I am feeding biomaRt a list of ensembl IDs (object: `ensemblIDs`) from an RNA-Seq experiment

Then I am running the following function calls

ensembl <- useMart('ensembl', dataset='hsapiens_gene_ensembl')
symbols.a <- getBM(attributes = c('ensembl_gene_id',
                                       'ensembl_transcript_id',
                                  'hgnc_symbol',
                                  'external_gene_name',
                                  'gene_biotype',
                                  'description',
                                  'name_1006',
                                  'definition_1006'),
                   filters = 'ensembl_gene_id',
                   ensemblIDs,
                   mart = ensembl)

After matching, I get back a list of results but although I am feeding the function ensembl IDs, the resulting data.frame returns a large number of NA. Taking a few examples:

ENSG00000139131, ENSG00000167157, ENSG00000149547

These all have ensembl gene web entries so it seems weird that it isn't matching them.

R version 3.4.0 (2017-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.2 LTS

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
[1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8     LC_MONETARY=en_GB.UTF-8  
[6] LC_MESSAGES=en_GB.UTF-8    LC_PAPER=en_GB.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C           
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] doParallel_1.0.10                                  RSQLite_2.0                                      
[3] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0 GenomicFeatures_1.28.4                           
[5] Rtsne_0.13                                         plyr_1.8.4                                       
[7] pheatmap_1.0.8                                     NMF_0.20.6                                       
[9] cluster_2.0.6                                      rngtools_1.2.4                                   
[11] pkgmaker_0.22                                      registry_0.3                                     
[13] minfi_1.22.1                                       bumphunter_1.16.0                                
[15] locfit_1.5-9.1                                     iterators_1.0.8                                  
[17] Biostrings_2.44.1                                  XVector_0.16.0                                   
[19] limma_3.32.2                                       igraph_1.0.1                                     
[21] hugene11sttranscriptcluster.db_8.6.0               hugene10sttranscriptcluster.db_8.6.0             
[23] hthgu133a.db_3.2.3                                 hgug4112a.db_3.2.3                               
[25] hgu95av2.db_3.2.3                                  hgu133plus2.db_3.2.3                             
[27] hgu133b.db_3.2.3                                   hgu133a2.db_3.2.3                                
[29] hgu133a.db_3.2.3                                   org.Hs.eg.db_3.4.1                               
[31] gplots_3.0.1                                       GEOquery_2.42.0                                  
[33] genefilter_1.58.1                                  foreach_1.4.3                                    
[35] DESeq2_1.16.1                                      SummarizedExperiment_1.6.3                       
[37] DelayedArray_0.2.7                                 matrixStats_0.52.2                               
[39] GenomicRanges_1.28.3                               GenomeInfoDb_1.12.2                              
[41] biomaRt_2.32.1                                     beadarray_2.26.1                                 
[43] ggplot2_2.2.1                                      annotate_1.54.0                                  
[45] XML_3.98-1.9                                       AnnotationDbi_1.38.1                             
[47] IRanges_2.10.2                                     S4Vectors_0.14.3                                 
[49] affy_1.54.0                                        Biobase_2.36.2                                   
[51] BiocGenerics_0.22.0                               

loaded via a namespace (and not attached):
[1] colorspace_1.3-2         siggenes_1.50.0          mclust_5.3               htmlTable_1.9            base64enc_0.1-3          base64_2.0             
[7] affyio_1.46.0            bit64_0.9-7              codetools_0.2-15         splines_3.4.0            geneplotter_1.54.0       knitr_1.16             
[13] Formula_1.2-2            Rsamtools_1.28.0         gridBase_0.4-7           compiler_3.4.0           httr_1.2.1               backports_1.1.0        
[19] Matrix_1.2-10            lazyeval_0.2.0           BeadDataPackR_1.28.0     acepack_1.4.1            htmltools_0.3.6          tools_3.4.0            
[25] gtable_0.2.0             GenomeInfoDbData_0.99.0  reshape2_1.4.2           doRNG_1.6.6              Rcpp_0.12.11             multtest_2.32.0        
[31] nlme_3.1-131             gdata_2.18.0             preprocessCore_1.38.1    rtracklayer_1.36.3       stringr_1.2.0            gtools_3.5.0           
[37] beanplot_1.2             MASS_7.3-47              zlibbioc_1.22.0          scales_0.4.1             BiocInstaller_1.26.0     RColorBrewer_1.1-2     
[43] memoise_1.1.0            gridExtra_2.2.1          rpart_4.1-11             reshape_0.8.6            latticeExtra_0.6-28      stringi_1.1.5          
[49] checkmate_1.8.3          caTools_1.17.1           BiocParallel_1.10.1      rlang_0.1.1              pkgconfig_2.0.1          bitops_1.0-6           
[55] nor1mix_1.2-2            lattice_0.20-35          GenomicAlignments_1.12.1 htmlwidgets_0.9          bit_1.1-12               magrittr_1.5           
[61] R6_2.2.2                 Hmisc_4.0-3              DBI_0.7                  foreign_0.8-69           survival_2.41-3          RCurl_1.95-4.8         
[67] nnet_7.3-12              tibble_1.3.3             KernSmooth_2.23-15       grid_3.4.0               data.table_1.10.4        blob_1.1.0             
[73] digest_0.6.12            xtable_1.8-2             illuminaio_0.18.0        openssl_0.9.6            munsell_0.4.3            quadprog_1.5-5   

I think this is probably related to a problem the Ensembl BioMart has when your list of query values is very long.  You'll notice on the web interface it only recommends submitting up to 500 values at a time.  If you submit something longer than this the query may time out, but it does so silently and still returns some results.  The same is true if you do the query with biomaRt, and it's basically impossible to tell from the returned values that this has happened.

I recently patched the developmental version of biomaRt (https://www.bioconductor.org/packages/devel/bioc/html/biomaRt.html) to break large queries into smaller chunks and submit them independently, so you this shouldn't happen in the future.  You can install the devel version using the following code, and then see if the problem persists:

library(BiocInstaller)
biocLite("Bioconductor-mirror/biomaRt")

Have you checked if the input ids are correct? I mean, are you telling what they're correctly? If so, try using another annotation.