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PubMed2003

SMASHing regulatory sites in DNA by human-mouse sequence comparisons.

Regulatory sequence elements provide important clues to understanding and predicting gene expression. Although the binding sites for hundreds of transcription factors are known, there has been no systematic attempt to incorporate this information in the annotation of the human genome. Cross species sequence comparisons are critical to a meaningful annotation of regulatory elements since they generally reside in conserved non-coding regions. To take advantage of the recently completed drafts of the mouse and human genomes for annotating transcription factor binding sites, we developed SMASH, a computational pipeline that identifies thousands of orthologous human/ mouse proteins, maps them to genomic sequences, extracts and compares upstream regions and annotates putative regulatory elements in conserved, non-coding, upstream regions. Our current dataset consists of approximately 2,500 human/mouse gene pairs. Transcription start sites were estimated by mapping quasi-full length cDNA sequences. SMASH uses a novel probabilistic method to identify putative conserved binding sites that takes into account the competition between transcription factors for binding DNA. SMASH presents the results via a genome browser web interface which displays the predicted regulatory information together with the current annotations for the human genome. Our results are validated by comparison to previously published experimental data. SMASH results compare favorably to other existing computational approaches.

Algorithms↗
PubMed2003

Mouse models of Down syndrome: how useful can they be? Comparison of the gene content of human chromosome 21 with orthologous mouse genomic regions.

With an incidence of approximately 1 in 700 live births, Down syndrome (DS) remains the most common genetic cause of mental retardation. The phenotype is assumed to be due to overexpression of some number of the >300 genes encoded by human chromosome 21. Mouse models, in particular the chromosome 16 segmental trisomies, Ts65Dn and Ts1Cje, are indispensable for DS-related studies of gene-phenotype correlations. Here we compare the updated gene content of the finished sequence of human chromosome 21 (364 genes and putative genes) with the gene content of the homologous mouse genomic regions (291 genes and putative genes) obtained from annotation of the public sector C57Bl/6 draft sequence. Annotated genes fall into one of three classes. First, there are 170 highly conserved, human/mouse orthologues. Second, there are 83 minimally conserved, possible orthologues. Included among the conserved and minimally conserved genes are 31 antisense transcripts. Third, there are species-specific genes: 111 spliced human transcripts show no orthologues in the syntenic mouse regions although 13 have homologous sequences elsewhere in the mouse genomic sequence, and 38 spliced mouse transcripts show no identifiable human orthologues. While these species-specific genes are largely based solely on spliced EST data, a majority can be verified in RNA expression experiments. In addition, preliminary data suggest that many human-specific transcripts may represent a novel class of primate-specific genes. Lastly, updated functional annotation of orthologous genes indicates genes encoding components of several cellular pathways are dispersed throughout the orthologous mouse chromosomal regions and are not completely represented in the Down syndrome segmental mouse models. Together, these data point out the potential for existing mouse models to produce extraneous phenotypes and to fail to produce DS-relevant phenotypes.

Alternative Splicing↗
PubMedDate not supplied

Experiments in searching small proteins in unannotated large eukaryotic genomes.

There is growing interest to use mass spectrometry data to search genome sequences directly. Previous work by other authors demonstrated that this approach is able to correct and complement available genome annotations. We discuss the practical difficulty of searching large eukaryotic genomes with peptide ion trap tandem mass spectra of small proteins (<40 kDa). The challenging problem of automatically identifying peptides that span across exon/intron boundaries is explored for the first time by using experimental data. In a human genome search, we find that roughly 30% of the peptides are missed, due to various reasons, compared to a Swiss-Prot search. We show that this percentage is significantly reduced with improved parent mass accuracy. We finally provide several examples of predicted gene structures that could be improved by proteomics data, in particular by peptides spanning across exon/intron boundaries.

Adult↗
PubMed1996

Integrated multimedia timeline of medical images and data for thoracic oncology patients.

A prototype multimedia medical database has been developed to provide image and textual data for thoracic oncology patients undergoing treatment of advanced malignancies. The database integrates image data from the hospital picture archiving and communication system with textual reports from the radiology information system, alphanumeric data contained in the hospital information system, and other electronic medical data. The database presents information in a timeline format and also contains visualization programs that permit the user to view and annotate radiographic measurements in tabular or graphic form. The database provides an efficient and intuitive display of the changing status of oncology patients. The ability to integrate, manage, and access relevant multimedia information may substantially enhance communication among distributed multidisciplinary health care providers and may ensure greater consistency and completeness of patient-related data.

Databases, Factual↗
PubMed2026

Clinically Relevant Pharmacogenomic Variant Frequencies in Kazakh, Russian, and Uzbek Population Groups Residing in Kazakhstan.

Central Asian populations remain underrepresented in pharmacogenomic research, limiting the availability of population-specific data for genotype-informed prescribing and precision medicine. This study analyzed clinically relevant pharmacogenomic variant frequencies in Kazakh, Russian, and Uzbek population groups residing in Kazakhstan using genome-wide genotype data from 1301 individuals: Kazakh (n = 1111), Russian (n = 156), and Uzbek (n = 34). ClinPGx, a PharmGKB-based clinical annotation framework that prioritizes variant-drug associations according to levels of evidence, was used to select variants with evidence levels 1A, 1B, and 2A. In total, 112 directly genotyped variants were retained for population-specific allele and genotype frequency analysis. All 112 variants were queried against the gnomAD v4.1 genome and exome reference datasets. Of these, matching allele-frequency data for the predefined reported allele were available in at least one of the two gnomAD datasets for 103 variants, whereas for 9 variants the VEP-based query did not return a matching gnomAD frequency for that allele. Frequencies were reported for the same predefined reported allele across all groups, and differences between the study groups were assessed using 95% confidence intervals, Fisher's exact tests, and false discovery rate correction. Genotype counts and the proportions of individuals carrying at least one copy of the reported allele were also summarized for all selected variants. Several pharmacogenomic variants showed population-specific frequency patterns, including NUDT15 rs116855232, SLCO1B1 rs4149056, VKORC1 rs9934438, and UGT1A1 rs10929302. Comparison with gnomAD showed that the observed frequencies were variant-specific and could not be consistently approximated by a single broad genetic ancestry group. Reference-based population structure analysis provided additional ancestry context and supported separate reporting by population group. The study did not evaluate clinical outcomes or make individual prescribing recommendations, and the small Uzbek sample size limits the precision of frequency estimates for this group, particularly for rare variants. Overall, this study provides a clinically prioritized pharmacogenomic frequency resource for underrepresented population groups in Kazakhstan and supports broader Central Asian representation in pharmacogenomic implementation research.

Central Asia↗
PubMed2002

The human genome browser at UCSC.

As vertebrate genome sequences near completion and research refocuses to their analysis, the issue of effective genome annotation display becomes critical. A mature web tool for rapid and reliable display of any requested portion of the genome at any scale, together with several dozen aligned annotation tracks, is provided at http://genome.ucsc.edu. This browser displays assembly contigs and gaps, mRNA and expressed sequence tag alignments, multiple gene predictions, cross-species homologies, single nucleotide polymorphisms, sequence-tagged sites, radiation hybrid data, transposon repeats, and more as a stack of coregistered tracks. Text and sequence-based searches provide quick and precise access to any region of specific interest. Secondary links from individual features lead to sequence details and supplementary off-site databases. One-half of the annotation tracks are computed at the University of California, Santa Cruz from publicly available sequence data; collaborators worldwide provide the rest. Users can stably add their own custom tracks to the browser for educational or research purposes. The conceptual and technical framework of the browser, its underlying MYSQL database, and overall use are described. The web site currently serves over 50,000 pages per day to over 3000 different users.

California↗
PubMed2005

Detecting clusters of different geometrical shapes in microarray gene expression data.

MOTIVATION: Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Many clustering methods have been proposed for clustering gene-expression data, including the hierarchical clustering, k-means clustering and self-organizing map (SOM). However, the conventional methods are limited to identify different shapes of clusters because they use a fixed distance norm when calculating the distance between genes. The fixed distance norm imposes a fixed geometrical shape on the clusters regardless of the actual data distribution. Thus, different distance norms are required for handling the different shapes of clusters. RESULTS: We present the Gustafson-Kessel (GK) clustering method for microarray gene-expression data. To detect clusters of different shapes in a dataset, we use an adaptive distance norm that is calculated by a fuzzy covariance matrix (F) of each cluster in which the eigenstructure of F is used as an indicator of the shape of the cluster. Moreover, the GK method is less prone to falling into local minima than the k-means and SOM because it makes decisions through the use of membership degrees of a gene to clusters. The algorithmic procedure is accomplished by the alternating optimization technique, which iteratively improves a sequence of sets of clusters until no further improvement is possible. To test the performance of the GK method, we applied the GK method and well-known conventional methods to three recently published yeast datasets, and compared the performance of each method using the Saccharomyces Genome Database annotations. The clustering results of the GK method are more significantly relevant to the biological annotations than those of the other methods, demonstrating its effectiveness and potential for clustering gene-expression data. AVAILABILITY: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://dragon.kaist.ac.kr/gk.

Algorithms↗
PubMed2004

Single nucleotide polymorphisms associated with rat expressed sequences.

Single nucleotide polymorphisms (SNPs) are the most common source of genetic variation in populations and are thus most likely to account for the majority of phenotypic and behavioral differences between individuals or strains. Although the rat is extensively studied for the latter, data on naturally occurring polymorphisms are mostly lacking. We have used publicly available sequences consisting of whole-genome shotgun (WGS), expressed sequence tag (EST), and mRNA data as a source for the in silico identification of SNPs in gene-coding regions and have identified a large collection of 33,305 high-quality candidate SNPs. Experimental verification of 471 candidate SNPs using a limited set of rat isolates revealed a confirmation rate of approximately 50%. Although the majority of SNPs were identified between Sprague-Dawley (EST data) and Brown Norway (WGS data) strains, we found that 66% of the verified variations are common among different rat strains. All SNPs were extensively annotated, including chromosomal and genetic map information, and nonsynonymous SNPs were analyzed by SIFT and PolyPhen prediction programs for their potential deleterious effect on protein function. Interestingly, we retrieved three SNPs from the database that result in the introduction of a premature stop codon and that could be confirmed experimentally. Two of these "in silico-identified knockouts" reside in interesting QTL regions. Data are publicly available via a Web interface (http://cascad.niob.knaw.nl), allowing simple and advanced search queries.

Animals↗
PubMed2006

Recurrent use of evolutionary importance for functional annotation of proteins based on local structural similarity.

The annotation of protein function has not kept pace with the exponential growth of raw sequence and structure data. An emerging solution to this problem is to identify 3D motifs or templates in protein structures that are necessary and sufficient determinants of function. Here, we demonstrate the recurrent use of evolutionary trace information to construct such 3D templates for enzymes, search for them in other structures, and distinguish true from spurious matches. Serine protease templates built from evolutionarily important residues distinguish between proteases and other proteins nearly as well as the classic Ser-His-Asp catalytic triad. In 53 enzymes spanning 33 distinct functions, an automated pipeline identifies functionally related proteins with an average positive predictive power of 62%, including correct matches to proteins with the same function but with low sequence identity (the average identity for some templates is only 17%). Although these template building, searching, and match classification strategies are not yet optimized, their sequential implementation demonstrates a functional annotation pipeline which does not require experimental information, but only local molecular mimicry among a small number of evolutionarily important residues.

Algorithms↗
PubMed2006

Comparative genomics as a tool for gene discovery.

With the increasing availability of data from multiple eukaryotic genome sequencing projects, attention has focused on interspecific comparisons to discover novel genes and transcribed genomic sequences. Generally, these extrinsic strategies combine ab initio gene prediction with expression and/or homology data to identify conserved gene candidates between two or more genomes. Interspecific sequence analyses have proven invaluable for the improvement of existing annotations, automation of annotation, and identification of novel coding regions and splice variants. Further, comparative genomic approaches hold the promise of improved prediction of terminal or small exons, microRNA precursors, and small peptide-encoding open reading frames--sequence elements that are difficult to identify through purely intrinsic methodologies in the absence of experimental data.

Computational Biology↗
PubMedDate not supplied

The DNA sequence and comparative analysis of human chromosome 20.

The finished sequence of human chromosome 20 comprises 59,187,298 base pairs (bp) and represents 99.4% of the euchromatic DNA. A single contig of 26 megabases (Mb) spans the entire short arm, and five contigs separated by gaps totalling 320 kb span the long arm of this metacentric chromosome. An additional 234,339 bp of sequence has been determined within the pericentromeric region of the long arm. We annotated 727 genes and 168 pseudogenes in the sequence. About 64% of these genes have a 5' and a 3' untranslated region and a complete open reading frame. Comparative analysis of the sequence of chromosome 20 to whole-genome shotgun-sequence data of two other vertebrates, the mouse Mus musculus and the puffer fish Tetraodon nigroviridis, provides an independent measure of the efficiency of gene annotation, and indicates that this analysis may account for more than 95% of all coding exons and almost all genes.

Animals↗
PubMed2006

NOPdb: Nucleolar Proteome Database.

The Nucleolar Proteome Database (NOPdb) archives data on >700 proteins that were identified by multiple mass spectrometry (MS) analyses from highly purified preparations of human nucleoli, the most prominent nuclear organelle. Each protein entry is annotated with information about its corresponding gene, its domain structures and relevant protein homologues across species, as well as documenting its MS identification history including all the peptides sequenced by tandem MS/MS. Moreover, data showing the quantitative changes in the relative levels of approximately 500 nucleolar proteins are compared at different timepoints upon transcriptional inhibition. Correlating changes in protein abundance at multiple timepoints, highlighted by visualization means in the NOPdb, provides clues regarding the potential interactions and relationships between nucleolar proteins and thereby suggests putative functions for factors within the 30% of the proteome which comprises novel/uncharacterized proteins. The NOPdb (http://www.lamondlab.com/NOPdb) is searchable by either gene names, nucleotide or protein sequences, Gene Ontology terms or motifs, or by limiting the range for isoelectric points and/or molecular weights and links to other databases (e.g. LocusLink, OMIM and PubMed).

Cell Nucleolus↗
PubMed2025

Expanding kinetoplastid genome annotation through protein structure comparison.

Kinetoplastids belong to the Discoba supergroup, an early divergent eukaryotic clade. Although the amount of genomic information on these parasites has grown substantially, assigning gene functions through traditional sequence-based homology methods remains challenging. Recently, significant advancements have been made in in-silico protein structure prediction and algorithms for rapid and precise large-scale protein structure comparisons. In this work, we developed a protein structure-based homology search pipeline (ASC, Annotation by Structural Comparisons) and applied it to transfer biological information to all kinetoplastid proteins available in TriTrypDB, the reference database for this lineage. Our pipeline enabled the assignment of structural similarity to a substantial portion of kinetoplastid proteins, improving current knowledge through annotation transfer. Additionally, we identified structural homologs for representatives of 6,700 uncharacterized proteins across 33 kinetoplastid species, proteins that could not be annotated using existing sequence-based tools and databases. As a result, this approach allowed us to infer potential biological information for a considerable number of kinetoplastid proteins. Among these, we identified structural homologs to ubiquitous eukaryotic proteins that are challenging to detect in kinetoplastid genomes through standard genome annotation pipelines. The results (KASC, Kinetoplastid Annotation by Structural Comparison) are openly accessible to the community at kasc.fcien.edu.uy through a user-friendly, gene-by-gene interface that enables visual inspection of the data.

Kinetoplastida↗
PubMed2005

Data preparation and interannotator agreement: BioCreAtIvE task 1B.

BACKGROUND: We prepared and evaluated training and test materials for an assessment of text mining methods in molecular biology. The goal of the assessment was to evaluate the ability of automated systems to generate a list of unique gene identifiers from PubMed abstracts for the three model organisms Fly, Mouse, and Yeast. This paper describes the preparation and evaluation of answer keys for training and testing. These consisted of lists of normalized gene names found in the abstracts, generated by adapting the gene list for the full journal articles found in the model organism databases. For the training dataset, the gene list was pruned automatically to remove gene names not found in the abstract; for the testing dataset, it was further refined by manual annotation by annotators provided with guidelines. A critical step in interpreting the results of an assessment is to evaluate the quality of the data preparation. We did this by careful assessment of interannotator agreement and the use of answer pooling of participant results to improve the quality of the final testing dataset. RESULTS: Interannotator analysis on a small dataset showed that our gene lists for Fly and Yeast were good (87% and 91% three-way agreement) but the Mouse gene list had many conflicts (mostly omissions), which resulted in errors (69% interannotator agreement). By comparing and pooling answers from the participant systems, we were able to add an additional check on the test data; this allowed us to find additional errors, especially in Mouse. This led to 1% change in the Yeast and Fly "gold standard" answer keys, but to an 8% change in the mouse answer key. CONCLUSION: We found that clear annotation guidelines are important, along with careful interannotator experiments, to validate the generated gene lists. Also, abstracts alone are a poor resource for identifying genes in paper, containing only a fraction of genes mentioned in the full text (25% for Fly, 36% for Mouse). We found that there are intrinsic differences between the model organism databases related to the number of synonymous terms and also to curation criteria. Finally, we found that answer pooling was much faster and allowed us to identify more conflicting genes than interannotator analysis.

Animals↗
PubMed2025

ATAC-seq in Emerging Model Organisms: Challenges and Strategies.

The Assay for Transposase-Accessible Chromatin with sequencing (ATAC-seq) is a versatile and widely utilized method for identifying potential regulatory regions, such as promoters and enhancers, within a genome. ATAC-seq has been successfully applied to a wide range of established and emerging model organisms. However, implementing this method in emerging model systems, such as arthropods, can be challenging due to several factors that influence data quality. These factors include the availability of a sufficient amount and quality of tissue or cells, the need for species- and tissue-specific protocol optimization, the completeness and accuracy of the reference genome, and the quality of the genome annotation. In this article, we emphasize the key steps in the ATAC-seq protocol that, based on our experience, have the greatest impact on data quality when adapting this method for emerging model organisms. Specifically, we discuss the importance of nuclei isolation, the incubation conditions of the Tn5 transposase, and PCR amplification of the library. Furthermore, we outline essential quality checkpoints during the bioinformatic analysis of ATAC-seq data to assist in assessing data integrity and consistency. Given that many emerging model organisms may not be readily available in laboratory cultures, we also emphasize the importance of evaluating how different preservation methods affect ATAC-seq data quality. Based on examples in one spider and one ant species, we demonstrate that replication and thorough quality controls at all steps of the protocol and data analysis are essential to assess the usability of ATAC-seq data. Our data highlights the importance of isolating the right number of intact nuclei, as well as ensuring optimal amplification conditions during library preparation to obtain good-quality sequence data for downstream analyses. We recommend using fresh tissue samples if possible because we show that direct cryopreservation of the tissue may affect chromatin integrity. This effect could be avoided or reduced by preserving the homogenate in cell culture medium. Overall, we explain the ATAC-seq protocol and downstream analyses in detail and give step-by-step advice to researchers who are new to the field and want to implement this method. With careful planning and validation, ATAC-seq can reveal the regulatory landscape of a genome and aid in identifying elements that govern gene expression.

Animals↗
PubMed1999

Comparative sequence analysis of the mouse and human Lgn1/SMA interval.

Human chromosome 5q11.2-q13.3 and its ortholog on mouse chromosome 13 contain candidate genes for an inherited human neurodegenerative disorder called spinal muscular atrophy (SMA) and for an inherited mouse susceptibility to infection with Legionella pneumophila (Lgn1). These homologous genomic regions also have unusual repetitive organizations that create practical difficulties in mapping and raise interesting issues about the evolutionary origin of the repeats. In an attempt to analyze this region in detail, and as a way to identify additional candidate genes for these diseases, we have determined the sequence of 179 kb of the mouse Lgn1/SMA interval. We have analyzed this sequence using BLAST searches and various exon prediction programs to identify potential genes. Since these methods can generate false-positive exon declarations, our alignments of the mouse sequence with available human orthologous sequence allowed us to discriminate rapidly among this collection of potential coding regions by indicating which regions were well conserved and were more likely to represent actual coding sequence. As a result of our analysis, we accurately mapped two additional genes in the SMA interval that can be tested for involvement in the pathogenesis of SMA. While no new Lgn1 candidates emerged, we have identified new genetic markers that exclude Smn as an Lgn1 candidate. In addition to providing important resources for studying SMA and Lgn1, our data provide further evidence of the value of sequencing the mouse genome as a means to help with the annotation of the human genomic sequence and vice versa.

Animals↗
PubMed2006

Integration of genomic and metabonomic data in systems biology--are we 'there' yet?

The measurement of genes, proteins and metabolites has gained increasing acceptance as a means by which to study the response of an organism to stimuli, whether they are environmental, genetic, pharmacological, toxicological, etc. Typically referred to as genomics, proteomics, and metabonomics or metabolomics, respectively, these methods as independent entities have undoubtedly provided new biological insight that was not attainable a decade ago. Not surprisingly, scientists continue to push the boundaries to extract knowledge from data, and it is currently recognized that the full realization of these technologies is limited by a lack of tools to enable data integration. Integration of these 'omic datasets, or integromics, is desirable as it links the individual biological elements together to provide a more complete understanding of dynamic biological processes. Accordingly, in addition to developing new data analysis methods to extract further details from each of the high-content datasets individually, effort is also being expended to create or improve statistical methods, databases, annotations and pathway mapping to maximize our learning. There are several recent examples, in both mammalian and non-mammalian systems, in which genes, proteins and/or metabolites have been integrated using either biology- or data-driven strategies. Herein, key findings are reviewed, gaps in our current tools and technologies are identified and illustrated, and perspective is provided on the potential of integromics in biological research.

Acetaminophen↗