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PubMed2026

PubMind: literature-based genetic variant extraction and functional annotation using large language models.

Biomedical literature contains extensive functional knowledge on genetic variants, but much remains inaccessible in unstructured text. Existing resources such as ClinVar and HGMD remain limited by coverage, submission bias, update frequency, and sparse annotation. We develop PubMind, an artificial intelligence (AI) framework that uses large language models (LLMs) to triage and extract variant-function-disease associations and supporting evidence from biomedical text. PubMind captures single-nucleotide, copy-number, structural, and gene-fusion variants, and normalizes records to genomic and transcriptomic coordinates. Benchmarking shows >90% accuracy for variant recognition and 99% precision for disease extraction. Applied to >41 million PubMed abstracts and >5 million full-text articles, PubMind generates PubMind-DB, a database of ~1.3 million unique variants with contextual annotations, accessible via web interface and API. Only ~10% of PubMind variants overlap with ClinVar, and >80% of them show concordant pathogenicity labels. PubMind transforms unstructured biomedical text into structured genomic knowledge, advancing variant interpretation for precision medicine.

Large Language Models↗
PubMed2002

Identification of genomic organisation, sequence variants and analysis of the role of the human dishevelled 1 gene in late onset Alzheimer's disease.

Alzheimer's disease (AD) is a disorder characterised by a progressive deterioration in memory and other cognitive functions. Neurofibrillary tangles (NFT) are a major pathological hallmark of AD, these are aggregations of paired helical filaments (PHF) comprised of the hyperphosphorylated microtubule associated protein tau. Several kinases, such as glycogen synthase kinase 3 beta (GSK3beta) and c-Jun N-terminal kinase (JNK), phosphorylate tau at sites that are phosphorylated in PHF. Dishevelled 1 (DVL1) is thought to act as a positive regulator of the wnt signalling pathway, and inhibits GSK3beta activity preventing beta-catenin degradation and thus allowing wnt target gene expression. JNK activation is also regulated by DVL1, however it is unclear if this is via the wnt signalling pathway. These observations suggest a central role for DVL1 in tau phosphorylation and AD and led us to investigate DVL1 as a candidate gene for this disorder. We determined the genomic structure of the DVL1 gene by sequencing and data mining and searched for sequence variations in the coding sequences and flanking introns. The DVL1 gene spans a region of approximately 13.8 kb (not including the 5' untranslated region) and is encoded by 15 exons. Analysis of over 4.3 kb of sequence, including 98% of exonic sequences and introns 2, 3, 6, 7, 9, 10, 11 and 12, revealed there to be six rare (< or =6%) sequence variations. None of these had any association with late onset AD. This would suggest that polymorphic variations in the coding sequences of DVL1 are not important in AD. However further analysis of regulatory regions may lead to the identification of other sequence variations which may be implicated in AD.

Adaptor Proteins, Signal Transducing↗
PubMed2006

Phosphatidylinositol synthesis is essential in bloodstream form Trypanosoma brucei.

PI (phosphatidylinositol) is a ubiquitous eukaryotic phospholipid which serves as a precursor for messenger molecules and GPI (glycosylphosphatidylinositol) anchors. PI is synthesized either de novo or by head group exchange by a PIS (PI synthase). The synthesis of GPI anchors has previously been validated both genetically and chemically as a drug target in Trypanosoma brucei, the causative parasite of African sleeping sickness. However, nothing is known about the synthesis of PI in this organism. Database mining revealed a putative TbPIS gene in the T. brucei genome and by recombinant expression and characterization it was shown to encode a catalytically active PIS, with a high specificity for myo-inositol. Immunofluorescence revealed that in T. brucei, PIS is found in both the endoplasmic reticulum and Golgi. We created a conditional double knockout of TbPIS in the bloodstream form of T. brucei, which when grown under non-permissive conditions, clearly showed that TbPIS is an essential gene. In vivo labelling of these conditional double knockout cells confirmed this result, showing a decrease in the amount of PI formed by the cells when grown under non-permissive conditions. Furthermore, quantitative and qualitative analysis by GLC-MS and ESI-MS/MS (electrospray ionization MS/MS) respectively showed a significant decrease (70%) in cellular PI, which appears to affect all major PI species equally. A consequence of this fall in PI level is a knock-on reduction in GPI biosynthesis which is essential for the parasite's survival. The results presented here show that PI synthesis is essential for bloodstream form T. brucei, and to our knowledge this is the first report of the dependence on PI synthesis of a protozoan parasite by genetic validation.

Amino Acid Sequence↗
PubMed2005

STRING: known and predicted protein-protein associations, integrated and transferred across organisms.

A full description of a protein's function requires knowledge of all partner proteins with which it specifically associates. From a functional perspective, 'association' can mean direct physical binding, but can also mean indirect interaction such as participation in the same metabolic pathway or cellular process. Currently, information about protein association is scattered over a wide variety of resources and model organisms. STRING aims to simplify access to this information by providing a comprehensive, yet quality-controlled collection of protein-protein associations for a large number of organisms. The associations are derived from high-throughput experimental data, from the mining of databases and literature, and from predictions based on genomic context analysis. STRING integrates and ranks these associations by benchmarking them against a common reference set, and presents evidence in a consistent and intuitive web interface. Importantly, the associations are extended beyond the organism in which they were originally described, by automatic transfer to orthologous protein pairs in other organisms, where applicable. STRING currently holds 730,000 proteins in 180 fully sequenced organisms, and is available at http://string.embl.de/.

Databases, Protein↗
PubMed2006

ForestTreeDB: a database dedicated to the mining of tree transcriptomes.

ForestTreeDB is intended as a resource that centralizes large-scale expressed sequence tag (EST) sequencing results from several tree species (http://foresttree.org/ftdb). It currently encompasses 344,878 quality sequences from 68 libraries, from diverse organs of conifer and hybrid poplar trees. It utilizes the Nimbus data model to provide a hosting system for multiple projects, and uses object-relational mapping APIs in Java and Perl for data accesses within an Oracle database designed to be scalable, maintainable and extendable. Transcriptome builds or unigene sets occupy the focal point of the system. Several of the five current species-specific unigenes were used to design microarrays and SNP resources. The ForestTreeDB web application provides the means for multiple combination database queries. It presents the user with a list of discrete queries to retrieve and download large EST datasets or sequences from precompiled unigene assemblies. Functional annotation assignment is not trivial in conifers which are distantly related to angiosperm model plants. Optimal annotations are achieved through database queries that integrate results from several procedures based open-source tools. ForestTreeDB aims to facilitate sequence mining of coherent annotations in multiple species to support comparative genomic approaches. We plan to continuously enrich ForestTreeDB with other resources through collaborations with other genomic projects.

Databases, Nucleic Acid↗
PubMed2006

Human fetal neuroblast and neuroblastoma transcriptome analysis confirms neuroblast origin and highlights neuroblastoma candidate genes.

BACKGROUND: Neuroblastoma tumor cells are assumed to originate from primitive neuroblasts giving rise to the sympathetic nervous system. Because these precursor cells are not detectable in postnatal life, their transcription profile has remained inaccessible for comparative data mining strategies in neuroblastoma. This study provides the first genome-wide mRNA expression profile of these human fetal sympathetic neuroblasts. To this purpose, small islets of normal neuroblasts were isolated by laser microdissection from human fetal adrenal glands. RESULTS: Expression of catecholamine metabolism genes, and neuronal and neuroendocrine markers in the neuroblasts indicated that the proper cells were microdissected. The similarities in expression profile between normal neuroblasts and malignant neuroblastomas provided strong evidence for the neuroblast origin hypothesis of neuroblastoma. Next, supervised feature selection was used to identify the genes that are differentially expressed in normal neuroblasts versus neuroblastoma tumors. This approach efficiently sifted out genes previously reported in neuroblastoma expression profiling studies; most importantly, it also highlighted a series of genes and pathways previously not mentioned in neuroblastoma biology but that were assumed to be involved in neuroblastoma pathogenesis. CONCLUSION: This unique dataset adds power to ongoing and future gene expression studies in neuroblastoma and will facilitate the identification of molecular targets for novel therapies. In addition, this neuroblast transcriptome resource could prove useful for the further study of human sympathoadrenal biogenesis.

Databases, Genetic↗
PubMed2005

Bioinformatics for whole-genome shotgun sequencing of microbial communities.

The application of whole-genome shotgun sequencing to microbial communities represents a major development in metagenomics, the study of uncultured microbes via the tools of modern genomic analysis. In the past year, whole-genome shotgun sequencing projects of prokaryotic communities from an acid mine biofilm, the Sargasso Sea, Minnesota farm soil, three deep-sea whale falls, and deep-sea sediments have been reported, adding to previously published work on viral communities from marine and fecal samples. The interpretation of this new kind of data poses a wide variety of exciting and difficult bioinformatics problems. The aim of this review is to introduce the bioinformatics community to this emerging field by surveying existing techniques and promising new approaches for several of the most interesting of these computational problems.

Journal Article↗
PubMed2001

Genomic approaches to elucidating the pathophysiology of renal diseases.

The physiological and pathological processes of the kidney as a whole can now be analyzed with a molecular precision at a genomic-scale. Using massively parallel cDNA microarray technology, the mRNA expression of thousands of genes can be quantified simultaneously. The advantages of microarray analyses include the ability to examine the interaction of several genes or the entire genome in a single experiment. Bioinformatics approaches such as data mining through mathematical condensation of the massive gene expression profiles are essential for elucidating molecular and biological logic underlying gene expression programs. Genes that encode similar protein components are often coordinately regulated. Recent application of gene expression profiling to the normal human renal cortical tissue, experimental in vitro and in vivo models has shown that cellular activation is accompanied by changes of hundreds of genes in parallel. The databases of gene expression emerging from these studies will be used to interpret the pathological changes in gene expression that accompany a variety of human renal diseases.

Gene Expression Profiling↗
PubMedDate not supplied

Bioinformatics-based discovery of a novel factor with apparent specificity to colon cancer.

In a previous study, a data mining tool called Digital Differential Display (DDD) from the Cancer Genome Anatomy Project (CGAP) was used to predict solid tumor- and organ-specific genes from the expressed sequence tag (EST) database. To validate the use of bioinformatics approaches in gene discovery, one of the ESTs, which was predicted to be colon tumor-specific, was chosen for further study. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) analysis of matched sets of cDNAs from normal and colon tumor tissues indicated that the EST was specifically expressed in the majority of colon tumors. Expression was also detected in early adenomas. Among other normal tissues, EST expression was detected only in the small intestine. The colon tumor specificity of this EST was inferred from the lack of expression in carcinomas of the breast, lung, ovary, pancreas and prostate. To validate the computational prediction of specificity, a full-length cDNA encompassing the entire open reading frame was cloned and, in view of its apparent specificity to the colon tumors, this gene was termed Colon Carcinoma Related Gene (CCRG). CCRG encodes a novel cysteine-rich motif and a putative signal peptide sequence. Supernatant from COS cells transfected with the CCRG expression vector stimulated proliferation of colon cancer cells. Immunoreactive CCRG was also detected in the paraffin sections of colon tumor samples. CCRG belongs to a new class of growth factors and may be important in the diagnosis and treatment of colon cancers. Identification of CCRG using bioinformatics approaches validates gene discovery using computational approaches.

Amino Acid Sequence↗
PubMed2003

Identification of novel tropomyosin 1 genes of pufferfish (Fugu rubripes) on genomic sequences and tissue distribution of their transcripts.

Fugu genome database enabled us to identify two novel tropomyosin 1 (TPM1) genes through in silico data mining and isolation of their corresponding cDNAs in vivo. The duplicate TPM1 genes in Japanese pufferfish Fugu rubripes suggest that additional an ancient segmental duplication or whole genome duplication occurred in fish lineage, which, like many other reported Fugu genes, showed reduction in genomic size in comparison with their human homologue. Computer analysis predicted that the coiled-coil probabilities, that were thought to be the most major function of TPM, were the same between the two TPM1 isoforms. We confirmed that the tissue expression profiles of the two TPM1 genes differed from each other, which implied that changes in expression pattern could fix duplicated TPM1 genes although the two TPM1 isoforms appear to have similar function.

Animals↗
PubMed2000

UTR reconstruction and analysis using genomically aligned EST sequences.

Untranslated regions (UTR) play important roles in the posttranscriptional regulation of mRNA processing. There is a wealth of UTR-related information to be mined from the rapidly accumulating EST collections. A computational tool, UTR-extender, has been developed to infer UTR sequences from genomically aligned ESTs. It can completely and accurately reconstruct 72% of the 3' UTRs and 15% of the 5' UTRs when tested using 908 functionally cloned transcripts. In addition, it predicts extensions for 11% of the 5' UTRs and 28% of the 3' UTRs. These extension regions are validated by examining splicing frequencies and conservation levels. We also developed a method called polyadenylation site scan (PASS) to precisely map polyadenylation sites in human genomic sequences. A PASS analysis of 908 genic regions estimates that 40-50% of human genes undergo alternative polyadenylation. Using EST redundancy to assess expression levels, we also find that genes with short 3' UTRs tend to be highly expressed.

Algorithms↗
PubMed2003

EST mining and functional expression assays identify extracellular effector proteins from the plant pathogen Phytophthora.

Plant pathogenic microbes have the remarkable ability to manipulate biochemical, physiological, and morphological processes in their host plants. These manipulations are achieved through a diverse array of effector molecules that can either promote infection or trigger defense responses. We describe a general functional genomics approach aimed at identifying extracellular effector proteins from plant pathogenic microorganisms by combining data mining of expressed sequence tags (ESTs) with virus-based high-throughput functional expression assays in plants. PexFinder, an algorithm for automated identification of extracellular proteins from EST data sets, was developed and applied to 2147 ESTs from the oomycete plant pathogen Phytophthora infestans. The program identified 261 ESTs (12.2%) corresponding to a set of 142 nonredundant Pex (Phytophthora extracellular protein) cDNAs. Of these, 78 (55%) Pex cDNAs were novel with no significant matches in public databases. Validation of PexFinder was performed using proteomic analysis of secreted protein of P. infestans. To identify which of the Pex cDNAs encode effector proteins that manipulate plant processes, high-throughput functional expression assays in plants were performed on 63 of the identified cDNAs using an Agrobacterium tumefaciens binary vector carrying the potato virus X (PVX) genome. This led to the discovery of two novel necrosis-inducing cDNAs, crn1 and crn2, encoding extracellular proteins that belong to a large and complex protein family in Phytophthora. Further characterization of the crn genes indicated that they are both expressed in P. infestans during colonization of the host plant tomato and that crn2 induced defense-response genes in tomato. Our results indicate that combining data mining using PexFinder with PVX-based functional assays can facilitate the discovery of novel pathogen effector proteins. In principle, this strategy can be applied to a variety of eukaryotic plant pathogens, including oomycetes, fungi, and nematodes.

Algal Proteins↗
PubMed2024

Discovery and characterization of complete genomes of 38 head-tailed proviruses in four predominant phyla of archaea.

Archaea play a significant role in natural ecosystems and the human body. Archaeal viruses exert a considerable influence on the structure and composition of archaeal communities and their associated ecological environments. The present study revealed the complete genomes of 38 archaeal head-tailed proviruses through comprehensive data mining. The hosts of these proviruses were identified as belonging to the following four dominant phyla: Halobacteriota, Thermoplasmatota, Thermoproteota, and Nanoarchaeota. In addition to the 14 proviruses of halophilic archaea related to the Graaviviridae family, the remaining proviruses exhibited limited genetic similarities to known (pro)viruses, suggesting the existence of 14 potential novel families. Of the 38 archaeal proviruses, 30 have the potential to lyse host cells. Eleven proviruses contain genes linked to antiviral defense mechanisms, including those involved in restriction modification (RM), clustered regularly interspaced short palindromic repeat (CRISPR)-associated (CRISPR-Cas) nucleases, defense island system associated with restriction-modification (DISARM), and DNA degradation (Dnd). Moreover, auxiliary metabolic genes were identified in the proviruses of Bathyarchaeia and Halobacteriota archaea, including those involved in carbohydrate and amino acid metabolism. Our findings indicate the diversity of archaeal viruses, their interactions with archaeal hosts, and their roles in the adaptation of the host.IMPORTANCEThe field of archaeal virology has seen a rapid expansion through the use of metagenomics, yet the diversity of these viruses remains largely uncharted. In this study, the complete genomes of 38 novel archaeal proviruses were identified for the following four dominant phyla: Halobacteriota, Thermoplasmatota, Thermoproteota, and Nanoarchaeota. Two families and six genera of Archaea were the first to be identified as hosts for viruses. The proviruses were found to contain diverse genes that were involved in distinct adaptation strategies of viruses to hosts. Our findings contribute to the expansion of the lineages of archaeal viruses and highlight their intricate interactions and essential roles in enabling host survival and adaptation to diverse environmental conditions.

Archaea↗
PubMed2002

Distribution patterns of over-represented k-mers in non-coding yeast DNA.

MOTIVATION: Over-represented k-mers in genomic DNA regions are often of particular biological interest. For example, over-represented k-mers in co-regulated families of genes are associated with the DNA binding sites of transcription factors. To measure over-representation, we introduce a statistical background model based on single-mismatches, and apply it to the pooled 500 bp ORF Upstream Regions (USRs) of yeast. More importantly, we investigate the context and spatial distribution of over-represented k-mers in yeast USRs. RESULTS: Single and double-stranded spatial distributions of most over-represented k-mers are highly non-random, and predominantly cluster into a small number of classes that are robust with respect to over-representation measures. Specifically, we show that the three most common distribution patterns can be related to DNA structure, function, and evolution and correspond to: (a) homologous ORF clusters associated with sharply localized distributions; (b) regulatory elements associated with a symmetric broad hill-shaped distribution in the 50-200 bp USR; and (c) runs of As, Ts, and ATs associated with a broad hill-shaped distribution also in the 50-200 bp USR, with extreme structural properties. Analysis of over-representation, homology, localization, and DNA structure are essential components of a general data-mining approach to finding biologically important k-mers in raw genomic DNA and understanding the 'lexicon' of regulatory regions.

Amino Acid Motifs↗
PubMed2003

LTR_STRUC: a novel search and identification program for LTR retrotransposons.

MOTIVATION: Long terminal repeat (LTR) retrotransposons constitute a substantial fraction of most eukaryotic genomes and are believed to have a significant impact on genome structure and function. Conventional methods used to search for LTR retrotransposons in genome databases are labor intensive. We present an efficient, reliable and automated method to identify and analyze members of this important class of transposable elements. RESULTS: We have developed a new data-mining program, LTR_STRUC (LTR retrotransposon structure program) which identifies and automatically analyzes LTR retrotransposons in genome databases by searching for structural features characteristic of such elements. LTR_STRUC has significant advantages over conventional search methods in the case of LTR retrotransposon families having low sequence homology to known queries or families with atypical structure (e.g. non-autonomous elements lacking canonical retroviral ORFs) and is thus a discovery tool that complements established methods. LTR_STRUC finds LTR retrotransposons using an algorithm that encompasses a number of tasks that would otherwise have to be initiated individually by the user. For each LTR retrotransposon found, LTR_STRUC automatically generates an analysis of a variety of structural features of biological interest. AVAILABILITY: The LTR_STRUC program is currently available as a console application free of charge to academic users from the authors.

Algorithms↗
PubMed2005

MILANO--custom annotation of microarray results using automatic literature searches.

BACKGROUND: High-throughput genomic research tools are becoming standard in the biologist's toolbox. After processing the genomic data with one of the many available statistical algorithms to identify statistically significant genes, these genes need to be further analyzed for biological significance in light of all the existing knowledge. Literature mining--the process of representing literature data in a fashion that is easy to relate to genomic data--is one solution to this problem. RESULTS: We present a web-based tool, MILANO (Microarray Literature-based Annotation), that allows annotation of lists of genes derived from microarray results by user defined terms. Our annotation strategy is based on counting the number of literature co-occurrences of each gene on the list with a user defined term. This strategy allows the customization of the annotation procedure and thus overcomes one of the major limitations of the functional annotations usually provided with microarray results. MILANO expands the gene names to include all their informative synonyms while filtering out gene symbols that are likely to be less informative as literature searching terms. MILANO supports searching two literature databases: GeneRIF and Medline (through PubMed), allowing retrieval of both quick and comprehensive results. We demonstrate MILANO's ability to improve microarray analysis by analyzing a list of 150 genes that were affected by p53 overproduction. This analysis reveals that MILANO enables immediate identification of known p53 target genes on this list and assists in sorting the list into genes known to be involved in p53 related pathways, apoptosis and cell cycle arrest. CONCLUSIONS: MILANO provides a useful tool for the automatic custom annotation of microarray results which is based on all the available literature. MILANO has two major advances over similar tools: the ability to expand gene names to include all their informative synonyms while removing synonyms that are not informative and access to the GeneRIF database which provides short summaries of curated articles relevant to known genes. MILANO is available at http://milano.md.huji.ac.il.

Algorithms↗
PubMed2026

Mining the sHSP20 (small heat-shock protein) gene family in finger millet (Eleusine coracana (L.) Gaertn.): structural, evolutionary and predicted abiotic-stress-responsive insights.

Small heat-shock proteins (sHSPs, the HSP20 family) are ATP-independent molecular chaperones that hold partially unfolded substrates and protect the proteome during heat and other abiotic stresses; every member is defined by a conserved &#x3b1;-crystallin domain (ACD). Finger millet (Eleusine coracana) is a climate-resilient, calcium-rich allotetraploid cereal of the semi-arid tropics whose HSP20 repertoire had not been catalogued. The present study is an entirely computational (in silico) analysis of the chromosome-scale reference genome of finger millet (NCBI GenBank assembly GCA_032690845.1, cultivar KNE 796-S). Mining the predicted proteome with the ACD profile (Pfam PF00011) and confirming every candidate by NCBI CD-search recovered 76 non-redundant ACD-bearing HSP20 genes (EcHSP20-1-EcHSP20-76). Based on phylogeny and TargetP-predicted localization, the members were classified into ten subfamilies: seven cytosolic/nuclear classes (C-I to C-VII, 60 members) together with chloroplastic (11), mitochondrial (3) and endoplasmic-reticulum (2) groups. The proteins ranged from 110 to 355 amino acids (12.1-39.2&#xa0;kDa) with theoretical pI of 4.85-9.69. The 76 loci were distributed over 14 of the 18 chromosomes and were conspicuously absent from chromosomes 8&#xa0;A, 8B, 9&#xa0;A and 9B, with pronounced clustering on chromosomes 1, 2, 3 and 6. Duplication analysis detected 149 paralogous pairs (49 homoeologous, 80 segmental/dispersed and 18 tandem); 147 of 148 pairs for which substitution rates could be calculated returned Ka/Ks&#x2009;<&#x2009;1 (mean 0.20), indicating strong purifying selection consistent with retention after whole-genome/allopolyploid duplication. Promoter analysis (PlantCARE) revealed enrichment of abscisic-acid-responsive (ABRE), MYB/MYC drought-related, STRE, DRE, low-temperature (LTR) and methyl-jasmonate/salicylic-acid elements, whereas canonical heat-shock elements (HSE) were not recovered. Expression profiling against a public drought transcriptome (SRP081350) showed that about half of the genes (39 of 76) are transcribed in leaf tissue, the expressed fraction being dominated by the cytosolic class C-I. This first finger-millet HSP20 catalogue provides a verified, reproducible framework and nominates computationally predicted candidate genes for future functional work on thermotolerance in cereals.

Allotetraploid↗