ValueError and Invalid Target End Point Errors during Mikado Serialization

[joseph144155]

Description:

I encountered multiple errors while running the Mikado serialization process using the following command:

singularity exec --cleanenv ../../mikado_2.3.2.sandbox mikado serialise --json-conf mikado_2.3.2_custom.conf --xml mikado_prepared.blast.tsv --orfs mikado_prepared.fasta.transdecoder.bed --junctions portcullis_filtered.pass.junctions.bed

The errors captured in the SLURM log file are as follows:

Process Preparer-44:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/local/lib/python3.10/site-packages/Mikado/serializers/blast_serializer/tabular_utils.py", line 387, in run
curr_hit, curr_hsps = prep_hit(key, rows)
File "/usr/local/lib/python3.10/site-packages/Mikado/serializers/blast_serializer/tabular_utils.py", line 247, in prepare_tab_hit
hit_dict["target_start"] = int(t_aligned.min())
File "/usr/local/lib/python3.10/site-packages/numpy/core/_methods.py", line 44, in _amin
return umr_minimum(a, axis, None, out, keepdims, initial, where)
ValueError: zero-size array to reduction operation minimum which has no identity

...

Process Preparer-45:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/local/lib/python3.10/site-packages/Mikado/serializers/blast_serializer/tabular_utils.py", line 387, in run
curr_hit, curr_hsps = prep_hit(key, rows)
File "/usr/local/lib/python3.10/site-packages/Mikado/serializers/blast_serializer/tabular_utils.py", line 250, in prepare_tab_hit
raise ValueError("Invalid target end point: {}, {}".format(hit_dict["target_end"], sends))
ValueError: Invalid target end point: 202, (449,)

...
Scoring File Used:(YAML)

requirements:
expression:

• cdna_length and ((exon_num.multi and verified_introns_num and min_intron_length and max_intron_length) or (exon_num.mono and combined_cds_length))
  parameters:
  cdna_length: {operator: ge, value: 300}
  exon_num.multi: {operator: ge, value: 2}
  verified_introns_num: {operator: gt, value: 0}
  min_intron_length: {operator: ge, value: 5}
  max_intron_length: {operator: le, value: 2000}
  exon_num.mono: {operator: eq, value: 1}
  combined_cds_length: {operator: gt, value: 0}
  scoring:
  snowy_blast_score: {rescaling: max}
  is_complete: {rescaling: target, value: true}
  has_start_codon: {rescaling: target, value: true}
  has_stop_codon: {rescaling: target, value: true}
  number_internal_orfs: {rescaling: target, value: 1}
  cds_not_maximal: {rescaling: min}
  cds_not_maximal_fraction: {rescaling: min}
  selected_cds_fraction: {rescaling: target, value: 0.7}
  selected_cds_length: {rescaling: max}
  selected_cds_intron_fraction: {rescaling: max}
  selected_cds_intron_fraction: {rescaling: max}
  cdna_length: {rescaling: max}
  exon_num: {rescaling: max, filter: {operator: ge, value: 3}}
  five_utr_num: {rescaling: target, value: 2, filter: {operator: lt, value: 4}}
  five_utr_length: {rescaling: target, value: 100, filter: {operator: le, value: 2500}}
  three_utr_num: {rescaling: target, value: 1, filter: {operator: lt, value: 3}}
  three_utr_length: {rescaling: target, value: 200, filter: {operator: lt, value: 2500}}
  proportion_verified_introns_inlocus: {rescaling: max}
  non_verified_introns_num: {rescaling: min}
  end_distance_from_junction: {rescaling: min, filter: {operator: lt, value: 55}}
  as_requirements:
  expression: [cdna_length and three_utr_length and five_utr_length and utr_length and suspicious_splicing]
  parameters:
  cdna_length: {operator: ge, value: 200}
  utr_length: {operator: le, value: 2500}
  five_utr_length: {operator: le, value: 2500}
  three_utr_length: {operator: le, value: 2500}
  suspicious_splicing: {operator: ne, value: true}
  not_fragmentary:
  expression: [((exon_num.multi and (cdna_length.multi or selected_cds_length.multi)), or, (exon_num.mono and ((snowy_blast_score and selected_cds_length.zero) or selected_cds_length.mono)))]
  parameters:
  selected_cds_length.zero: {operator: gt, value: 300} # 600
  exon_num.multi: {operator: gt, value: 2}
  cdna_length.multi: {operator: ge, value: 300}
  selected_cds_length.multi: {operator: gt, 250}
  exon_num.mono: {operator: eq, value: 1}
  snowy_blast_score: {operator: gt, value: 0} # 0.3
  selected_cds_length.mono: {operator: gt, value: 600} # 900
  exon_num.mono: {operator: le, value: 2}
  Error Explanation:

ValueError: zero-size array to reduction operation minimum which has no identity:

This error occurs when attempting to find the minimum value of an empty array. It suggests that the input array t_aligned is empty during the execution of int(t_aligned.min()) in prepare_tab_hit.
ValueError: Invalid target end point:

This error indicates that the target end points are not matching expected values, causing the prepare_tab_hit function to raise an error.

Questions:

What could be the underlying cause for these arrays being empty or target end points being invalid?
Are there any specific input checks or preprocessing steps that I might be missing to prevent these errors?
Is it safe to ignore these errors, or do they indicate a critical problem that needs addressing?
Additional Information:

The serialized.log file does not output any errors; these are only captured in the SLURM log file when the job is submitted using sbatch.
I used docker pull quay.io/biocontainers/mikado:2.3.2--py37h9c5868f_0 to build the Singularity image.
Any guidance on how to resolve or debug these issues would be greatly appreciated.
Thank you for your assistance.