Auto-saving for axelle.loriot on branch master from commit 38f4ae17

R01_count_matrix.R

+library(tidyverse)
+rWSBIM2122::prepare_shell()
+
+samples <- list.files("wsbim2122_data/count_data/", pattern = "*tsv.gz")
+
+counts <- read_tsv(file = paste0("wsbim2122_data/count_data/", samples[1])) %>%  
+  select(ends_with('.bam'))
+
+for (sample in samples[-1]){
+  tmp <- read_tsv(file = paste0("wsbim2122_data/count_data/", sample)) %>% 
+    select(ends_with('.bam'))
+  counts <- cbind(counts, tmp)
+}
+names(counts) <- sub(pattern = ".bam$", '', names(counts))
+names(counts) <- sub(pattern = '../processed_data/bam/', '', names(counts))
+
+Geneids <- read_tsv(file = paste0("wsbim2122_data/count_data/", samples[1])) %>%  
+  dplyr::select(Geneid)
+rownames(counts) <- Geneids$Geneid
+
+

R01_deseq2.R

+library(DESeq2)
+library(tidyverse)
+load("wsbim2122_data/deseq2/counts.rda")
+load("wsbim2122_data/deseq2/coldata.rda")
+coldata
+head(counts)
+
+# Create dds object
+dds <- DESeqDataSetFromMatrix(countData = counts,
+                              colData = coldata,
+                              design = ~ Condition)
+
+
+head(assay(dds))
+colData(dds)
+rowData(dds)
+
+# Inspect counts distribution
+as_tibble(assay(dds)) %>%
+  gather(sample, value = counts) %>% 
+  ggplot(aes(x = log2(counts + 1), fill = sample)) +
+  geom_histogram(bins = 20) +
+  facet_wrap(~ sample)
+
+# Run DESeq2
+dds <- DESeq(dds)
+
+#PCA with plotPCA
+rld <- rlogTransformation(dds)
+
+
+# effect of the rlog transformation 
+
+# picked a very highly expressed gene
+assay(dds["ENSG00000198804"])
+log2(assay(dds["ENSG00000198804"]))
+assay(rld["ENSG00000198804"])
+
+# picked a lowly expressed gene
+assay(dds["ENSG00000248671"])
+log2(assay(dds["ENSG00000248671"]))
+assay(rld["ENSG00000248671"])
+
+plotPCA(rld, intgroup = "Condition")
+
+# Values can be extracted to customise the plot
+x <- plotPCA(rld, intgroup = "Condition", return = TRUE)
+ggplot(x, aes(x = PC1, y = PC2, color = Condition, label = name)) +
+  geom_point() +
+  geom_text()
+
+
+# Size Factors
+sizeFactors(dds)
+
+# Compare with sequencing depths
+enframe(sizeFactors(dds), name = "sample", value = "Size_Factor")
+
+SF <- enframe(sizeFactors(dds), name = "sample", value = "Size_Factor") %>%
+  ggplot(aes(x = sample, y = Size_Factor)) +
+  geom_bar(stat = "identity")
+SF
+SD <- enframe(colSums(assay(dds, "counts")), name = "sample", value = "n_reads") %>%
+  ggplot(aes(x = sample, y = n_reads)) +
+  geom_bar(stat = "identity")
+
+library("patchwork")
+SF / SD
+
+
+
+# Available results
+resultsNames(dds)
+dds$Condition
+
+dds$Condition <- relevel(dds$Condition, ref = "mock")
+dds$Condition
+dds <- DESeq(dds)
+resultsNames(dds)
+
+res <- results(dds,
+               name = "Condition_KD_vs_mock")
+
+res
+res_tbl <- as_tibble(res, rownames = "ENSEMBL")
+
+## Exercices
+
+# 1. Inspect the results table and identify the 5 “best genes” showing the lowest padjusted value.
+res_tbl %>%
+  arrange(padj) %>%
+  head(5)
+
+# 2. Calculate the mean expression level of these 5 "best genes" using 
+# the function `count()`. Compare with baseMean values.
+
+best_genes <- res_tbl %>%
+  arrange(padj) %>%
+  head(5) %>% pull(ENSEMBL)
+best_genes
+counts(dds[best_genes], normalize = TRUE)
+rowMeans(counts(dds[best_genes], normalize = TRUE))
+
+
+# 3. Extract the ß coefficient of these 5 "best genes" from the GLM 
+# using the function `coefficient()`. Compare with log2FoldChange values.
+
+coefficients(dds[best_genes])
+# 4. using the function `count()`, calculate the expression levels (in log2) 
+# of these 5 "best genes" in mock cells. Compare with ß coefficients.
+
+log2(rowMeans(counts(dds[best_genes, 1:3], normalize = TRUE)))
+
+
+# 5. Calculate the expression levels (in log2) 
+#of these 5 "best genes" in KD cells. Compare with ß coefficients.
+
+log2(rowMeans(counts(dds[best_genes, 4:6], normalize = TRUE)))
+
+# log2 expression levels in KD cells evaluated from ß coefficients
+coefficients(dds[best_genes])[,1] + coefficients(dds[best_genes])[,2]
+
+# 6. How many genes have no padjusted value? Why?
+summary(res$padj)
+
+# filter genes with no padjusted values
+res_tbl %>%
+  filter(is.na(padj))
+
+
+metadata(res)
+metadata(res)$filterThreshold
+
+quantile(res$baseMean, 0.7169)
+
+metadata(res)$filterNumRej
+as_tibble(metadata(res)$filterNumRej) %>%
+  ggplot(aes(x = theta, y = numRej)) +
+  geom_point() +
+  geom_vline(xintercept = 0.7169,
+             color = 'red')
+
+
+# Evaluate how many genes were really filtered by the independent
+# filtering procedure.
+
+# Number of genes with basemean == 0
+res_tbl %>%
+  filter(baseMean == 0) %>%
+  nrow()
+
+# Number of genes filtered by the independent filtering procedure
+res_tbl %>%
+  filter(baseMean > 0 & baseMean < metadata(res)$filterThreshold) %>%
+  nrow()
+
+# Re-run the results() function on the same dds object,
+#but set the independent filtering parameter to FALSE.
+# Check how many genes have no padj?
+res_no_IF <- results(dds,
+                     name = "Condition_KD_vs_mock",
+                     independentFiltering = FALSE)
+as_tibble(res_no_IF, rownames = "ENSEMBL") %>%
+  filter(is.na(padj)) %>% nrow()
+
+
+# Imagine another way of filtering genes with very low counts
+
+# filter the data to remove genes with few counts
+filtering_thr <- 10
+# keep genes with counts > 5 in 3 or more samples
+keep <- rowSums(counts(dds, normalized = TRUE) >= filtering_thr) >=3
+dds_bis <- DESeq(dds[keep, ])
+res_bis <- results(dds_bis,
+                   name = "Condition_KD_vs_mock",
+                   independentFiltering = FALSE)
+
+as_tibble(res_bis, rownames = "ENSEMBL") %>%
+  filter(is.na(padj)) %>% nrow()
+
+# => Number of genes kept
+as_tibble(res_bis, rownames = "ENSEMBL") %>%
+  filter(!is.na(padj)) %>% nrow()
+
+# Compare with previous anaylsis with the independant filtering thr fixed by deseq2
+as_tibble(res, rownames = "ENSEMBL") %>%
+  filter(!is.na(padj)) %>% nrow()
+
+
+# Histogram of pvalues
+hist(res_tbl$pvalue)
+
+res_tbl %>%
+  filter(pvalue > 0.8 & pvalue < 0.85) %>%
+  head(10)
+
+res_tbl %>%
+  filter(baseMean > metadata(res)$filterThreshold) %>% 
+  pull(pvalue) %>% 
+  hist()
+            
+# plot MA
+plotMA(res)
+
+# Volcano plot
+res_tbl %>%
+  filter(!is.na(padj)) %>%
+  ggplot(aes(x = log2FoldChange, y = -log10(padj), 
+             color = padj < 0.05 & abs(log2FoldChange) > 1)) +
+  scale_colour_manual(values = c("gray", "red")) +
+  geom_point(size = 0.5) + 
+  geom_hline(yintercept = -log10(0.05)) +
+  geom_vline(xintercept = 1) +
+  geom_vline(xintercept = -1)
+
+
+# plot counts of 6 best genes
+best_genes <- res_tbl %>%
+  arrange(padj)  %>%
+  head(6)
+
+as_tibble(counts(dds[best_genes$ENSEMBL, ], normalize = T),
+          rownames = 'ENSEMBL') %>%
+  gather(sample, counts, -ENSEMBL) %>%
+  left_join(as_tibble(coldata, rownames = "sample")) %>%
+  ggplot(aes(x = sample, y = counts, fill = Condition)) +
+  geom_bar(stat = 'identity', color = "gray30") +
+  facet_wrap( ~ ENSEMBL, scales = "free", ncol = 3) +
+  theme(axis.text.x = element_text(size = 7, angle = 90),
+        axis.title.x = element_blank(),
+        legend.position = "right",
+        legend.text = element_text(size = 7),
+        legend.title = element_text(size = 7))
+
+
+
+#Identify and inspect counts of the genes plotted in red in the volcano-plot. 
+#These genes have a very large log2FC (|log2FC| > 5) but are far from bearing 
+#the lowest padjusted value (their padjusted value is between 0.05 and 1e-5).
+selected_genes <- res_tbl %>%
+  filter(padj < 0.05 & padj > 1e-5 & abs(log2FoldChange) > 5)
+
+as_tibble(counts(dds[selected_genes$ENSEMBL, ], normalize = T),
+          rownames = 'ENSEMBL') %>%
+  gather(sample, counts, -ENSEMBL) %>%
+  left_join(as_tibble(coldata, rownames = "sample")) %>%
+  ggplot(aes(x = sample, y = counts, fill = Condition)) +
+  geom_bar(stat = 'identity', color = "gray30") +
+  facet_wrap( ~ ENSEMBL, scales = "free", ncol = 3) +
+  theme(axis.text.x = element_text(size = 7, angle = 90),
+        axis.title.x = element_blank(),
+        legend.position = "right",
+        legend.text = element_text(size = 7),
+        legend.title = element_text(size = 7))
+
+# Using dispersion() function, compare dispersion values for both group 
+#of genes
+dispersions(dds[best_genes$ENSEMBL,])
+dispersions(dds[selected_genes$ENSEMBL,])
+
+
+# Add genes names
+library("biomaRt")
+library("org.Hs.eg.db")
+
+# Load homo sapiens ensembl dataset
+mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl"))
+
+#Attributes define the values we are interested to retrieve.
+#listAttributes(mart)
+ensembl_to_geneName <- getBM(attributes = c("ensembl_gene_id", "external_gene_name"), mart = mart)
+ensembl_to_geneName
+names(ensembl_to_geneName) <- c("ENSEMBL", "gene")
+
+res_tbl <- res_tbl %>%
+  left_join(ensembl_to_geneName) %>%
+  dplyr::select(ENSEMBL, gene, everything()) %>%
+  arrange(padj)

test.R

+library(DESeq2)
+library(tidyverse)
+load("wsbim2122_data/deseq2/counts.rda")
+load("wsbim2122_data/deseq2/coldata.rda")
+coldata
+head(counts)
+
+# Create dds object
+dds <- DESeqDataSetFromMatrix(countData = counts,
+                              colData = coldata,
+                              design = ~ Condition)
+
+
+head(assay(dds))
+colData(dds)
+rowData(dds)
+
+# Inspect counts distribution
+as_tibble(assay(dds)) %>%
+  gather(sample, value = counts) %>%
+  ggplot(aes(x = log2(counts + 1), fill = sample)) +
+  geom_histogram(bins = 20) +
+  facet_wrap(~ sample)
+
+# Run DESeq2
+dds <- DESeq(dds)
+
+#PCA with plotPCA
+dim(rld)
+rld <- rlogTransformation(dds)
+plotPCA(rld, intgroup = "Condition")
+
+# Values can be extracted to customise the plot
+x <- plotPCA(rld, intgroup = "Condition", return = TRUE)
+ggplot(x, aes(x = PC1, y = PC2, color = Condition, label = name)) +
+  geom_point() +
+  geom_text()
+
+# PCA with prcomp()
+pca <- prcomp(t(assay(rld)))
+summary(pca)
+var <- pca$sdev^2
+pve <- var/sum(var) # % var explained
+as_tibble(pca$x, rownames = "Sample") %>% 
+  select(Sample, PC1, PC2) %>% 
+  left_join(as_tibble(coldata, rownames = "Sample")) %>% 
+  ggplot(aes(x = PC1, y = PC2, color = Condition)) +
+  geom_point() +
+  xlab(paste0("PC1:", round(pve[1]*100), " % variance")) +
+  ylab(paste0("PC2:", round(pve[2]*100), " % variance"))
+
+# PCA with prcomp() taking 500 genes with higher variance
+rv <- rowVars(assay(rld))
+select <- order(rv, decreasing = TRUE)[seq_len(min(500, length(rv)))]
+pca <- prcomp(t(assay(rld)[select, ]))
+pve <- pca$sdev^2/sum(pca$sdev^2)
+as_tibble(pca$x, rownames = "Sample") %>% 
+  select(Sample, PC1, PC2) %>% 
+  left_join(as_tibble(coldata, rownames = "Sample")) %>% 
+  ggplot(aes(x = PC1, y = PC2, color = Condition)) +
+  geom_point() +
+  xlab(paste0("PC1:", round(pve[1]*100), " % variance")) +
+  ylab(paste0("PC2:", round(pve[2]*100), " % variance"))
+
+
+# Size Factors
+sizeFactors(dds)
+
+# Compare with sequencing depths
+SF <- enframe(sizeFactors(dds), name = "sample", value = "Size_Factor") %>%
+  ggplot(aes(x = sample, y = Size_Factor)) +
+  geom_bar(stat = "identity")
+
+SD <- enframe(colSums(assay(dds, "counts")), name = "sample", value = "n_reads") %>%
+  ggplot(aes(x = sample, y = n_reads)) +
+  geom_bar(stat = "identity")
+
+library("patchwork")
+SF / SD
+
+
+
+# Available results
+resultsNames(dds)
+dds$Condition
+
+dds$Condition <- relevel(dds$Condition, ref = "mock")
+dds$Condition
+dds <- DESeq(dds)
+resultsNames(dds)
+
+res <- results(dds,
+               name = "Condition_KD_vs_mock")
+res_tbl <- as_tibble(res, rownames = "ENSEMBL")
+
+## Exercices
+
+# 1. Inspect the results table and identify the 5 “best genes” showing the lowest padjusted value.
+res_tbl %>%
+  arrange(padj) %>%
+  head(5)
+
+# 2. Calculate the mean expression level of these 5 "best genes" using 
+# the function `count()`. Compare with baseMean values.
+
+best_genes <- res_tbl %>%
+  arrange(padj) %>%
+  head(5) %>% pull(ENSEMBL)
+
+rowMeans(counts(dds[best_genes], normalize = TRUE))
+
+
+rowMeans(counts(dds[best_genes], normalize = TRUE))
+
+# 3. Extract the ß coefficient of these 5 "best genes" from the GLM 
+# using the function `coefficient()`. Compare with log2FoldChange values.
+
+coefficients(dds[best_genes])
+
+# 4. using the function `count()`, calculate the expression levels (in log2) 
+# of these 5 "best genes" in mock cells. Compare with ß coefficients.
+
+log2(rowMeans(counts(dds[best_genes, 1:3], normalize = TRUE)))
+
+
+# 5. Calculate the expression levels (in log2) 
+#of these 5 "best genes" in KD cells. Compare with ß coefficients.
+
+log2(rowMeans(counts(dds[best_genes, 4:6], normalize = TRUE)))
+
+# log2 expression levels in KD cells evaluated from ß coefficients
+coefficients(dds[best_genes])[,1] + coefficients(dds[best_genes])[,2]
+
+# 6. How many genes have no padjusted value? Why?
+summary(res$padj)
+
+# filter genes with no padjusted values
+res_tbl %>%
+  filter(is.na(padj))
+
+head(metadata(res)$filterNumRej)
+as_tibble(metadata(res)$filterNumRej) %>%
+  ggplot(aes(x = theta, y = numRej)) +
+  geom_point() +
+  geom_vline(xintercept = 0.7169,
+             color = 'red')
+
+
+# Evaluate how many genes were really filtered by the independent
+# filtering procedure.
+
+# Number of genes with basemean == 0
+res_tbl %>%
+  filter(baseMean == 0) %>%
+  nrow()
+
+# Number of genes filtered by the independent filtering procedure
+res_tbl %>%
+  filter(baseMean > 0 & baseMean < metadata(res)$filterThreshold) %>%
+  nrow()
+
+# Re-run the results() function on the same dds object,
+#but set the independent filtering parameter to FALSE.
+# Check how many genes have no padj?
+res_no_IF <- results(dds,
+                     name = "Condition_KD_vs_mock",
+                     independentFiltering = FALSE)
+as_tibble(res_no_IF, rownames = "ENSEMBL") %>%
+  filter(is.na(padj)) %>% nrow()
+
+
+# Imagine another way of filtering genes with very low counts
+
+# filter the data to remove genes with few counts
+filtering_thr <- 5
+# keep genes with counts > 5 in 3 or more samples
+keep <- rowSums(counts(dds, normalized = TRUE) >= filtering_thr) >=3
+dds_bis <- DESeq(dds[keep, ])
+res_bis <- results(dds_bis,
+                   name = "Condition_KD_vs_mock",
+                   independentFiltering = FALSE)
+
+as_tibble(res_bis, rownames = "ENSEMBL") %>%
+  filter(is.na(padj)) %>% nrow()
+
+as_tibble(res_bis, rownames = "ENSEMBL") %>%
+  filter(!is.na(padj)) %>% nrow()
+
+# Histogram of pvalues
+hist(res_tbl$pvalue)
+
+res_tbl %>%
+  filter(pvalue > 0.8 & pvalue < 0.85) %>%
+  head(10)
+
+res_tbl %>%
+  filter(baseMean > metadata(res)$filterThreshold) %>% 
+  pull(pvalue) %>% 
+  hist()
+            
+# plot MA
+plotMA(res, alpha = 0.05)
+
+# Volcano plot
+res_tbl %>%
+  filter(!is.na(padj)) %>%
+  ggplot(aes(x= log2FoldChange, y = -log10(padj))) +
+  geom_point(size = 0.5)
+
+

wsbim2122-20200917.Rproj

+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

wsbim2122_data/RNAseq_pipeline/example.fastq

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wsbim2122_data/RNAseq_pipeline/example.sam

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+DRR016707.42561021	81	1	19626494	60	101M	=	19626495	0	TGTAACAATGTTATCAGTAATGCTTTAAACTCCAGCACCTGGTTATGCATTTGACACCAAGTCTGTTTCTTGTTTTGTATTTTCGCTCTGGAAGTTGTAAG	(9(??==3??>==;5(55((6.((9>;..;67>)>).5(A;;>=/))8.=>9)((?AB?92=00*;=::1)=:3=74=A7<)0<+<AAAB?=<+4AA<;==	AS:i:-5	ZS:i:-19	XN:i:0	XM:i:2	XO:i:0	XG:i:0	NM:i:2	MD:Z:54A29T16	YT:Z:UP	NH:i:1
+DRR016707.43465443	163	1	19626483	60	101M	=	19626486	104	TGCCATTCTTCTGTAACAATGTTATCAGTAATGCTTTAAACTCCAGCACCTGGTTATGCATTTGAAACCAAGTCTGTTTCTTGTTTTGTATTTTCTCTCTG	B@@FAFFFHHHHHHIIIIJJJHJJIJJIIJJJJJJJFJJJJJJJJJJJJJHIJFHJJIJJJIJJIIJIJJHIHHIIIJJJJJHEHHHFEFFFFFEDEEEEE	AS:i:0	XN:i:0	XM:i:0	XO:i:0	XG:i:0	NM:i:0	MD:Z:101	YS:i:-1	YT:Z:CP	NH:i:1
+DRR016707.43465443	83	1	19626486	60	100M1S	=	19626483	-104	CATTCTTCTGTAACAATGTTATCAGTAATGCTTTAAACTCCAGCACCTGGTTATGCATTTGAAACCAAGTCTGTTTCTTGTTTTGTATTTTCTCTCTGGAG	DDDDDDCDDDDCCDEEEEDFFFFFDBHHHHHGJJGHDJIJJIGIHJGGJJJJJJJJJJJJJHG?JJJJJJJJJJJIIJJJJIJJJJJIGHGHHFFFFFCCC	AS:i:-1	ZS:i:-12	XN:i:0	XM:i:0	XO:i:0	XG:i:0	NM:i:0	MD:Z:100	YS:i:0	YT:Z:CP	NH:i:1
+DRR016707.64407509	147	1	19626484	60	101M	=	19626484	-101	GCCATTCTTCTGTAACAATGTTATCAGTAATGCTTTAAACTCCAGCACCTGGTTATGCATTTGAAACCAAGTCTGTTTCTTGTTTTGTATTTTCTCTCTGG	DDDDDCDDDDDDEEDEEEEFFFFFFHHHHHHJJIJIGHFIHJJJIHFIHHFJJJJJIJJJJJJJIHJJJJJIJJJJJJJJJJJJJJJJHHHHHFFFFFBB@	AS:i:0	XN:i:0	XM:i:0	XO:i:0	XG:i:0	NM:i:0	MD:Z:101	YS:i:0	YT:Z:CP	NH:i:1
+DRR016707.64407509	99	1	19626484	60	101M	=	19626484	-101	GCCATTCTTCTGTAACAATGTTATCAGTAATGCTTTAAACTCCAGCACCTGGTTATGCATTTGAAACCAAGTCTGTTTCTTGTTTTGTATTTTCTCTCTGG	CCCFFFFFHHHHHJJJJJJJIJJJJJJJJJJJJJJJJJJJJJIJJJJJJIJJDGIJJJJJJJJCGIJJJIIHHJJJHHIJJJHHHHHDFFFFFFEEEEEED	AS:i:0	XN:i:0	XM:i:0	XO:i:0	XG:i:0	NM:i:0	MD:Z:101	YS:i:0	YT:Z:CP	NH:i:1
+DRR016707.68628658	163	1	19626487	60	97M4S	=	19626487	-105	ATTCTTCTGTAACAATGTTATCAGTAATGCTTTAAACTCCAGCACCTGGTTATGCATTTGAAACCAAGTCTGTTTCTTGTTTTGTATTTTCTCTCTGAGAT	CCCFFFFFGHHHHJJJJJJIJJJJJJIJJJJJJJJJIJGJIIEIIJJJIE:DGIJJJJJJJJJJJIJIHIJGGHJJJJJHJHHHHHHFFFFFFFEEEDEDC	AS:i:-4	ZS:i:-17	XN:i:0	XM:i:0	XO:i:0	XG:i:0	NM:i:0	MD:Z:97	YS:i:-4	YT:Z:CP	NH:i:1
+DRR016707.68628658	83	1	19626487	60	4S97M	=	19626487	105	ATCTATTCTTCTGTAACAATGTTATCAGTAATGCTTTAAACTCCAGCACCTGGTTATGCATTTGAAACCAAGTCTGTTTCTTGTTTTGTATTTTCTCTCTG	EDDDDDDDDCDEEEEEEEFFFEEFHCHHHHHIGJJJJIHCIJJJJJIHIJJJIFIJJIJJJJJIIHFJJJJJJJJJJJJJJJJJJJJJHHHHHFFFFFCCC	AS:i:-4	XN:i:0	XM:i:0	XO:i:0	XG:i:0	NM:i:0	MD:Z:97	YS:i:-4	YT:Z:CP	NH:i:1

wsbim2122_data/data/seq_1.fas

+> SEQUENCE 1 (Thu Oct  1 16:21:22 2020)
+TATAAGCCACTATCTCATTATTTGTACTGCTCACACGATGTGGATGTGGA
+CATGTTCCGAGAGCATTCCTAGCGCTCCCAGTCTCCATAACGTTACTTGT
+CCTTAAGGAATGTTAATTCCCGCGGGCGTGCCAAGAGGGAATTTCGATTT
+AAAAGTTATACTCTTCCTAAGTATAAGAGCGCTCTCATAAGTACAGACAC
+CTGCATTGTTAAGAAGGTAATTCAGATCTGCTACGGAGTCCGTGTTCGAC
+ATAGTGAGTGGCAGGCTAGACGAACACTATGATCACCGACTTAATCTTCA
+GCTGAAATGCTTGGAACCACCAGTCCGATGTTATACCTACACAATTTTCG
+GTGGTTTAAATGCAAGCGATAAGGCTCAAATTAACTAGTAACTTGTCTCA
+CGTGATTCATTTCATAACGTGGTGTATAGGATGTGCGACTACTAGCGACG
+CACTTACGGAGACTGGATGGTCTGAGTCTAGCGTTTCGTAAAAGGTAAGG
+TATTGCTTTATAGAGATACACTTAGTTACGACTTCCCAGATGCTTCGGGG
+GTCTGTCTTGATGACAATGGACACATATGGTGCTCATTAAATGTCATCCG
+AAACGACAACATGATCCGCGTGATAAATTTTTCTAATTCTAGAGTACGTC
+CCGTGGGTGCCTCACGCCGGGGCCCATAACGCGACCTATAGAAGACAAAT
+TGGATGCGTTACGACCAGCCTCCAGTTAGGCCCCTCAGTCCAGAGTATGG
+ACTTGGGATAATTGGTTTTGCTCGAGTGAGACATCGTGGCGACCGCCATA
+AACGTTACAAAAGCAGAATGTGGTTCAACCGTCATTTGTCCCTCGTTAAC
+GGTTCGTCGCCTTCCGCCGTTGCCACAGTGTCGAGTATGATCACGATAGC
+GAATCCTGGCGACTGGGGACACCACGTCCCAGTGAGTTCTTTCTGTCTTA
+GTTAACAGTAAGGGTTTTACACCCTACCGCAAAAAGCGGGCAAAAGTTTG
+TCTGCACGCGTCCTAATCCGAATCACGCAAAATCCGTAATAAGGAAGTAT
+AACATAGTTGGTGCCGGGCAGTTTATACAATAGATCGTTAACCGACAGGG
+GTTCAACAAACTATGCAGGATCTGGCTGACCCACATGTTGATTCCCTCAT
+GAGGTCTGCAATTGGTGTTCGTCTTGTCCTGGTCATATTGGATGGCGAAG
+GATTATTGTTAAGGCCACTCGTAATTCCTGAGGTCCGCGAGCAATCTACC
+AGTGATCGGAGATATGCTCACCCCGGGCCTGTTATTTCTCTACAATCGTC
+TGTAACACAAGCGAGCTGGGTTGATCTTCGAGGGGACAACACCTCGCCGC
+GATATTTTCGGGAGCCAGGGTTACGTGACGGGCGCATGGGGCAATTAGCG
+TTTGCCTCAAAGCCATTCGGTTGTATCTGAGACACTTAGCGACCGCGAAT
+CCTGGGTCCATCCCGAGAGCGCGCTTGTGGATGACTCCCATCCTAGCCGG
+AAAAGACGTGTCATACAGCTCGACGAGTTGTGAACGGACCTCCGCAGAAA
+TAATCATTAGTGCTCCCCTCCCTCCCATGTATTAGTGCAAAATAGGATCA
+GGACCTGGTTACTAGTAACGGTTTCTTCCTCGGATTCTTAGAAGCGCAGG
+AGCCTATTGCTCGAGACACAGTCACAGCAGAACCATGAAGGAGACGCTCC
+TGTGCAGACCGCCGCCATTGACGTTGCCACGGCTGCAAAACCTTGCTAGG