There is no function to fetch automatically the information on the reported pubmed articles links in the tair to be used for the language models, so i coded this function which will take the tair information, a gene or locus tag and will fetch the corresponding pubmed and then from the pubmed the corresponding abstracts, which can be directly used for the text summarization into langchain or the llm models.
readTairNCBI("/Users/gauravsablok/Desktop/CodeCheck/release/ATH_GO_GOSLIM.txt", \
"/Users/gauravsablok/Desktop/CodeCheck/release/ATH_GO_GOSLIM.out", \
"2200935")
# this is just a reference id for checking the functionality
[('https://pubmed.ncbi.nlm.nih.gov/30356219/',
'Nitrogen is an essential macronutrient for plant growth and basic metabolic processes.
The application of nitrogen-containing fertilizer increases yield, which has been a
substantial factor in the green revolution1. Ecologically, however, excessive application
of fertilizer has disastrous effects such as eutrophication2. A better understanding
of how plants regulate nitrogen metabolism is critical to increase plant yield and reduce
fertilizer overuse. Here we present a transcriptional regulatory network and twenty-one
transcription factors that regulate the architecture of root and shoot systems in response
to changes in nitrogen availability. Genetic perturbation of a subset of these transcription
factors revealed coordinate transcriptional regulation of enzymes involved in nitrogen metabolism.
Transcriptional regulators in the network are transcriptionally modified by feedback via
genetic perturbation of nitrogen metabolism. The network, genes and gene-regulatory modules
identified here will prove critical to increasing agricultural productivity.'),
('https://pubmed.ncbi.nlm.nih.gov/34562334/',
'Unraveling gene function is pivotal to understanding the signaling cascades that control plant
development and stress responses. As experimental profiling is costly and labor intensive,
there is a clear need for high-confidence computational annotation. In contrast to detailed
gene-specific functional information, transcriptomics data are widely available for both model and
crop species. Here, we describe a novel automated function prediction method, which leverages
complementary information from multiple expression datasets by analyzing study-specific gene
co-expression networks. First, we benchmarked the prediction performance on recently characterized
Arabidopsis thaliana genes, and showed that our method outperforms state-of-the-art expression-based
approaches. Next, we predicted biological process annotations for known (n = 15 790) and unknown
(n = 11 865) genes in A. thaliana and validated our predictions using experimental protein-DNA and
protein-protein interaction data (covering >220 000 interactions in total), obtaining a set of
high-confidence functional annotations. Our method assigned at least one validated annotation to
5054 (42.6%) unknown genes, and at least one novel validated function to 3408 (53.0%) genes with
computational annotations only. These omics-supported functional annotations shed light on a
variety of developmental processes and molecular responses, such as flower and root development,
defense responses to fungi and bacteria, and phytohormone signaling, and help fill the information
gap on biological process annotations in Arabidopsis. An in-depth analysis of two context-specific
networks, modeling seed development and response to water deprivation, shows how previously
uncharacterized genes function within the respective networks. Moreover, our automated function
prediction approach can be applied in future studies to facilitate gene discovery for crop improvement.'),
('https://pubmed.ncbi.nlm.nih.gov/11118137/',
'The completion of the Arabidopsis thaliana genome sequence allows a comparative analysis of
transcriptional regulators across the three eukaryotic kingdoms. Arabidopsis dedicates over
5% of its genome to code for more than 1500 transcription factors, about 45% of which are from
families specific to plants. Arabidopsis transcription factors that belong to families common
to all eukaryotes do not share significant similarity with those of the other kingdoms beyond the
conserved DNA binding domains, many of which have been arranged in combinations specific to each
lineage. The genome-wide comparison reveals the evolutionary generation of diversity in the
regulation of transcription.')]Gaurav
Academic Staff Member
Bioinformatics
Institute for Biochemistry and Biology
University of Potsdam
Potsdam,Germany