adding two new promoter af datasets

README.md

@@ -467,11 +467,30 @@ features:
             - compound: peptone
               concentration_percent: 2
 
+- applies_to:
+  - chec_mahendrawada_m2025_af_replicates_intergenic
+  - chec_mahendrawada_m2025_af_combined_intergenic
+  fields:
+  - name: ir_name
+    dtype: string
+    description: >-
+      Unique identifier of the intergenic region. See
+      yeast_genome_resources/intergenic_regions_metadata_5_1.csv for details on
+      the region (location, etc). Note that these intergenic regions are defined
+      as the region between the end of one ORF and the start of the next, and
+      are named according to the locus tags of the flanking ORFs (e.g.,
+      YAL001C-YAL002W). A intergenic  region is assigned to a promoter only when
+      the 5' end is continuous with the region.
+
 - applies_to:
   - chec_mahendrawada_m2025_af_replicates
   - chec_mahendrawada_m2025_af_combined
   - chec_mahendrawada_m2025_af_replicates_mindel
   - chec_mahendrawada_m2025_af_combined_mindel
+  - chec_mahendrawada_m2025_af_replicates_start_codon_500bp
+  - chec_mahendrawada_m2025_af_combined_start_codon_500bp
+  - chec_mahendrawada_m2025_af_replicates_intergenic
+  - chec_mahendrawada_m2025_af_combined_intergenic
   fields:
   - name: seqnames
     dtype: string
@@ -621,7 +640,9 @@ configs:
 - config_name: mahendrawada_chec_seq
   description: >-
     ChEC-seq transcription factor binding data with peak scores
-    (original authors' processed data)
+    (original authors' processed data). Note that this is the only the standard
+    condition data for the 178 transcription factors in that condition in the
+    original authors' processed data.
   default: true
   dataset_type: annotated_features
   metadata_fields:
@@ -635,8 +656,9 @@ configs:
     - name: sample_id
       dtype: integer
       description: >-
-        unique identifier for a specific sample, which uniquely identifies one of the 178 TFs.
-        Across datasets in this repo, the a given sample_id identifies the same regulator.
+        NOTE: this needs to be checked -- I think it should be deprecated/removed and 
+        replaced with the sample_id in the chec_genome_map_meta. This dataset was 
+        not used for awhile as the rest of the repo developed.
     - name: peak_score
       dtype: float64
       description: >-
@@ -648,7 +670,7 @@ configs:
     Sample-level metadata for ChEC-seq experiments including regulator information,
     experimental conditions, and replicate structure
   dataset_type: metadata
-  applies_to: ["chec_mahendrawada_m2025_af_replicates", "chec_mahendrawada_m2025_af_replicates_mindel", "chec_genome_map"]
+  applies_to: ["chec_mahendrawada_m2025_af_replicates", "chec_mahendrawada_m2025_af_replicates_mindel", "chec_mahendrawada_m2025_af_replicates_start_codon_500bp", "chec_mahendrawada_m2025_af_replicates_intergenic", "chec_genome_map"]
   data_files:
   - split: train
     path: chec_genome_map_meta.parquet
@@ -810,6 +832,52 @@ configs:
       description: SRA (Sequence Read Archive) accession identifier for this biological replicate
       role: sample_id
 
+- config_name: chec_mahendrawada_m2025_af_replicates_start_codon_500bp
+  description: >-
+    Promoter significance scores using promoters defined as 500bp upstream of the 
+    start codon. See scripts/mahendrawada_annotated_features.R. This is a reprocessing
+    of the original authors' data which counts all tags over a specific promoter
+    region in the TF tagged sample and calculates an enrichment relative to the free
+    MNase control.
+  dataset_type: annotated_features
+  genome_resources:
+    region_sets:
+      start_codon_500bp:
+        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/start_codon_500bp_upstream_promoters.bed
+        join_column: target_locus_tag
+  data_files:
+  - split: train
+    path: chec_mahendrawada_m2025_af_replicates_start_codon_500bp.parquet
+  dataset_info:
+    features:
+    - name: sra_accession
+      dtype: string
+      description: SRA (Sequence Read Archive) accession identifier for this biological replicate
+      role: sample_id
+
+- config_name: chec_mahendrawada_m2025_af_replicates_intergenic
+  description: >-
+    Promoter significance scores using intergenic regions that are continuous with 
+    the 5' end of a target feature. See scripts/mahendrawada_annotated_features.R.
+    This is a reprocessing of the original authors' data which counts all tags over a
+    specific promoter region in the TF tagged sample and calculates an enrichment
+    relative to the free MNase control.
+  dataset_type: annotated_features
+  genome_resources:
+    region_sets:
+      intergenic:
+        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/intergenic_regions_metadata_5_1.csv
+        join_column: ir_name
+  data_files:
+  - split: train
+    path: chec_mahendrawada_m2025_af_replicates_intergenic.parquet
+  dataset_info:
+    features:
+    - name: sra_accession
+      dtype: string
+      description: SRA (Sequence Read Archive) accession identifier for this biological replicate
+      role: sample_id
+
 - config_name: chec_mahendrawada_m2025_af_combined_meta
   description: Sample-level metadata for combined ChEC-seq experiments with regulator information and experimental conditions
   dataset_type: metadata
@@ -868,6 +936,53 @@ configs:
         for a given regulator and condition prior to having promoter enrichment
         and significance calculated. 
 
+- config_name: chec_mahendrawada_m2025_af_combined_start_codon_500bp
+  description: >-
+    Annotated feature dataset with binding score and statistics performed on combined
+    replicates for a single sample per regulator/condition, using promoters defined as
+    500bp upstream of the start codon. See scripts/mahendrawada_annotated_features.R.
+  dataset_type: annotated_features
+  genome_resources:
+    region_sets:
+      start_codon_500bp:
+        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/start_codon_500bp_upstream_promoters.bed
+        join_column: target_locus_tag
+  data_files:
+  - split: train
+    path: chec_mahendrawada_m2025_af_combined_start_codon_500bp.parquet
+  dataset_info:
+    features:
+    - name: sample_id
+      dtype: int64
+      description: >-
+        Unique identifier for a sample. Each sample is the combination of replicates
+        for a given regulator and condition prior to having promoter enrichment
+        and significance calculated. 
+
+- config_name: chec_mahendrawada_m2025_af_combined_intergenic
+  description: >-
+    Annotated feature dataset with binding score and statistics performed on combined
+    replicates for a single sample per regulator/condition, using intergenic regions
+    that are continuous with the 5' end of a target feature.
+    See scripts/mahendrawada_annoted_features.R.
+  dataset_type: annotated_features
+  genome_resources:
+    region_sets:
+      intergenic:
+        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/intergenic_regions_metadata_5_1.csv
+        join_column: ir_name
+  data_files:
+  - split: train
+    path: chec_mahendrawada_m2025_af_combined_intergenic.parquet
+  dataset_info:
+    features:
+    - name: sample_id
+      dtype: int64
+      description: >-
+        Unique identifier for a sample. Each sample is the combination of replicates
+        for a given regulator and condition prior to having promoter enrichment
+        and significance calculated. 
+
 - config_name: rna_seq
   description: Nascent RNA-seq differential expression data following transcription factor depletion using 4TU metabolic labeling
   dataset_type: annotated_features

chec_mahendrawada_m2025_af_combined_intergenic.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3965e354a9d39bcd0c717998e94613696e5c677519608e6ebeafd3a7aba57c9e
+size 67168608

chec_mahendrawada_m2025_af_combined_start_codon_500bp.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8027a224f1dafc0ad00b0e471f765ed1c436fc2cfa1d883d7dfab482f51a262e
+size 74534135

chec_mahendrawada_m2025_af_replicates_intergenic.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:236ca43bfc9c8025cc7f4d900e2ada144df6d994383785e47a8fb7f33ebf2ab5
+size 200162982

chec_mahendrawada_m2025_af_replicates_start_codon_500bp.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61fb0dd266901ed76d27cc221609ace595c6b550f5ec885a355f78a653dff318
+size 221640272

scripts/mahendrawada_annotated_features.R

@@ -475,7 +475,7 @@ enrichment_analysis <- function(sampleid,
     )
 }
 
-PROMOTER_SET = 'mindel'
+PROMOTERS = 'start_codon_500bp'
 
 genomic_features = arrow::read_parquet("~/code/hf/yeast_genome_resources/brentlab_features.parquet")
 
@@ -495,48 +495,254 @@ samplid_list = chec_genomemap_meta %>%
     pull(sample_id) %>%
     unique()
 
-regions = list(
+regions_list = list(
     yiming = read_tsv("~/code/hf/yeast_genome_resources/yiming_promoters.bed",
         col_names = c('chr', 'start', 'end', 'locus_tag', 'score', 'strand')) %>%
         bed_to_granges(),
     mindel =  arrow::read_parquet("~/code/hf/yeast_genome_resources/mindel_promoters.parquet",
         col_names = c('chr', 'start', 'end', 'locus_tag', 'score', 'strand')) %>%
-        bed_to_granges()
+        bed_to_granges(),
+    start_codon_500bp = rtracklayer::import("~/code/hf/yeast_genome_resources/start_codon_500bp_upstream_promoters.bed"),
+    intergenic = rtracklayer::import("~/code/hf/yeast_genome_resources/intergenic_regions_5_1.bed")
 )
 
-regions_gr <- regions[[PROMOTER_SET]]
+# rename name to target_locus_tag
+colnames(GenomicRanges::mcols(regions_list$yiming))[1] <- "target_locus_tag"
+colnames(GenomicRanges::mcols(regions_list$start_codon_500bp))[1] <- "target_locus_tag"
+
+intergenic_meta = read_csv("~/code/hf/yeast_genome_resources/intergenic_regions_metadata_5_1.csv") |>
+    dplyr::select(ir_name, chr, start, end, feature_left, feature_right) |>
+    pivot_longer(-c(ir_name, chr, start, end), names_to = 'side', values_to = 'target_locus_tag') |>
+    left_join(dplyr::select(genomic_features, target_locus_tag = locus_tag,
+                            target_symbol = symbol, target_strand = strand,
+                            target_start = start, target_end = end)) |>
+    filter(chr != "chrM") |>
+    filter(!is.na(target_strand)) |>
+    mutate(valid = (target_strand == '-' & target_end <= start)
+           | (target_strand == '+' & target_start >= end)) |>
+    filter(valid)
+
+regions_gr <- regions_list[[PROMOTERS]]
 
 # removes AAD6 and AAD16 from mindel. pseudogene no longer in annotations
-regions_gr = regions_gr[!is.na(regions_gr$target_locus_tag)]
+if(PROMOTERS != "intergenic"){
+    # really just for mindel
+    regions_gr = regions_gr[!is.na(regions_gr$target_locus_tag)]
+} else if(PROMOTERS == "intergenic"){
+    regions_gr = regions_gr[regions_gr$name %in% unique(intergenic_meta$ir_name)]
+}
 
 m2025_control = combine_control_af()
 
-annotated_feature_counts = map(samplid_list, combine_replicates_af)
+# annotated_feature_counts = map(samplid_list, combine_replicates_af)
+library(parallel)
+
+n_cores = 25
+annotated_feature_counts <- mclapply(
+    samplid_list,
+    combine_replicates_af,
+    mc.cores = n_cores
+)
 names(annotated_feature_counts) = samplid_list
 
-annotated_feature_quants = map(
-    samplid_list, ~{
-        enrichment_analysis(
-            .x,
-            m2025_control$af$score,
-            sum(m2025_control$library_totals$n)
-        )
-    }
+# annotated_feature_quants = map(
+#     samplid_list, ~{
+#         enrichment_analysis(
+#             .x,
+#             m2025_control$af$score,
+#             sum(m2025_control$library_totals$n)
+#         )
+#     }
+# )
+
+
+annotated_feature_quants <- mclapply(
+    samplid_list,
+    \(x) enrichment_analysis(
+        x,
+        m2025_control$af$score,
+        sum(m2025_control$library_totals$n)
+    ),
+    mc.cores = n_cores
 )
 
 names(annotated_feature_quants) = samplid_list
 
 sra_accession_for_quants = map(annotated_feature_quants, ~names(.x$replicates))
 
-annotated_features_quants_replicates_mindel =
+annotated_features_quants_replicates_raw =
     map(annotated_feature_quants, ~{
         map(.x$replicates, as_tibble) %>%
             list_rbind(names_to = "sra_accession")}) %>%
     list_rbind(names_to = "sample_id") %>%
     mutate(sample_id = as.integer(sample_id)) %>%
     arrange(sample_id) %>%
-    select(-sample_id) %>%
+    select(-sample_id)
+
+if(PROMOTERS == "intergenic"){
+    annotated_features_quants_replicates = annotated_features_quants_replicates_raw |>
+        dplyr::rename(ir_name = name) |>
+        left_join(dplyr::select(intergenic_meta, ir_name, target_locus_tag, target_symbol),
+                  relationship = "many-to-many")
+} else if(PROMOTERS == "yiming" | PROMOTERS == "start_codon_500bp"){
+    annotated_features_quants_replicates = annotated_features_quants_replicates_raw |>
+        left_join(dplyr::select(genomic_features, target_locus_tag = locus_tag, target_symbol = symbol)) |>
+    dplyr::relocate(sra_accession, target_locus_tag, target_symbol)
+
+} else{
+    annotated_features_quants_replicates = annotated_features_quants_replicates_raw |>
     dplyr::relocate(sra_accession, target_locus_tag, target_symbol)
+}
+
+annotated_feature_quants_combined_raw =
+    map(annotated_feature_quants, ~as_tibble(.x$combined)) %>%
+    list_rbind(names_to = "sample_id") %>%
+    mutate(sample_id = as.integer(sample_id)) %>%
+    arrange(sample_id)
+
+if(PROMOTERS == "intergenic"){
+    annotated_feature_quants_combined = annotated_feature_quants_combined_raw |>
+        dplyr::rename(ir_name = name) |>
+        left_join(dplyr::select(intergenic_meta, ir_name, target_locus_tag, target_symbol),
+                  relationship = "many-to-many") |>
+        dplyr::relocate(sample_id, target_locus_tag, target_symbol, ir_name)
+} else if(PROMOTERS == "yiming" | PROMOTERS == "start_codon_500bp"){
+    annotated_feature_quants_combined = annotated_feature_quants_combined_raw |>
+        left_join(dplyr::select(genomic_features, target_locus_tag = locus_tag, target_symbol = symbol)) |>
+        dplyr::relocate(sample_id, target_locus_tag, target_symbol)
+
+} else{
+    annotated_feature_quants_combined = annotated_feature_quants_combined_raw |>
+        dplyr::relocate(sample_id, target_locus_tag, target_symbol)
+}
+
+if(PROMOTERS == 'yiming'){
+    # annotated_features_quants_replicates %>%
+    #   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_replicates.parquet",
+    #                 compression = "zstd",
+    #                 write_statistics = TRUE,
+    #                 chunk_size = 6708,
+    #                 use_dictionary = c(
+    #                   sra_accession = TRUE,
+    #                   seqnames = TRUE,
+    #                   target_locus_tag = TRUE,
+    #                   target_symbol = TRUE
+    #                 )
+    #   )
+    #
+    # annotated_feature_quants_combined %>%
+    #   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined.parquet",
+    #                 compression = "zstd",
+    #                 write_statistics = TRUE,
+    #                 chunk_size = 6708,
+    #                 use_dictionary = c(
+    #                   sample_id = TRUE,
+    #                   seqnames = TRUE,
+    #                   target_locus_tag = TRUE,
+    #                   target_symbol = TRUE
+    #                 )
+    #   )
+
+} else if(PROMOTERS == 'mindel'){
+    # annotated_features_quants_replicates_mindel %>%
+    #   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_replicates_mindel.parquet",
+    #                 compression = "zstd",
+    #                 write_statistics = TRUE,
+    #                 chunk_size = 5358,
+    #                 use_dictionary = c(
+    #                   sra_accession = TRUE,
+    #                   seqnames = TRUE,
+    #                   target_locus_tag = TRUE,
+    #                   target_symbol = TRUE
+    #                 )
+    #   )
+    #
+    # annotated_feature_quants_combined_mindel %>%
+    #   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_mindel.parquet",
+    #                 compression = "zstd",
+    #                 write_statistics = TRUE,
+    #                 chunk_size = 5358,
+    #                 use_dictionary = c(
+    #                   sample_id = TRUE,
+    #                   seqnames = TRUE,
+    #                   target_locus_tag = TRUE,
+    #                   target_symbol = TRUE
+    #                 )
+    #   )
+
+} else if(PROMOTERS == 'start_codon_500bp'){
+    # annotated_features_quants_replicates %>%
+    #     dplyr::select(-score) |>
+    #   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_replicates_start_codon_500bp.parquet",
+    #                 compression = "zstd",
+    #                 write_statistics = TRUE,
+    #                 chunk_size = 5945,
+    #                 use_dictionary = c(
+    #                   sra_accession = TRUE,
+    #                   seqnames = TRUE,
+    #                   target_locus_tag = TRUE,
+    #                   target_symbol = TRUE
+    #                 )
+    #   )
+    #
+    # annotated_feature_quants_combined %>%
+    #     dplyr::select(-score) |>
+    #   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_start_codon_500bp.parquet",
+    #                 compression = "zstd",
+    #                 write_statistics = TRUE,
+    #                 chunk_size = 5945,
+    #                 use_dictionary = c(
+    #                   sample_id = TRUE,
+    #                   seqnames = TRUE,
+    #                   target_locus_tag = TRUE,
+    #                   target_symbol = TRUE
+    #                 )
+    #   )
+
+} else if(PROMOTERS == 'intergenic'){
+    # annotated_features_quants_replicates %>%
+    #     dplyr::select(-score) |>
+    #   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_replicates_intergenic.parquet",
+    #                 compression = "zstd",
+    #                 write_statistics = TRUE,
+    #                 chunk_size = 6040,
+    #                 use_dictionary = c(
+    #                   sra_accession = TRUE,
+    #                   seqnames = TRUE,
+    #                   ir_name = TRUE,
+    #                   target_locus_tag = TRUE,
+    #                   target_symbol = TRUE
+    #                 )
+    #   )
+    #
+    # annotated_feature_quants_combined %>%
+    #     dplyr::select(-score) |>
+    #   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_intergenic.parquet",
+    #                 compression = "zstd",
+    #                 write_statistics = TRUE,
+    #                 chunk_size = 6040,
+    #                 use_dictionary = c(
+    #                   sample_id = TRUE,
+    #                   seqnames = TRUE,
+    #                   ir_name = TRUE,
+    #                   target_locus_tag = TRUE,
+    #                   target_symbol = TRUE
+    #                 )
+    #   )
+
+}
+
+
+
+# annotated_features_quants_replicates_mindel =
+#     map(annotated_feature_quants, ~{
+#         map(.x$replicates, as_tibble) %>%
+#             list_rbind(names_to = "sra_accession")}) %>%
+#     list_rbind(names_to = "sample_id") %>%
+#     mutate(sample_id = as.integer(sample_id)) %>%
+#     arrange(sample_id) %>%
+#     select(-sample_id) %>%
+#     dplyr::relocate(sra_accession, target_locus_tag, target_symbol)
 
 # annotated_features_quants_replicates =
 #     map(annotated_feature_quants, ~{
@@ -552,31 +758,7 @@ annotated_features_quants_replicates_mindel =
 #     select(-score)
 #
 
-# annotated_features_quants_replicates %>%
-#   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_replicates.parquet",
-#                 compression = "zstd",
-#                 write_statistics = TRUE,
-#                 chunk_size = 6708,
-#                 use_dictionary = c(
-#                   sra_accession = TRUE,
-#                   seqnames = TRUE,
-#                   target_locus_tag = TRUE,
-#                   target_symbol = TRUE
-#                 )
-#   )
-
-# annotated_features_quants_replicates_mindel %>%
-#   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_replicates_mindel.parquet",
-#                 compression = "zstd",
-#                 write_statistics = TRUE,
-#                 chunk_size = 5358,
-#                 use_dictionary = c(
-#                   sra_accession = TRUE,
-#                   seqnames = TRUE,
-#                   target_locus_tag = TRUE,
-#                   target_symbol = TRUE
-#                 )
-#   )
+
 
 # annotated_feature_quants_combined =
 #     map(annotated_feature_quants, ~as_tibble(.x$combined)) %>%
@@ -588,36 +770,12 @@ annotated_features_quants_replicates_mindel =
 #     dplyr::relocate(sample_id, target_locus_tag, target_symbol) %>%
 #     select(-score)
 
-annotated_feature_quants_combined_mindel =
-    map(annotated_feature_quants, ~as_tibble(.x$combined)) %>%
-    list_rbind(names_to = "sample_id") %>%
-    mutate(sample_id = as.integer(sample_id)) %>%
-    arrange(sample_id) %>%
-    dplyr::relocate(sample_id, target_locus_tag, target_symbol)
-
-# annotated_feature_quants_combined_mindel %>%
-#   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_mindel.parquet",
-#                 compression = "zstd",
-#                 write_statistics = TRUE,
-#                 chunk_size = 5358,
-#                 use_dictionary = c(
-#                   sample_id = TRUE,
-#                   seqnames = TRUE,
-#                   target_locus_tag = TRUE,
-#                   target_symbol = TRUE
-#                 )
-#   )
-
-
-# annotated_feature_quants_combined %>%
-#   write_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined.parquet",
-#                 compression = "zstd",
-#                 write_statistics = TRUE,
-#                 chunk_size = 6708,
-#                 use_dictionary = c(
-#                   sample_id = TRUE,
-#                   seqnames = TRUE,
-#                   target_locus_tag = TRUE,
-#                   target_symbol = TRUE
-#                 )
-#   )
+# annotated_feature_quants_combined_mindel =
+#     map(annotated_feature_quants, ~as_tibble(.x$combined)) %>%
+#     list_rbind(names_to = "sample_id") %>%
+#     mutate(sample_id = as.integer(sample_id)) %>%
+#     arrange(sample_id) %>%
+#     dplyr::relocate(sample_id, target_locus_tag, target_symbol)
+
+
+