1 Introduction

It is understood that the mammary gland in cattle undergoes extensive morphological and physiological transformations to facilitate the production of milk. These changes are regulated at the genetic and epigenetic level, with specific patterns of expression driving maturation of the gland. This study aims to uncover the temporal expression patterns of mRNA in bovine mammary glands by performing timewise differential expression of sequencing data, then clustering based on temporal expression pattern.

1.1 Boxplot Generation

2 Galaxy

All paired end reads were loaded into galaxy as single end. After preliminary QC was performed, alignment to the bosTau9 reference genome was performed. After alignment, statistics were again drawn in order to ensure quality of reads. Mapping to genome to generate counts was performed by Feature counts. Read mapping took in to account the strandedness of the reads, All stranded reads were read in as forward stranded. In reference to the metadata, the column ‘batch’ delimitates which samples were stranded and unstranded (1,0 respectively).

3 mRNA

3.1 Setup

3.1.1 Libraries

library(DESeq2)
library(dplyr)
library(AnnotationDbi)
library(org.Bt.eg.db)
library(sva)
library(readxl)
library(tidyr)
library(knitr)
library(kableExtra)
library(ggplot2)
library(plotly)
library(ggrepel)
library(readr)

3.1.2 Data

The raw count files can be downloaded directly from Github. The data was loaded using the read.delim function from the base package, setting the Ensembl ids as rownames. The first column, which was redundant (gene-ids), was removed.

file_path <- 'https://raw.githubusercontent.com/blamp23/Cattle-lac-proj/main/Raw_data/Raw_Cattle_Counts.tabular'
df <- read.delim(file_path, header = TRUE, row.names = 1)
df <- df[-1]

3.1.3 Metadata

Sample metadata was compiled to reflect the stages of pregnancy as well as the batches associated with strandedness. The metadata table includes the animal identifier, stage of pregnancy, and batch for each sample.

Before finalizing the raw counts, it is necessary to adjust the dataframe’s column names to simplify data manipulation. The desired format combines the condition with the sample identifier (e.g., XX_123), which mirrors the row names in the metadata dataframe. The format mimics the row names of the metadata dataframe. Additionally, it is important to note that the samples from Early Lactation 610 and Late Pregnancy 610 were switched prior to sequencing and must be corrected before proceeding with the analysis.

Sample Data
	condition	animal	batch
V_468	V	468	1
V_502	V	502	1
V_504	V	504	1
V_507	V	507	1
V_509	V	509	1
V_598	V	598	1
V_610	V	610	1
MP_468	MP	468	0
MP_502	MP	502	1
MP_504	MP	504	0
MP_507	MP	507	1
MP_509	MP	509	0
MP_598	MP	598	0
MP_610	MP	610	0
LP_468	LP	468	0
LP_502	LP	502	1
LP_504	LP	504	0
LP_509	LP	509	0
LP_507	LP	507	1
LP_598	LP	598	0
LP_610	LP	610	0
EL_468	EL	468	0
EL_502	EL	502	1
EL_504	EL	504	0
EL_507	EL	507	1
EL_509	EL	509	0
EL_598	EL	598	0
EL_610	EL	610	0
PL_468	PL	468	1
PL_502	PL	502	1
PL_504	PL	504	1
PL_507	PL	507	1
PL_509	PL	509	1
PL_598	PL	598	1
PL_610	PL	610	1

3.2 Filtering/loading

3.2.1 Filtering

To determine which genes should be included in the study, each gene is evaluated within each lactation stage based on its expression count across the 7 samples in that stage. A gene is considered for inclusion in a specific lactation stage if its count exceeds a threshold of 3 in at least 4 of the 7 samples at that time point.

A gene will be included in the final study if it meets the above criteria in at least 1 out of the 5 time points (i.e., lactation stages). This means that a gene must have high counts in at least one of the lactation stages to be included in the study.

# Step 1: Splitting Data by Condition
conditions <- unique(samples$condition)
split_data <- lapply(conditions, function(cond) {
  df[, samples$condition == cond]
})

# Step 2: Define Filtering Function
count_high_expressions <- function(data) {
  apply(data, 1, function(gene) {
    sum(gene >= 3) >= 4  # Returns TRUE if at least 4 out of 7 samples have counts ≥ 3
  })
}
high_exp_counts <- lapply(split_data, count_high_expressions)

# Step 3: Aggregate Results for inclusion
gene_inclusion_filter <- rowSums(do.call(cbind, high_exp_counts)) >= 1 # If 1/5 timepoints meet crit, gene is included

# Step 4: Filter Genes
df <- df[gene_inclusion_filter, ]

3.3 Normalization

3.3.1 ComBatSeq

Strandedness as a batch effect was seen to have distorted our data when using principal component analysis as a diagnostic tool. In order to correct the batch effect, the ComBat_seq function from the sva package was used. This function adjusts for batch effects using the batch variable from our metadata and considers the experimental design (like time course variables) to prevent overfitting.

count_matrix <- as.matrix(df)

cdf <- ComBat_seq(count_matrix,
                  batch = samples$batch,
                  group = samples$condition)

## Found 2 batches
## Using full model in ComBat-seq.
## Adjusting for 4 covariate(s) or covariate level(s)
## Estimating dispersions
## Fitting the GLM model
## Shrinkage off - using GLM estimates for parameters
## Adjusting the data

3.3.2 DESeq2

A standard DESeq function was applied using the counts corrected by ComBat_seq and the experimental design from the ‘samples’ metadata frame.

dds <- DESeqDataSetFromMatrix(
  countData = cdf,
  colData = samples,
  design = ~ condition
)

dds <- DESeq(dds)

3.3.3 PCA

3.3.3.1 ComBat Corrected + DESeq Norm PCA

3.3.3.2 DESeq Norm PCA

3.4 Timecourse Analysis

Mean of Normalized Counts
	V	MP	LP	EL	PL
ENSBTAG00000000005	222.8397152	208.9822665	216.3579474	395.1031964	295.86744
ENSBTAG00000000008	0.0910119	0.2631827	0.2745275	0.7202736	0.00000
ENSBTAG00000000009	94.5829692	106.9566598	68.6357260	64.1902184	94.56270
ENSBTAG00000000010	290.5563095	266.2848146	303.4057908	666.3469239	628.63636
ENSBTAG00000000011	36.7905454	21.0850876	23.4233891	18.0733364	19.97571
ENSBTAG00000000012	158.7158759	115.4934772	129.0799472	81.0886870	94.71675

3.4.1 Pairwise Comparison

To perform differential expression analysis, comparative structures need to be formed between timepoints. The loop below iterates through each successive comparison and generates ‘res’ objects from DESeq2, which contain statistics like log fold change and adjusted p-values for each comparison. After the False Discovery Rate adjusted p-value (padj) is generated, the mean of the normalized counts is evaluated between each comparison. If gene expression increases from time n to n+1, an ‘I’ will be assigned to the gene. If gene expression decreases from n to n+1, a ‘D’ will be assigned to the gene.

The adjusted p-value generated from DESeq2 is then used to evaluate whether there is significance. If the padj value is greater than 0.05, the gene will be deemed to have an insignificant change in expression between timepoints and will be assigned an ‘S’

contrasts <- list(
  c("condition", "PL", "V"),
  #
  c("condition", "EL", "V"),
  c("condition", "PL", "MP"),
  #
  c("condition", "LP", "V"),
  c("condition", "EL", "MP"),
  c("condition", "PL", "LP"),
  #
  c("condition", "MP", "V"),
  c("condition", "LP", "MP"),
  c("condition", "EL", "LP"),
  c("condition", "PL", "EL")
)

contrast_results <- list()

# DE for pairs in contrasts
for (i in seq_along(contrasts)) {
  contrast = contrasts[[i]]
  res <- results(dds,
                 contrast=contrast)
  
  # Determine if the mean expression increases or decreases
  condition1_mean <- averages_df[[contrast[3]]]  # Mean for the first condition
  condition2_mean <- averages_df[[contrast[2]]]  # Mean for the second condition
  expressionDirection <- ifelse(condition1_mean < condition2_mean, "I", "D")
  
  # Hypothesis test, FTR null assign 'S', Rej null assign I/D
  res$expressionChange <- ifelse(res$padj > 0.05, "S", expressionDirection)
  
  contrast_results[[paste(contrast[3], "to", contrast[2])]] <- res
}

3.4.2 Motif Structuring

The variables (I,S,D) that corespond to expression are currently being stored in the list of DESeq objects. In order to create an index of gene expression, genes are added to a dataframe that completley describes their patterns of expression over the time course. This tabe can be manipulated in order to generate a singular index of pattern structure depending on the needs of the research.

The complete time course analysis can be organized into a piecewise hierarchical structure with 4 tiers,

Primary: V-PL
Secondary: V-EL MP-PL
Tertiary: V-LP MP-EL LP-PL
Quaternary: V-MP MP-LP LP-EL EL-PL

A complete model vector will be structured as
Primary-Secondary-Tertiary-Quaternary
X-XX-XXX-XXXX

combined_results <- data.frame(gene=rownames(contrast_results[[1]]))
# Loop through each contrast result to combine them
for (i in seq_along(contrast_results)) {
  contrast_name <- names(contrast_results)[i]  # Get the name of the current contrast
  # Create a temporary dataframe with genes and their expression changes for the current contrast
  temp_df <- data.frame(gene=rownames(contrast_results[[i]]), 
                        expressionChange=contrast_results[[i]]$expressionChange)
  colnames(temp_df)[2] <- contrast_name  # Rename the second column to the current contrast name
  # Merge the temporary dataframe with the combined_results dataframe
  combined_results <- merge(combined_results, temp_df, by="gene", all=TRUE)
}

# Concatenate expression patterns to make model vector
combined_results$modelVector <- apply(combined_results[, -1], 1, 
                                      function(x) paste(x, collapse = ""))

# for Quaternary tree structure ###############################################################
combined_results$primary <- combined_results[,2]
combined_results$secondary <- paste0(combined_results[,3], combined_results[,4])
combined_results$tertiary <- paste0(combined_results[,5], combined_results[,6], combined_results[,7])
combined_results$quaternary <- paste0(combined_results[,8], combined_results[,9], combined_results[,10], combined_results[,11])

Incidence of Increasing, Decreasing and Steady Genes

Incedence of Increasing, Decreasing and Steady Genes
transition	D	I	S
EL to PL	838	488	16018
LP to EL	2082	2714	12548
LP to PL	3033	3463	10848
MP to EL	1889	2411	13044
MP to LP	8	6	17330
MP to PL	3030	3509	10805
V to EL	3862	3266	10216
V to LP	2747	2028	12569
V to MP	2656	1892	12796
V to PL	2920	2457	11967

Head of pattern dataframe

First 5 Rows of Pattern Dataframe
gene	V to PL	V to EL	MP to PL	V to LP	MP to EL	LP to PL	V to MP	MP to LP	LP to EL	EL to PL
ENSBTAG00000000005	S	I	S	S	I	S	S	S	I	S
ENSBTAG00000000008	S	S	S	S	S	S	S	S	S	S
ENSBTAG00000000009	S	S	S	S	S	S	S	S	S	S
ENSBTAG00000000010	I	I	I	S	I	I	S	S	I	S
ENSBTAG00000000011	S	S	S	S	S	S	S	S	S	S
ENSBTAG00000000012	D	D	S	S	S	S	S	S	D	S


gene	modelVector	primary	secondary	tertiary	quaternary
ENSBTAG00000000005	SISSISSSIS	S	IS	SIS	SSIS
ENSBTAG00000000008	SSSSSSSSSS	S	SS	SSS	SSSS
ENSBTAG00000000009	SSSSSSSSSS	S	SS	SSS	SSSS
ENSBTAG00000000010	IIISIISSIS	I	II	SII	SSIS
ENSBTAG00000000011	SSSSSSSSSS	S	SS	SSS	SSSS
ENSBTAG00000000012	DDSSSSSSDS	D	DS	SSS	SSDS

3.4.3 Pattern Generation

Reducing the number of comparisons within a pattern structure can significantly decrease the total number of patterns identified. Additionally, depending on the objectives of the experiment, different patterns can be constructed that reveal more biologically relevant structures. Below, the ‘vector’ variable was constructed to create a pattern that depicts the total timecourse change in expression (virgin to peak lactation) followed by the Quaternary structure that depicts the sequential changes (1->2 2->3 3->4 4->5). This pattern is useful because one can check to see if there was a significant change of expression throughout the time course (v to l) while also monitoring the pairwise sequential changes.

motif_index <- list()
x <- combined_results
vector <- paste0(x$`V to PL`,
                 x$quaternary)

# Loop through each unique modelVector
for(model in unique(vector)) {
  # Subset the genes that match the current modelVector
  genes <- combined_results$gene[vector == model]
  # Store
  motif_index[[model]] <- genes
}

Throughout this entire process, Ensembl IDs have been used as gene identifiers. To make the mRNA easier to identify, mapping to symbols can be done using AnnotationDbi and the org.Bt.eg.db package for bovine gene symbols.

mapped_motif_index <- list()
for(model in names(motif_index)) {
  ensembl_ids <- motif_index[[model]]
  gene_symbols <- tryCatch({
    mapIds(org.Bt.eg.db,
           keys = ensembl_ids,
           column = "SYMBOL",
           keytype = "ENSEMBL",
           multivals = 'first')
  }, error = function(e) {
    message("Error with model ", model, ": ", e$message)
    return(NA)  # Return NA or any other placeholder that indicates an error
  })
  mapped_motif_index[[model]] <- gene_symbols
}

mmi_mrna <- mapped_motif_index # mapped motif index mrna

4 miRNA

4.1 Setup

4.1.1 Raw Counts

Count data from Galaxy can be imported to your R session directly from github

# 
file_path <- 'https://raw.githubusercontent.com/blamp23/Cattle-lac-proj/main/Raw_data/Filtered_Raw_Counts_fix.csv'
df <- read.csv(file_path)

4.1.2 Metadata

The experimental design in the miRNA samples is a bit ‘easier’ to develop because there are no batch issues between strandedness. It is for this reason ComBatSeq will not need to be run. From a coding & logistical standpoint, the count table developed has already been formatted in a way where sample id and timepoint can directly be pulled from the column names of the dataframe.

samples <- data.frame(
  row.names = c(colnames(df)),
  condition = c((substr(colnames(df), 5, nchar(colnames(df)))))
)

## 
## <style>
## table caption {
##   font-size: 16pt;
##   font-weight: bold;
## }
## </style>

Sample Data
	condition
468 V	V
490 V	V
504 V	V
507 V	V
509 V	V
531 V	V
581 V	V
595 V	V
598 V	V
610 V	V
615 V	V
468 MP	MP
502 MP	MP
504 MP	MP
507 MP	MP
509 MP	MP
595 MP	MP
598 MP	MP
610 MP	MP
468 LP	LP
502 LP	LP
504 LP	LP
507 LP	LP
509 LP	LP
595 LP	LP
598 LP	LP
610 LP	LP
468 EL	EL
502 EL	EL
504 EL	EL
507 EL	EL
509 EL	EL
595 EL	EL
598 EL	EL
610 EL	EL
468 PL	PL
479 PL	PL
490 PL	PL
502 PL	PL
504 PL	PL
509 PL	PL
507 PL	PL
531 PL	PL
581 PL	PL
590 PL	PL
595 PL	PL
610 PL	PL
615 PL	PL

4.2 DESeq2

General commands of DESeq2 generation for dds and a normalized count table were run in a similar fashion as was done in mRNA.

df <- as.matrix(df)
dds <- DESeqDataSetFromMatrix(
  countData = df,
  colData = samples,
  design = ~ condition
)

dds <- DESeq(dds)

normalized_counts <- counts(dds, normalized = TRUE)

4.3 PCA

4.4 Timecourse

4.4.1 Mean Counts

# Normalized Count table and dds object from DESeq are needed to run code
col_names <- c(rep("V", 11),
               rep("MP", 8),
               rep("LP", 8),
               rep("EL", 8),
               rep("PL", 13))
names(normalized_counts) <- col_names


# Time-point Averages of Normalized Counts ######################################
normalized_counts_df <- as.data.frame(normalized_counts)


# Prepare an empty data frame for storing averages, with correct dimensions and column names
averages_df <- data.frame(matrix(ncol = length(unique(col_names)),
                                 nrow = nrow(normalized_counts_df)))
names(averages_df) <- unique(col_names)
rownames(averages_df) <- rownames(normalized_counts_df)

# Loop through each time-point to calculate and store row means
for (timepoint in unique(col_names)) {
  # Identify columns belonging to the current time-point
  cols <- which(col_names == timepoint)
  # Calculate and store
  averages_df[[timepoint]] <- rowMeans(normalized_counts_df[, cols], na.rm = TRUE)
}

Mean of Normalized Counts
	V	MP	LP	EL	PL
bta.let.7a.5p	1997414.5641	1668136.0098	1960049.6716	1300440.2823	2176869.5549
bta.let.7a.3p	671.5617	255.3875	494.1430	322.5810	208.2733
bta.let.7a.5p.1	1998686.5496	1671423.8782	1963007.7650	1303161.9017	2180046.4306
bta.let.7a.5p.2	1995373.5839	1666503.6297	1958226.5896	1299040.8489	2174136.3242
bta.let.7a.3p.1	666.8361	251.4582	490.1695	320.1862	207.0597
bta.let.7b	313548.8013	530406.6025	893827.5224	406621.2068	935932.1697

4.4.2 Pairwise Comparison

Pairwise comparisons are run in the same fashion as in mRNA. The only caviat being a line that filters for miRNAs that do not show a significant change in expression due to a low number of counts. If the mean number of counts in timepoint n and n+1 are both under 100, the miRNA is labled as ‘S’ for pattern structuring.

# pairwise comparisons, hypothesis testing, and assignment of expression pattern ###################################
contrasts <- list(
  c("condition", "PL", "V"),
  #
  c("condition", "EL", "V"),
  c("condition", "PL", "MP"),
  #
  c("condition", "LP", "V"),
  c("condition", "EL", "MP"),
  c("condition", "PL", "LP"),
  #
  c("condition", "MP", "V"),
  c("condition", "LP", "MP"),
  c("condition", "EL", "LP"),
  c("condition", "PL", "EL")
)

contrast_results <- list()

for (i in seq_along(contrasts)) {
  contrast = contrasts[[i]]
  res <- results(dds,
                 contrast=contrast,
                 independentFiltering = FALSE)
  
  # Determine if the mean expression increases or decreases
  condition1_mean <- averages_df[[contrast[3]]]  # Mean for the first condition
  condition2_mean <- averages_df[[contrast[2]]]  # Mean for the second condition
  
  # Determine direction of expression based on means
  expressionDirection <- ifelse(condition1_mean < condition2_mean, "I", "D")
  
  # Check if either condition mean is below the threshold
  lowExpression <- condition1_mean < 100 & condition2_mean < 100
  
  # Hypothesis test: if p-value > 0.05 or low expression, assign 'S', else assign I/D
  res$expressionChange <- ifelse(res$padj > 0.05 | lowExpression, "S", expressionDirection)
  
  # Save results in a list with a unique name for each contrast
  contrast_results[[paste(contrast[3], "to", contrast[2])]] <- res
}

4.4.3 Expression Motif

Parallel to mRNA, the combined results dataframe is generated from the list of deseq results of the pairwise comparisons.

# Generation of expression motif ###############################################

# Initialize a dataframe with gene names
combined_results <- data.frame(gene=rownames(contrast_results[[1]]))

# Loop through each contrast result to combine them
for (i in seq_along(contrast_results)) {
  contrast_name <- names(contrast_results)[i]  # Get the name of the current contrast
  # Create a temporary dataframe with genes and their expression changes for the current contrast
  temp_df <- data.frame(gene=rownames(contrast_results[[i]]),
                        expressionChange=contrast_results[[i]]$expressionChange)
  colnames(temp_df)[2] <- contrast_name  # Rename the second column to the current contrast name
  # Merge the temporary dataframe with the combined_results dataframe
  combined_results <- merge(combined_results, temp_df, by="gene", all=TRUE)
}

# Concatenate expression patterns to make model vector
combined_results$modelVector <- apply(combined_results[, -1], 1,
                                      function(x) paste(x, collapse = ""))
# for Quaternary tree structure ###############################################################
combined_results$primary <- combined_results[,2]
combined_results$secondary <- paste0(combined_results[,3], combined_results[,4])
combined_results$tertiary <- paste0(combined_results[,5], combined_results[,6], combined_results[,7])
combined_results$quaternary <- paste0(combined_results[,8], combined_results[,9], combined_results[,10], combined_results[,11])

Incedence of Increasing, Decreasing and Steady miRNA

Incedence of Increasing, Decreasing and Steady Genes
transition	D	I	S
EL to PL	66	60	737
LP to EL	37	31	795
LP to PL	100	85	678
MP to EL	27	10	826
MP to LP	0	0	863
MP to PL	86	83	694
V to EL	109	87	667
V to LP	89	84	690
V to MP	82	71	710
V to PL	113	83	667

Head of of Pattern Dataframe

First 5 Rows of Pattern Dataframe
gene	V to PL	V to EL	MP to PL	V to LP	MP to EL	LP to PL	V to MP	MP to LP	LP to EL	EL to PL
bta.let.7a.3p	D	S	S	S	S	S	S	S	S	S
bta.let.7a.3p.1	D	S	S	S	S	S	S	S	S	S
bta.let.7a.5p	S	S	S	S	S	S	S	S	S	I
bta.let.7a.5p.1	S	S	S	S	S	S	S	S	S	I
bta.let.7a.5p.2	S	S	S	S	S	S	S	S	S	I
bta.let.7b	I	S	S	I	S	S	S	S	S	I


gene	modelVector	primary	secondary	tertiary	quaternary
bta.let.7a.3p	DSSSSSSSSS	D	SS	SSS	SSSS
bta.let.7a.3p.1	DSSSSSSSSS	D	SS	SSS	SSSS
bta.let.7a.5p	SSSSSSSSSI	S	SS	SSS	SSSI
bta.let.7a.5p.1	SSSSSSSSSI	S	SS	SSS	SSSI
bta.let.7a.5p.2	SSSSSSSSSI	S	SS	SSS	SSSI
bta.let.7b	ISSISSSSSI	I	SS	ISS	SSSI

4.4.4 Pattern Generation

Manipulating the ‘vector’ variable, it is possible to generate any motif index of your choosing as already performed in the mRNA samples. Here the same structure was generated, detailing the expression of the whole timecourse, followed by the pairwise expression patterns. Due to the suppressive mechanism of action that miRNA presents, it can be hypothesized that mRNA targets will present with an opposite pattern of expression when compared to miRNA.

# Genes indexed by model vector ################################################
motif_index <- list()
x <- combined_results
vector <- paste0(x$`V to PL`,
                 x$quaternary)

# Loop through each unique modelVector
for(model in unique(vector)) {
  # Subset the genes that match the current modelVector
  genes <- combined_results$gene[vector == model]
  # Store
  motif_index[[model]] <- genes
}
mi_miRNA <- motif_index

Click to view motif index of miRNA

mi_miRNA

## $DSSSS
##  [1] "bta.let.7a.3p"    "bta.let.7a.3p.1"  "bta.let.7g"       "bta.miR.106b"    
##  [5] "bta.miR.1271"     "bta.miR.146b"     "bta.miR.2284k"    "bta.miR.2285au"  
##  [9] "bta.miR.2317"     "bta.miR.2369"     "bta.miR.2378"     "bta.miR.2400"    
## [13] "bta.miR.2420"     "bta.miR.2484"     "bta.miR.30e.5p"   "bta.miR.32"      
## [17] "bta.miR.328"      "bta.miR.362.5p"   "bta.miR.365.3p"   "bta.miR.365.3p.1"
## [21] "bta.miR.452"      "bta.miR.532"      "bta.miR.6524"     "bta.miR.664b"    
## [25] "bta.miR.767"      "bta.miR.95"       "bta.miR.96"      
## 
## $SSSSI
##  [1] "bta.let.7a.5p"   "bta.let.7a.5p.1" "bta.let.7a.5p.2" "bta.let.7d"     
##  [5] "bta.miR.130a"    "bta.miR.181a"    "bta.miR.181a.1"  "bta.miR.224"    
##  [9] "bta.miR.27b"     "bta.miR.30d"     "bta.miR.505"     "bta.miR.8549"   
## 
## $ISSSI
##  [1] "bta.let.7b"      "bta.let.7c"      "bta.let.7i"      "bta.miR.140"    
##  [5] "bta.miR.151.3p"  "bta.miR.155"     "bta.miR.181b"    "bta.miR.181b.1" 
##  [9] "bta.miR.193b"    "bta.miR.214"     "bta.miR.221"     "bta.miR.2389"   
## [13] "bta.miR.3432a"   "bta.miR.3432a.1" "bta.miR.3432b"   "bta.miR.378"    
## [17] "bta.miR.378.1"   "bta.miR.652"    
## 
## $ISSSS
##  [1] "bta.let.7e"      "bta.miR.11980"   "bta.miR.11987"   "bta.miR.1260b"  
##  [5] "bta.miR.132"     "bta.miR.134"     "bta.miR.139"     "bta.miR.142.5p" 
##  [9] "bta.miR.1468"    "bta.miR.150"     "bta.miR.16a"     "bta.miR.193a.5p"
## [13] "bta.miR.24.3p"   "bta.miR.24.3p.1" "bta.miR.2419.5p" "bta.miR.2904"   
## [17] "bta.miR.2904.1"  "bta.miR.2904.2"  "bta.miR.320a"    "bta.miR.320a.1" 
## [21] "bta.miR.379"     "bta.miR.424.3p"  "bta.miR.484"     "bta.miR.485"    
## [25] "bta.miR.486"     "bta.miR.7859"    "bta.miR.99b"    
## 
## $SSSSS
##   [1] "bta.let.7f"          "bta.let.7f.1"        "bta.miR.100"        
##   [4] "bta.miR.10020"       "bta.miR.10162.5p"    "bta.miR.10164.3p"   
##   [7] "bta.miR.10166.5p"    "bta.miR.10169.3p"    "bta.miR.10170.5p"   
##  [10] "bta.miR.10171.3p"    "bta.miR.10172.5p"    "bta.miR.10173.5p"   
##  [13] "bta.miR.10174.3p"    "bta.miR.10174.5p"    "bta.miR.10175.5p"   
##  [16] "bta.miR.10176.5p"    "bta.miR.10177.5p"    "bta.miR.10178.5p"   
##  [19] "bta.miR.10179.5p"    "bta.miR.10180.3p"    "bta.miR.10181.5p"   
##  [22] "bta.miR.10182.3p"    "bta.miR.10182.5p"    "bta.miR.10183.5p"   
##  [25] "bta.miR.10185.5p"    "bta.miR.10225a"      "bta.miR.1185"       
##  [28] "bta.miR.1197"        "bta.miR.11971"       "bta.miR.11973"      
##  [31] "bta.miR.11978"       "bta.miR.11981"       "bta.miR.11982"      
##  [34] "bta.miR.11983"       "bta.miR.11984"       "bta.miR.11986c"     
##  [37] "bta.miR.11988"       "bta.miR.11990"       "bta.miR.11991"      
##  [40] "bta.miR.11992"       "bta.miR.11993"       "bta.miR.11994"      
##  [43] "bta.miR.11995"       "bta.miR.11997"       "bta.miR.11998"      
##  [46] "bta.miR.12001"       "bta.miR.12002b"      "bta.miR.12003"      
##  [49] "bta.miR.12004"       "bta.miR.12005"       "bta.miR.12005.1"    
##  [52] "bta.miR.12006"       "bta.miR.12010"       "bta.miR.12014"      
##  [55] "bta.miR.12017"       "bta.miR.12019"       "bta.miR.12025"      
##  [58] "bta.miR.12026"       "bta.miR.12027"       "bta.miR.12032"      
##  [61] "bta.miR.12033"       "bta.miR.12036"       "bta.miR.12039"      
##  [64] "bta.miR.12046"       "bta.miR.12048"       "bta.miR.12051"      
##  [67] "bta.miR.12054"       "bta.miR.12056"       "bta.miR.12057"      
##  [70] "bta.miR.12059"       "bta.miR.12060"       "bta.miR.12061"      
##  [73] "bta.miR.12064"       "bta.miR.122"         "bta.miR.1224"       
##  [76] "bta.miR.1247.3p"     "bta.miR.1248"        "bta.miR.1248.1"     
##  [79] "bta.miR.1249"        "bta.miR.1256"        "bta.miR.125a"       
##  [82] "bta.miR.126.5p"      "bta.miR.127"         "bta.miR.1277"       
##  [85] "bta.miR.128"         "bta.miR.128.1"       "bta.miR.1281"       
##  [88] "bta.miR.1287"        "bta.miR.129"         "bta.miR.129.1"      
##  [91] "bta.miR.129.5p"      "bta.miR.129.5p.1"    "bta.miR.1291"       
##  [94] "bta.miR.1296"        "bta.miR.1298"        "bta.miR.1301"       
##  [97] "bta.miR.1306"        "bta.miR.1343.5p"     "bta.miR.138"        
## [100] "bta.miR.138.1"       "bta.miR.1388.3p"     "bta.miR.1388.5p"    
## [103] "bta.miR.141"         "bta.miR.1434.3p"     "bta.miR.144"        
## [106] "bta.miR.148a"        "bta.miR.149.3p"      "bta.miR.149.5p"     
## [109] "bta.miR.152"         "bta.miR.154b"        "bta.miR.154c"       
## [112] "bta.miR.1584.3p"     "bta.miR.1584.5p"     "bta.miR.15a"        
## [115] "bta.miR.1721"        "bta.miR.1777a"       "bta.miR.1777b"      
## [118] "bta.miR.1814c"       "bta.miR.1835"        "bta.miR.1839"       
## [121] "bta.miR.184"         "bta.miR.1842"        "bta.miR.1843"       
## [124] "bta.miR.185"         "bta.miR.187"         "bta.miR.188"        
## [127] "bta.miR.18b"         "bta.miR.190b"        "bta.miR.191"        
## [130] "bta.miR.191b"        "bta.miR.192"         "bta.miR.193a"       
## [133] "bta.miR.1949"        "bta.miR.194b"        "bta.miR.194b.1"     
## [136] "bta.miR.194b.3p"     "bta.miR.197"         "bta.miR.199b"       
## [139] "bta.miR.202"         "bta.miR.205"         "bta.miR.206"        
## [142] "bta.miR.21.3p"       "bta.miR.215"         "bta.miR.218"        
## [145] "bta.miR.218.1"       "bta.miR.219"         "bta.miR.219.3p"     
## [148] "bta.miR.2284a"       "bta.miR.2284aa"      "bta.miR.2284aa.1"   
## [151] "bta.miR.2284aa.2"    "bta.miR.2284aa.3"    "bta.miR.2284ab"     
## [154] "bta.miR.2284ac"      "bta.miR.2284b"       "bta.miR.2284d"      
## [157] "bta.miR.2284g"       "bta.miR.2284h.5p"    "bta.miR.2284j"      
## [160] "bta.miR.2284l"       "bta.miR.2284m"       "bta.miR.2284r"      
## [163] "bta.miR.2284w"       "bta.miR.2284z"       "bta.miR.2284z.1"    
## [166] "bta.miR.2284z.2"     "bta.miR.2284z.3"     "bta.miR.2284z.4"    
## [169] "bta.miR.2284z.5"     "bta.miR.2284z.6"     "bta.miR.2285a"      
## [172] "bta.miR.2285aa"      "bta.miR.2285ab"      "bta.miR.2285ac"     
## [175] "bta.miR.2285ad"      "bta.miR.2285ag.3p"   "bta.miR.2285ah.5p"  
## [178] "bta.miR.2285ai.5p"   "bta.miR.2285ak.5p"   "bta.miR.2285al.5p"  
## [181] "bta.miR.2285am.5p"   "bta.miR.2285an"      "bta.miR.2285ap"     
## [184] "bta.miR.2285ar"      "bta.miR.2285as"      "bta.miR.2285as.1"   
## [187] "bta.miR.2285as.2"    "bta.miR.2285at"      "bta.miR.2285at.1"   
## [190] "bta.miR.2285at.2"    "bta.miR.2285at.3"    "bta.miR.2285aw"     
## [193] "bta.miR.2285ax"      "bta.miR.2285ax.1"    "bta.miR.2285ax.2"   
## [196] "bta.miR.2285ay"      "bta.miR.2285az"      "bta.miR.2285b"      
## [199] "bta.miR.2285b.1"     "bta.miR.2285ba"      "bta.miR.2285ba.1"   
## [202] "bta.miR.2285bb"      "bta.miR.2285bc"      "bta.miR.2285bd"     
## [205] "bta.miR.2285be"      "bta.miR.2285bg"      "bta.miR.2285bh"     
## [208] "bta.miR.2285bi"      "bta.miR.2285bi.1"    "bta.miR.2285bj"     
## [211] "bta.miR.2285bj.1"    "bta.miR.2285bk"      "bta.miR.2285bl"     
## [214] "bta.miR.2285bm"      "bta.miR.2285bo"      "bta.miR.2285bp"     
## [217] "bta.miR.2285bq"      "bta.miR.2285br"      "bta.miR.2285bs"     
## [220] "bta.miR.2285bt"      "bta.miR.2285bu"      "bta.miR.2285bu.1"   
## [223] "bta.miR.2285by"      "bta.miR.2285bz"      "bta.miR.2285c"      
## [226] "bta.miR.2285cc"      "bta.miR.2285cj"      "bta.miR.2285ck"     
## [229] "bta.miR.2285cl"      "bta.miR.2285cm"      "bta.miR.2285co"     
## [232] "bta.miR.2285cp"      "bta.miR.2285cr"      "bta.miR.2285cr.1"   
## [235] "bta.miR.2285cs"      "bta.miR.2285ct"      "bta.miR.2285cw"     
## [238] "bta.miR.2285cw.1"    "bta.miR.2285cy"      "bta.miR.2285cz"     
## [241] "bta.miR.2285d"       "bta.miR.2285da"      "bta.miR.2285db"     
## [244] "bta.miR.2285dc"      "bta.miR.2285dd"      "bta.miR.2285de"     
## [247] "bta.miR.2285dh"      "bta.miR.2285dl.3p"   "bta.miR.2285dl.3p.1"
## [250] "bta.miR.2285e"       "bta.miR.2285e.1"     "bta.miR.2285g"      
## [253] "bta.miR.2285g.1"     "bta.miR.2285g.2"     "bta.miR.2285h"      
## [256] "bta.miR.2285i"       "bta.miR.2285j"       "bta.miR.2285j.1"    
## [259] "bta.miR.2285l"       "bta.miR.2285m"       "bta.miR.2285m.1"    
## [262] "bta.miR.2285m.2"     "bta.miR.2285m.3"     "bta.miR.2285m.4"    
## [265] "bta.miR.2285n"       "bta.miR.2285n.1"     "bta.miR.2285n.2"    
## [268] "bta.miR.2285n.3"     "bta.miR.2285n.4"     "bta.miR.2285n.5"    
## [271] "bta.miR.2285n.6"     "bta.miR.2285p"       "bta.miR.2285q"      
## [274] "bta.miR.2285r"       "bta.miR.2285v"       "bta.miR.2285x"      
## [277] "bta.miR.2286"        "bta.miR.2287"        "bta.miR.2288"       
## [280] "bta.miR.2290"        "bta.miR.2291"        "bta.miR.2292"       
## [283] "bta.miR.2295"        "bta.miR.2299.3p"     "bta.miR.2299.5p"    
## [286] "bta.miR.2300b.3p"    "bta.miR.2303"        "bta.miR.2304"       
## [289] "bta.miR.2305"        "bta.miR.2307"        "bta.miR.2308"       
## [292] "bta.miR.2309"        "bta.miR.2310"        "bta.miR.2313.5p"    
## [295] "bta.miR.2316"        "bta.miR.2318"        "bta.miR.2320.3p"    
## [298] "bta.miR.2321"        "bta.miR.2324"        "bta.miR.2325a"      
## [301] "bta.miR.2325c"       "bta.miR.2328.3p"     "bta.miR.2328.5p"    
## [304] "bta.miR.2330.5p"     "bta.miR.2331.5p"     "bta.miR.2335"       
## [307] "bta.miR.2336"        "bta.miR.2338"        "bta.miR.2339"       
## [310] "bta.miR.2341"        "bta.miR.2342"        "bta.miR.2343"       
## [313] "bta.miR.2344"        "bta.miR.2346"        "bta.miR.2347"       
## [316] "bta.miR.2348"        "bta.miR.2349"        "bta.miR.2352"       
## [319] "bta.miR.2353"        "bta.miR.2354"        "bta.miR.2355.3p"    
## [322] "bta.miR.2356"        "bta.miR.2357"        "bta.miR.2359"       
## [325] "bta.miR.2360"        "bta.miR.2361"        "bta.miR.2364"       
## [328] "bta.miR.2366"        "bta.miR.2367.3p"     "bta.miR.2367.5p"    
## [331] "bta.miR.2370.5p"     "bta.miR.2371"        "bta.miR.2372"       
## [334] "bta.miR.2373.3p"     "bta.miR.2373.5p"     "bta.miR.2374"       
## [337] "bta.miR.2375"        "bta.miR.2376"        "bta.miR.2379"       
## [340] "bta.miR.2381"        "bta.miR.2382.5p"     "bta.miR.2384"       
## [343] "bta.miR.2387"        "bta.miR.2388.3p"     "bta.miR.2390"       
## [346] "bta.miR.2392"        "bta.miR.2397.3p"     "bta.miR.2397.5p"    
## [349] "bta.miR.2399.5p"     "bta.miR.23b.3p"      "bta.miR.23b.5p"     
## [352] "bta.miR.24"          "bta.miR.2404"        "bta.miR.2404.1"     
## [355] "bta.miR.2406"        "bta.miR.2407"        "bta.miR.2410"       
## [358] "bta.miR.2411.5p"     "bta.miR.2412"        "bta.miR.2413"       
## [361] "bta.miR.2415.5p"     "bta.miR.2416"        "bta.miR.2417"       
## [364] "bta.miR.2418"        "bta.miR.2419.3p"     "bta.miR.2421"       
## [367] "bta.miR.2422"        "bta.miR.2426"        "bta.miR.2427"       
## [370] "bta.miR.2428"        "bta.miR.2430"        "bta.miR.2431.5p"    
## [373] "bta.miR.2433"        "bta.miR.2434"        "bta.miR.2436.3p"    
## [376] "bta.miR.2436.5p"     "bta.miR.2438"        "bta.miR.2439.5p"    
## [379] "bta.miR.2440"        "bta.miR.2441"        "bta.miR.2442"       
## [382] "bta.miR.2443"        "bta.miR.2447"        "bta.miR.2448.3p"    
## [385] "bta.miR.2450b"       "bta.miR.2454.5p"     "bta.miR.2455"       
## [388] "bta.miR.2456"        "bta.miR.2457"        "bta.miR.2459"       
## [391] "bta.miR.2460"        "bta.miR.2462"        "bta.miR.2463"       
## [394] "bta.miR.2464.3p"     "bta.miR.2464.5p"     "bta.miR.2465"       
## [397] "bta.miR.2466.3p"     "bta.miR.2466.5p"     "bta.miR.2467.3p"    
## [400] "bta.miR.2468"        "bta.miR.2473"        "bta.miR.2474"       
## [403] "bta.miR.2475"        "bta.miR.2481"        "bta.miR.2483.3p"    
## [406] "bta.miR.2483.5p"     "bta.miR.2486.5p"     "bta.miR.27a.5p"     
## [409] "bta.miR.2881"        "bta.miR.2882"        "bta.miR.2889"       
## [412] "bta.miR.2890"        "bta.miR.2892"        "bta.miR.2893"       
## [415] "bta.miR.2898"        "bta.miR.2900"        "bta.miR.2902"       
## [418] "bta.miR.296.5p"      "bta.miR.299"         "bta.miR.299.2"      
## [421] "bta.miR.29a"         "bta.miR.29c"         "bta.miR.29d.5p"     
## [424] "bta.miR.301b"        "bta.miR.3065"        "bta.miR.3141"       
## [427] "bta.miR.320b"        "bta.miR.323"         "bta.miR.323b.3p"    
## [430] "bta.miR.324"         "bta.miR.326"         "bta.miR.329a"       
## [433] "bta.miR.329b"        "bta.miR.330"         "bta.miR.331.3p"     
## [436] "bta.miR.338"         "bta.miR.33a"         "bta.miR.33b"        
## [439] "bta.miR.342"         "bta.miR.3431"        "bta.miR.345.5p"     
## [442] "bta.miR.34b"         "bta.miR.34c"         "bta.miR.3533"       
## [445] "bta.miR.365.5p"      "bta.miR.3660"        "bta.miR.370"        
## [448] "bta.miR.374c"        "bta.miR.376a"        "bta.miR.376b"       
## [451] "bta.miR.376c"        "bta.miR.377"         "bta.miR.378b"       
## [454] "bta.miR.378c"        "bta.miR.378d"        "bta.miR.380.3p"     
## [457] "bta.miR.380.5p"      "bta.miR.382"         "bta.miR.383"        
## [460] "bta.miR.3956"        "bta.miR.3957"        "bta.miR.409a"       
## [463] "bta.miR.411b"        "bta.miR.411c.3p"     "bta.miR.412"        
## [466] "bta.miR.421"         "bta.miR.431"         "bta.miR.432"        
## [469] "bta.miR.433"         "bta.miR.4444"        "bta.miR.4449"       
## [472] "bta.miR.449a"        "bta.miR.449b"        "bta.miR.449d"       
## [475] "bta.miR.4523"        "bta.miR.454"         "bta.miR.455.3p"     
## [478] "bta.miR.4657"        "bta.miR.483"         "bta.miR.487a"       
## [481] "bta.miR.487b"        "bta.miR.490"         "bta.miR.491"        
## [484] "bta.miR.493"         "bta.miR.496"         "bta.miR.499"        
## [487] "bta.miR.502a"        "bta.miR.502a.1"      "bta.miR.502b"       
## [490] "bta.miR.503.3p"      "bta.miR.503.5p"      "bta.miR.507.3p"     
## [493] "bta.miR.507b"        "bta.miR.539"         "bta.miR.541"        
## [496] "bta.miR.542.5p"      "bta.miR.543"         "bta.miR.545.5p"     
## [499] "bta.miR.582"         "bta.miR.584"         "bta.miR.584.1"      
## [502] "bta.miR.584.2"       "bta.miR.584.3"       "bta.miR.584.4"      
## [505] "bta.miR.584.5"       "bta.miR.584.6"       "bta.miR.584.7"      
## [508] "bta.miR.6120.3p"     "bta.miR.6121.3p"     "bta.miR.6121.5p"    
## [511] "bta.miR.6122.3p"     "bta.miR.6122.5p"     "bta.miR.6123"       
## [514] "bta.miR.628"         "bta.miR.6516"        "bta.miR.6517"       
## [517] "bta.miR.6518"        "bta.miR.6519"        "bta.miR.6520"       
## [520] "bta.miR.6522"        "bta.miR.6523a"       "bta.miR.6525"       
## [523] "bta.miR.6527"        "bta.miR.6528"        "bta.miR.6529a"      
## [526] "bta.miR.6530"        "bta.miR.6531"        "bta.miR.6532"       
## [529] "bta.miR.6533"        "bta.miR.6534"        "bta.miR.6535"       
## [532] "bta.miR.6536"        "bta.miR.6536.1"      "bta.miR.654"        
## [535] "bta.miR.656"         "bta.miR.658"         "bta.miR.660"        
## [538] "bta.miR.664a"        "bta.miR.665"         "bta.miR.668.3p"     
## [541] "bta.miR.669"         "bta.miR.671"         "bta.miR.6715"       
## [544] "bta.miR.6775"        "bta.miR.7"           "bta.miR.7.1"        
## [547] "bta.miR.7.2"         "bta.miR.708"         "bta.miR.7180"       
## [550] "bta.miR.758"         "bta.miR.760.5p"      "bta.miR.763"        
## [553] "bta.miR.7857.3p"     "bta.miR.7857.5p"     "bta.miR.7857.5p.1"  
## [556] "bta.miR.7858"        "bta.miR.7860"        "bta.miR.7861"       
## [559] "bta.miR.7862"        "bta.miR.7863"        "bta.miR.7865"       
## [562] "bta.miR.874"         "bta.miR.875"         "bta.miR.9.3p"       
## [565] "bta.miR.9.3p.1"      "bta.miR.9.5p"        "bta.miR.9.5p.1"     
## [568] "bta.miR.940"         "bta.miR.98"          "bta.miR.9851"       
## 
## $IISSS
##  [1] "bta.miR.1"        "bta.miR.1.1"      "bta.miR.10167.3p" "bta.miR.10a"     
##  [5] "bta.miR.10b"      "bta.miR.151.5p"   "bta.miR.199c"     "bta.miR.212"     
##  [9] "bta.miR.28"       "bta.miR.2888"     "bta.miR.2888.1"   "bta.miR.411a"    
## [13] "bta.miR.615"     
## 
## $DDSSS
##  [1] "bta.miR.101"       "bta.miR.101.1"     "bta.miR.11986b"   
##  [4] "bta.miR.12030"     "bta.miR.1247.5p"   "bta.miR.147"      
##  [7] "bta.miR.148b"      "bta.miR.190a"      "bta.miR.200a"     
## [10] "bta.miR.204"       "bta.miR.210"       "bta.miR.2284v"    
## [13] "bta.miR.2284x"     "bta.miR.2285aj.5p" "bta.miR.2332"     
## [16] "bta.miR.2411.3p"   "bta.miR.2424"      "bta.miR.2454.3p"  
## [19] "bta.miR.26b"       "bta.miR.29d.3p"    "bta.miR.301a"     
## [22] "bta.miR.31"        "bta.miR.339a"      "bta.miR.339b"     
## [25] "bta.miR.340"       "bta.miR.345.3p"    "bta.miR.34a"      
## [28] "bta.miR.361"       "bta.miR.504"       "bta.miR.6119.5p"  
## [31] "bta.miR.677"       "bta.miR.885"       "bta.miR.99a.3p"   
## 
## $IISIS
## [1] "bta.miR.10172.3p" "bta.miR.126.3p"   "bta.miR.143"      "bta.miR.195"     
## [5] "bta.miR.369.5p"   "bta.miR.451"      "bta.miR.497"     
## 
## $SDSSI
## [1] "bta.miR.103"    "bta.miR.103.1"  "bta.miR.11977"  "bta.miR.11989" 
## [5] "bta.miR.222"    "bta.miR.2285av" "bta.miR.2285cf" "bta.miR.331.5p"
## [9] "bta.miR.425.3p"
## 
## $DSSDS
## [1] "bta.miR.106a"  "bta.miR.1246"  "bta.miR.17.3p" "bta.miR.181c" 
## [5] "bta.miR.182"   "bta.miR.200b"  "bta.miR.20a"  
## 
## $SDSIS
## [1] "bta.miR.107"   "bta.miR.29b"   "bta.miR.29b.1"
## 
## $SDSSS
##  [1] "bta.miR.11972"   "bta.miR.11975"   "bta.miR.11976"   "bta.miR.11976.1"
##  [5] "bta.miR.11985"   "bta.miR.12023"   "bta.miR.12034"   "bta.miR.2340"   
##  [9] "bta.miR.23a"     "bta.miR.27a.3p"  "bta.miR.2885"    "bta.miR.2886"   
## [13] "bta.miR.2887"    "bta.miR.2887.1"  "bta.miR.2899"    "bta.miR.3154"   
## [17] "bta.miR.92b"    
## 
## $SSSDS
##  [1] "bta.miR.125b"    "bta.miR.125b.1"  "bta.miR.1343.3p" "bta.miR.15b"    
##  [5] "bta.miR.17.5p"   "bta.miR.183"     "bta.miR.200c"    "bta.miR.2285cd" 
##  [9] "bta.miR.296.3p"  "bta.miR.423.3p"  "bta.miR.92a"     "bta.miR.92a.1"  
## 
## $DDSSI
##  [1] "bta.miR.1307"    "bta.miR.196b"    "bta.miR.2284y"   "bta.miR.2284y.1"
##  [5] "bta.miR.2284y.2" "bta.miR.2284y.3" "bta.miR.2284y.4" "bta.miR.2284y.5"
##  [9] "bta.miR.2284y.6" "bta.miR.26a"     "bta.miR.26a.1"  
## 
## $DSSSD
##  [1] "bta.miR.130b"    "bta.miR.135a"    "bta.miR.135a.1"  "bta.miR.136"    
##  [5] "bta.miR.1434.5p" "bta.miR.18a"     "bta.miR.19a"     "bta.miR.2477"   
##  [9] "bta.miR.30c"     "bta.miR.3601"    "bta.miR.374a"    "bta.miR.455.5p" 
## [13] "bta.miR.6119.3p" "bta.miR.769"    
## 
## $SSSIS
## [1] "bta.miR.133a"   "bta.miR.133a.1" "bta.miR.223"    "bta.miR.494"   
## 
## $SSSID
## [1] "bta.miR.135b"    "bta.miR.2285o"   "bta.miR.2285o.1" "bta.miR.2285o.2"
## [5] "bta.miR.2285o.3" "bta.miR.2285o.4"
## 
## $IISSD
## [1] "bta.miR.142.3p" "bta.miR.146a"   "bta.miR.22.3p"  "bta.miR.2478"  
## [5] "bta.miR.424.5p" "bta.miR.425.5p" "bta.miR.4286"   "bta.miR.4286.1"
## 
## $ISSIS
## [1] "bta.miR.145"   "bta.miR.21.5p" "bta.miR.22.5p"
## 
## $SISSS
## [1] "bta.miR.16b"       "bta.miR.193a.3p"   "bta.miR.199a.3p"  
## [4] "bta.miR.199a.3p.1" "bta.miR.199a.5p"   "bta.miR.199a.5p.1"
## [7] "bta.miR.2285u"     "bta.miR.410"       "bta.miR.495"      
## 
## $DDSDI
## [1] "bta.miR.181d" "bta.miR.25"   "bta.miR.744" 
## 
## $SISSD
##  [1] "bta.miR.186"     "bta.miR.2285bn"  "bta.miR.2285ce"  "bta.miR.2285f"  
##  [5] "bta.miR.2285f.1" "bta.miR.2285k"   "bta.miR.2285k.1" "bta.miR.2285k.2"
##  [9] "bta.miR.2285k.3" "bta.miR.2285k.4" "bta.miR.335"     "bta.miR.450a"   
## [13] "bta.miR.450a.1"  "bta.miR.450b"    "bta.miR.500"    
## 
## $SSSSD
## [1] "bta.miR.194"    "bta.miR.194.1"  "bta.miR.369.3p"
## 
## $ISSII
## [1] "bta.miR.196a"   "bta.miR.196a.1" "bta.miR.375"   
## 
## $DDSDD
## [1] "bta.miR.19b"   "bta.miR.19b.1"
## 
## $DISDS
## [1] "bta.miR.20b"
## 
## $DISDD
## [1] "bta.miR.211"    "bta.miR.2285t"  "bta.miR.30a.5p" "bta.miR.30f"   
## [5] "bta.miR.363"   
## 
## $DISSD
## [1] "bta.miR.2285bf"   "bta.miR.2285bf.1" "bta.miR.2285bf.2" "bta.miR.30b.5p"  
## [5] "bta.miR.362.3p"   "bta.miR.374b"    
## 
## $SSSDI
## [1] "bta.miR.30b.3p"
## 
## $DDSSD
## [1] "bta.miR.3613a" "bta.miR.429"   "bta.miR.592"  
## 
## $SISID
## [1] "bta.miR.376d" "bta.miR.381"  "bta.miR.655" 
## 
## $IISID
## [1] "bta.miR.376e"
## 
## $SISIS
## [1] "bta.miR.411c.5p"
## 
## $ISSDI
## [1] "bta.miR.423.5p"
## 
## $IISDS
## [1] "bta.miR.574"
## 
## $SDSDI
## [1] "bta.miR.760.3p"
## 
## $ISSDS
## [1] "bta.miR.877"
## 
## $DSSDI
## [1] "bta.miR.93"
## 
## $SISDS
## [1] "bta.miR.99a.5p"

5 miRNA Target Mapping Use Case

5.1 Pattern of Interest

The patterning structure generated wich depicts a wide view of expression change confounded with the smaller ‘jumps’ can be deciphered to understand what patterns are interesting/occur at the highest frequencies. For this use case, the pattern S-DSSI was interesting because it indicates mirs that decrease during pregnancy and increase after partruition. Considering the way mirna affects mrna, it can also be deduced that the mRNA pattern S-ISSD could potentially be directly impacted by the mirs in this dataset.

mi_miRNA$SDSSI

## [1] "bta.miR.103"    "bta.miR.103.1"  "bta.miR.11977"  "bta.miR.11989" 
## [5] "bta.miR.222"    "bta.miR.2285av" "bta.miR.2285cf" "bta.miR.331.5p"
## [9] "bta.miR.425.3p"

mi_miRNA$SISSD

##  [1] "bta.miR.186"     "bta.miR.2285bn"  "bta.miR.2285ce"  "bta.miR.2285f"  
##  [5] "bta.miR.2285f.1" "bta.miR.2285k"   "bta.miR.2285k.1" "bta.miR.2285k.2"
##  [9] "bta.miR.2285k.3" "bta.miR.2285k.4" "bta.miR.335"     "bta.miR.450a"   
## [13] "bta.miR.450a.1"  "bta.miR.450b"    "bta.miR.500"

5.2 miRwalk

We can use miRwalk to identify target genes for the selected miRNA. The following code assumes that the miRwalk results for the miRNAs of interest have been downloaded locally. This code integrates analyses of miRNA and mRNA. Assuming the mapped motif index for mRNA has a well-defined structural pattern, the loop will extract the targets from the gene symbol column and then filter the mapped motif index to reveal the patterns exhibited by the miRNA targets.

5.2.1 mir SDSSI

# Specify the file path
file_path <- "C:/Users/12142/Bioinformatics/mirwalk csv/btamir SDSSI mining.csv"
# Import the CSV 
mirwalk_csv <- read.csv(file_path, header = TRUE)

miRNA_genes <- mirwalk_csv$genesymbol

results <- list()

# Iterate over each category in mmi_mrna
for (category in names(mmi_mrna)) {
  # Find common genes between miRNA_genes and the current category
  common_genes <- intersect(miRNA_genes, mmi_mrna[[category]])
  
  # If there are common genes, store them in the results list
  if (length(common_genes) > 0) {
    results[[category]] <- common_genes
  }
}

Click to expand results and summary

summary(results)

##       Length Class  Mode     
## SSSIS  222   -none- character
## SSSSS 1733   -none- character
## ISSIS  306   -none- character
## DSSDS  282   -none- character
## SSSDS  158   -none- character
## SDSSS  272   -none- character
## SSSSI   12   -none- character
## IISIS   56   -none- character
## SISSS  112   -none- character
## DDSSS  190   -none- character
## DISSD   10   -none- character
## SISSD   70   -none- character
## IISSD   12   -none- character
## ISSSS  192   -none- character
## DSSSS  223   -none- character
## IISSS   89   -none- character
## DISDS   11   -none- character
## SSSDI    6   -none- character
## SDSIS  102   -none- character
## DDSDS   58   -none- character
## SDSSI   46   -none- character
## SDSDS    5   -none- character
## IDSIS   12   -none- character
## SISID    1   -none- character
## SISDS   48   -none- character
## SSSSD   17   -none- character
## DDSIS    5   -none- character
## DSSSD   15   -none- character
## DDSDI    6   -none- character
## DISDD   13   -none- character
## IDSSI    5   -none- character
## SSDIS    1   -none- character
## DDSSD    1   -none- character
## SSSID    9   -none- character
## SISIS    5   -none- character
## ISSII    4   -none- character
## ISSSI   15   -none- character
## IISDS    2   -none- character
## DSSDD    4   -none- character
## DSSIS    1   -none- character
## DSSID    2   -none- character
## DSSDI    1   -none- character
## IISID    6   -none- character
## SDSDI    5   -none- character
## IDSSS    3   -none- character
## SISDD   12   -none- character
## DDSSI    2   -none- character
## IDSII    3   -none- character
## DDDIS    1   -none- character
## DISSS    2   -none- character
## SSIDS    1   -none- character

The pattern opposite of the miRNA pattern selected is S-ISSD. Pulling from the targest that were filtered through our mapped motif index, we can see what genes are present.

print(results$SISSD)

##  [1] "MRPL57"    "TOLLIP"    "DUSP23"    "ZHX3"      "HIP1R"     "EME2"     
##  [7] "ZNF697"    "ZNF575"    "KBTBD11"   "INAFM2"    "KSR1"      "PPP1R12B" 
## [13] "BICDL1"    "RAB43"     "SNED1"     "SHANK3"    "SH3BP2"    "REX1BD"   
## [19] "TBX2"      "TSC22D4"   "SRSF4"     "WDR86"     "DMWD"      "NEURL1B"  
## [25] "TOM1L2"    "SKI"       "ZDHHC8"    "PAM16"     "FAM53B"    "BRD1"     
## [31] "CTBP1"     "PPP1R37"   "LOC783012" "DGKQ"      "CPLANE2"   "ITPRIPL2" 
## [37] "TTYH3"     "SOX12"     "HES4"      "FBRSL1"    "SH3PXD2B"  "MAZ"      
## [43] "ELL"       "CBARP"     "MED19"     "HES6"      "LMOD1"     "AXIN1"    
## [49] "SMIM10L1"  "REXO1"     "FRAT2"     "ZBTB42"    "ZNF598"    "NRARP"    
## [55] "DIDO1"     "FBF1"      "MRPS26"    "ESS2"      "NELFA"     "ZNF771"   
## [61] "FUT4"      "FRAT1"     "SHROOM2"   "NUBP2"     "TMEM160"   "POLR1D"   
## [67] "PDCD1"     "GNB1L"     "MEGF6"     "KLF16"

5.2.2 mir SISSD

# Specify the file path
file_path <- "C:/Users/12142/Bioinformatics/mirwalk csv/btamir SISSD mining.csv"
# Import the CSV 
mirwalk_csv <- read.csv(file_path, header = TRUE)

miRNA_genes <- mirwalk_csv$genesymbol

results <- list()

# Iterate over each category in mmi_mrna
for (category in names(mmi_mrna)) {
  # Find common genes between miRNA_genes and the current category
  common_genes <- intersect(miRNA_genes, mmi_mrna[[category]])
  
  # If there are common genes, store them in the results list
  if (length(common_genes) > 0) {
    results[[category]] <- common_genes
  }
}

Click to expand results and summary

The pattern opposite of the miRNA pattern selected is S-ISSD. Pulling from the targest that were filtered through our mapped motif index, we can see what genes are present.

print(results$SDSSI)

##  [1] "IFITM3"     "CDC42SE2"   "C9H6orf120" "GTF2A1"     "TP53INP1"  
##  [6] "ANGEL2"     "NRXN1"      "MBLAC2"     "C7H5orf15"  "BLOC1S6"   
## [11] "CERS5"      "SYPL1"      "MAPK8"      "PAK2"       "HMGB1"

5.2.3 bta-miR-542-5p

# Specify the file path
file_path <- "C:/Users/12142/Bioinformatics/mirwalk csv/btamir542_5p mirwalk.csv"
# Import the CSV 
mirwalk_csv <- read.csv(file_path, header = TRUE)

miRNA_genes <- mirwalk_csv$genesymbol

results <- list()

# Iterate over each category in mmi_mrna
for (category in names(mmi_mrna)) {
  # Find common genes between miRNA_genes and the current category
  common_genes <- intersect(miRNA_genes, mmi_mrna[[category]])
  
  # If there are common genes, store them in the results list
  if (length(common_genes) > 0) {
    results[[category]] <- common_genes
  }
}

for (key in names(results)) {
  if (substr(key, 3, 3) == "D") {
    print(paste("Key:", key))
    print(results[[key]])
  }
}

## [1] "Key: SSDIS"
## [1] "NRGN"
## [1] "Key: DDDIS"
## [1] "VTCN1"

Click to expand results and summary

print(results$SSDIS)

## [1] "NRGN"

5.2.4 V-MP 2284/2285

5.2.4.1 Decrease 2284

# Specify the file path
file_path <- "C:/Users/12142/Bioinformatics/mirwalk csv/vmp2284.csv"
# Import the CSV 
mirwalk_csv <- read.csv(file_path, header = TRUE)

miRNA_genes <- mirwalk_csv$genesymbol

results <- list()

# Iterate over each category in mmi_mrna
for (category in names(mmi_mrna)) {
  # Find common genes between miRNA_genes and the current category
  common_genes <- intersect(miRNA_genes, mmi_mrna[[category]])
  
  # If there are common genes, store them in the results list
  if (length(common_genes) > 0) {
    results[[category]] <- common_genes
  }
}


# V-MP Increase in expression
for (key in names(results)) {
  if (substr(key, 2, 2) == "I") {
    print(paste("Key:", key))
    print(results[[key]])
  }
}

## [1] "Key: IISIS"
## [1] "CD3E"   "GIMAP8"
## [1] "Key: SISSS"
## [1] "RNF220"  "EAF1"    "ZC3H18"  "NECTIN2" "DAPP1"   "CEBPD"  
## [1] "Key: SISSD"
## [1] "BRD1" "GPC1" "HES4"
## [1] "Key: IISSS"
## [1] "SHISA5"  "MCOLN2"  "TMEM51"  "RAPGEF1" "GPR146"  "SLC1A2"  "FOSL2"  
## [1] "Key: DISDS"
## [1] "DENND1B"
## [1] "Key: SISDS"
## [1] "FBN3"   "ZNF710" "MLLT1" 
## [1] "Key: SISDD"
## [1] "RASL10B"

5.2.4.2 Decrease 2285

# Specify the file path
file_path <- "C:/Users/12142/Bioinformatics/mirwalk csv/vmp2285dec.csv"
# Import the CSV 
mirwalk_csv <- read.csv(file_path, header = TRUE)

miRNA_genes <- mirwalk_csv$genesymbol

results <- list()

# Iterate over each category in mmi_mrna
for (category in names(mmi_mrna)) {
  # Find common genes between miRNA_genes and the current category
  common_genes <- intersect(miRNA_genes, mmi_mrna[[category]])
  
  # If there are common genes, store them in the results list
  if (length(common_genes) > 0) {
    results[[category]] <- common_genes
  }
}


# V-MP Increase in expression
for (key in names(results)) {
  if (substr(key, 2, 2) == "I") {
    print(paste("Key:", key))
    print(results[[key]])
  }
}

## [1] "Key: IISIS"
## [1] "BHLHE22"
## [1] "Key: SISSS"
## [1] "GNA13" "SYNJ2" "DAPP1" "CEBPD"
## [1] "Key: SISSD"
## [1] "HES4"
## [1] "Key: IISSS"
## [1] "UBE2G2" "RASAL1" "SLC1A2" "FOSL2" 
## [1] "Key: SISDS"
## [1] "MCAT"   "ZNF710"
## [1] "Key: SISIS"
## [1] "RAB38"
## [1] "Key: IISDS"
## [1] "CLDN8"

5.2.4.3 Increase 2285

# Specify the file path
file_path <- "C:/Users/12142/Bioinformatics/mirwalk csv/vmp2285inc.csv"
# Import the CSV 
mirwalk_csv <- read.csv(file_path, header = TRUE)

miRNA_genes <- mirwalk_csv$genesymbol

results <- list()

# Iterate over each category in mmi_mrna
for (category in names(mmi_mrna)) {
  # Find common genes between miRNA_genes and the current category
  common_genes <- intersect(miRNA_genes, mmi_mrna[[category]])
  
  # If there are common genes, store them in the results list
  if (length(common_genes) > 0) {
    results[[category]] <- common_genes
  }
}


# V-MP Increase in expression
for (key in names(results)) {
  if (substr(key, 2, 2) == "D") {
    print(paste("Key:", key))
    print(results[[key]])
  }
}

## [1] "Key: SDSSS"
##  [1] "PI15"     "SGMS2"    "RBM41"    "GOLGB1"   "SEPTIN11" "TMEM254" 
##  [7] "ESCO1"    "PPP1R12A" "CAVIN4"   "TBCEL"    "HMMR"     "IDNK"    
## [13] "MAMLD1"   "TMTC4"    "MARF1"    "UBE2G1"   "MAST4"   
## [1] "Key: DDSSS"
##  [1] "BTBD11"    "C1H3orf52" "MAP2"      "GPD2"      "SAXO2"     "SECISBP2L"
##  [7] "BARD1"     "HMGXB4"    "LYPD1"     "SLC13A3"   "NRIP3"     "IFNLR1"   
## [13] "RALGPS2"   "PPFIBP2"   "MAGI3"     "CREB5"     "PEX5L"    
## [1] "Key: SDSIS"
## [1] "CD109" "KCNQ5"
## [1] "Key: DDSDS"
## [1] "PPIL4"  "TPX2"   "ITCH"   "ZNF548"
## [1] "Key: SDSSI"
## [1] "HBEGF"
## [1] "Key: SDSDS"
## [1] "HELLS"
## [1] "Key: IDSIS"
## [1] "RASEF"  "STEAP4" "ADD3"  
## [1] "Key: DDSDI"
## [1] "PERP"    "SLC38A1"
## [1] "Key: IDSII"
## [1] "SH3BGRL2"

5.2.5 EL-PL 2284/2285

5.2.5.1 Decrease 2285

# Specify the file path
file_path <- "C:/Users/12142/Bioinformatics/mirwalk csv/decelpl2285.csv"
# Import the CSV 
mirwalk_csv <- read.csv(file_path, header = TRUE)

miRNA_genes <- mirwalk_csv$genesymbol

results <- list()

# Iterate over each category in mmi_mrna
for (category in names(mmi_mrna)) {
  # Find common genes between miRNA_genes and the current category
  common_genes <- intersect(miRNA_genes, mmi_mrna[[category]])
  
  # If there are common genes, store them in the results list
  if (length(common_genes) > 0) {
    results[[category]] <- common_genes
  }
}


# V-MP Increase in expression
for (key in names(results)) {
  if (substr(key, 5, 5) == "I") {
    print(paste("Key:", key))
    print(results[[key]])
  }
}

## [1] "Key: SSSSI"
## [1] "PRPS1" "TMED2"
## [1] "Key: SDSSI"
## [1] "HBEGF"
## [1] "Key: DDSDI"
## [1] "PERP"    "SLC38A1"
## [1] "Key: IDSII"
## [1] "SH3BGRL2"

5.2.5.2 Increase 2285

# Specify the file path
file_path <- "C:/Users/12142/Bioinformatics/mirwalk csv/elpl2285avcf.csv"
# Import the CSV 
mirwalk_csv <- read.csv(file_path, header = TRUE)

miRNA_genes <- mirwalk_csv$genesymbol

results <- list()

# Iterate over each category in mmi_mrna
for (category in names(mmi_mrna)) {
  # Find common genes between miRNA_genes and the current category
  common_genes <- intersect(miRNA_genes, mmi_mrna[[category]])
  
  # If there are common genes, store them in the results list
  if (length(common_genes) > 0) {
    results[[category]] <- common_genes
  }
}


# V-MP Increase in expression
for (key in names(results)) {
  if (substr(key, 5, 5) == "D") {
    print(paste("Key:", key))
    print(results[[key]])
  }
}

## [1] "Key: SISSD"
## [1] "HES4"
## [1] "Key: SSSSD"
## [1] "SLC12A7"

5.2.5.3 Increase 2284

# Specify the file path
file_path <- "C:/Users/12142/Bioinformatics/mirwalk csv/btamir2284y.csv"
# Import the CSV 
mirwalk_csv <- read.csv(file_path, header = TRUE)

miRNA_genes <- mirwalk_csv$genesymbol

results <- list()

# Iterate over each category in mmi_mrna
for (category in names(mmi_mrna)) {
  # Find common genes between miRNA_genes and the current category
  common_genes <- intersect(miRNA_genes, mmi_mrna[[category]])
  
  # If there are common genes, store them in the results list
  if (length(common_genes) > 0) {
    results[[category]] <- common_genes
  }
}


# V-MP Increase in expression
for (key in names(results)) {
  if (substr(key, 5, 5) == "D") {
    print(paste("Key:", key))
    print(results[[key]])
  }
}

## [1] "Key: SISSD"
## [1] "GPC1" "HES4"
## [1] "Key: SSSSD"
## [1] "SLC12A7"

5.2.6 miR patterns

print(mi_miRNA)

## $DSSSS
##  [1] "bta.let.7a.3p"    "bta.let.7a.3p.1"  "bta.let.7g"       "bta.miR.106b"    
##  [5] "bta.miR.1271"     "bta.miR.146b"     "bta.miR.2284k"    "bta.miR.2285au"  
##  [9] "bta.miR.2317"     "bta.miR.2369"     "bta.miR.2378"     "bta.miR.2400"    
## [13] "bta.miR.2420"     "bta.miR.2484"     "bta.miR.30e.5p"   "bta.miR.32"      
## [17] "bta.miR.328"      "bta.miR.362.5p"   "bta.miR.365.3p"   "bta.miR.365.3p.1"
## [21] "bta.miR.452"      "bta.miR.532"      "bta.miR.6524"     "bta.miR.664b"    
## [25] "bta.miR.767"      "bta.miR.95"       "bta.miR.96"      
## 
## $SSSSI
##  [1] "bta.let.7a.5p"   "bta.let.7a.5p.1" "bta.let.7a.5p.2" "bta.let.7d"     
##  [5] "bta.miR.130a"    "bta.miR.181a"    "bta.miR.181a.1"  "bta.miR.224"    
##  [9] "bta.miR.27b"     "bta.miR.30d"     "bta.miR.505"     "bta.miR.8549"   
## 
## $ISSSI
##  [1] "bta.let.7b"      "bta.let.7c"      "bta.let.7i"      "bta.miR.140"    
##  [5] "bta.miR.151.3p"  "bta.miR.155"     "bta.miR.181b"    "bta.miR.181b.1" 
##  [9] "bta.miR.193b"    "bta.miR.214"     "bta.miR.221"     "bta.miR.2389"   
## [13] "bta.miR.3432a"   "bta.miR.3432a.1" "bta.miR.3432b"   "bta.miR.378"    
## [17] "bta.miR.378.1"   "bta.miR.652"    
## 
## $ISSSS
##  [1] "bta.let.7e"      "bta.miR.11980"   "bta.miR.11987"   "bta.miR.1260b"  
##  [5] "bta.miR.132"     "bta.miR.134"     "bta.miR.139"     "bta.miR.142.5p" 
##  [9] "bta.miR.1468"    "bta.miR.150"     "bta.miR.16a"     "bta.miR.193a.5p"
## [13] "bta.miR.24.3p"   "bta.miR.24.3p.1" "bta.miR.2419.5p" "bta.miR.2904"   
## [17] "bta.miR.2904.1"  "bta.miR.2904.2"  "bta.miR.320a"    "bta.miR.320a.1" 
## [21] "bta.miR.379"     "bta.miR.424.3p"  "bta.miR.484"     "bta.miR.485"    
## [25] "bta.miR.486"     "bta.miR.7859"    "bta.miR.99b"    
## 
## $SSSSS
##   [1] "bta.let.7f"          "bta.let.7f.1"        "bta.miR.100"        
##   [4] "bta.miR.10020"       "bta.miR.10162.5p"    "bta.miR.10164.3p"   
##   [7] "bta.miR.10166.5p"    "bta.miR.10169.3p"    "bta.miR.10170.5p"   
##  [10] "bta.miR.10171.3p"    "bta.miR.10172.5p"    "bta.miR.10173.5p"   
##  [13] "bta.miR.10174.3p"    "bta.miR.10174.5p"    "bta.miR.10175.5p"   
##  [16] "bta.miR.10176.5p"    "bta.miR.10177.5p"    "bta.miR.10178.5p"   
##  [19] "bta.miR.10179.5p"    "bta.miR.10180.3p"    "bta.miR.10181.5p"   
##  [22] "bta.miR.10182.3p"    "bta.miR.10182.5p"    "bta.miR.10183.5p"   
##  [25] "bta.miR.10185.5p"    "bta.miR.10225a"      "bta.miR.1185"       
##  [28] "bta.miR.1197"        "bta.miR.11971"       "bta.miR.11973"      
##  [31] "bta.miR.11978"       "bta.miR.11981"       "bta.miR.11982"      
##  [34] "bta.miR.11983"       "bta.miR.11984"       "bta.miR.11986c"     
##  [37] "bta.miR.11988"       "bta.miR.11990"       "bta.miR.11991"      
##  [40] "bta.miR.11992"       "bta.miR.11993"       "bta.miR.11994"      
##  [43] "bta.miR.11995"       "bta.miR.11997"       "bta.miR.11998"      
##  [46] "bta.miR.12001"       "bta.miR.12002b"      "bta.miR.12003"      
##  [49] "bta.miR.12004"       "bta.miR.12005"       "bta.miR.12005.1"    
##  [52] "bta.miR.12006"       "bta.miR.12010"       "bta.miR.12014"      
##  [55] "bta.miR.12017"       "bta.miR.12019"       "bta.miR.12025"      
##  [58] "bta.miR.12026"       "bta.miR.12027"       "bta.miR.12032"      
##  [61] "bta.miR.12033"       "bta.miR.12036"       "bta.miR.12039"      
##  [64] "bta.miR.12046"       "bta.miR.12048"       "bta.miR.12051"      
##  [67] "bta.miR.12054"       "bta.miR.12056"       "bta.miR.12057"      
##  [70] "bta.miR.12059"       "bta.miR.12060"       "bta.miR.12061"      
##  [73] "bta.miR.12064"       "bta.miR.122"         "bta.miR.1224"       
##  [76] "bta.miR.1247.3p"     "bta.miR.1248"        "bta.miR.1248.1"     
##  [79] "bta.miR.1249"        "bta.miR.1256"        "bta.miR.125a"       
##  [82] "bta.miR.126.5p"      "bta.miR.127"         "bta.miR.1277"       
##  [85] "bta.miR.128"         "bta.miR.128.1"       "bta.miR.1281"       
##  [88] "bta.miR.1287"        "bta.miR.129"         "bta.miR.129.1"      
##  [91] "bta.miR.129.5p"      "bta.miR.129.5p.1"    "bta.miR.1291"       
##  [94] "bta.miR.1296"        "bta.miR.1298"        "bta.miR.1301"       
##  [97] "bta.miR.1306"        "bta.miR.1343.5p"     "bta.miR.138"        
## [100] "bta.miR.138.1"       "bta.miR.1388.3p"     "bta.miR.1388.5p"    
## [103] "bta.miR.141"         "bta.miR.1434.3p"     "bta.miR.144"        
## [106] "bta.miR.148a"        "bta.miR.149.3p"      "bta.miR.149.5p"     
## [109] "bta.miR.152"         "bta.miR.154b"        "bta.miR.154c"       
## [112] "bta.miR.1584.3p"     "bta.miR.1584.5p"     "bta.miR.15a"        
## [115] "bta.miR.1721"        "bta.miR.1777a"       "bta.miR.1777b"      
## [118] "bta.miR.1814c"       "bta.miR.1835"        "bta.miR.1839"       
## [121] "bta.miR.184"         "bta.miR.1842"        "bta.miR.1843"       
## [124] "bta.miR.185"         "bta.miR.187"         "bta.miR.188"        
## [127] "bta.miR.18b"         "bta.miR.190b"        "bta.miR.191"        
## [130] "bta.miR.191b"        "bta.miR.192"         "bta.miR.193a"       
## [133] "bta.miR.1949"        "bta.miR.194b"        "bta.miR.194b.1"     
## [136] "bta.miR.194b.3p"     "bta.miR.197"         "bta.miR.199b"       
## [139] "bta.miR.202"         "bta.miR.205"         "bta.miR.206"        
## [142] "bta.miR.21.3p"       "bta.miR.215"         "bta.miR.218"        
## [145] "bta.miR.218.1"       "bta.miR.219"         "bta.miR.219.3p"     
## [148] "bta.miR.2284a"       "bta.miR.2284aa"      "bta.miR.2284aa.1"   
## [151] "bta.miR.2284aa.2"    "bta.miR.2284aa.3"    "bta.miR.2284ab"     
## [154] "bta.miR.2284ac"      "bta.miR.2284b"       "bta.miR.2284d"      
## [157] "bta.miR.2284g"       "bta.miR.2284h.5p"    "bta.miR.2284j"      
## [160] "bta.miR.2284l"       "bta.miR.2284m"       "bta.miR.2284r"      
## [163] "bta.miR.2284w"       "bta.miR.2284z"       "bta.miR.2284z.1"    
## [166] "bta.miR.2284z.2"     "bta.miR.2284z.3"     "bta.miR.2284z.4"    
## [169] "bta.miR.2284z.5"     "bta.miR.2284z.6"     "bta.miR.2285a"      
## [172] "bta.miR.2285aa"      "bta.miR.2285ab"      "bta.miR.2285ac"     
## [175] "bta.miR.2285ad"      "bta.miR.2285ag.3p"   "bta.miR.2285ah.5p"  
## [178] "bta.miR.2285ai.5p"   "bta.miR.2285ak.5p"   "bta.miR.2285al.5p"  
## [181] "bta.miR.2285am.5p"   "bta.miR.2285an"      "bta.miR.2285ap"     
## [184] "bta.miR.2285ar"      "bta.miR.2285as"      "bta.miR.2285as.1"   
## [187] "bta.miR.2285as.2"    "bta.miR.2285at"      "bta.miR.2285at.1"   
## [190] "bta.miR.2285at.2"    "bta.miR.2285at.3"    "bta.miR.2285aw"     
## [193] "bta.miR.2285ax"      "bta.miR.2285ax.1"    "bta.miR.2285ax.2"   
## [196] "bta.miR.2285ay"      "bta.miR.2285az"      "bta.miR.2285b"      
## [199] "bta.miR.2285b.1"     "bta.miR.2285ba"      "bta.miR.2285ba.1"   
## [202] "bta.miR.2285bb"      "bta.miR.2285bc"      "bta.miR.2285bd"     
## [205] "bta.miR.2285be"      "bta.miR.2285bg"      "bta.miR.2285bh"     
## [208] "bta.miR.2285bi"      "bta.miR.2285bi.1"    "bta.miR.2285bj"     
## [211] "bta.miR.2285bj.1"    "bta.miR.2285bk"      "bta.miR.2285bl"     
## [214] "bta.miR.2285bm"      "bta.miR.2285bo"      "bta.miR.2285bp"     
## [217] "bta.miR.2285bq"      "bta.miR.2285br"      "bta.miR.2285bs"     
## [220] "bta.miR.2285bt"      "bta.miR.2285bu"      "bta.miR.2285bu.1"   
## [223] "bta.miR.2285by"      "bta.miR.2285bz"      "bta.miR.2285c"      
## [226] "bta.miR.2285cc"      "bta.miR.2285cj"      "bta.miR.2285ck"     
## [229] "bta.miR.2285cl"      "bta.miR.2285cm"      "bta.miR.2285co"     
## [232] "bta.miR.2285cp"      "bta.miR.2285cr"      "bta.miR.2285cr.1"   
## [235] "bta.miR.2285cs"      "bta.miR.2285ct"      "bta.miR.2285cw"     
## [238] "bta.miR.2285cw.1"    "bta.miR.2285cy"      "bta.miR.2285cz"     
## [241] "bta.miR.2285d"       "bta.miR.2285da"      "bta.miR.2285db"     
## [244] "bta.miR.2285dc"      "bta.miR.2285dd"      "bta.miR.2285de"     
## [247] "bta.miR.2285dh"      "bta.miR.2285dl.3p"   "bta.miR.2285dl.3p.1"
## [250] "bta.miR.2285e"       "bta.miR.2285e.1"     "bta.miR.2285g"      
## [253] "bta.miR.2285g.1"     "bta.miR.2285g.2"     "bta.miR.2285h"      
## [256] "bta.miR.2285i"       "bta.miR.2285j"       "bta.miR.2285j.1"    
## [259] "bta.miR.2285l"       "bta.miR.2285m"       "bta.miR.2285m.1"    
## [262] "bta.miR.2285m.2"     "bta.miR.2285m.3"     "bta.miR.2285m.4"    
## [265] "bta.miR.2285n"       "bta.miR.2285n.1"     "bta.miR.2285n.2"    
## [268] "bta.miR.2285n.3"     "bta.miR.2285n.4"     "bta.miR.2285n.5"    
## [271] "bta.miR.2285n.6"     "bta.miR.2285p"       "bta.miR.2285q"      
## [274] "bta.miR.2285r"       "bta.miR.2285v"       "bta.miR.2285x"      
## [277] "bta.miR.2286"        "bta.miR.2287"        "bta.miR.2288"       
## [280] "bta.miR.2290"        "bta.miR.2291"        "bta.miR.2292"       
## [283] "bta.miR.2295"        "bta.miR.2299.3p"     "bta.miR.2299.5p"    
## [286] "bta.miR.2300b.3p"    "bta.miR.2303"        "bta.miR.2304"       
## [289] "bta.miR.2305"        "bta.miR.2307"        "bta.miR.2308"       
## [292] "bta.miR.2309"        "bta.miR.2310"        "bta.miR.2313.5p"    
## [295] "bta.miR.2316"        "bta.miR.2318"        "bta.miR.2320.3p"    
## [298] "bta.miR.2321"        "bta.miR.2324"        "bta.miR.2325a"      
## [301] "bta.miR.2325c"       "bta.miR.2328.3p"     "bta.miR.2328.5p"    
## [304] "bta.miR.2330.5p"     "bta.miR.2331.5p"     "bta.miR.2335"       
## [307] "bta.miR.2336"        "bta.miR.2338"        "bta.miR.2339"       
## [310] "bta.miR.2341"        "bta.miR.2342"        "bta.miR.2343"       
## [313] "bta.miR.2344"        "bta.miR.2346"        "bta.miR.2347"       
## [316] "bta.miR.2348"        "bta.miR.2349"        "bta.miR.2352"       
## [319] "bta.miR.2353"        "bta.miR.2354"        "bta.miR.2355.3p"    
## [322] "bta.miR.2356"        "bta.miR.2357"        "bta.miR.2359"       
## [325] "bta.miR.2360"        "bta.miR.2361"        "bta.miR.2364"       
## [328] "bta.miR.2366"        "bta.miR.2367.3p"     "bta.miR.2367.5p"    
## [331] "bta.miR.2370.5p"     "bta.miR.2371"        "bta.miR.2372"       
## [334] "bta.miR.2373.3p"     "bta.miR.2373.5p"     "bta.miR.2374"       
## [337] "bta.miR.2375"        "bta.miR.2376"        "bta.miR.2379"       
## [340] "bta.miR.2381"        "bta.miR.2382.5p"     "bta.miR.2384"       
## [343] "bta.miR.2387"        "bta.miR.2388.3p"     "bta.miR.2390"       
## [346] "bta.miR.2392"        "bta.miR.2397.3p"     "bta.miR.2397.5p"    
## [349] "bta.miR.2399.5p"     "bta.miR.23b.3p"      "bta.miR.23b.5p"     
## [352] "bta.miR.24"          "bta.miR.2404"        "bta.miR.2404.1"     
## [355] "bta.miR.2406"        "bta.miR.2407"        "bta.miR.2410"       
## [358] "bta.miR.2411.5p"     "bta.miR.2412"        "bta.miR.2413"       
## [361] "bta.miR.2415.5p"     "bta.miR.2416"        "bta.miR.2417"       
## [364] "bta.miR.2418"        "bta.miR.2419.3p"     "bta.miR.2421"       
## [367] "bta.miR.2422"        "bta.miR.2426"        "bta.miR.2427"       
## [370] "bta.miR.2428"        "bta.miR.2430"        "bta.miR.2431.5p"    
## [373] "bta.miR.2433"        "bta.miR.2434"        "bta.miR.2436.3p"    
## [376] "bta.miR.2436.5p"     "bta.miR.2438"        "bta.miR.2439.5p"    
## [379] "bta.miR.2440"        "bta.miR.2441"        "bta.miR.2442"       
## [382] "bta.miR.2443"        "bta.miR.2447"        "bta.miR.2448.3p"    
## [385] "bta.miR.2450b"       "bta.miR.2454.5p"     "bta.miR.2455"       
## [388] "bta.miR.2456"        "bta.miR.2457"        "bta.miR.2459"       
## [391] "bta.miR.2460"        "bta.miR.2462"        "bta.miR.2463"       
## [394] "bta.miR.2464.3p"     "bta.miR.2464.5p"     "bta.miR.2465"       
## [397] "bta.miR.2466.3p"     "bta.miR.2466.5p"     "bta.miR.2467.3p"    
## [400] "bta.miR.2468"        "bta.miR.2473"        "bta.miR.2474"       
## [403] "bta.miR.2475"        "bta.miR.2481"        "bta.miR.2483.3p"    
## [406] "bta.miR.2483.5p"     "bta.miR.2486.5p"     "bta.miR.27a.5p"     
## [409] "bta.miR.2881"        "bta.miR.2882"        "bta.miR.2889"       
## [412] "bta.miR.2890"        "bta.miR.2892"        "bta.miR.2893"       
## [415] "bta.miR.2898"        "bta.miR.2900"        "bta.miR.2902"       
## [418] "bta.miR.296.5p"      "bta.miR.299"         "bta.miR.299.2"      
## [421] "bta.miR.29a"         "bta.miR.29c"         "bta.miR.29d.5p"     
## [424] "bta.miR.301b"        "bta.miR.3065"        "bta.miR.3141"       
## [427] "bta.miR.320b"        "bta.miR.323"         "bta.miR.323b.3p"    
## [430] "bta.miR.324"         "bta.miR.326"         "bta.miR.329a"       
## [433] "bta.miR.329b"        "bta.miR.330"         "bta.miR.331.3p"     
## [436] "bta.miR.338"         "bta.miR.33a"         "bta.miR.33b"        
## [439] "bta.miR.342"         "bta.miR.3431"        "bta.miR.345.5p"     
## [442] "bta.miR.34b"         "bta.miR.34c"         "bta.miR.3533"       
## [445] "bta.miR.365.5p"      "bta.miR.3660"        "bta.miR.370"        
## [448] "bta.miR.374c"        "bta.miR.376a"        "bta.miR.376b"       
## [451] "bta.miR.376c"        "bta.miR.377"         "bta.miR.378b"       
## [454] "bta.miR.378c"        "bta.miR.378d"        "bta.miR.380.3p"     
## [457] "bta.miR.380.5p"      "bta.miR.382"         "bta.miR.383"        
## [460] "bta.miR.3956"        "bta.miR.3957"        "bta.miR.409a"       
## [463] "bta.miR.411b"        "bta.miR.411c.3p"     "bta.miR.412"        
## [466] "bta.miR.421"         "bta.miR.431"         "bta.miR.432"        
## [469] "bta.miR.433"         "bta.miR.4444"        "bta.miR.4449"       
## [472] "bta.miR.449a"        "bta.miR.449b"        "bta.miR.449d"       
## [475] "bta.miR.4523"        "bta.miR.454"         "bta.miR.455.3p"     
## [478] "bta.miR.4657"        "bta.miR.483"         "bta.miR.487a"       
## [481] "bta.miR.487b"        "bta.miR.490"         "bta.miR.491"        
## [484] "bta.miR.493"         "bta.miR.496"         "bta.miR.499"        
## [487] "bta.miR.502a"        "bta.miR.502a.1"      "bta.miR.502b"       
## [490] "bta.miR.503.3p"      "bta.miR.503.5p"      "bta.miR.507.3p"     
## [493] "bta.miR.507b"        "bta.miR.539"         "bta.miR.541"        
## [496] "bta.miR.542.5p"      "bta.miR.543"         "bta.miR.545.5p"     
## [499] "bta.miR.582"         "bta.miR.584"         "bta.miR.584.1"      
## [502] "bta.miR.584.2"       "bta.miR.584.3"       "bta.miR.584.4"      
## [505] "bta.miR.584.5"       "bta.miR.584.6"       "bta.miR.584.7"      
## [508] "bta.miR.6120.3p"     "bta.miR.6121.3p"     "bta.miR.6121.5p"    
## [511] "bta.miR.6122.3p"     "bta.miR.6122.5p"     "bta.miR.6123"       
## [514] "bta.miR.628"         "bta.miR.6516"        "bta.miR.6517"       
## [517] "bta.miR.6518"        "bta.miR.6519"        "bta.miR.6520"       
## [520] "bta.miR.6522"        "bta.miR.6523a"       "bta.miR.6525"       
## [523] "bta.miR.6527"        "bta.miR.6528"        "bta.miR.6529a"      
## [526] "bta.miR.6530"        "bta.miR.6531"        "bta.miR.6532"       
## [529] "bta.miR.6533"        "bta.miR.6534"        "bta.miR.6535"       
## [532] "bta.miR.6536"        "bta.miR.6536.1"      "bta.miR.654"        
## [535] "bta.miR.656"         "bta.miR.658"         "bta.miR.660"        
## [538] "bta.miR.664a"        "bta.miR.665"         "bta.miR.668.3p"     
## [541] "bta.miR.669"         "bta.miR.671"         "bta.miR.6715"       
## [544] "bta.miR.6775"        "bta.miR.7"           "bta.miR.7.1"        
## [547] "bta.miR.7.2"         "bta.miR.708"         "bta.miR.7180"       
## [550] "bta.miR.758"         "bta.miR.760.5p"      "bta.miR.763"        
## [553] "bta.miR.7857.3p"     "bta.miR.7857.5p"     "bta.miR.7857.5p.1"  
## [556] "bta.miR.7858"        "bta.miR.7860"        "bta.miR.7861"       
## [559] "bta.miR.7862"        "bta.miR.7863"        "bta.miR.7865"       
## [562] "bta.miR.874"         "bta.miR.875"         "bta.miR.9.3p"       
## [565] "bta.miR.9.3p.1"      "bta.miR.9.5p"        "bta.miR.9.5p.1"     
## [568] "bta.miR.940"         "bta.miR.98"          "bta.miR.9851"       
## 
## $IISSS
##  [1] "bta.miR.1"        "bta.miR.1.1"      "bta.miR.10167.3p" "bta.miR.10a"     
##  [5] "bta.miR.10b"      "bta.miR.151.5p"   "bta.miR.199c"     "bta.miR.212"     
##  [9] "bta.miR.28"       "bta.miR.2888"     "bta.miR.2888.1"   "bta.miR.411a"    
## [13] "bta.miR.615"     
## 
## $DDSSS
##  [1] "bta.miR.101"       "bta.miR.101.1"     "bta.miR.11986b"   
##  [4] "bta.miR.12030"     "bta.miR.1247.5p"   "bta.miR.147"      
##  [7] "bta.miR.148b"      "bta.miR.190a"      "bta.miR.200a"     
## [10] "bta.miR.204"       "bta.miR.210"       "bta.miR.2284v"    
## [13] "bta.miR.2284x"     "bta.miR.2285aj.5p" "bta.miR.2332"     
## [16] "bta.miR.2411.3p"   "bta.miR.2424"      "bta.miR.2454.3p"  
## [19] "bta.miR.26b"       "bta.miR.29d.3p"    "bta.miR.301a"     
## [22] "bta.miR.31"        "bta.miR.339a"      "bta.miR.339b"     
## [25] "bta.miR.340"       "bta.miR.345.3p"    "bta.miR.34a"      
## [28] "bta.miR.361"       "bta.miR.504"       "bta.miR.6119.5p"  
## [31] "bta.miR.677"       "bta.miR.885"       "bta.miR.99a.3p"   
## 
## $IISIS
## [1] "bta.miR.10172.3p" "bta.miR.126.3p"   "bta.miR.143"      "bta.miR.195"     
## [5] "bta.miR.369.5p"   "bta.miR.451"      "bta.miR.497"     
## 
## $SDSSI
## [1] "bta.miR.103"    "bta.miR.103.1"  "bta.miR.11977"  "bta.miR.11989" 
## [5] "bta.miR.222"    "bta.miR.2285av" "bta.miR.2285cf" "bta.miR.331.5p"
## [9] "bta.miR.425.3p"
## 
## $DSSDS
## [1] "bta.miR.106a"  "bta.miR.1246"  "bta.miR.17.3p" "bta.miR.181c" 
## [5] "bta.miR.182"   "bta.miR.200b"  "bta.miR.20a"  
## 
## $SDSIS
## [1] "bta.miR.107"   "bta.miR.29b"   "bta.miR.29b.1"
## 
## $SDSSS
##  [1] "bta.miR.11972"   "bta.miR.11975"   "bta.miR.11976"   "bta.miR.11976.1"
##  [5] "bta.miR.11985"   "bta.miR.12023"   "bta.miR.12034"   "bta.miR.2340"   
##  [9] "bta.miR.23a"     "bta.miR.27a.3p"  "bta.miR.2885"    "bta.miR.2886"   
## [13] "bta.miR.2887"    "bta.miR.2887.1"  "bta.miR.2899"    "bta.miR.3154"   
## [17] "bta.miR.92b"    
## 
## $SSSDS
##  [1] "bta.miR.125b"    "bta.miR.125b.1"  "bta.miR.1343.3p" "bta.miR.15b"    
##  [5] "bta.miR.17.5p"   "bta.miR.183"     "bta.miR.200c"    "bta.miR.2285cd" 
##  [9] "bta.miR.296.3p"  "bta.miR.423.3p"  "bta.miR.92a"     "bta.miR.92a.1"  
## 
## $DDSSI
##  [1] "bta.miR.1307"    "bta.miR.196b"    "bta.miR.2284y"   "bta.miR.2284y.1"
##  [5] "bta.miR.2284y.2" "bta.miR.2284y.3" "bta.miR.2284y.4" "bta.miR.2284y.5"
##  [9] "bta.miR.2284y.6" "bta.miR.26a"     "bta.miR.26a.1"  
## 
## $DSSSD
##  [1] "bta.miR.130b"    "bta.miR.135a"    "bta.miR.135a.1"  "bta.miR.136"    
##  [5] "bta.miR.1434.5p" "bta.miR.18a"     "bta.miR.19a"     "bta.miR.2477"   
##  [9] "bta.miR.30c"     "bta.miR.3601"    "bta.miR.374a"    "bta.miR.455.5p" 
## [13] "bta.miR.6119.3p" "bta.miR.769"    
## 
## $SSSIS
## [1] "bta.miR.133a"   "bta.miR.133a.1" "bta.miR.223"    "bta.miR.494"   
## 
## $SSSID
## [1] "bta.miR.135b"    "bta.miR.2285o"   "bta.miR.2285o.1" "bta.miR.2285o.2"
## [5] "bta.miR.2285o.3" "bta.miR.2285o.4"
## 
## $IISSD
## [1] "bta.miR.142.3p" "bta.miR.146a"   "bta.miR.22.3p"  "bta.miR.2478"  
## [5] "bta.miR.424.5p" "bta.miR.425.5p" "bta.miR.4286"   "bta.miR.4286.1"
## 
## $ISSIS
## [1] "bta.miR.145"   "bta.miR.21.5p" "bta.miR.22.5p"
## 
## $SISSS
## [1] "bta.miR.16b"       "bta.miR.193a.3p"   "bta.miR.199a.3p"  
## [4] "bta.miR.199a.3p.1" "bta.miR.199a.5p"   "bta.miR.199a.5p.1"
## [7] "bta.miR.2285u"     "bta.miR.410"       "bta.miR.495"      
## 
## $DDSDI
## [1] "bta.miR.181d" "bta.miR.25"   "bta.miR.744" 
## 
## $SISSD
##  [1] "bta.miR.186"     "bta.miR.2285bn"  "bta.miR.2285ce"  "bta.miR.2285f"  
##  [5] "bta.miR.2285f.1" "bta.miR.2285k"   "bta.miR.2285k.1" "bta.miR.2285k.2"
##  [9] "bta.miR.2285k.3" "bta.miR.2285k.4" "bta.miR.335"     "bta.miR.450a"   
## [13] "bta.miR.450a.1"  "bta.miR.450b"    "bta.miR.500"    
## 
## $SSSSD
## [1] "bta.miR.194"    "bta.miR.194.1"  "bta.miR.369.3p"
## 
## $ISSII
## [1] "bta.miR.196a"   "bta.miR.196a.1" "bta.miR.375"   
## 
## $DDSDD
## [1] "bta.miR.19b"   "bta.miR.19b.1"
## 
## $DISDS
## [1] "bta.miR.20b"
## 
## $DISDD
## [1] "bta.miR.211"    "bta.miR.2285t"  "bta.miR.30a.5p" "bta.miR.30f"   
## [5] "bta.miR.363"   
## 
## $DISSD
## [1] "bta.miR.2285bf"   "bta.miR.2285bf.1" "bta.miR.2285bf.2" "bta.miR.30b.5p"  
## [5] "bta.miR.362.3p"   "bta.miR.374b"    
## 
## $SSSDI
## [1] "bta.miR.30b.3p"
## 
## $DDSSD
## [1] "bta.miR.3613a" "bta.miR.429"   "bta.miR.592"  
## 
## $SISID
## [1] "bta.miR.376d" "bta.miR.381"  "bta.miR.655" 
## 
## $IISID
## [1] "bta.miR.376e"
## 
## $SISIS
## [1] "bta.miR.411c.5p"
## 
## $ISSDI
## [1] "bta.miR.423.5p"
## 
## $IISDS
## [1] "bta.miR.574"
## 
## $SDSDI
## [1] "bta.miR.760.3p"
## 
## $ISSDS
## [1] "bta.miR.877"
## 
## $DSSDI
## [1] "bta.miR.93"
## 
## $SISDS
## [1] "bta.miR.99a.5p"

5.3 Interesting notes 3’UTR CDS 5’UTR

3’ UTR Binding: - miRNAs binding to the 3’ UTR lead to mRNA degradation and translational repression.
- binding in the 3’ UTR recruits RISC complex which leads to deadenylation, decapping -> degradation
- These sites are generally considered the most effective in down-regulating gene expression by reducing mRNA levels
- 3’ UTR binding is also the most well studied mechanism of regulation by miRNA

5’ UTR Binding:
- miRNA binding in the 5’ UTR is less understood but may influence translation initiation
- Binding near the 5’ cap can interfere with the assembly of the translation initiation complex, inhibiting initiation.
- The secondary structure near the 5’ cap in miRNA-regulated mRNAs is more stable, suggesting a mechanism for miRNA-mediated regulation at this region.
- Less common and seems to be less potent compared to 3’ UTR binding sites in terms of inducing mRNA degradation

CDS Binding:
- miRNAs binding within the CDS can inhibit translation, often without affecting mRNA stability.
- The binding of miRNAs to the CDS can stall ribosomes during translation elongation, reducing protein synthesis.
- less effective at reducing mRNA levels compared to 3’ UTR binding, CDS binding can significantly inhibit translation after miRNA transfection

6 Session Info

Click to view

sessionInfo()

## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22631)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: America/Chicago
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] readr_2.1.5                 ggrepel_0.9.5              
##  [3] plotly_4.10.4               ggplot2_3.5.0              
##  [5] kableExtra_1.4.0            knitr_1.46                 
##  [7] tidyr_1.3.1                 readxl_1.4.3               
##  [9] sva_3.48.0                  BiocParallel_1.34.2        
## [11] genefilter_1.82.1           mgcv_1.9-0                 
## [13] nlme_3.1-163                org.Bt.eg.db_3.17.0        
## [15] AnnotationDbi_1.62.2        dplyr_1.1.4                
## [17] DESeq2_1.40.2               SummarizedExperiment_1.30.2
## [19] Biobase_2.60.0              MatrixGenerics_1.12.3      
## [21] matrixStats_1.2.0           GenomicRanges_1.52.1       
## [23] GenomeInfoDb_1.36.4         IRanges_2.34.1             
## [25] S4Vectors_0.38.2            BiocGenerics_0.46.0        
## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.2.2               bitops_1.0-7            rlang_1.1.3            
##  [4] magrittr_2.0.3          compiler_4.3.2          RSQLite_2.3.5          
##  [7] png_0.1-8               systemfonts_1.0.6       vctrs_0.6.5            
## [10] stringr_1.5.1           pkgconfig_2.0.3         crayon_1.5.2           
## [13] fastmap_1.1.1           XVector_0.40.0          labeling_0.4.3         
## [16] utf8_1.2.4              rmarkdown_2.26          tzdb_0.4.0             
## [19] purrr_1.0.2             bit_4.0.5               xfun_0.43              
## [22] zlibbioc_1.46.0         cachem_1.0.8            jsonlite_1.8.8         
## [25] blob_1.2.4              highr_0.10              DelayedArray_0.26.7    
## [28] parallel_4.3.2          R6_2.5.1                bslib_0.7.0            
## [31] stringi_1.8.3           limma_3.56.2            jquerylib_0.1.4        
## [34] cellranger_1.1.0        Rcpp_1.0.12             Matrix_1.6-1.1         
## [37] splines_4.3.2           tidyselect_1.2.1        rstudioapi_0.16.0      
## [40] abind_1.4-5             yaml_2.3.8              codetools_0.2-19       
## [43] lattice_0.21-9          tibble_3.2.1            withr_3.0.0            
## [46] KEGGREST_1.40.1         evaluate_0.23           survival_3.5-7         
## [49] xml2_1.3.6              Biostrings_2.68.1       pillar_1.9.0           
## [52] generics_0.1.3          RCurl_1.98-1.14         hms_1.1.3              
## [55] munsell_0.5.1           scales_1.3.0            xtable_1.8-4           
## [58] glue_1.7.0              lazyeval_0.2.2          tools_4.3.2            
## [61] data.table_1.15.4       annotate_1.78.0         locfit_1.5-9.9         
## [64] XML_3.99-0.16.1         grid_4.3.2              edgeR_3.42.4           
## [67] colorspace_2.1-0        GenomeInfoDbData_1.2.10 cli_3.6.2              
## [70] fansi_1.0.6             S4Arrays_1.0.6          viridisLite_0.4.2      
## [73] svglite_2.1.3           gtable_0.3.4            sass_0.4.9             
## [76] digest_0.6.35           farver_2.1.1            htmlwidgets_1.6.4      
## [79] memoise_2.0.1           htmltools_0.5.8.1       lifecycle_1.0.4        
## [82] httr_1.4.7              MASS_7.3-60             bit64_4.0.5

Transcriptional Dynamics of Milk Production in Bovine Mammary Glands

Benji Lamp

4/24/24

1 Introduction

1.1 Boxplot Generation

2 Galaxy

3 mRNA

3.1 Setup

3.1.1 Libraries

3.1.2 Data

3.1.3 Metadata

3.2 Filtering/loading

3.2.1 Filtering

3.3 Normalization

3.3.1 ComBatSeq

3.3.2 DESeq2

3.3.3 PCA

3.3.3.1 ComBat Corrected + DESeq Norm PCA

3.3.3.2 DESeq Norm PCA

3.4 Timecourse Analysis

3.4.1 Pairwise Comparison

3.4.2 Motif Structuring

3.4.3 Pattern Generation

4 miRNA

4.1 Setup

4.1.1 Raw Counts

4.1.2 Metadata

4.2 DESeq2

4.3 PCA

4.4 Timecourse

4.4.1 Mean Counts

4.4.2 Pairwise Comparison

4.4.3 Expression Motif

4.4.4 Pattern Generation

5 miRNA Target Mapping Use Case

5.1 Pattern of Interest

5.2 miRwalk

5.2.1 mir SDSSI

5.2.2 mir SISSD

5.2.3 bta-miR-542-5p

5.2.4 V-MP 2284/2285

5.2.4.1 Decrease 2284

5.2.4.2 Decrease 2285

5.2.4.3 Increase 2285

5.2.5 EL-PL 2284/2285

5.2.5.1 Decrease 2285

5.2.5.2 Increase 2285

5.2.5.3 Increase 2284

5.2.6 miR patterns

5.3 Interesting notes 3’UTR CDS 5’UTR

6 Session Info