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Identification and classification of ion channels across the tree of life provide functional insights into understudied CALHM channels.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § Methods › Corpus construction and vectorization ↔ query_rag_langchain copy.py, lines 21–63 · score 0.68 · vector database, OpenAI, metadata, LangChain, page, PubMed
  2. [2] § Methods › Query formulation and similarity search ↔ query_rag_autogen.py, lines 66–128 · score 0.67 · ion selectivity, article provide evidence, gating mechanism, ion channel, Query
  3. [3] § Results › Defining the IC complement of the human genome using informatics approaches ↔ query_rag_langchain copy.py, lines 21–63 · score 0.67 · retrieval augmented generation, RAG system, predicted, unable, database, models
  4. [4] § Methods › Corpus construction and vectorization ↔ create_chunk_vectors_qdrant.py, lines 78–106 · score 0.66 · CharacterTextSplitter, PubMed, Qdrant, overlapping, metadata, page
  5. [5] § Methods › RAG system for verifying ion selectivity and gating mechanism annotations ↔ query_rag_autogen.py, lines 66–128 · score 0.55 · ion selectivity, gating mechanism, human IC, RAG, query

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 68 lines · 2.6 KB · GPL-3.0 · 2 matches

  1. import os
  2. from langchain_openai import OpenAIEmbeddings
  3. from langchain_core.prompts import ChatPromptTemplate
  4. from langchain_qdrant import Qdrant
  5. from qdrant_client import QdrantClient
  6. from langchain_community.chat_models import ChatOpenAI
  7. from dotenv import load_dotenv
  8. load_dotenv()
  10. OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
  11. OPENAI_EMBEDDING_MODEL = "text-embedding-3-small"
  12. OPENAI_MODEL = "gpt-4o"
  14. PROMPT_TEMPLATE = """
  15. Answer the question based only on the following context:
  16. {context}
  17. - -
  18. Answer the question based on the above context: {question}
  19. """
  21. def query(query_text):
  22. """
  23. Query a Retrieval-Augmented Generation (RAG) system using a vector database and OpenAI.
  24. Args:
  25. - query_text (str): The text to query the RAG system with.
  26. Returns:
  27. - formatted_response (str): Formatted response including the generated text and sources.
  28. - response_text (str): The generated response text.
  29. """
  30. # YOU MUST - Use same embedding function as before
  31. embedding_function = OpenAIEmbeddings(model=OPENAI_EMBEDDING_MODEL, api_key=OPENAI_API_KEY)
  33. # Prepare the database
  34. qdrant_client = QdrantClient(path="./qdrant")
  36. db = Qdrant(client=qdrant_client, collection_name="pubmed", embeddings=embedding_function)
  38. # Retrieving the context from the DB using similarity search
  39. results = db.similarity_search_with_relevance_scores(query_text, k=3)
  41. # Check if there are any matching results or if the relevance score is too low
  42. if len(results) == 0: #or results[0][1] < 0.7:
  43. print(f"Unable to find matching results.")
  45. # Combine context from matching documents
  46. context_text = "\n\n - -\n\n".join([doc.page_content for doc, _score in results])
  48. # Create prompt template using context and query text
  49. prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
  50. prompt = prompt_template.format(context=context_text, question=query_text)
  52. # Initialize OpenAI chat model
  53. model = ChatOpenAI(model_name=OPENAI_MODEL, api_key=OPENAI_API_KEY)
  55. # Generate response text based on the prompt
  56. response_text = model.predict(prompt)
  58. # Get sources of the matching documents
  59. sources = [doc.metadata.get("source", None) for doc, _score in results]
  61. # Format and return response including generated text and sources
  62. formatted_response = f"Response: {response_text}\nSources: {sources}"
  63. return formatted_response, response_text
  65. # Let's call our function we have defined
  66. formatted_response, response_text = query("anion")# query("Is there any evidence that `anion` is the ion selectivity of the `Golgi pH regulator A` ion channel?")
  67. # and finally, inspect our final response!
  68. print(response_text)

query_rag_langchain copy.py at commit fb76374, under GPL-3.0 · at the source

Overview

Authors: Rahil Taujale1,2, Sung Jin Park3, Nathan Gravel1, Saber Soleymani2, Rayna Carter2, Kennady Boyd2, Sarah I Keuning3, Zheng Ruan4, Wei Lü3,5,6, Natarajan Kannan1,2
ORCID iDs: Rahil Taujale, Sarah I Keuning, Wei Lü, Natarajan Kannan
  1. Institute of Bioinformatics, University of Georgia Athens United States
  2. Department of Biochemistry and Molecular Biology, University of Georgia Athens United States
  3. Department of Molecular Biosciences, Northwestern University Evanston United States
  4. Department of Biochemistry & Molecular Biology, Thomas Jefferson University Philadelphia United States
  5. Department of Pharmacology, Northwestern University Chicago United States
  6. Chemistry of Life Processes Institute, Northwestern University Evanston United States
Institutions: University of Georgia (United States); Northwestern University (United States); Thomas Jefferson University (United States)
Journal: eLife, volume 14, article RP106134
Dates: published online 18 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.106134 · PMID 42149114 · PMCID PMC13183375 · OpenAlex W4410231828
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials
Keywords: tree of life, eukaryotes, metazoa, Human
MeSH: Calcium Channels*, Ion Channels*, Animals, Humans, Phylogeny (* major topic)
Journal subjects: Biochemistry and Chemical Biology, Computational and Systems Biology
Topic: Ion channel regulation and function (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institutes of Health (U01CA271376); National Institute of Neurological Disorders and Stroke (5R00NS128258)
Citations: cited by 2 papers (Europe PMC); 75 references in the paper

Abstract

The ion channel (IC) genes encoded in the human genome play fundamental roles in cellular functions and disease, and are one of the largest classes of druggable proteins. However, limited knowledge of the diverse molecular and cellular functions carried out by ICs presents a major bottleneck in developing selective chemical probes for modulating their functions in disease states. The wealth of sequence data available on ICs from diverse organisms provides a valuable source of untapped information for illuminating the unique modes of channel regulation and functional specialization. However, the extensive diversification of IC sequences and the lack of a unified resource present a challenge in effectively using existing data for IC research. Here, we perform integrative mining of available sequence, structure, and functional data on 419 human ICs across disparate sources, including extensive literature mining by leveraging advances in LLMs to annotate and curate the full complement of the ‘channelome’. We employ a well-established orthology inference approach to identify and extend the IC orthologs across diverse organisms to above 48,000. We show that the depth of conservation and taxonomic representation of IC sequences can further be translated to functional similarities by clustering them into functionally relevant groups, which can be used for downstream functional prediction on understudied members. We demonstrate this by delineating co-conserved patterns characteristic of the understudied family of the calcium homeostasis modulator (CALHM) family of ICs. Through mutational analysis of co-conserved residues altered in human diseases and electrophysiological studies, we show that these evolutionarily constrained residues play an important role in channel gating functions. Thus, by providing new tools and resources for performing large comparative analyses on ICs, this study addresses the unique needs of the IC community and provides the groundwork for accelerating the functional characterization of dark channels for therapeutic intervention.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

esbgkannan/ionchannels-final-pdf

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: fb763748ef6bd00e53b974d83b205a156edc31b9, 14 May 2026
Languages: Python (12)
Size: 437 files, 12 scripts
Software Heritage: archived
Found in: the text, “RAG system for verifying ion selectivity and gat”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: pandas (6 files), Matplotlib (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
14 files
At the source: github.com/esbgkannan/ionchannels-final-pdf

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The human IC annotation table with all the curation information is made available with the manuscript as Supplementary file 1A. The fasta sequences for the human ICs (both full-length sequences and the pore domain sequences) are available through Zenodo (https://doi.org/10.5281/zenodo.16232528). The full length sequences for all the identified orthologs are also available through Zenodo (https://doi.org/10.5281/zenodo.16232528). The code and results related to the RAG annotation pipeline are available at GitHub (https://github.com/esbgkannan/ionchannels-final-pdf) (copy archived at Kannan, 2026).

The following dataset was generated:

TaujaleR KannanN 2025Identification and classification of ion-channels across the tree of life: Insights into understudied CALHM channelsZenodo10.5281/zenodo.16232528PMC1318337542149114

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 10 authors, 4 keywords, 5 MeSH terms, 2 funders, 74 references.

Cite

This paper

Taujale, R., Park, S. J., Gravel, N., Soleymani, S., Carter, R., Boyd, K., Keuning, S. I., Ruan, Z., Lü, W., & Kannan, N. (2026). Identification and classification of ion channels across the tree of life provide functional insights into understudied CALHM channels. eLife, 14, RP106134. https://doi.org/10.7554/elife.106134

BibTeX

@article{taujale2026identification,
author = {Taujale, Rahil and Park, Sung Jin and Gravel, Nathan and Soleymani, Saber and Carter, Rayna and Boyd, Kennady and Keuning, Sarah I and Ruan, Zheng and Lü, Wei and Kannan, Natarajan},
title = {{Identification and classification of ion channels across the tree of life provide functional insights into understudied CALHM channels}},
journal = {eLife},
year = {2026},
month = may,
volume = {14},
pages = {RP106134},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.106134},
url = {https://doi.org/10.7554/elife.106134},
pmid = {42149114},
pmcid = {PMC13183375}
}

RIS

TY - JOUR
AU - Taujale, Rahil
AU - Park, Sung Jin
AU - Gravel, Nathan
AU - Soleymani, Saber
AU - Carter, Rayna
AU - Boyd, Kennady
AU - Keuning, Sarah I
AU - Ruan, Zheng
AU - Lü, Wei
AU - Kannan, Natarajan
TI - Identification and classification of ion channels across the tree of life provide functional insights into understudied CALHM channels
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/05/18
VL - 14
SP - RP106134
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.106134
UR - https://doi.org/10.7554/elife.106134
LA - en
ER -

CSL-JSON

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"author": [
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"family": "Taujale",
"given": "Rahil"
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"family": "Park",
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"family": "Gravel",
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"container-title-short": "eLife",
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"DOI": "10.7554/elife.106134",
"PMID": "42149114",
"PMCID": "PMC13183375",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
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"issued": {
"date-parts": [
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