diff --git a/bin/cdhit_wrapper.R b/bin/cdhit_wrapper.R
new file mode 100644
index 0000000..ed5ea7f
--- /dev/null
+++ b/bin/cdhit_wrapper.R
@@ -0,0 +1,75 @@
+#! /bin/Rscript
+library("optparse")
+
+option_list <- list(
+  make_option(opt_str = c("-i", "--input"), default = NULL, help = "Input bed-file. Last column must be sequences.", metavar = "character"),
+  make_option(opt_str = c("-s", "--similarity"), default = 0.8, help = "Similarity threshold. Default = %default", metavar = "double >= 0.8"),
+  make_option(opt_str = c("-A", "--coverage"), default = 8, help = "Minimal alignment length for both sequences in nucelotides. Default = %default", metavar = "integer"),
+  make_option(opt_str = c("-o", "--output"), default = "cluster.bed", help = "Output file. Default = %default", metavar = "character"),
+  make_option(opt_str = c("-c", "--clean"), default = TRUE, help = "Delete all temporary files. Default = %default", metavar = "logical")
+  # TODO more args
+)
+
+opt_parser <- OptionParser(option_list = option_list, 
+                           description = "CD-HIT-EST Wrapper function.")
+
+opt <- parse_args(opt_parser)
+
+#' cd-hit wrapper
+#' 
+#' @param input
+#' @param similarity Similarity threshold.
+#' @param coverage Minimal alignment length for both sequences in nucelotides.
+#' @param output Clustered bedfile. Adds cluster number in last column (lower number = bigger cluster).
+#' @param clean Clean up after run.
+#' 
+#' @return bed_table with cluster in last column
+#' TODO add all cdhit parameter
+#' TODO check whether cdhit is installed
+cdhitest <- function(input, similarity = 0.8, coverage = 8, output = "cluster.bed", clean = TRUE) {
+  # load bed if neccessary
+  if (!data.table::is.data.table(input)) {
+    bed_table <- data.table::fread(input = input, header = FALSE)
+  } else {
+    bed_table <- input
+  }
+  
+  ### convert bed to fasta
+  # 4th column = name
+  # last column = sequence
+  fasta_file <- "converted_bed.fasta"
+  seqinr::write.fasta(sequences = as.list(bed_table[[ncol(bed_table)]]), names = bed_table[[4]], as.string = TRUE, file.out = fasta_file)
+  
+  ### cd-hit-est
+  cdhit_output <- "cdhit_output"
+  cdhit_call <- paste("cd-hit-est -i", fasta_file, "-o", cdhit_output, "-c", similarity, "-A", coverage, "-G 0 -n 3 -g 1 -r 0 -l 5 -sc 1 -d 0")
+  
+  system(command = cdhit_call, wait = TRUE)
+  
+  # reformat cluster file
+  # columns: id, clstr, clstr_size, length, clstr_rep, clstr_iden, clstr_cov
+  cluster_file <- "reformated.clstr"
+  cluster_call <- paste("clstr2txt.pl", paste0(cdhit_output, ".clstr"), ">", cluster_file)
+  
+  system(command = cluster_call, wait = TRUE)
+  
+  # load reformated file
+  cluster <- data.table::fread(cluster_file)
+  
+  ### add cluster to bed_table
+  result <- merge(x = bed_table, y = cluster[, c("id", "clstr")], by.x = "V4", by.y = "id", sort = FALSE)[, union(names(bed_table), names(cluster)[2]), with = FALSE]
+  
+  # delete files
+  if (clean) {
+    file.remove(fasta_file, paste0(cdhit_output, ".clstr"), cdhit_output, cluster_file)
+  }
+  
+  data.table::fwrite(x = result, file = output, sep = "\t", col.names = FALSE)
+}
+
+# call function with given parameter if not in interactive context (e.g. run from shell)
+if (!interactive()) {
+  # remove last parameter (help param)
+  params <- opt[-length(opt)]
+  do.call(cdhitest, args = params)  
+}
diff --git a/bin/reduce_bed.R b/bin/reduce_bed.R
new file mode 100644
index 0000000..223c946
--- /dev/null
+++ b/bin/reduce_bed.R
@@ -0,0 +1,241 @@
+#! /bin/Rscript
+library("optparse")
+
+option_list <- list(
+  make_option(opt_str = c("-i", "--input"), default = NULL, help = "Input bed-file. Last column must be sequences.", metavar = "character"),
+  make_option(opt_str = c("-k", "--kmer"), default = 10, help = "Kmer length. Default = %default", metavar = "integer"),
+  make_option(opt_str = c("-m", "--motif"), default = 10, help = "Estimated motif length. Default = %default", metavar = "integer"),
+  make_option(opt_str = c("-o", "--output"), default = "reduced.bed", help = "Output file. Default = %default", metavar = "character"),
+  make_option(opt_str = c("-t", "--threads"), default = 1, help = "Number of threads to use. Use -1 for all available cores. Default = %default", metavar = "integer"),
+  make_option(opt_str = c("-c", "--clean"), default = TRUE, help = "Delete all temporary files. Default = %default", metavar = "logical"),
+  make_option(opt_str = c("-s", "--min_seq_length"), default = NULL, help = "Remove sequences below this length. Defaults to motif length.", metavar = "integer", type = "integer")
+  # TODO more args
+)
+
+opt_parser <- OptionParser(option_list = option_list, 
+                           description = "Reduce sequences to frequent regions.")
+
+opt <- parse_args(opt_parser)
+
+#' Reduce bed file to conserved regions
+#' 
+#' @param input bed file
+#' @param kmer Length of kmer.
+#' @param motif Estimated motif length.
+#' @param output Output file
+#' @param threads Number of threads.
+#' @param clean Delete all temporary files.
+#' @param minoverlap_kmer Minimum required overlap between kmers. # TODO
+#' @param minoverlap_motif Minimum required overlap between motif and kmer. # TODO
+#' @param min_seq_length Must be smaller or equal to kmer and motif. Default = motif.
+#' 
+#' @return reduced bed
+#' TODO check whether jellyfish is installed
+reduce_bed <- function(input, kmer = 10, motif = 10, output = "reduced.bed", threads = NULL, clean = TRUE, minoverlap_kmer, minoverlap_motif, min_seq_length = motif) {
+  # get number of available cores
+  if (threads == -1) {
+    threads <- parallel::detectCores()
+  }
+  
+  message("Loading bed...")
+  # load bed
+  # columns: chr, start, end, name, ..., sequence
+  bed_table <- data.table::fread(input = input, header = FALSE)
+  names(bed_table)[1:4] <- c("chr", "start", "end", "name")
+  names(bed_table)[ncol(bed_table)] <- "sequence"
+  # index
+  data.table::setkey(bed_table, name, physical = FALSE)
+  
+  # check for duplicated names
+  if (anyDuplicated(bed_table[, "name"])) {
+    warning("Found duplicated names. Making names unique.")
+    bed_table[, name := make.unique(name)]
+  }
+  
+  # remove sequences below minimum length
+  total_rows <- nrow(bed_table)
+  bed_table <- bed_table[nchar(sequence) > min_seq_length]
+  if (total_rows > nrow(bed_table)) {
+    message("Removed ", total_rows - nrow(bed_table), " sequence(s) below minimum length of ", min_seq_length)
+  }
+  
+  # TODO forward fasta file as parameter so no bed -> fasta conversion is needed.
+  message("Writing fasta...")
+  # save as fasta
+  fasta_file <- paste0(basename(input), ".fasta")
+  seqinr::write.fasta(sequences = as.list(bed_table[[ncol(bed_table)]]), names = bed_table[[4]], as.string = TRUE, file.out = fasta_file)
+  
+  message("Counting kmer...")
+  # count k-mer
+  hashsize <- 4 ^ kmer
+  count_output_binary <- "mer_count_binary.jf"
+  input <- fasta_file
+  jellyfish_call <- paste("jellyfish count ", "-m", kmer, "-s", hashsize, "-o", count_output_binary, input)
+
+  system(command = jellyfish_call, wait = TRUE)
+  
+  mer_count_table <- "mer_count_table.jf"
+  jellyfish_dump_call <- paste("jellyfish dump --column --tab --output", mer_count_table, count_output_binary)
+  
+  system(command = jellyfish_dump_call, wait = TRUE)
+
+  message("Reduce kmer.")
+  # load mer table
+  # columns: kmer, count
+  kmer_counts <- data.table::fread(input = mer_count_table, header = FALSE)
+  # order kmer descending
+  data.table::setorder(kmer_counts, -V2)
+  
+  # compute number of hits to keep
+  keep_hits <- significant_kmer(bed_table, kmer = kmer, motif = motif)
+  
+  # reduce kmer
+  reduced_kmer <- reduce_kmer(kmer = kmer_counts, keep_hits)
+
+  message("Find kmer in sequences.")
+  # find k-mer in sequences
+  # TODO minoverlap as parameter
+  # columns: name, start, end, width
+  ranges_table <- find_kmer_regions(bed = bed_table, kmer_counts = reduced_kmer, minoverlap = kmer - 1, threads = threads)
+  names(ranges_table)[1:2] <- c("relative_start", "relative_end")
+  
+  # merge ranged_table with bed_table + keep column order
+  merged <- merge(x = bed_table, y = ranges_table, by = "name", sort = FALSE)[, union(names(bed_table), names(ranges_table)), with = FALSE]
+  
+  # delete sequences without hit
+  merged <- na.omit(merged, cols = c("relative_start", "relative_end"))
+  message("Removed ", nrow(bed_table) - nrow(merged), " sequence(s) without hit.")
+  
+  message("Reduce sequences.")
+  # create subsequences
+  merged[, sequence := stringr::str_sub(sequence, relative_start, relative_end)]  
+  
+  # bed files count from 0
+  merged[, `:=`(relative_start = relative_start - 1, relative_end = relative_end - 1)]
+  # change start end location
+  merged[, `:=`(start = start + relative_start, end = start + relative_end)]
+  
+  # clean table
+  merged[, `:=`(relative_start = NULL, relative_end = NULL, width = NULL)]
+  
+  if (clean) {
+    file.remove(fasta_file, count_output_binary, mer_count_table)
+  }
+  
+  data.table::fwrite(merged, file = output, sep = "\t", col.names = FALSE)
+}
+
+#' returns sum of top x kmer frequencies to keep
+#' 
+#' @param bed Bed table with sequences in last column
+#' @param kmer Length of kmer
+#' @param motif Length of motif
+#' @param minoverlap Minimum number of bases overlapping between kmer and motif. Must be <= motif & <= kmer. Defaults to ceiling(motif / 2).
+#' 
+#' @return Number of interesting kmer.
+significant_kmer <- function(bed, kmer, motif, minoverlap = ceiling(motif / 2)) {
+  if (minoverlap > kmer || minoverlap > motif) {
+    stop("Kmer & motif must be greater or equal than minoverlap!")
+  }
+  
+  # minimum sequence length to get all interesting overlaps
+  min_seq_length <- motif + 2 * (kmer - minoverlap)
+  
+  seq_lengths <- nchar(bed[[ncol(bed)]])
+  
+  # reduce to max interesting length
+  seq_lengths <- ifelse(seq_lengths > min_seq_length, min_seq_length, seq_lengths)
+  
+  # calculate max possible kmer
+  topx <- sum(seq_lengths - kmer + 1)
+  
+  return(topx)
+}
+
+#' @param kmer Kmer table
+#' @param significant
+reduce_kmer <- function(kmer, significant) {
+  kmer[, cumsum := cumsum(V2)]
+  
+  return(kmer[cumsum <= significant])
+}
+
+#' create list of significant ranges (one for each bed entry)
+#' 
+#' @param bed Data.table of bed with sequence in last column
+#' @param kmer_counts Data.table of counted kmer. Column1 = kmer, column2 = count.
+#' @param minoverlap Minimum overlapping nucleotides between kmers to be merged. Positive integer. Must be smaller than kmer length.
+#' @param threads Number of threads.
+#' 
+#' @return Data.table with relative positions and width (start, end, width).
+find_kmer_regions <- function(bed, kmer_counts, minoverlap = 1 , threads = NULL) {
+  if (nchar(kmer_counts[1, 1]) <= minoverlap) {
+    stop("Minoverlap must be smaller than kmer length!")
+  }
+  
+  names(kmer_counts)[1:2] <- c("kmer", "count")
+  data.table::setorder(kmer_counts, -count)
+  
+  seq_ranges <- pbapply::pblapply(seq_len(nrow(bed)), cl = threads, function(x) {
+    seq <- bed[x][[ncol(bed)]]
+    
+    #### locate ranges
+    ranges <- data.table::data.table(do.call(rbind, stringi::stri_locate_all_fixed(seq, pattern = kmer_counts[[1]])))
+    
+    ranges <- na.omit(ranges, cols = c("start", "end"))
+    
+    if (nrow(ranges) < 1) {
+      return(data.table::data.table(start = NA, end = NA, width = NA))
+    }
+    
+    # add kmer sequences
+    ranges[, sub_seq := stringr::str_sub(seq, start, end)]
+    # add kmer count
+    ranges[, count := kmer_counts[ranges[["sub_seq"]], "count", on = "kmer"]]
+    
+    #### reduce ranges
+    reduced_ranges <- IRanges::IRanges(start = ranges[["start"]], end = ranges[["end"]], names = ranges[["sub_seq"]])
+    
+    # list of overlapping ranges
+    edge_list <- as.matrix(IRanges::findOverlaps(reduced_ranges, minoverlap = minoverlap, drop.self = FALSE, drop.redundant = TRUE))
+    
+    # get components (groups of connected ranges)
+    graph <- igraph::graph_from_edgelist(edge_list, directed = FALSE)
+    # vector of node membership (indices correspond to ranges above)
+    member <- as.factor(igraph::components(graph)$membership)
+    
+    # list of membership vectors
+    node_membership <- lapply(levels(member), function(x) {
+      which(member == x)
+    })
+    
+    # calculate component score (= sum of kmer count)
+    score <- vapply(node_membership, FUN.VALUE = numeric(1), function(x) {
+      sum(kmer_counts[x, "count"])
+    })
+    
+    selected_ranges <- node_membership[[which(score == max(score))[1]]]
+    
+    # reduce selected ranges
+    reduced_ranges <- IRanges::reduce(reduced_ranges[selected_ranges])
+    
+    reduced_ranges <- data.table::as.data.table(reduced_ranges)
+    
+    return(reduced_ranges)
+  })
+  
+  # create ranges table
+  conserved_regions_table <- data.table::rbindlist(seq_ranges)
+  conserved_regions_table[, name := bed[[4]]]
+  
+  return(conserved_regions_table)
+}
+
+# call function with given parameter if not in interactive context (e.g. run from shell)
+if (!interactive()) {
+  # show apply progressbar
+  pbo <- pbapply::pboptions(type = "timer")
+  # remove last parameter (help param)
+  params <- opt[-length(opt)]
+  do.call(reduce_bed, args = params)  
+}
diff --git a/masterenv.yml b/masterenv.yml
index 175f53a..2c1879f 100644
--- a/masterenv.yml
+++ b/masterenv.yml
@@ -3,12 +3,19 @@ name: masterenv
 channels:
   - bioconda
   - conda-forge
-  - defaults
-  - r
 dependencies:
   - bedtools
   - python
-  - r-essentials
   - r-seqinr
   - numpy
   - pybigWig
+  - cd-hit
+  - jellyfish
+  - r-base>=3.5.1
+  - r-data.table
+  - r-pbapply
+  - r-igraph
+  - r-stringi
+  - r-stringr
+  - r-optparse
+  - bioconductor-iranges