Hello,

I'm trying to annotate genes in DESeq2 results object with chrom, strand, start, end. Code works fine but many genes have incorrect TSS/TTS annotation on IGV/UCSC browser. If I convert GENEID to ENSEML the problem is solved but not all GENEIDs map, which is suboptimal. How to fix? Thanks in advance!

B_samples <- read.table(file.path(dir, "sample_info.tsv"), header = TRUE)
B_files <- file.path(dir, B_samples$name)
names(B_files) <- file.path(B_samples$sample)
txdb <- (TxDb.Mmusculus.UCSC.mm10.knownGene)
k <- keys(txdb, keytype = "GENEID")
df <- AnnotationDbi::select(txdb, keys = k, keytype = "GENEID", columns = "TXNAME")
tx2gene <- df[, 2:1]
B_txi <- tximport(B_files, type = "kallisto", tx2gene = tx2gene)
rownames(B_samples) <- B_samples$sample
B_ddsTxi <- DESeqDataSetFromTximport(B_txi, colData = B_samples, design = ~sequencer + condition)
B_ddsTxi <- DESeq(B_ddsTxi)
B_res <- results(B_ddsTxi, c("condition", "Y", "X"))

B_res$symbol <- mapIds(Mus.musculus, keys=row.names(B_res), column="SYMBOL", keytype="GENEID", multiVals="first")
B_res$chrom <- mapIds(TxDb.Mmusculus.UCSC.mm10.knownGene, keys=row.names(B_res), column="TXCHROM", keytype="GENEID", multiVals="first")
B_res$strand <- mapIds(TxDb.Mmusculus.UCSC.mm10.knownGene, keys=row.names(B_res), column="TXSTRAND", keytype="GENEID", multiVals="first")
B_res$start <- mapIds(TxDb.Mmusculus.UCSC.mm10.knownGene, keys=row.names(B_res), column="TXSTART", keytype="GENEID", multiVals="first")
B_res$end <- mapIds(TxDb.Mmusculus.UCSC.mm10.knownGene, keys=row.names(B_res), column="TXEND", keytype="GENEID", multiVals="first")

> head(tx2gene)
      TXNAME    GENEID
1 uc009veu.1 100009600
2 uc033jjg.1 100009600
3 uc012fog.1 100009609
4 uc011xhj.2 100009614
5 uc007inp.3 100009664
6 uc008vqx.2    100012

> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.6

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] grid      parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] TxDb.Mmusculus.UCSC.mm10.ensGene_3.4.0   BiocInstaller_1.32.1                     gdtools_0.2.1                           
 [4] UpSetR_1.4.0                             gprofiler2_0.1.9                         GeneOverlap_1.18.0                      
 [7] sva_3.30.1                               genefilter_1.64.0                        mgcv_1.8-31                             
[10] nlme_3.1-148                             rafalib_1.0.0                            ggpubr_0.3.0                            
[13] ggsignif_0.6.0                           ashr_2.2-47                              gridExtra_2.3                           
[16] gtools_3.8.2                             naturalsort_0.1.3                        ggthemes_4.2.0                          
[19] viridis_0.5.1                            viridisLite_0.3.0                        RColorBrewer_1.1-2                      
[22] pheatmap_1.0.12                          circlize_0.4.10                          ComplexHeatmap_1.20.0                   
[25] reshape2_1.4.4                           scales_1.1.1                             forcats_0.5.0                           
[28] stringr_1.4.0                            purrr_0.3.4                              tidyr_1.1.0                             
[31] tibble_3.0.1                             tidyverse_1.3.0                          dplyr_1.0.0                             
[34] ggrepel_0.8.2                            eulerr_6.1.0                             gplots_3.0.3                            
[37] ggplot2_3.3.1                            VennDiagram_1.6.20                       futile.logger_1.4.3                     
[40] Mus.musculus_1.3.1                       org.Mm.eg.db_3.7.0                       GO.db_3.7.0                             
[43] OrganismDbi_1.24.0                       TxDb.Mmusculus.UCSC.mm10.knownGene_3.4.4 GenomicFeatures_1.34.8                  
[46] AnnotationDbi_1.44.0                     limma_3.38.3                             DESeq2_1.22.2                           
[49] SummarizedExperiment_1.12.0              DelayedArray_0.8.0                       BiocParallel_1.16.6                     
[52] matrixStats_0.56.0                       Biobase_2.42.0                           GenomicRanges_1.34.0                    
[55] GenomeInfoDb_1.18.2                      IRanges_2.16.0                           S4Vectors_0.20.1                        
[58] BiocGenerics_0.28.0                      readr_1.3.1                              tximport_1.10.1                         

Multiple things. First, you are using a very outdated version of R and Bioconductor. You should update. Second, if you are using Ensembl gene mappings on IGV and NCBI mappings in R, then yes there are disagreements. As you noted when you tried to map between NCBI and Ensembl. The fix is to not do that. If your data are mapped to a NCBI-annotated genome, then use NCBI IDs throughout. And vice versa for Ensembl.

Although it depends on what you mean by 'incorrect annotation on UCSC browser'. The TxDb.Mmusculus.UCSC.mm10.knownGene, is, as its name suggests, based on gene information from UCSC. So it seems rather unlikely that the data from UCSC doesn't match up to the data on the UCSC browser, given that the data source is the same. Do note that you are taking the (random) first TSS/TTS, of possibly many. Which is a convenient simplification, but it is a simplification.