Further preparatory comments.

small_RNA-seq/small_RNA_seq_annotate.py

@@ -187,6 +187,9 @@ def make_annotation_processor(getter_type):
         # For miRNA, we also want the coordinates to match exactly.
         # For piRNA, we also want the coordinates to match exactly
         # and the read to be 21U (actually, we want to accept longer reads).
+        # TODO (21/10/2021): Add a tRNA category
+        # with no constraints on read extremities coordinates.
+        # Where in the priority order? After pi and mi, before si?
         #
         # If not a miRNA or piRNA, then it might be an RdRP-derived endosiRNA.
         # We want to keep the annotations for further analyses.
@@ -259,7 +262,6 @@ def make_annotation_processor(getter_type):
         # It is faster using at:
         # compositions[annot].at[read_len, seq[0]] += 1
         return (annot_name, signature), (annotations, (name, seq, qual, read_len, strand))
-        # return annot_name, (annotations, get_read_info(ali))
     return process_annotations
 
 # First: Count bona fide piRNA and miRNA
@@ -477,7 +479,7 @@ def main():
             # The functions in the list passed to the FuncApplier object
             # process chunks of annotations.
             # The FuncApplier object is a callable that replicates
-            # the chunks for these functions to process, and put the
+            # the chunks for these functions to process, and puts the
             # corresponding results in a tuple.
             # stream_processor = FuncApplier([
             #     count_annots, count_pimis, count_sis, count_all_sis,
@@ -485,10 +487,22 @@ def main():
             stream_processor = FuncApplier([
                 count_annots, count_small,
                 count_first_bases])
+            # TODO (21/10/2021): Add tRNA-derived sRNAs:
+            # small_types = [
+            #     "pi", "mi", "ti", "all_si"
+            #     f"all_si_{SI_MIN}G", f"all_si_{SI_MAX}G", *SI_TYPES]
             small_types = [
                 "pi", "mi", "all_si",
                 f"all_si_{SI_MIN}G", f"all_si_{SI_MAX}G", *SI_TYPES]
+            # The same classification logic is applied to reads
+            # that had a polyU tail, remapped after polyU removal,
+            # but read and output file names are different.
             if args.remapped_polyU:
+                # We don't really care about piu and miu,
+                # but they are here for code simplicity.
+                # One reason is that elements in small_types_u
+                # should be in the same order as small_types
+                # in order to be able to build the u2nonu dict.
                 small_types_u = [
                     "piu", "miu", "all_siu",
                     f"all_siu_{SI_MIN}G", f"all_siu_{SI_MAX}G", *SIU_TYPES]
@@ -496,6 +510,9 @@ def main():
                 # via the construction of SIU_TYPES and SI_TYPES
                 # from SMALL_TYPES_TREE
                 u2nonu = dict(zip(small_types_u, small_types)).get
+                # File paths dictionaries use u2nonu to have the base
+                # small type names as keys, so that further code can
+                # ignore the U vs. non-U distinction (see fq_writer).
                 fq_paths = {u2nonu(small_type): OPJ(
                     args.reads_dir, f"{small_type}RNA.fastq.gz") for small_type in small_types_u}
                 ambig_type_path = OPJ(args.out_dir, "ambig_typeU.txt")