re-doing chexmix peaks to annotate to nearest downstream orf

README.md

@@ -81,8 +81,7 @@ features:
   - rossi_2021_af_combined
   - rossi_2021_af_replicates_mindel
   - rossi_2021_af_combined_mindel
-  - yep_filtered_peaks_yiming
-  - yep_filtered_peaks_mindel
+  - yep_filtered_peaks_combined
   fields:
   - name: target_locus_tag
     dtype: string
@@ -102,6 +101,22 @@ features:
     dtype: int64
     description: Promoter region end position (1-based, inclusive)
 
+- applies_to:
+  - rossi_2021_af_replicates
+  - rossi_2021_af_combined
+  - rossi_2021_af_replicates_mindel
+  - rossi_2021_af_combined_mindel
+  fields:
+  - name: seqnames
+    dtype: string
+    description: Chromosome identifier (e.g., chrI, chrII, chrXVI)
+  - name: start
+    dtype: int64
+    description: Promoter region start position (1-based coordinate)
+  - name: end
+    dtype: int64
+    description: Promoter region end position (1-based, inclusive)
+
 - applies_to:
   - rossi_2021_af_replicates
   - rossi_2021_af_combined
@@ -156,8 +171,6 @@ features:
 - applies_to:
   - rossi_2021_af_replicates_mindel
   - rossi_2021_af_combined_mindel
-  - yep_filtered_peaks_yiming
-  - yep_filtered_peaks_mindel
   fields:
   - name: width
     dtype: int64
@@ -187,20 +200,28 @@ features:
       this is a feature from the Mindel data that I have not documented. It is a TODO.
 
 - applies_to:
-  - yep_filtered_peaks_yiming
-  - yep_filtered_peaks_mindel
+  - yep_filtered_peaks_combined
   fields:
-  - name: score
+  - name: peak_score
     dtype: float64
     description: >-
       The filtered bed file representing the high confidence ChExMix peaks from
-      yeastepigenome.org score. Where multiple peaks intersect the same promoter,
-      the median score is taken for that promoter. Replicates are then combined. When 
-      combining replicates, the median score across all replicates is taken. Note that
-      when a replicate does not have a peak score for a promoter in the promoter set,
-      that promoter is assigned a score of 0. So if replicate A as a peak score of
-      500 and replicate B did not have any peak intersecting with that promoter, then
-      the combined score for that promoter would be 250.
+      yeastepigenome.org score. Where multiple peaks are within 500bp upstream of a target 
+      ORF, we take the median peak score. Replicates are then combined. When 
+      combining replicates, the median score across all replicates is taken.
+  - name: n_peaks
+    dtype: float64
+    description: >-
+      The number of peaks annotated to the same target. This is both within replicates and 
+      across (ie, there might be 3 peaks in 2 replicates for a total of 6 in this data)
+  - name: max_distance
+    dtype: float64
+    description: >-
+      The maximum distance a peak in the set (see n_peaks) is from the ORF
+  - name: peak_n_replicates
+    dtype: float64
+    description: >-
+      The number of replicates which have peaks for this target
 
 configs:
 - config_name: rossi_2021_metadata_replicate
@@ -231,7 +252,7 @@ configs:
 - config_name: rossi_2021_metadata_sample
   description: Sample-level metadata for combined ChIP-exo experiments including experimental conditions
   dataset_type: metadata
-  applies_to: ["rossi_2021_af_combined", "rossi_2021_af_combined_mindel", "yep_filtered_peaks_yiming", "yep_filtered_peaks_mindel"]
+  applies_to: ["rossi_2021_af_combined", "rossi_2021_af_combined_mindel", "yep_filtered_peaks_combined"]
   data_files:
   - split: train
     path: rossi_2021_metadata_sample.parquet
@@ -395,47 +416,17 @@ configs:
       description: Unique identifier combining regulator and replicates
       role: sample_id
 
-- config_name: yep_filtered_peaks_yiming
+- config_name: yep_filtered_peaks_combined
   description: >-
-    The filtered high confidence peaks reported at yeastepigenome.org intersected 
-    with the Kang promoter regions. see `scripts/parse_rossi_filtered_peaks.R`
-    for more details. NOTE: be sure to read the definition of the score column, 
-    which is at the top of this README in the common features section, as the
-    score has been combined across peaks intersecting the same promoter in the same
-    replicate, and then also across replicates.
-  dataset_type: annotated_features
-  genome_resources:
-    region_sets:
-      Kang:
-        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/yiming_promoters.bed
-        join_column: target_locus_tag
-  data_files:
-  - split: train
-    path: yep_filtered_peaks_yiming.parquet
-  dataset_info:
-    features:
-    - name: sample_id
-      dtype: int64
-      description: Unique identifier for the biological replicate
-      role: sample_id
-
-- config_name: yep_filtered_peaks_mindel
-  description: >-
-    The filtered high confidence peaks reported at yeastepigenome.org intersected 
-    with the Mindel promoter regions. see `scripts/parse_rossi_filtered_peaks.R`
-    for more details. NOTE: be sure to read the definition of the score column, 
-    which is at the top of this README in the common features section, as the
-    score has been combined across peaks intersecting the same promoter in the same
-    replicate, and then also across replicates.
-  dataset_type: annotated_features
-  genome_resources:
-    region_sets:
-      Mindel:
-        path: https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/mindel_promoters.csv.gz
-        join_column: target_locus_tag
+    The filtered high confidence peaks reported at yeastepigenome.org. The peaks 
+    are annotated to the nearest downstream gene (not dubious). Peaks within 500 
+    bp of the ORF are keep. Within a given replicate, if a target has more than 1 
+    peak, then the median score is taken. Replicates are combined in the same way, 
+    with the median target score across replicates. see
+    `scripts/parse_rossi_filtered_peaks.R` for more details.
   data_files:
   - split: train
-    path: yep_filtered_peaks_mindel.parquet
+    path: yep_filtered_peaks_combined.parquet
   dataset_info:
     features:
     - name: sample_id

scripts/CopyOfparse_rossi_filtered_peaks.R

@@ -0,0 +1,210 @@
+# fetch the zip files from the yeastepigenome.org server
+# https://www.datacommons.psu.edu/download/eberly/pughlab/yeast-epigenome-project/
+# and then extract them
+# with
+#!/usr/bin/env bash
+# fetch_yep_peaks.sh
+#
+# Extracts *_chexmix_filtered_peaks.bed from each *_YEP.zip in ZIP_DIR.
+# Output: yep_filtered_peaks/ in the current working directory.
+#
+# set -euo pipefail
+#
+# ZIP_DIR="${1:-/lts/mblab/downloaded_data/chipexo/yeastepigenome_data}"
+# OUT_DIR="$(pwd)/yep_filtered_peaks"
+#
+# if [[ ! -d "$ZIP_DIR" ]]; then
+# echo "[error] ZIP_DIR not found: $ZIP_DIR" >&2
+# exit 1
+# fi
+#
+# mkdir -p "$OUT_DIR"
+#
+# COUNT=0; SKIP=0; FAIL=0
+#
+# for zip_path in "$ZIP_DIR"/*_YEP.zip; do
+# zip_name=$(basename "$zip_path")
+# sample_id="${zip_name%_YEP.zip}"
+# bed_name="${sample_id}_chexmix_filtered_peaks.bed"
+# dest="$OUT_DIR/$bed_name"
+#
+# if [[ -f "$dest" ]]; then
+# echo "[skip] $bed_name already exists"
+# (( SKIP++ )) || true
+# continue
+# fi
+#
+# # file path inside the zip may vary — find it
+# bed_in_zip=$(unzip -Z1 "$zip_path" 2>/dev/null | grep -m1 "${bed_name}$" || true)
+#
+# if [[ -z "$bed_in_zip" ]]; then
+# echo "[warn] $bed_name not found in $zip_name — skipping" >&2
+# (( FAIL++ )) || true
+# continue
+# fi
+#
+# echo "[ext ] $zip_name  →  $bed_name"
+# unzip -p "$zip_path" "$bed_in_zip" > "$dest"
+# (( COUNT++ )) || true
+# done
+#
+# echo ""
+# echo "[done] extracted=$COUNT  skipped=$SKIP  failed=$FAIL"
+# echo "[done] output in: $OUT_DIR"
+
+# the following can be run on the output
+
+library(tidyverse)
+library(arrow)
+library(GenomicRanges)
+library(here)
+
+#' Convert BED format data frame to GRanges
+#'
+#' Handles coordinate system conversion from 0-indexed half-open BED format
+#' to 1-indexed closed GenomicRanges format
+#'
+#' @param bed_df Data frame with chr, start, end columns in BED format (0-indexed, half-open)
+#' @param zero_indexed Logical, whether input is 0-indexed (default: TRUE)
+#' @return GRanges object
+bed_to_granges <- function(bed_df, zero_indexed = TRUE) {
+
+    if (!all(c("chr", "start", "end") %in% names(bed_df))) {
+        stop("bed_df must have columns: chr, start, end")
+    }
+
+    # Convert from 0-indexed half-open [start, end) to 1-indexed closed [start, end]
+    if (zero_indexed) {
+        gr_start <- bed_df$start + 1
+        gr_end <- bed_df$end
+    } else {
+        gr_start <- bed_df$start
+        gr_end <- bed_df$end
+    }
+
+    # Create GRanges object (strand-agnostic for calling cards)
+    gr <- GenomicRanges::GRanges(
+        seqnames = bed_df$chr,
+        ranges = IRanges::IRanges(start = gr_start, end = gr_end),
+        strand = "*"
+    )
+
+    # Add any additional metadata columns
+    extra_cols <- setdiff(names(bed_df), c("chr", "start", "end", "strand"))
+    if (length(extra_cols) > 0) {
+        GenomicRanges::mcols(gr) <- bed_df[, extra_cols, drop = FALSE]
+    }
+
+    return(gr)
+}
+
+#' Annotate genomic regions with ChExMix peak scores
+#'
+#' Intersects a set of genomic regions (e.g. promoters) with ChExMix filtered
+#' peaks and assigns each region a score. Regions overlapping a single peak
+#' receive that peak's score; regions overlapping multiple peaks receive the
+#' median score across all overlapping peaks. Non-overlapping regions are
+#' scored 0.
+#'
+#' @param peaks_df Data frame with BED-format peak calls (0-indexed, half-open)
+#'   and a \code{score} column, as produced by \code{read_in_filtered_bed}.
+#' @param regions_gr GRanges object of genomic regions to annotate
+#'   (e.g. an element of \code{regions_list}).
+#'
+#' @return A copy of \code{regions_gr} with an additional metadata column
+#'   \code{score}: the peak score for overlapping regions, 0 otherwise.
+#'
+#' @seealso \code{\link{bed_to_granges}}, \code{\link{read_in_filtered_bed}}
+peaks_to_promoter_scores <- function(peaks_df, regions_gr) {
+
+    peaks_gr <- bed_to_granges(peaks_df)
+
+    hits <- GenomicRanges::findOverlaps(regions_gr, peaks_gr)
+
+    # for each hit, pull the peak score
+    scores_df <- tibble(
+        region_idx = S4Vectors::queryHits(hits),
+        score      = peaks_df$score[S4Vectors::subjectHits(hits)]
+    ) |>
+        summarise(score = median(score), .by = region_idx)
+
+    # initialize all promoters to 0, fill in overlapping ones
+    result <- rep(0, length(regions_gr))
+    result[scores_df$region_idx] <- scores_df$score
+
+    regions_gr_tmp <- regions_gr
+    regions_gr_tmp$score <- result
+    regions_gr_tmp
+}
+
+
+rossi_meta = arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_metadata.parquet")
+chrmap = read_csv("~/code/hf/yeast_genome_resources/chrmap.csv.gz")
+
+brentlab_features = read_csv("~/code/hf/yeast_genome_resources/brentlab_features.csv.gz")
+
+# Load regions
+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") |>
+        bed_to_granges()
+)
+
+filtered_bed = list.files(here("data/yep_filtered_peaks"), full.names = TRUE)
+names(filtered_bed) = str_extract(basename(filtered_bed), "^\\d+")
+
+read_in_filtered_bed = function(path){
+    read_tsv(path, col_names = c('chr', 'start', 'end', 'source', 'score')) |>
+        left_join(dplyr::select(chrmap, chr, ucsc)) |>
+        dplyr::select(-chr) |>
+        dplyr::rename(chr = ucsc) |>
+        dplyr::relocate(chr)
+}
+
+filtered_bed_df_list = map(filtered_bed, read_in_filtered_bed)
+
+filtered_bed_df_list_no_null = filtered_bed_df_list[unlist(map(filtered_bed_df_list, ~nrow(.) > 0))]
+
+filtered_bed_df_list_no_null_in_meta = filtered_bed_df_list_no_null[
+    as.integer(names(filtered_bed_df_list_no_null)) %in% rossi_meta$yeastepigenome_id]
+
+af_list = list(
+    yiming = map(filtered_bed_df_list_no_null_in_meta, peaks_to_promoter_scores, regions_list$yiming),
+    mindel = map(filtered_bed_df_list_no_null_in_meta, peaks_to_promoter_scores, regions_list$mindel)
+)
+
+af_list_dfs = list(
+    yiming = bind_rows(map(af_list$yiming, as_tibble), .id = "yeastepigenome_id") |>
+        mutate(yeastepigenome_id = as.integer(yeastepigenome_id)) |>
+        left_join(dplyr::select(brentlab_features, locus_tag, symbol)) |>
+        dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol) |>
+        left_join(rossi_meta) |>
+        group_by(sample_id, seqnames, start, end, width, strand, target_locus_tag, target_symbol) |>
+        reframe(score = median(score)) |>
+        group_by(sample_id) |>
+        arrange(desc(score)),
+    mindel = bind_rows(map(af_list$mindel, as_tibble), .id = "yeastepigenome_id") |>
+        mutate(yeastepigenome_id = as.integer(yeastepigenome_id)) |>
+        left_join(rossi_meta) |>
+        group_by(sample_id, seqnames, start, end, width, strand, target_locus_tag, target_symbol) |>
+        reframe(score = median(score)) |>
+        group_by(sample_id) |>
+        arrange(desc(score))
+)
+
+# arrow::write_parquet(
+#     af_list_dfs$yiming,
+#     "~/code/hf/rossi_2021/yep_filtered_peaks_yiming.parquet",
+#     compression = "zstd",
+#     write_statistics = TRUE
+# )
+#
+# arrow::write_parquet(
+#     af_list_dfs$mindel,
+#     "~/code/hf/rossi_2021/yep_filtered_peaks_mindel.parquet",
+#     compression = "zstd",
+#     write_statistics = TRUE
+# )

scripts/parse_rossi_filtered_peaks.R

@@ -98,60 +98,96 @@ bed_to_granges <- function(bed_df, zero_indexed = TRUE) {
     return(gr)
 }
 
-#' Annotate genomic regions with ChExMix peak scores
+#' Build a GRanges of protein-coding gene features for peak annotation
 #'
-#' Intersects a set of genomic regions (e.g. promoters) with ChExMix filtered
-#' peaks and assigns each region a score. Regions overlapping a single peak
-#' receive that peak's score; regions overlapping multiple peaks receive the
-#' median score across all overlapping peaks. Non-overlapping regions are
-#' scored 0.
+#' Restricts the brentlab feature set to protein-coding genes, excluding
+#' dubious ORFs, and returns a strand-aware GRanges carrying the locus tag
+#' and gene symbol as metadata. Coordinates in the feature table are 1-indexed
+#' closed (SGD convention) and are used directly. Also removes mito genes
+#'
+#' @param features_df Data frame from \code{brentlab_features.parquet} with
+#'   columns chr, start, end, strand, type, locus_tag, symbol, note.
+#' @return GRanges of retained gene features with metadata columns
+#'   \code{locus_tag} and \code{symbol}.
+prepare_gene_features <- function(features_df) {
+
+    genes <- features_df |>
+        dplyr::filter(type == "gene",
+                      stringr::str_detect(note, stringr::fixed("dubious", ignore_case = TRUE),
+                                          negate = TRUE),
+                      chr != "chrM")
+
+    GenomicRanges::GRanges(
+        seqnames = genes$chr,
+        ranges   = IRanges::IRanges(start = genes$start, end = genes$end),
+        strand   = genes$strand,
+        locus_tag = genes$locus_tag,
+        symbol    = genes$symbol
+    )
+}
+
+#' Annotate ChExMix peaks to the nearest downstream gene feature
+#'
+#' Assigns each peak to its nearest downstream feature by genomic coordinate
+#' (strand-agnostic, since peaks carry no strand) and records the gap distance.
+#' This proceeds in two rounds:
+#' \enumerate{
+#'   \item Every peak is annotated to the nearest downstream gene and the
+#'     distance to that gene is computed. Peaks whose nearest downstream gene
+#'     lies farther than \code{nbp} (or have no downstream gene) are dropped.
+#'   \item Retained peak-to-gene assignments are aggregated by target gene,
+#'     and the median peak score across the set of peaks annotated to a gene is
+#'     reported as that gene's \code{peak_score}.
+#' }
 #'
 #' @param peaks_df Data frame with BED-format peak calls (0-indexed, half-open)
 #'   and a \code{score} column, as produced by \code{read_in_filtered_bed}.
-#' @param regions_gr GRanges object of genomic regions to annotate
-#'   (e.g. an element of \code{regions_list}).
+#' @param features_gr GRanges of gene features, as produced by
+#'   \code{prepare_gene_features}.
+#' @param nbp Maximum allowed distance (bp) between a peak and its nearest
+#'   downstream gene for the peak to be retained. Default 500.
 #'
-#' @return A copy of \code{regions_gr} with an additional metadata column
-#'   \code{score}: the peak score for overlapping regions, 0 otherwise.
+#' @return A tibble with one row per annotated gene and columns
+#'   \code{target_locus_tag}, \code{target_symbol}, \code{peak_score} (median
+#'   peak score across annotated peaks) and \code{n_peaks} (number of peaks
+#'   annotated to the gene).
 #'
-#' @seealso \code{\link{bed_to_granges}}, \code{\link{read_in_filtered_bed}}
-peaks_to_promoter_scores <- function(peaks_df, regions_gr) {
+#' @seealso \code{\link{bed_to_granges}}, \code{\link{prepare_gene_features}}
+annotate_peaks_to_features <- function(peaks_df, features_gr, nbp = 500) {
 
     peaks_gr <- bed_to_granges(peaks_df)
 
-    hits <- GenomicRanges::findOverlaps(regions_gr, peaks_gr)
-
-    # for each hit, pull the peak score
-    scores_df <- tibble(
-        region_idx = S4Vectors::queryHits(hits),
-        score      = peaks_df$score[S4Vectors::subjectHits(hits)]
-    ) |>
-        summarise(score = median(score), .by = region_idx)
-
-    # initialize all promoters to 0, fill in overlapping ones
-    result <- rep(0, length(regions_gr))
-    result[scores_df$region_idx] <- scores_df$score
-
-    regions_gr_tmp <- regions_gr
-    regions_gr_tmp$score <- result
-    regions_gr_tmp
+    # round 1: nearest downstream feature (strand-agnostic) and its distance
+    ds_idx <- GenomicRanges::precede(peaks_gr, features_gr, ignore.strand = TRUE)
+    keep   <- !is.na(ds_idx)
+
+    peaks_gr  <- peaks_gr[keep]
+    feat_hits <- features_gr[ds_idx[keep]]
+
+    round1 <- tibble::tibble(
+        score            = peaks_df$score[keep],
+        target_locus_tag = feat_hits$locus_tag,
+        target_symbol    = feat_hits$symbol,
+        distance         = GenomicRanges::distance(peaks_gr, feat_hits,
+                                                   ignore.strand = TRUE)) |>
+        dplyr::filter(distance <= nbp)
+
+    # round 2: aggregate peaks per target gene, median score as the peak score
+    round1 |>
+        dplyr::group_by(target_locus_tag, target_symbol) |>
+        dplyr::reframe(peak_score = median(score),
+                       n_peaks    = dplyr::n(),
+                       max_distance = max(distance))
 }
 
 
 rossi_meta = arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_metadata.parquet")
 chrmap = read_csv("~/code/hf/yeast_genome_resources/chrmap.csv.gz")
 
-brentlab_features = read_csv("~/code/hf/yeast_genome_resources/brentlab_features.csv.gz")
+brentlab_features = arrow::read_parquet("~/code/hf/yeast_genome_resources/brentlab_features.parquet")
 
-# Load regions
-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") |>
-        bed_to_granges()
-)
+# protein-coding gene features used to annotate peaks (excludes dubious ORFs)
+gene_features_gr = prepare_gene_features(brentlab_features)
 
 filtered_bed = list.files(here("data/yep_filtered_peaks"), full.names = TRUE)
 names(filtered_bed) = str_extract(basename(filtered_bed), "^\\d+")
@@ -171,40 +207,24 @@ filtered_bed_df_list_no_null = filtered_bed_df_list[unlist(map(filtered_bed_df_l
 filtered_bed_df_list_no_null_in_meta = filtered_bed_df_list_no_null[
     as.integer(names(filtered_bed_df_list_no_null)) %in% rossi_meta$yeastepigenome_id]
 
-af_list = list(
-    yiming = map(filtered_bed_df_list_no_null_in_meta, peaks_to_promoter_scores, regions_list$yiming),
-    mindel = map(filtered_bed_df_list_no_null_in_meta, peaks_to_promoter_scores, regions_list$mindel)
-)
-
-af_list_dfs = list(
-    yiming = bind_rows(map(af_list$yiming, as_tibble), .id = "yeastepigenome_id") |>
-        mutate(yeastepigenome_id = as.integer(yeastepigenome_id)) |>
-        left_join(dplyr::select(brentlab_features, locus_tag, symbol)) |>
-        dplyr::rename(target_locus_tag = locus_tag, target_symbol = symbol) |>
-        left_join(rossi_meta) |>
-        group_by(sample_id, seqnames, start, end, width, strand, target_locus_tag, target_symbol) |>
-        reframe(score = median(score)) |>
-        group_by(sample_id) |>
-        arrange(desc(score)),
-    mindel = bind_rows(map(af_list$mindel, as_tibble), .id = "yeastepigenome_id") |>
-        mutate(yeastepigenome_id = as.integer(yeastepigenome_id)) |>
-        left_join(rossi_meta) |>
-        group_by(sample_id, seqnames, start, end, width, strand, target_locus_tag, target_symbol) |>
-        reframe(score = median(score)) |>
-        group_by(sample_id) |>
-        arrange(desc(score))
-)
+# annotate each sample's peaks to nearest downstream genes within 500bp
+annotated = map(filtered_bed_df_list_no_null_in_meta,
+                annotate_peaks_to_features, gene_features_gr, nbp = 500)
+
+annotated_df = bind_rows(annotated, .id = "yeastepigenome_id") |>
+    mutate(yeastepigenome_id = as.integer(yeastepigenome_id)) |>
+    left_join(rossi_meta, by = "yeastepigenome_id") |>
+    group_by(sample_id, target_locus_tag, target_symbol) |>
+    reframe(
+        peak_score = median(peak_score),
+        n_peaks = sum(n_peaks),
+        max_distance = max(max_distance),
+        peak_n_replicates = n()) |>
+    arrange(desc(peak_score), .by_group = TRUE)
 
 # arrow::write_parquet(
-#     af_list_dfs$yiming,
-#     "~/code/hf/rossi_2021/yep_filtered_peaks_yiming.parquet",
-#     compression = "zstd",
-#     write_statistics = TRUE
-# )
-#
-# arrow::write_parquet(
-#     af_list_dfs$mindel,
-#     "~/code/hf/rossi_2021/yep_filtered_peaks_mindel.parquet",
+#     annotated_df,
+#     "~/code/hf/rossi_2021/yep_filtered_peaks_combined.parquet",
 #     compression = "zstd",
 #     write_statistics = TRUE
 # )

yep_filtered_peaks_mindel.parquet → yep_filtered_peaks_combined.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1f8473129d802f994ddc01c9e2d5d1625da8b541dc3439b7a389397568a93662
-size 24383507
+oid sha256:201410f57fe0368cf14ab1dce12192792293f885517d31f86a07494c9ef86d2a
+size 527546

yep_filtered_peaks_yiming.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cbd1506072d97198bce01b497cb93a33a79772843bd6fa22bfe5dd04e3037795
-size 30745732