diff --git a/conf/modules.config b/conf/modules.config
index 28ede9d..30f057e 100644
--- a/conf/modules.config
+++ b/conf/modules.config
@@ -230,7 +230,7 @@ process {
             path: { "${params.outdir}/minimap2/index" },
             mode: params.publish_dir_mode,
             enabled: params.save_hostremoval_index,
-            pattern: 'minimap2'
+            pattern: '*.mmi'
         ]
     }
 
diff --git a/docs/usage.md b/docs/usage.md
index 3a4e55c..d37ba60 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -64,6 +64,8 @@ ERR3201952,ERR3201952,OXFORD_NANOPORE,/<path>/<to>/fastq/ERR3201952.fastq.gz,,
 
 > ⚠️ Input FASTQ and FASTA files _must_ be gzipped
 
+> ⚠️ While one can include both short-read and long-read data in one run, we recommend that you split these across _two_ pipeline runs and database sheets (see below). This will allow classification optimisation for each data type, and make MultiQC run-reports more readable (due to run statistics having vary large number differences).
+
 | Column                | Description                                                                                                                                                                                              |
 | --------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `sample`              | Unique sample name [required].                                                                                                                                                                           |
diff --git a/subworkflows/local/db_check.nf b/subworkflows/local/db_check.nf
index 7490b72..270f597 100644
--- a/subworkflows/local/db_check.nf
+++ b/subworkflows/local/db_check.nf
@@ -44,6 +44,7 @@ workflow DB_CHECK {
                         .filter {
                           params["run_${it[0]['tool']}"]
                         }
+
     UNTAR (ch_input_untar)
     ch_versions = ch_versions.mix(UNTAR.out.versions.first())
     ch_final_dbs = ch_dbs_for_untar.skip.mix( UNTAR.out.untar )
diff --git a/subworkflows/local/profiling.nf b/subworkflows/local/profiling.nf
index 4b4952c..1fe4cbb 100644
--- a/subworkflows/local/profiling.nf
+++ b/subworkflows/local/profiling.nf
@@ -203,6 +203,8 @@ workflow PROFILING {
                             .filter{
                                 if (it[0].is_fasta) log.warn "[nf-core/taxprofiler] MetaPhlAn3 currently does not accept FASTA files as input. Skipping MetaPhlAn3 for sample ${it[0].id}."
                                 !it[0].is_fasta
+                                if (it[0].instrument_platform == 'OXFORD_NANOPORE') log.warn "[nf-core/taxprofiler] MetaPhlAn3 has not been evaluated for Nanopore data. Skipping MetaPhlAn3 for sample ${it[0].id}."
+                                !it[0].instrument_platform == 'OXFORD_NANOPORE'
                             }
                             .multiMap {
                                 it ->
@@ -277,14 +279,13 @@ workflow PROFILING {
                                             [[id: db_meta.db_name, single_end: meta.single_end], reads, db_meta, db]
                                     }
                                     .groupTuple(by: [0,2,3])
-                                    .dump(tag: "krakenuniq_premultimap")
                                     .multiMap {
                                         single_meta, reads, db_meta, db ->
                                             reads: [ single_meta + db_meta, reads.flatten() ]
                                             db: db
                                 }
         // Hardcode to _always_ produce the report file (which is our basic otput, and goes into)
-        KRAKENUNIQ_PRELOADEDKRAKENUNIQ ( ch_input_for_krakenuniq.reads.dump(tag: "krakenuniq_input"), ch_input_for_krakenuniq.db.dump(tag: "krakenuniq_db"), params.krakenuniq_ram_chunk_size, params.krakenuniq_save_reads, true, params.krakenuniq_save_readclassifications )
+        KRAKENUNIQ_PRELOADEDKRAKENUNIQ ( ch_input_for_krakenuniq.reads, ch_input_for_krakenuniq.db, params.krakenuniq_ram_chunk_size, params.krakenuniq_save_reads, true, params.krakenuniq_save_readclassifications )
         ch_multiqc_files       = ch_multiqc_files.mix( KRAKENUNIQ_PRELOADEDKRAKENUNIQ.out.report )
         ch_versions            = ch_versions.mix( KRAKENUNIQ_PRELOADEDKRAKENUNIQ.out.versions.first() )
         ch_raw_classifications = ch_raw_classifications.mix( KRAKENUNIQ_PRELOADEDKRAKENUNIQ.out.classified_assignment )