed using themicrobenchmark package.





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Windows XP SP3 Ultimate Edition VALR

Stephen Henry