I'm running the Dropseq pipeline on a de-novo assembled diatom with gene calls from Maker. The pipeline suggests using STAR-aligner for the read mapping. The only problem is that I'm getting a REALLY low mapping.

Does anybody know if there are parameters I can adjust for this particular dataset in STAR or another aligner designed to specifically address these types of issues?

Here is my command:

STAR --genomeDir star_index_extended --readFilesIn unaligned_mc_tagged_polyA_filtered.fastq --outFileNamePrefix star_ --runThreadN 16 --outFilterScoreMinOverLread 0 --outFilterMatchNminOverLread 0 --outFilterMatchNmin 50 --outReadsUnmapped Fastx

Here is the summary output:

bash-4.1$ cat star_Log.final.out
                                 Started job on |   May 10 14:58:11
                             Started mapping on |   May 10 14:58:16
                                    Finished on |   May 10 20:57:36
       Mapping speed, Million of reads per hour |   85.18

                          Number of input reads |   510139798
                      Average input read length |   57
                                    UNIQUE READS:
                   Uniquely mapped reads number |   9914109
                        Uniquely mapped reads % |   1.94%
                          Average mapped length |   58.89
                       Number of splices: Total |   689950
            Number of splices: Annotated (sjdb) |   0
                       Number of splices: GT/AG |   415996
                       Number of splices: GC/AG |   11503
                       Number of splices: AT/AC |   297
               Number of splices: Non-canonical |   262154
                      Mismatch rate per base, % |   4.32%
                         Deletion rate per base |   0.07%
                        Deletion average length |   1.54
                        Insertion rate per base |   0.03%
                       Insertion average length |   1.64
                             MULTI-MAPPING READS:
        Number of reads mapped to multiple loci |   11525318
             % of reads mapped to multiple loci |   2.26%
        Number of reads mapped to too many loci |   11342282
             % of reads mapped to too many loci |   2.22%
                                  UNMAPPED READS:
       % of reads unmapped: too many mismatches |   0.00%
                 % of reads unmapped: too short |   84.30%
                     % of reads unmapped: other |   9.27%
                                  CHIMERIC READS:
                       Number of chimeric reads |   0
                            % of chimeric reads |   0.00%