diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 2fb38462..4b5406a8 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -18,7 +18,8 @@
     _OutputCombinedSourceFilepath,
     _SourceWeights,
 )
-from como.utils import LogLevel, log_and_raise_error, _num_columns, get_missing_gene_data
+from como.pipelines.identifier import convert
+from como.utils import LogLevel, get_missing_gene_data, log_and_raise_error, num_columns
 
 
 def _combine_z_distribution_for_batch(
@@ -55,9 +56,13 @@ def _combine_z_distribution_for_batch(
         f"source: '{source.value}', "
         f"context: '{context_name}'"
     )
-    if _num_columns(matrix) < 2:
-        logger.trace(f"A single sample exists for batch '{batch.batch_num}'. Returning as-is because no additional combining can be done")
-        return matrix
+    if num_columns(matrix) < 2:
+        logger.trace(
+            f"A single sample exists for batch '{batch.batch_num}'. Returning as-is because no additional combining can be done"
+        )
+        with_batch_num = matrix.copy()
+        with_batch_num.columns = [batch.batch_num]
+        return with_batch_num
 
     values = matrix.values
     weighted_matrix = np.clip(
@@ -116,7 +121,7 @@ def _combine_z_distribution_for_source(
           A pandas dataframe of the weighted z-distributions
     """
     # If only one batch column exists, return as-is (rename like R path-through).
-    if _num_columns(merged_source_data) <= 1:
+    if num_columns(merged_source_data) <= 1:
         logger.warning("A single batch exists for this source; returning matrix as-is.")
         out_df = merged_source_data.copy()
         out_df.columns = ["combine_z"]
@@ -203,8 +208,10 @@ def _combine_z_distribution_for_context(
         z_matrices.append(matrix)
 
     z_matrix = pd.concat(z_matrices, axis="columns", join="outer", ignore_index=False)
-    if _num_columns(z_matrix) <= 1:
-        logger.trace(f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine")
+    if num_columns(z_matrix) <= 1:
+        logger.trace(
+            f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine"
+        )
         z_matrix.columns = ["combine_z"]
         return z_matrix
 
@@ -275,8 +282,26 @@ async def _begin_combining_distributions(
         batch_results: list[pd.DataFrame] = []
         for batch in batch_names[source.value]:
             batch: _BatchEntry
-            matrix_subset = cast(pd.DataFrame, matrix[[GeneIdentifier.ensembl_gene_id.value, *batch.sample_names]])
-            matrix_subset = matrix_subset.set_index(keys=[GeneIdentifier.ensembl_gene_id.value], drop=True)
+            if isinstance(matrix, pd.DataFrame):
+                matrix_subset = matrix[[GeneIdentifier.entrez_gene_id.value, *batch.sample_names]]
+                matrix_subset = matrix_subset.set_index(keys=[GeneIdentifier.entrez_gene_id.value], drop=True)
+                matrix_subset = matrix_subset.drop(columns=["gene_symbol", "ensembl_gene_id"], errors="ignore")
+            elif isinstance(matrix, sc.AnnData):
+                conversion = convert(ids=matrix.var_names.tolist(), taxon=taxon)
+                conversion.reset_index(drop=False, inplace=True)
+                matrix: pd.DataFrame = matrix.to_df().T
+                matrix.reset_index(inplace=True, drop=False, names=["gene_symbol"])
+                matrix: pd.DataFrame = matrix.merge(
+                    conversion, left_on="gene_symbol", right_on="gene_symbol", how="left"
+                )
+                matrix_subset: pd.DataFrame = matrix[[GeneIdentifier.entrez_gene_id.value, *batch.sample_names]]
+                matrix_subset: pd.DataFrame = matrix_subset.set_index(
+                    keys=[GeneIdentifier.entrez_gene_id.value], drop=True
+                )
+            else:
+                raise TypeError(
+                    f"Unexpected matrix type for source '{source.value}'; expected 'pandas.DataFrame' or 'scanpy.AnnData': {type(matrix)}"
+                )
 
             output_fp = output_filepaths[source.value].parent / f"{source.value}_batch{batch.batch_num}_combined_z_distrobution_{context_name}.csv"
             batch_results.append(
diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index f1e04a81..96d6a3a9 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -1,14 +1,12 @@
 from __future__ import annotations
 
-import asyncio
 import re
 import sys
 from pathlib import Path
-from typing import TextIO, cast
+from typing import TextIO
 
 import numpy as np
 import pandas as pd
-from fast_bioservices.biothings.mygene import MyGene
 from loguru import logger
 
 from como.combine_distributions import _begin_combining_distributions
@@ -24,7 +22,7 @@
     _SourceWeights,
 )
 from como.project import Config
-from como.utils import log_and_raise_error, read_file, set_up_logging, get_missing_gene_data, return_placeholder_data
+from como.utils import get_missing_gene_data, log_and_raise_error, read_file, return_placeholder_data, set_up_logging
 
 
 class _MergedHeaderNames:
@@ -348,17 +346,16 @@ async def _update_missing_data(input_matrices: _InputMatrices, taxon_id: int) ->
         "proteomics": [input_matrices.proteomics],
     }
     logger.trace(f"Gathering missing data for data sources: {','.join(key for key in matrix_keys if key is not None)}")
+    # ruff: disable[E501]
     # fmt: off
-    results = await asyncio.gather(
-        *[
-            # Using 'is not None' is required because the truth value of a Dataframe is ambiguous
-            get_missing_gene_data(values=input_matrices.trna, taxon_id=taxon_id) if input_matrices.trna is not None else asyncio.sleep(0),
-            get_missing_gene_data(values=input_matrices.mrna, taxon_id=taxon_id) if input_matrices.mrna is not None else asyncio.sleep(0),
-            get_missing_gene_data(values=input_matrices.scrna, taxon_id=taxon_id) if input_matrices.scrna is not None else asyncio.sleep(0),
-            get_missing_gene_data(values=input_matrices.proteomics, taxon_id=taxon_id) if input_matrices.proteomics is not None else asyncio.sleep(0),
-        ]
+    results: tuple[pd.DataFrame | None, ...] = (
+        get_missing_gene_data(values=input_matrices.trna, taxon_id=taxon_id) if input_matrices.trna is not None else None,
+        get_missing_gene_data(values=input_matrices.mrna, taxon_id=taxon_id) if input_matrices.mrna is not None else None,
+        get_missing_gene_data(values=input_matrices.scrna, taxon_id=taxon_id) if input_matrices.scrna is not None else None,
+        get_missing_gene_data(values=input_matrices.proteomics, taxon_id=taxon_id) if input_matrices.proteomics is not None else None,
     )
     # fmt: on
+    # ruff: enable[E501]
     for i, key in enumerate(matrix_keys):
         matrix_keys[key].append(results[i])
 
@@ -591,7 +588,7 @@ async def merge_xomics(  # noqa: C901
     log_location: str | TextIO = sys.stderr,
 ):
     """Merge expression tables of multiple sources (RNA-seq, proteomics) into one."""
-    _set_up_logging(level=log_level, location=log_location)
+    set_up_logging(level=log_level, location=log_location)
     logger.info(f"Starting to merge all omics data for context: '{context_name}'")
 
     # fmt: off
diff --git a/main/como/pipelines/identifier.py b/main/como/pipelines/identifier.py
new file mode 100644
index 00000000..59fe7444
--- /dev/null
+++ b/main/como/pipelines/identifier.py
@@ -0,0 +1,100 @@
+from collections.abc import Sequence
+from typing import Literal
+
+import pandas as pd
+from bioservices.mygeneinfo import MyGeneInfo
+
+__all__ = [
+    "convert",
+    "determine_gene_type",
+]
+
+T_MG_SCOPE = Literal["entrezgene", "ensembl.gene", "symbol"]
+T_MG_TRANSLATE = Literal["entrez_gene_id", "ensembl_gene_id", "gene_symbol"]
+T_MG_RETURN = list[dict[T_MG_TRANSLATE, str]]
+
+
+def _get_conversion(info: MyGeneInfo, values: list[str], scope: T_MG_SCOPE, fields: str, taxon: str) -> T_MG_RETURN:
+    value_str = ",".join(map(str, values))
+    results = info.get_queries(query=value_str, dotfield=True, scopes=scope, fields=fields, species=taxon)
+    if not isinstance(results, list):
+        raise TypeError(f"Expected results to be a list, but got {type(results)}")
+    if not isinstance(results[0], dict):
+        raise TypeError(f"Expected each result to be a dict, but got {type(results[0])}")
+
+    data: T_MG_RETURN = []
+    for result in results:
+        ensembl = result.get("query" if scope == "ensembl.gene" else "ensembl.gene")
+        entrez = result.get("query" if scope == "entrezgene" else "entrezgene")
+        symbol = result.get("query" if scope == "symbol" else "symbol")
+        data.append({"ensembl_gene_id": ensembl, "entrez_gene_id": entrez, "gene_symbol": symbol})
+    return data
+
+
+def convert(ids: int | str | Sequence[int] | Sequence[str] | Sequence[int | str], taxon: int | str, cache: bool = True):
+    """Convert between genomic identifiers.
+
+    This function will convert between the following components:
+        - Entrez Gene ID
+        - Ensembl Gene ID
+        - Gene Symbol
+
+    :param ids: IDs to be converted
+    :param taxon: Taxonomic identifier
+    :param: scope: The type of identifier provided in `ids`
+    :param cache: Should local caching be used for queries
+    :return: DataFrame with columns "entrez_gene_id", "ensembl_gene_id", and "gene_symbol"
+    """
+    my_geneinfo = MyGeneInfo(cache=cache)
+    chunk_size = 1000
+    id_list = list(map(str, [ids] if isinstance(ids, (int, str)) else ids))
+    chunks = list(range(0, len(id_list), chunk_size))
+
+    data_type = determine_gene_type(id_list)
+    if not all(v == data_type[id_list[0]] for v in data_type.values()):
+        raise ValueError("All items in ids must be of the same type (Entrez, Ensembl, or symbols).")
+
+    scope = next(iter(data_type.values()))
+    fields = ",".join({"ensembl.gene", "entrezgene", "symbol"} - {scope})
+    taxon_str = str(taxon)
+    return pd.DataFrame(
+        [
+            row
+            for i in chunks
+            for row in _get_conversion(
+                info=my_geneinfo,
+                values=id_list[i : i + chunk_size],
+                scope=scope,
+                fields=fields,
+                taxon=taxon_str,
+            )
+        ]
+    )
+
+
+def determine_gene_type(items: str | list[str], /) -> dict[str, T_MG_SCOPE]:
+    """Determine the genomic data type.
+
+    :param items: A string or list of strings representing gene identifiers.
+        The function will determine whether each identifier is an Entrez Gene ID,
+        Ensembl Gene ID, or a gene symbol based on its format.
+
+    :return: A dictionary mapping each input item to its determined type, which can be one of:
+        - "entrez_gene_id": If the item consists solely of digits.
+        - "ensembl_gene_id": If the item starts with "ENS" and is
+            followed by a specific format (length greater than 11 and the last 11 characters are digits).
+        - "gene_symbol": If the item does not match the above criteria, it is assumed to be a gene symbol.
+    """
+    items = [items] if isinstance(items, str) else items
+
+    determine: dict[str, Literal["entrezgene", "ensembl.gene", "symbol"]] = {}
+    for i in items:
+        i_str = str(i).split(".")[0] if isinstance(i, float) else str(i)
+        if i_str.isdigit():
+            determine[i_str] = "entrezgene"
+        elif i_str.startswith("ENS") and (len(i_str) > 11 and all(i.isdigit() for i in i_str[-11:])):
+            determine[i_str] = "ensembl.gene"
+        else:
+            determine[i_str] = "symbol"
+
+    return determine
diff --git a/main/como/proteomics/Crux.py b/main/como/proteomics/Crux.py
index 9351cf4e..1703a857 100644
--- a/main/como/proteomics/Crux.py
+++ b/main/como/proteomics/Crux.py
@@ -1,5 +1,6 @@
 # ruff: noqa
 
+from typing import Type
 import asyncio
 import multiprocessing
 import os
@@ -8,10 +9,11 @@
 from multiprocessing.sharedctypes import Synchronized
 from pathlib import Path
 
+from bioservices.biodbnet import BioDBNet
 import numpy as np
 import pandas as pd
 import tqdm
-from fast_bioservices import BioDBNet
+
 
 from como.proteomics.FileInformation import FileInformation, clear_print
 
@@ -128,7 +130,9 @@ def mzml_to_sqt(self) -> None:
             )
 
             # Replace all "comet.*" in output directory with the name of the file being processed
-            comet_files = [str(file) for file in os.listdir(file_information.sqt_base_path) if str(file).startswith("comet.")]
+            comet_files = [
+                str(file) for file in os.listdir(file_information.sqt_base_path) if str(file).startswith("comet.")
+            ]
             for file_name in comet_files:
                 # Determine the old file path
                 old_file_path: Path = Path(file_information.sqt_base_path, file_name)
@@ -240,7 +244,9 @@ def collect_uniprot_ids_and_ion_intensity(self) -> None:
 
             # Assign the file_information intensity dataframe to the gathered values
             self._file_information[i].intensity_df = pd.DataFrame(average_intensities_dict)
-            self._file_information[i].intensity_df = self._file_information[i].intensity_df.groupby("uniprot", as_index=False).mean()
+            self._file_information[i].intensity_df = (
+                self._file_information[i].intensity_df.groupby("uniprot", as_index=False).mean()
+            )
 
     async def _convert_uniprot_wrapper(self) -> None:
         """This function is a multiprocessing wrapper around the convert_ids function"""
@@ -248,7 +254,9 @@ async def _convert_uniprot_wrapper(self) -> None:
 
         # Create a progress bar of results
         # From: https://stackoverflow.com/a/61041328/
-        progress_bar = tqdm.tqdm(desc="Starting UniProt to Gene Symbol conversion... ", total=len(self._file_information))
+        progress_bar = tqdm.tqdm(
+            desc="Starting UniProt to Gene Symbol conversion... ", total=len(self._file_information)
+        )
         for i, result in enumerate(asyncio.as_completed(values)):
             await result  # Get result from asyncio.as_completed
             progress_bar.set_description(f"Working on {i + 1} of {len(self._file_information)}")
@@ -271,13 +279,11 @@ async def async_convert_uniprot(self, file_information: FileInformation) -> None
             loop = asyncio.get_event_loop()
             # async with self._semaphore:
             #     gene_symbols: pd.DataFrame = await loop.run_in_executor(None, self._biodbnet.db2db, "UniProt Accession", "Gene Symbol", input_values)
-            gene_symbols: pd.DataFrame = await loop.run_in_executor(
-                None,
-                self._biodbnet.db2db,
-                input_values,
-                "UniProt Accession",
-                "Gene Symbol",
+            gene_symbols = self._biodbnet.db2db(
+                input_db="UniProt Accession", output_db="Gene Symbol", input_values=input_values
             )
+            if not isinstance(gene_symbols, pd.DataFrame):
+                raise TypeError(f"Expected gene_symbols to be a DataFrame, but got {type(gene_symbols)}")
 
             # The index is UniProt IDs. Create a new column of these values
             gene_symbols["uniprot"] = gene_symbols.index
@@ -372,7 +378,9 @@ def split_abundance_values(self) -> None:
                 # Create a new dataframe to split the S# columns from
                 split_frame: pd.DataFrame = dataframe.copy()
                 # Get the current S{i} columns in
-                abundance_columns: list[str] = [column for column in split_frame.columns if re.match(rf"{cell_type}_S{i}R\d+", column)]
+                abundance_columns: list[str] = [
+                    column for column in split_frame.columns if re.match(rf"{cell_type}_S{i}R\d+", column)
+                ]
                 take_columns: list[str] = ["symbol"] + abundance_columns
                 average_intensity_name: str = f"{cell_type}_S{i}"
 
diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index fa76c618..a2714e69 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -71,15 +71,18 @@ async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None
         biodbnet.services.settings.TIMEOUT = 60
         for i in range(0, len(gene_symbols), step_size):
             # Operations: Goes from gene_symbols=["A", "B;C", "D"] -> gene.split=[["A"], ["B", "C"], ["D"]] -> chain.from_iterable["A", "B", "C", "D"]
-            chunk: list[str] = list(itertools.chain.from_iterable(gene.split(";") for gene in gene_symbols[i : i + step_size]))
-            dataframes.append(
-                biodbnet.db2db(
-                    input_values=chunk,
-                    input_db=Input.GENE_SYMBOL.value,
-                    output_db=[Output.GENE_ID.value, Output.ENSEMBL_GENE_ID.value],
-                    taxon=9606,
-                )
+            chunk: list[str] = list(
+                itertools.chain.from_iterable(gene.split(";") for gene in gene_symbols[i : i + step_size])
             )
+            result = biodbnet.db2db(
+                input_db="Gene Symbol",
+                output_db=["Gene ID", "Ensembl Gene ID"],
+                input_values=chunk,
+                taxon=9606,
+            )
+            if not isinstance(result, pd.DataFrame):
+                raise TypeError(f"Got {type(result)}, expected pd.DataFrame")
+            dataframes.append(result)
         df = pd.concat(dataframes, axis="columns")
         print(df)
         df.loc[df["gene_id"].isna(), ["gene_id"]] = np.nan
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 817b76d6..ba1d3df7 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -10,7 +10,6 @@
 from typing import NamedTuple, TextIO, cast
 
 import anndata as ad
-import boolean
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
@@ -19,13 +18,13 @@
 import sklearn.neighbors
 from anndata.compat import XDataArray
 from anndata.experimental.backed import Dataset2D
-from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 from scipy import sparse
 from zfpkm import zFPKM, zfpkm_plot
 
 from como.data_types import FilteringTechnique, LogLevel, RNAType
 from como.migrations import gene_info_migrations
+from como.pipelines.identifier import convert
 from como.project import Config
 from como.utils import log_and_raise_error, read_file, set_up_logging
 
@@ -173,7 +172,7 @@ async def _build_matrix_results(
             raise TypeError("AnnData.var is expected to be a pandas.DataFrame")
 
         matrix.var = matrix.var.reset_index(drop=False, names=["gene_symbol"])
-        conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=matrix.var["gene_symbol"].tolist(), taxon=taxon)
+        conversion = convert(ids=matrix.var["gene_symbol"].tolist(), taxon=taxon)
     else:
         if "ensembl_gene_id" not in matrix.columns:
             log_and_raise_error(
@@ -181,9 +180,7 @@ async def _build_matrix_results(
                 error=ValueError,
                 level=LogLevel.CRITICAL,
             )
-        conversion: pd.DataFrame = await ensembl_to_gene_id_and_symbol(
-            ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon
-        )
+        conversion: pd.DataFrame = convert(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
     # If the entrez gene id column is empty, it is indicative that the incorrect taxon id was provided
     if conversion["entrez_gene_id"].eq("-").all():
         logger.critical(
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index d2e9a8dc..6faf4862 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -15,11 +15,10 @@
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
-from fast_bioservices.biothings.mygene import MyGene
-from fast_bioservices.pipeline import gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 
 from como.data_types import LogLevel, RNAType
+from como.pipelines.identifier import convert
 from como.utils import log_and_raise_error, read_file, set_up_logging
 
 
@@ -183,6 +182,7 @@ def to_dataframe(cls, samples: list[SampleConfiguration]) -> tuple[pd.DataFrame,
 
         return config, lengths
 
+
 def _sample_name_from_filepath(file: Path) -> str:
     group = re.search(r".+_S\d+R\d+(r\d+)?", file.stem)
     if not group:
@@ -196,9 +196,11 @@ def _sample_name_from_filepath(file: Path) -> str:
         )
     return group.group()
 
+
 def _sample_name_from_filepath(file: Path) -> str:
     return re.search(r".+_S\d+R\d+(r\d+)?", file.stem).group()
 
+
 def _require_one(
     paths: list[Path | None],
     kind: Literal["layout", "strand", "preparation", "fragment"],
@@ -536,7 +538,7 @@ async def read_ensembl_gene_ids(file: Path) -> list[str]:
         if isinstance(data_, pd.DataFrame):
             return data_["ensembl_gene_id"].tolist()
         try:
-            conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=data_.var_names.tolist(), taxon=taxon)
+            conversion = convert(ids=data_.var_names.tolist(), taxon=taxon)
         except json.JSONDecodeError as e:
             log_and_raise_error(
                 f"Got a JSON decode error for file '{counts_matrix_filepaths}' ({e})",
diff --git a/main/como/utils.py b/main/como/utils.py
index 38db7d72..996adef1 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -3,27 +3,24 @@
 import contextlib
 import io
 import sys
-from collections.abc import Iterator
+from collections.abc import Iterator, Sequence
 from pathlib import Path
 from typing import Any, Literal, NoReturn, TextIO, TypeVar, overload
 
 import numpy.typing as npt
 import pandas as pd
 import scanpy as sc
-from fast_bioservices import BioDBNet, Output, Taxon
-from fast_bioservices.pipeline import (
-    determine_gene_type,
-    ensembl_to_gene_id_and_symbol,
-    gene_id_to_ensembl_and_gene_symbol,
-    gene_symbol_to_ensembl_and_gene_id,
-)
 from loguru import logger
 
 from como.data_types import LOG_FORMAT, Algorithm, LogLevel
+from como.pipelines.identifier import convert
 
 T = TypeVar("T")
 __all__ = [
+    "get_missing_gene_data",
     "log_and_raise_error",
+    "num_columns",
+    "num_rows",
     "read_file",
     "set_up_logging",
     "split_gene_expression_data",
@@ -124,48 +121,21 @@ def suppress_stdout() -> Iterator[None]:
             sys.stdout = sys.__stdout__
 
 
-async def _format_determination(
-    biodbnet: BioDBNet, *, requested_output: Output | list[Output], input_values: list[str], taxon: Taxon
-) -> pd.DataFrame:
-    """Determine the data type of the given input values (i.e., Entrez Gene ID, Gene Symbol, etc.).
+def get_missing_gene_data(values: Sequence[str] | pd.DataFrame | sc.AnnData, taxon_id: int | str) -> pd.DataFrame:
+    """Get missing gene data from a given set of values.
 
-    Args:
-        biodbnet: The BioDBNet to use for determination
-        requested_output: The data type to generate (of type `Output`)
-        input_values: The input values to determine
-        taxon: The Taxon ID
-
-    Returns:
-        A pandas DataFrame
+    This function will attempt to find gene identifiers in the provided values and convert them to a DataFrame
+    containing "entrez_gene_id", "ensembl_gene_id", and "gene_symbol"
 
+    :param values: The values to extract gene identifiers from.
+        This can be a list of strings, a pandas DataFrame, or a scanpy AnnData object.
+    :param taxon_id: The taxonomic identifier to use for gene consversion
+    :return: A DataFrame containing "entrez_gene_id", "ensembl_gene_id", and "gene_symbol" for the provided values
     """
-    requested_output = [requested_output] if isinstance(requested_output, Output) else requested_output
-    coercion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(
-        columns=["Input Type"]
-    )
-    coercion.columns = pd.Index(["input_value", *[o.value.replace(" ", "_").lower() for o in requested_output]])
-    return coercion
-
-
-async def get_missing_gene_data(  # noqa: C901
-    values: list[str] | pd.DataFrame | sc.AnnData, taxon_id: int | str | Taxon
-) -> pd.DataFrame:
-    if isinstance(values, list) and not isinstance(
-        values, pd.DataFrame
-    ):  # second isinstance required for static type check to be happy
-        gene_type = await determine_gene_type(values)
-        if all(v == "gene_symbol" for v in gene_type.values()):
-            return await gene_symbol_to_ensembl_and_gene_id(values, taxon=taxon_id)
-        elif all(v == "ensembl_gene_id" for v in gene_type.values()):
-            return await ensembl_to_gene_id_and_symbol(ids=values, taxon=taxon_id)
-        elif all(v == "entrez_gene_id" for v in gene_type.values()):
-            return await gene_id_to_ensembl_and_gene_symbol(ids=values, taxon=taxon_id)
-        else:
-            log_and_raise_error(
-                message="Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)",
-                error=ValueError,
-                level=LogLevel.CRITICAL,
-            )
+    # second isinstance required for static type check to be happy
+    # if isinstance(values, list) and not isinstance(values, pd.DataFrame):
+    if isinstance(values, list):
+        return convert(ids=values, taxon=taxon_id)
     elif isinstance(values, pd.DataFrame):
         # raise error if duplicate column names exist
         if any(values.columns.duplicated(keep=False)):
@@ -183,17 +153,17 @@ async def get_missing_gene_data(  # noqa: C901
         if values.index.name is not None:
             names.append(str(values.index.name))
         if "gene_symbol" in names:
-            return await get_missing_gene_data(
+            return get_missing_gene_data(
                 values["gene_symbol"].tolist() if "gene_symbol" in values.columns else values.index.tolist(),
                 taxon_id=taxon_id,
             )
         elif "entrez_gene_id" in names:
-            return await get_missing_gene_data(
+            return get_missing_gene_data(
                 values["entrez_gene_id"].tolist() if "entrez_gene_id" in values.columns else values.index.tolist(),
                 taxon_id=taxon_id,
             )
         elif "ensembl_gene_id" in names:
-            return await get_missing_gene_data(
+            return get_missing_gene_data(
                 values["ensembl_gene_id"].tolist() if "ensembl_gene_id" in values.columns else values.index.tolist(),
                 taxon_id=taxon_id,
             )
@@ -315,11 +285,21 @@ def _listify(value: T | list[T]) -> list[T]:
     return value if isinstance(value, list) else [value]
 
 
-def _num_rows(item: pd.DataFrame | npt.NDArray) -> int:
+def num_rows(item: pd.DataFrame | npt.NDArray) -> int:
+    """Return the number of rows in an object.
+
+    :param item: The object to check the number of rows of
+    :return: The number of rows in the provided object
+    """
     return item.shape[0]
 
 
-def _num_columns(item: pd.DataFrame | npt.NDArray) -> int:
+def num_columns(item: pd.DataFrame | npt.NDArray) -> int:
+    """Return the number of columns in an object.
+
+    :param item: The object to check the number of columns of
+    :return: The number of columns in the provided object
+    """
     return item.shape[1]
 
 
diff --git a/pyproject.toml b/pyproject.toml
index 070be7c5..ede17cb1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,9 +9,9 @@ requires-python = ">=3.11,<3.14"
 dependencies = [
     "aioftp>=0.23.1",
     "anndata>=0.12.0",
+    "bioservices>=1.12.1",
     "cobamp@git+https://github.com/JoshLoecker/cobamp@master",
     "cobra>=0.28.0",
-    "fast-bioservices>=0.3.9",
     "joypy>=0.2.6",
     "kaleido>=1.0.0",
     "loguru>=0.7.2",
diff --git a/tests/test_proteomics.py b/tests/test_proteomics.py
index defb0fef..0f38be27 100644
--- a/tests/test_proteomics.py
+++ b/tests/test_proteomics.py
@@ -2,11 +2,11 @@
 
 import aioftp
 import pytest
-
 from como.proteomics.Crux import MZMLtoSQT, RAWtoMZML, SQTtoCSV
 from como.proteomics.FileInformation import FileInformation
 from como.proteomics.FTPManager import Download, Reader, aioftp_client
 from como.proteomics.proteomics_preprocess import ParseCSVInput, PopulateInformation
+
 from tests.fixtures.fixture_ftp_server import fixture_ftp_server, ftp_file_names
 
 
diff --git a/tests/unit/test_identifier_convert.py b/tests/unit/test_identifier_convert.py
new file mode 100644
index 00000000..72118877
--- /dev/null
+++ b/tests/unit/test_identifier_convert.py
@@ -0,0 +1,274 @@
+# tests/test_gene_conversion.py
+from __future__ import annotations
+
+import importlib
+import sys
+import types
+from dataclasses import dataclass
+
+import pandas as pd
+import pytest
+
+MODULE_NAME = "como.pipelines.identifier"
+
+
+@pytest.fixture
+def mod(monkeypatch):
+    """Import como in a way that does NOT require bioservices to be installed (we stub it before import).
+
+    If bioservices *is* installed, this still keeps tests hermetic and avoids
+    any accidental network calls.
+    """
+    # Stub bioservices.mygeneinfo.MyGeneInfo so importing the module works
+    bioservices = types.ModuleType("bioservices")
+    mygeneinfo = types.ModuleType("bioservices.mygeneinfo")
+
+    class DummyMyGeneInfo:
+        def __init__(self, cache: bool = True):
+            self.cache = cache
+
+        def get_queries(self, **kwargs):
+            raise AssertionError(
+                "DummyMyGeneInfo.get_queries was called unexpectedly. "
+                "Patch mod.MyGeneInfo or use _get_conversion tests with a fake info object."
+            )
+
+    mygeneinfo.MyGeneInfo = DummyMyGeneInfo
+    bioservices.mygeneinfo = mygeneinfo
+
+    monkeypatch.setitem(sys.modules, "bioservices", bioservices)
+    monkeypatch.setitem(sys.modules, "bioservices.mygeneinfo", mygeneinfo)
+
+    # Ensure a clean import each test
+    if MODULE_NAME in sys.modules:
+        del sys.modules[MODULE_NAME]
+
+    return importlib.import_module(MODULE_NAME)
+
+
+def test_determine_gene_type_accepts_single_string(mod):
+    assert mod.determine_gene_type("1017") == {"1017": "entrezgene"}
+
+
+def test_determine_gene_type_classifies_common_formats_and_float(mod):
+    # Includes a float to exercise the float-specific split(".")[0] branch.
+    out = mod.determine_gene_type(["1017", "ENSG00000141510", "TP53", 123.0])
+
+    assert out["1017"] == "entrezgene"
+    assert out["ENSG00000141510"] == "ensembl.gene"
+    assert out["TP53"] == "symbol"
+    # float 123.0 -> "123"
+    assert out["123"] == "entrezgene"
+
+
+def test_determine_gene_type_ensembl_format_requires_ens_prefix_and_11_trailing_digits(mod):
+    # Should be Ensembl-like: starts with ENS, length > 11, last 11 chars are digits
+    assert mod.determine_gene_type(["ENSX00000123456"])["ENSX00000123456"] == "ensembl.gene"
+    # Not enough trailing digits / too short => symbol
+    assert mod.determine_gene_type(["ENS123"])["ENS123"] == "symbol"
+    # ENS but last 11 not all digits => symbol
+    assert mod.determine_gene_type(["ENSGABCDEFGHIJK"])["ENSGABCDEFGHIJK"] == "symbol"
+
+
+# ---------------------------------------------------------------------------
+# _get_conversion tests
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class FakeInfo:
+    return_value: object
+    calls: list[dict] = None
+
+    def __post_init__(self):  # noqa: D105
+        self.calls = []
+
+    def get_queries(self, **kwargs):
+        self.calls.append(kwargs)
+        return self.return_value
+
+
+def test_get_conversion_raises_typeerror_when_results_not_list(mod):
+    info = FakeInfo(return_value={"not": "a list"})
+    with pytest.raises(TypeError, match="Expected results to be a list"):
+        mod._get_conversion(info=info, values=["1017"], scope="entrezgene", fields="ensembl.gene,symbol", taxon="9606")
+
+
+def test_get_conversion_raises_typeerror_when_first_item_not_dict(mod):
+    info = FakeInfo(return_value=["not a dict"])
+    with pytest.raises(TypeError, match="Expected each result to be a dict"):
+        mod._get_conversion(info=info, values=["1017"], scope="entrezgene", fields="ensembl.gene,symbol", taxon="9606")
+
+
+def test_get_conversion_calls_get_queries_with_expected_arguments(mod):
+    info = FakeInfo(
+        return_value=[
+            {"query": "1017", "ensembl.gene": "ENSG00000123374", "symbol": "CDK2"},
+        ]
+    )
+
+    out = mod._get_conversion(
+        info=info, values=["1017"], scope="entrezgene", fields="ensembl.gene,symbol", taxon="9606"
+    )
+
+    assert out == [
+        {"ensembl_gene_id": "ENSG00000123374", "entrez_gene_id": "1017", "gene_symbol": "CDK2"},
+    ]
+
+    assert len(info.calls) == 1
+    call = info.calls[0]
+    assert call["query"] == "1017"
+    assert call["dotfield"] is True
+    assert call["scopes"] == "entrezgene"
+    assert call["fields"] == "ensembl.gene,symbol"
+    assert call["species"] == "9606"
+
+
+@pytest.mark.parametrize(
+    ("scope", "results", "expected"),
+    [
+        (
+            "entrezgene",
+            [
+                {"query": "1017", "ensembl.gene": "ENSG00000123374", "symbol": "CDK2"},
+                {"query": "1018", "ensembl.gene": "ENSG00000123400", "symbol": "CDK3"},
+            ],
+            [
+                {"ensembl_gene_id": "ENSG00000123374", "entrez_gene_id": "1017", "gene_symbol": "CDK2"},
+                {"ensembl_gene_id": "ENSG00000123400", "entrez_gene_id": "1018", "gene_symbol": "CDK3"},
+            ],
+        ),
+        (
+            "ensembl.gene",
+            [
+                {"query": "ENSG00000141510", "entrezgene": "7157", "symbol": "TP53"},
+            ],
+            [
+                {"ensembl_gene_id": "ENSG00000141510", "entrez_gene_id": "7157", "gene_symbol": "TP53"},
+            ],
+        ),
+        (
+            "symbol",
+            [
+                {"query": "TP53", "ensembl.gene": "ENSG00000141510", "entrezgene": "7157"},
+            ],
+            [
+                {"ensembl_gene_id": "ENSG00000141510", "entrez_gene_id": "7157", "gene_symbol": "TP53"},
+            ],
+        ),
+    ],
+)
+def test_get_conversion_scope_specific_mapping(mod, scope, results, expected):
+    info = FakeInfo(return_value=results)
+    out = mod._get_conversion(info=info, values=[r["query"] for r in results], scope=scope, fields="x", taxon="9606")
+    assert out == expected
+
+
+# ---------------------------------------------------------------------------
+# convert tests
+# ---------------------------------------------------------------------------
+
+
+def test_convert_rejects_mixed_id_types(mod):
+    # Mixed: Entrez-like ("1017") + symbol ("TP53") => ValueError
+    with pytest.raises(ValueError, match="All items in ids must be of the same type"):
+        mod.convert(ids=["1017", "TP53"], taxon=9606)
+
+
+def test_convert_single_id_passes_expected_scope_fields_taxon_and_cache(monkeypatch, mod):
+    calls = []
+
+    class CapturingMyGeneInfo:
+        def __init__(self, cache: bool = True):
+            self.cache = cache
+
+    def fake_get_conversion(*, info, values, scope, fields, taxon):
+        calls.append({"info": info, "values": values, "scope": scope, "fields": fields, "taxon": taxon})
+        # Return one row per value
+        return [{"ensembl_gene_id": "ENSG_TEST", "entrez_gene_id": v, "gene_symbol": "SYM_TEST"} for v in values]
+
+    monkeypatch.setattr(mod, "MyGeneInfo", CapturingMyGeneInfo)
+    monkeypatch.setattr(mod, "_get_conversion", fake_get_conversion)
+
+    df = mod.convert(ids=1017, taxon=9606, cache=False)
+
+    assert isinstance(df, pd.DataFrame)
+    assert set(df.columns) == {"ensembl_gene_id", "entrez_gene_id", "gene_symbol"}
+    assert len(df) == 1
+    assert df.loc[0, "entrez_gene_id"] == "1017"
+    assert df.loc[0, "ensembl_gene_id"] == "ENSG_TEST"
+    assert df.loc[0, "gene_symbol"] == "SYM_TEST"
+
+    assert len(calls) == 1
+    call = calls[0]
+    assert isinstance(call["info"], CapturingMyGeneInfo)
+    assert call["info"].cache is False
+    assert call["values"] == ["1017"]
+    assert call["scope"] == "entrezgene"
+    assert call["taxon"] == "9606"
+
+    # Fields are computed via set arithmetic; order is not guaranteed.
+    assert set(call["fields"].split(",")) == {"ensembl.gene", "symbol"}
+
+
+def test_convert_chunks_inputs_over_1000(monkeypatch, mod):
+    calls = []
+
+    class CapturingMyGeneInfo:
+        def __init__(self, cache: bool = True):
+            self.cache = cache
+
+    def fake_get_conversion(*, info, values, scope, fields, taxon):
+        calls.append({"values_len": len(values), "scope": scope, "fields": fields, "taxon": taxon})
+        return [
+            {"ensembl_gene_id": f"ENSG{v.zfill(11)}", "entrez_gene_id": v, "gene_symbol": f"SYM{v}"} for v in values
+        ]
+
+    monkeypatch.setattr(mod, "MyGeneInfo", CapturingMyGeneInfo)
+    monkeypatch.setattr(mod, "_get_conversion", fake_get_conversion)
+
+    ids = list(range(1, 1002))  # 1001 ids => should create 2 chunks: 1000 and 1
+    df = mod.convert(ids=ids, taxon="9606")
+
+    assert len(df) == 1001
+    assert set(df.columns) == {"ensembl_gene_id", "entrez_gene_id", "gene_symbol"}
+
+    # Ensure chunking happened as expected
+    assert [c["values_len"] for c in calls] == [1000, 1]
+    for c in calls:
+        assert c["scope"] == "entrezgene"
+        assert c["taxon"] == "9606"
+        assert set(c["fields"].split(",")) == {"ensembl.gene", "symbol"}
+
+    # Spot-check first and last rows keep ordering
+    assert df.loc[0, "entrez_gene_id"] == "1"
+    assert df.loc[0, "gene_symbol"] == "SYM1"
+    assert df.loc[1000, "entrez_gene_id"] == "1001"
+    assert df.loc[1000, "gene_symbol"] == "SYM1001"
+
+
+def test_convert_symbol_scope_fields_exclude_symbol(monkeypatch, mod):
+    calls = []
+
+    class CapturingMyGeneInfo:
+        def __init__(self, cache: bool = True):
+            self.cache = cache
+
+    def fake_get_conversion(*, info, values, scope, fields, taxon):
+        calls.append({"scope": scope, "fields": fields, "taxon": taxon, "values": values})
+        # Return minimal row(s)
+        return [{"ensembl_gene_id": None, "entrez_gene_id": None, "gene_symbol": v} for v in values]
+
+    monkeypatch.setattr(mod, "MyGeneInfo", CapturingMyGeneInfo)
+    monkeypatch.setattr(mod, "_get_conversion", fake_get_conversion)
+
+    df = mod.convert(ids=["TP53", "BRCA1"], taxon=9606)
+
+    assert len(df) == 2
+    assert set(df.columns) == {"ensembl_gene_id", "entrez_gene_id", "gene_symbol"}
+
+    assert len(calls) == 1
+    assert calls[0]["scope"] == "symbol"
+    assert calls[0]["taxon"] == "9606"
+    # When scope is "symbol", requested fields should be the other two
+    assert set(calls[0]["fields"].split(",")) == {"ensembl.gene", "entrezgene"}
diff --git a/uv.lock b/uv.lock
index eb47f1c6..9139aea2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -6,15 +6,6 @@ resolution-markers = [
     "python_full_version < '3.12'",
 ]
 
-[[package]]
-name = "aiofiles"
-version = "25.1.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
-]
-
 [[package]]
 name = "aioftp"
 version = "0.27.2"
@@ -188,6 +179,33 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" },
 ]
 
+[[package]]
+name = "bioservices"
+version = "1.12.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "appdirs" },
+    { name = "beautifulsoup4" },
+    { name = "click" },
+    { name = "colorlog" },
+    { name = "easydev" },
+    { name = "grequests" },
+    { name = "lxml" },
+    { name = "matplotlib" },
+    { name = "pandas" },
+    { name = "requests" },
+    { name = "requests-cache" },
+    { name = "rich-click" },
+    { name = "suds-community" },
+    { name = "tqdm" },
+    { name = "wrapt" },
+    { name = "xmltodict" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/51/1a8dc87ffc6c27e0a896b982da3ff1b483f5ef85a47ae1ffafbc6a479bb4/bioservices-1.12.1.tar.gz", hash = "sha256:0f31782ae50930d4ab82b43f98d1ca2cc9befaa699f3a7a6cba512a8f9e2cab0", size = 218752, upload-time = "2025-02-27T22:38:58.628Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b8/c9/656854139abbdc0a09d544bacc6fa3132245ebf9847dd45da9a8e416c5a5/bioservices-1.12.1-py3-none-any.whl", hash = "sha256:898033fe0158a0e31ea5ad93ff02f5000aa67ff9d06e9dc6477583d3f60ace80", size = 258032, upload-time = "2025-02-27T22:38:56.406Z" },
+]
+
 [[package]]
 name = "bleach"
 version = "6.3.0"
@@ -214,6 +232,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e5/ca/78d423b324b8d77900030fa59c4aa9054261ef0925631cd2501dd015b7b7/boolean_py-5.0-py3-none-any.whl", hash = "sha256:ef28a70bd43115208441b53a045d1549e2f0ec6e3d08a9d142cbc41c1938e8d9", size = 26577, upload-time = "2025-04-03T10:39:48.449Z" },
 ]
 
+[[package]]
+name = "cattrs"
+version = "25.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6e/00/2432bb2d445b39b5407f0a90e01b9a271475eea7caf913d7a86bcb956385/cattrs-25.3.0.tar.gz", hash = "sha256:1ac88d9e5eda10436c4517e390a4142d88638fe682c436c93db7ce4a277b884a", size = 509321, upload-time = "2025-10-07T12:26:08.737Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d8/2b/a40e1488fdfa02d3f9a653a61a5935ea08b3c2225ee818db6a76c7ba9695/cattrs-25.3.0-py3-none-any.whl", hash = "sha256:9896e84e0a5bf723bc7b4b68f4481785367ce07a8a02e7e9ee6eb2819bc306ff", size = 70738, upload-time = "2025-10-07T12:26:06.603Z" },
+]
+
 [[package]]
 name = "certifi"
 version = "2026.1.4"
@@ -341,6 +372,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/9f/d73dfb85d7a5b1a56a99adc50f2074029468168c970ff5daeade4ad819e4/choreographer-1.2.1-py3-none-any.whl", hash = "sha256:9af5385effa3c204dbc337abf7ac74fd8908ced326a15645dc31dde75718c77e", size = 49338, upload-time = "2025-11-09T23:04:43.154Z" },
 ]
 
+[[package]]
+name = "click"
+version = "8.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
+]
+
 [[package]]
 name = "cobamp"
 version = "0.2.2"
@@ -389,6 +432,18 @@ wheels = [
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