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At least 217 records · Page 12Linked to original sources
PubMed1999

Phylogeny of Marsileaceous Ferns and Relationships of the Fossil Hydropteris pinnata Reconsidered.

Recent phylogenetic studies have provided compelling evidence that confirms the once disputed hypothesis of monophyly for heterosporous leptosporangiate ferns (Marsileaceae and Salviniaceae). Hypotheses for relationships among the three genera of Marsileaceae (Marsilea, Regnellidium, and Pilularia), however, have continued to be in conflict. The phylogeny of Marsileaceae is investigated here using information from morphology and rbcL sequence data. In addition, relationships among all heterosporous ferns, including the whole-plant fossil Hydropteris pinnata are reconsidered. Data sets of 71 morphological and 1239 rbcL characters for 23 leptosporangiate ferns, including eight heterosporous ingroup taxa and 15 homosporous outgroup taxa, were subjected to maximum parsimony analysis. Morphological analyses were carried out both with and without the fossil Hydropteris, and it was excluded from all analyses with rbcL data. An annotated list of the 71 morphological characters is provided in the appendix. For comparative purposes, the Rothwell and Stockey (1994) data set was also reanalyzed here. The best estimate of phylogenetic relationships for Marsileaceae in all analyses is that Pilularia and Regnellidium are sister taxa and Marsilea is sister to that clade. Morphological synapomorphies for various nodes are discussed. Analyses that included Hydropteris resulted in two most-parsimonious trees that differ only in the placement of the fossil. One topology is identical to the relationship found by Rothwell and Stockey (1994), placing the fossil sister to the Azolla plus Salvinia clade. The alternative topology places Hydropteris as the most basal member of the heterosporous fern clade. Equivocal interpretations for character evolution in heterosporous ferns are discussed in the context of these two most-parsimonious trees. Because of the observed degree of character ambiguity, the phylogenetic placement of Hydropteris is best viewed as unresolved, and recognition of the suborder Hydropteridineae, as circumscribed by Rothwell and Stockey (1994), is regarded as premature. The two competing hypotheses of relationships for heterosporous ferns are also compared with the known temporal distribution of relevant taxa. Stratigraphic fit of the phylogenetic estimates is measured by using the Stratigraphic Consistency Index and by comparison with minimum divergence times.

Journal Article↗
PubMed2026

scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.

Recent advances in single-cell epigenomic techniques have increased the demand for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis. One key analytical task is to determine cell type identity based on epigenetic data. Here, we introduce scATAnno, a Python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates reference atlases from publicly available datasets, enabling accurate cell type annotation by integrating query data with reference atlases without the use of single-cell RNA sequencing (scRNA-seq) data. To enhance annotation accuracy, we incorporated k-nearest neighbors (KNN)-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compared and benchmarked scATAnno against five other published cell annotation approaches, demonstrating its superior performance across multiple datasets and metrics. We further showcased the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cells (PBMCs), triple-negative breast cancer (TNBC), and basal cell carcinoma (BCC), and demonstrated that scATAnno accurately annotates cell types across diverse biological conditions. Overall, scATAnno is a useful tool for scATAC-seq reference atlas construction and cell type annotation and can facilitate the interpretation of new scATAC-seq datasets in complex biological systems. scATAnno is publicly available at https://scatanno-main.readthedocs.io/.

Single-Cell Analysis↗
PubMed2006

Web-based analysis of the mouse transcriptome using Genevestigator.

BACKGROUND: Gene function analysis often requires a complex and laborious sequence of laboratory and computer-based experiments. Choosing an effective experimental design generally results from hypotheses derived from prior knowledge or experimentation. Knowledge obtained from meta-analyzing compendia of expression data with annotation libraries can provide significant clues in understanding gene and network function, resulting in better hypotheses that can be tested in the laboratory. DESCRIPTION: Genevestigator is a microarray database and analysis system allowing context-driven queries. Simple but powerful tools allow biologists with little computational background to retrieve information about when, where and how genes are expressed. We manually curated and quality-controlled 3110 mouse Affymetrix arrays from public repositories. Data queries can be run against an annotation library comprising 160 anatomy categories, 12 developmental stage groups, 80 stimuli, and 182 genetic backgrounds or modifications. The quality of results obtained through Genevestigator is illustrated by a number of biological scenarios that are substantiated by other types of experimentation in the literature. CONCLUSION: The Genevestigator-Mouse database effectively provides biologically meaningful results and can be accessed at https://www.genevestigator.ethz.ch.

Animals↗
PubMed2003

MOPAC: motif finding by preprocessing and agglomerative clustering from microarrays.

We propose a novel strategy for discovering motifs from gene expression data. The gene expression data in our experiments comes from DNA Microarray analysis of the bacterium E. coli in response to recovery from nutrient starvation. We have annotated the data and identified the upregulated genes. Our interest is to find common regulatory motifs that are responsible for the upregulation of these specific genes. We assume that a common motif that a regulatory protein can bind to will be present in the upstream region of the upregulated genes and will not be present in the upstream regions of genes that showed a constant level of expression over time. Our objective is to find the common motifs that are present in at least some of the upstream sequences of upregulated genes and not present in the control set, which is the set of genes whose expression remained the same. Because it is possible that there could be several subsets of co-regulated genes under different control mechanisms among the co-expressed genes, we do not want to require motifs to be present in all upregulated sequences. Therefore, we propose a new algorithm for finding such motifs through stages of pre-processing, denoising, agglomerative clustering and consensus checking. Through this process, we have found some motifs that are good candidates for further validation.

Algorithms↗
PubMed2003

PRECIS: protein reports engineered from concise information in SWISS-PROT.

MOTIVATION: There have been several endeavours to address the problem of annotating sequence data computationally, but the task is non-trivial and few tools have emerged that gather useful information on a given sequence, or set of sequences, in a simple and convenient manner. As more genome projects bear fruit, the mass of uncharacterized sequence data accumulating in public repositories grows ever larger. There is thus a pressing need for tools to support the process of automatic analysis and annotation of newly determined sequences. With this in mind, we have developed PRECIS, which automatically creates protein reports from sets of SWISS-PROT entries, collating results into structured reports, detailing known biological and medical information, literature and database cross-references, and relevant keywords.

Abstracting and Indexing↗
PubMed2005

Storing, linking, and mining microarray databases using SRS.

BACKGROUND: SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range of different scientific databases in SRS, it has become a useful platform to incorporate and mine microarray data to facilitate the analyses of biological questions and non-hypothesis driven quests. Here we report various solutions and tools for integrating and mining annotated expression data in SRS. RESULTS: We devised an Auto-Upload Tool by which microarray data can be automatically imported into SRS. The dataset can be linked to other databases and user access can be set. The linkage comprehensiveness of microarray platforms to other platforms and biological databases was examined in a network of scientific databases. The stored microarray data can also be made accessible to external programs for further processing. For example, we built an interface to a program called Venn Mapper, which collects its microarray data from SRS, processes the data by creating Venn diagrams, and saves the data for interpretation. CONCLUSION: SRS is a useful database system to store, link and query various scientific datasets, including microarray data. The user-friendly Auto-Upload Tool makes SRS accessible to biologists for linking and mining user-owned databases.

Computational Biology↗
PubMed2006

ASAP: a resource for annotating, curating, comparing, and disseminating genomic data.

ASAP is a comprehensive web-based system for community genome annotation and analysis. ASAP is being used for a large-scale effort to augment and curate annotations for genomes of enterobacterial pathogens and for additional genome sequences. New tools, such as the genome alignment program Mauve, have been incorporated into ASAP in order to improve display and analysis of related genomes. Recent improvements to the database and challenges for future development of the system are discussed. ASAP is available on the web at https://asap.ahabs.wisc.edu/asap/logon.php.

Databases, Nucleic Acid↗
PubMed2000

Human immunodeficiency virus reverse transcriptase and protease sequence database.

The HIV RT and Protease Sequence Database is an online relational database that catalogs evolutionary and drug-related human immunodeficiency virus (HIV) reverse transcriptase (RT) and protease sequence variation (http://hivdb.stanford.edu). The database contains a compilation of nearly all published HIV RT and protease sequences including International Collaboration database submissions (e.g., GenBank) and sequences published in journal articles. Sequences are linked to data about the source of the sequence sample and the antiretroviral drug treatment history of the individual from whom the isolate was obtained. The database is curated and sequences are annotated with data from >230 literature references. Users can retrieve additional data and view alignments of sequence sets meeting specific criteria (e.g., treatment history, subtype, presence of a particular mutation). A gene-specific sequence analysis program, new user-defined queries and nearly 2000 additional sequences were added in 1999.

Databases, Factual↗
PubMed2006

Integrated analysis of gene expression by Association Rules Discovery.

BACKGROUND: Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process. RESULTS: In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work. CONCLUSION: The integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Engene software package.

Algorithms↗
PubMed2006

Improving sensitivity in shotgun proteomics using a peptide-centric database with reduced complexity: protease cleavage and SCX elution rules from data mining of MS/MS spectra.

Correct identification of a peptide sequence from MS/MS data is still a challenging research problem, particularly in proteomic analyses of higher eukaryotes where protein databases are large. The scoring methods of search programs often generate cases where incorrect peptide sequences score higher than correct peptide sequences (referred to as distraction). Because smaller databases yield less distraction and better discrimination between correct and incorrect assignments, we developed a method for editing a peptide-centric database (PC-DB) to remove unlikely sequences and strategies for enabling search programs to utilize this peptide database. Rules for unlikely missed cleavage and nontryptic proteolysis products were identified by data mining 11 849 high-confidence peptide assignments. We also evaluated ion exchange chromatographic behavior as an editing criterion to generate subset databases. When used to search a well-annotated test data set of MS/MS spectra, we found no loss of critical information using PC-DBs, validating the methods for generating and searching against the databases. On the other hand, improved confidence in peptide assignments was achieved for tryptic peptides, measured by changes in DeltaCN and RSP. Decreased distraction was also achieved, consistent with the 3-9-fold decrease in database size. Data mining identified a major class of common nonspecific proteolytic products corresponding to leucine aminopeptidase (LAP) cleavages. Large improvements in identifying LAP products were achieved using the PC-DB approach when compared with conventional searches against protein databases. These results demonstrate that peptide properties can be used to reduce database size, yielding improved accuracy and information capture due to reduced distraction, but with little loss of information compared to conventional protein database searches.

Amino Acid Sequence↗
PubMed1999

Human Immunodeficiency Virus Reverse Transcriptase and Protease Sequence Database.

The HIV RT and Protease Sequence Database is an on-line relational database that catalogues evolutionary and drug-related human immunodeficiency virus reverse transcriptase (RT) and protease sequence variation (http://hivdb.stanford.edu). The database contains a compilation of nearly all published HIV RT and protease sequences including International Collaboration database submissions (e.g., GenBank) and sequences published in journal articles. Sequences are linked to data about the source of the sequence sample and the anti-HIV drug treatment history of the individual from whom the isolate was obtained. The database is curated and sequences are annotated with data from 180 literature references. Users can retrieve additional data and view alignments of sequences sets meeting specific criteria (e.g., treatment history, subtype, presence of a particular mutation).

Anti-HIV Agents↗
PubMed2002

PlasmoDB: the Plasmodium genome resource. An integrated database providing tools for accessing, analyzing and mapping expression and sequence data (both finished and unfinished).

PlasmoDB (http://PlasmoDB.org) is the official database of the Plasmodium falciparum genome sequencing consortium. This resource incorporates finished and draft genome sequence data and annotation emerging from Plasmodium sequencing projects. PlasmoDB currently houses information from five parasite species and provides tools for cross-species comparisons. Sequence information is also integrated with other genomic-scale data emerging from the Plasmodium research community, including gene expression analysis from EST, SAGE and microarray projects. The relational schemas used to build PlasmoDB [Genomics Unified Schema (GUS) and RNA Abundance Database (RAD)] employ a highly structured format to accommodate the diverse data types generated by sequence and expression projects. A variety of tools allow researchers to formulate complex, biologically based queries of the database. A version of the database is also available on CD-ROM (Plasmodium GenePlot), facilitating access to the data in situations where Internet access is difficult (e.g. by malaria researchers working in the field). The goal of PlasmoDB is to enhance utilization of the vast quantities of data emerging from genome-scale projects by the global malaria research community.

Animals↗
PubMed2006

PUMA2--grid-based high-throughput analysis of genomes and metabolic pathways.

The PUMA2 system (available at http://compbio.mcs.anl.gov/puma2) is an interactive, integrated bioinformatics environment for high-throughput genetic sequence analysis and metabolic reconstructions from sequence data. PUMA2 provides a framework for comparative and evolutionary analysis of genomic data and metabolic networks in the context of taxonomic and phenotypic information. Grid infrastructure is used to perform computationally intensive tasks. PUMA2 currently contains precomputed analysis of 213 prokaryotic, 22 eukaryotic, 650 mitochondrial and 1493 viral genomes and automated metabolic reconstructions for >200 organisms. Genomic data is annotated with information integrated from >20 sequence, structural and metabolic databases and ontologies. PUMA2 supports both automated and interactive expert-driven annotation of genomes, using a variety of publicly available bioinformatics tools. It also contains a suite of unique PUMA2 tools for automated assignment of gene function, evolutionary analysis of protein families and comparative analysis of metabolic pathways. PUMA2 allows users to submit batch sequence data for automated functional analysis and construction of metabolic models. The results of these analyses are made available to the users in the PUMA2 environment for further interactive sequence analysis and annotation.

Computational Biology↗
PubMed2006

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB.

BACKGROUND: Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support. RESULTS: We propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can be used for annotating and communicating microarray data in a MIAME compliant fashion. CONCLUSION: MAGE-TAB will enable laboratories without bioinformatics experience or support to manage, exchange and submit well-annotated microarray data in a standard format using a spreadsheet. The MAGE-TAB format is self-contained, and does not require an understanding of MAGE-ML or XML.

Computational Biology↗
PubMed2026

Whole metagenome sequencing: not deep enough for complete microbial function recovery.

BACKGROUND: Whole metagenome shotgun sequencing (WMS) is widely used to profile microbial function. However, technical variability in sequencing and analysis often obscures true biological patterns. Large-scale studies are particularly susceptible to batch effects, such as differences in sequencing depth and platform and annotation strategies, as well as sample-to-flow-cell assignments. However, the relative effects of these factors on functional inference in such studies have yet to be systematically evaluated. We analyzed oral-rinse WMS data from 671 Nigerian youths aged 9-18, sequenced on two Illumina platforms. Microbial molecular functionality encoded in these data was annotated using the mi-faser/Fusion pipeline, to capture the broad functional repertoire, and HUMAnN 3/EC numbers pipeline to characterize curated enzymatic activities. We then quantified how technical factors and batch effects shaped the recovery of microbial functionality. RESULTS: Three findings of our work were most salient. First, we observed that the choice of annotation strategy traded off between breadth and specificity of functional coverage. Second, we found that low-prevalence functions were disproportionately lost at shallow sequencing depths, indicating that in, e.g., case-control studies with few representatives of the minor class, sequencing depth could critically impact study resolution. Finally, using our newly developed model relating sequencing depth to functional recovery, we demonstrated that increasing sequencing depth does not directly or proportionally improve functional recall. That is, at as little as 10% of this study's sequencing depth, 30% of the estimated complete microbiome functional repertoire was detectable. However, even at the full depth used in this study, we were only able to recover an estimated 60% of that complete functional repertoire. We further showed that despite biomes differences in functional diversity and host contamination levels (e.g., soil, fecal), incomplete functional recovery at commonly used sequencing depths was consistently observed. CONCLUSIONS: Together, these findings and our depth-to-function mapping framework provide practical guidelines for the design and interpretation of WMS studies. Coordinating sequencing depth planning with annotation strategy, experimental design, and rigorous batch control is thus essential for robust detection of microbial functions and for ensuring reproducible microbiome insights. Video Abstract.

Humans↗
PubMed2003

Concept-match medical data scrubbing. How pathology text can be used in research.

CONTEXT: In the normal course of activity, pathologists create and archive immense data sets of scientifically valuable information. Researchers need pathology-based data sets, annotated with clinical information and linked to archived tissues, to discover and validate new diagnostic tests and therapies. Pathology records can be used for research purposes (without obtaining informed patient consent for each use of each record), provided the data are rendered harmless. Large data sets can be made harmless through 3 computational steps: (1) deidentification, the removal or modification of data fields that can be used to identify a patient (name, social security number, etc); (2) rendering the data ambiguous, ensuring that every data record in a public data set has a nonunique set of characterizing data; and (3) data scrubbing, the removal or transformation of words in free text that can be used to identify persons or that contain information that is incriminating or otherwise private. This article addresses the problem of data scrubbing. OBJECTIVE: To design and implement a general algorithm that scrubs pathology free text, removing all identifying or private information. METHODS: The Concept-Match algorithm steps through confidential text. When a medical term matching a standard nomenclature term is encountered, the term is replaced by a nomenclature code and a synonym for the original term. When a high-frequency "stop" word, such as a, an, the, or for, is encountered, it is left in place. When any other word is encountered, it is blocked and replaced by asterisks. This produces a scrubbed text. An open-source implementation of the algorithm is freely available. RESULTS: The Concept-Match scrub method transformed pathology free text into scrubbed output that preserved the sense of the original sentences, while it blocked terms that did not match terms found in the Unified Medical Language System (UMLS). The scrubbed product is safe, in the restricted sense that the output retains only standard medical terms. The software implementation scrubbed more than half a million surgical pathology report phrases in less than an hour. CONCLUSIONS: Computerized scrubbing can render the textual portion of a pathology report harmless for research purposes. Scrubbing and deidentification methods allow pathologists to create and use large pathology databases to conduct medical research.

Computing Methodologies↗
PubMed2004

Functional annotation of the Arabidopsis genome using controlled vocabularies.

Controlled vocabularies are increasingly used by databases to describe genes and gene products because they facilitate identification of similar genes within an organism or among different organisms. One of The Arabidopsis Information Resource's goals is to associate all Arabidopsis genes with terms developed by the Gene Ontology Consortium that describe the molecular function, biological process, and subcellular location of a gene product. We have also developed terms describing Arabidopsis anatomy and developmental stages and use these to annotate published gene expression data. As of March 2004, we used computational and manual annotation methods to make 85,666 annotations representing 26,624 unique loci. We focus on associating genes to controlled vocabulary terms based on experimental data from the literature and use The Arabidopsis Information Resource-developed PubSearch software to facilitate this process. Each annotation is tagged with a combination of evidence codes, evidence descriptions, and references that provide a robust means to assess data quality. Annotation of all Arabidopsis genes will allow quantitative comparisons between sets of genes derived from sources such as microarray experiments. The Arabidopsis annotation data will also facilitate annotation of newly sequenced plant genomes by using sequence similarity to transfer annotations to homologous genes. In addition, complete and up-to-date annotations will make unknown genes easy to identify and target for experimentation. Here, we describe the process of Arabidopsis functional annotation using a variety of data sources and illustrate several ways in which this information can be accessed and used to infer knowledge about Arabidopsis and other plant species.

Arabidopsis↗
PubMed2005

Pathology data integration with eXtensible Markup Language.

It is impossible to overstate the importance of XML (eXtensible Markup Language) as a data organization tool. With XML, pathologists can annotate all of their data (clinical and anatomic) in a format that can transform every pathology report into a database, without compromising narrative structure. The purpose of this manuscript is to provide an overview of XML for pathologists. Examples will demonstrate how pathologists can use XML to annotate individual data elements and to structure reports in a common format that can be merged with other XML files or queried using standard XML tools. This manuscript gives pathologists a glimpse into how XML allows pathology data to be linked to other types of biomedical data and reduces our dependence on centralized proprietary databases.

Database Management Systems↗