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The functional genomic distribution of protein divergence in two animal phyla: coevolution, genomic conflict, and constraint.

We compare the functional spectrum of protein evolution in two separate animal lineages with respect to two hypotheses: (1) rates of divergence are distributed similarly among functional classes within both lineages, indicating that selective pressure on the proteome is largely independent of organismic-level biological requirements; and (2) rates of divergence are distributed differently among functional classes within each lineage, indicating species-specific selective regimes impact genome-wide substitutional patterns. Integrating comparative genome sequence with data from tissue-specific expressed-sequence-tag (EST) libraries and detailed database annotations, we find a functional genomic signature of rapid evolution and selective constraint shared between mammalian and nematode lineages despite their extensive morphological and ecological differences and distant common ancestry. In both phyla, we find evidence of accelerated evolution among components of molecular systems involved in coevolutionary change. In mammals, lineage-specific fast evolving genes include those involved in reproduction, immunity, and possibly, maternal-fetal conflict. Likelihood ratio tests provide evidence for positive selection in these rapidly evolving functional categories in mammals. In contrast, slowly evolving genes, in terms of amino acid or insertion/deletion (indel) change, in both phyla are involved in core molecular processes such as transcription, translation, and protein transport. Thus, strong purifying selection appears to act on the same core cellular processes in both mammalian and nematode lineages, whereas positive and/or relaxed selection acts on different biological processes in each lineage.

Amino Acid Substitution↗

eVOC: a controlled vocabulary for unifying gene expression data.

Expression data contribute significantly to the biological value of the sequenced human genome, providing extensive information about gene structure and the pattern of gene expression. ESTs, together with SAGE libraries and microarray experiment information, provide a broad and rich view of the transcriptome. However, it is difficult to perform large-scale expression mining of the data generated by these diverse experimental approaches. Not only is the data stored in disparate locations, but there is frequent ambiguity in the meaning of terms used to describe the source of the material used in the experiment. Untangling semantic differences between the data provided by different resources is therefore largely reliant on the domain knowledge of a human expert. We present here eVOC, a system which associates labelled target cDNAs for microarray experiments, or cDNA libraries and their associated transcripts with controlled terms in a set of hierarchical vocabularies. eVOC consists of four orthogonal controlled vocabularies suitable for describing the domains of human gene expression data including Anatomical System, Cell Type, Pathology and Developmental Stage. We have curated and annotated 7016 cDNA libraries represented in dbEST, as well as 104 SAGE libraries,with expression information,and provide this as an integrated, public resource that allows the linking of transcripts and libraries with expression terms. Both the vocabularies and the vocabulary-annotated libraries can be retrieved from http://www.sanbi.ac.za/evoc/. Several groups are involved in developing this resource with the aim of unifying transcript expression information.

Animals↗

The transcriptome of the sea urchin embryo.

The sea urchin Strongylocentrotus purpuratus is a model organism for study of the genomic control circuitry underlying embryonic development. We examined the complete repertoire of genes expressed in the S. purpuratus embryo, up to late gastrula stage, by means of high-resolution custom tiling arrays covering the whole genome. We detected complete spliced structures even for genes known to be expressed at low levels in only a few cells. At least 11,000 to 12,000 genes are used in embryogenesis. These include most of the genes encoding transcription factors and signaling proteins, as well as some classes of general cytoskeletal and metabolic proteins, but only a minor fraction of genes encoding immune functions and sensory receptors. Thousands of small asymmetric transcripts of unknown function were also detected in intergenic regions throughout the genome. The tiling array data were used to correct and authenticate several thousand gene models during the genome annotation process.

Animals↗

Electrophysiological analysis of neuronal and muscle function in C. elegans.

The nematode Caenorhabditis elegans provides numerous experimental advantages for the identification and characterization of genes required for the function of the nervous system. These advantages include forward and reverse genetic tractability, a relatively simple body plan with an invariant cellular lineage, and a fully sequenced and well-annotated genome. However, one limitation of C. elegans is the relative scarcity of electrophysiological data from excitable cells. To address this limitation, high-resolution cellular techniques for probing the roles of specific gene products in the C. elegans nervous system have been recently developed. This chapter will provide an overview of the technical requirements for patch-clamp electrophysiological analysis of C. elegans neurons and muscle cells, as well as provide some illustrative examples of insights gained from the pairing of electrophysiological techniques with molecular and genetic analysis.

Animals↗

PhysioNet: a research resource for studies of complex physiologic and biomedical signals.

PhysioNet (http://www.physionet.org/) is a web-based resource supplying well-characterized physiologic signals and related open-source software to the biomedical research community. Inaugurated in September 1999 under the auspices of the NIH's National Center for Research Resources (NCRR), PhysioNet provides an on-line forum for free dissemination and exchange of research data and software, with facilities for cooperative analysis of data and evaluation of new analytic methods. As of September 2000, PhysioBank, the data archive made available via PhysioNet, contained roughly 35 gigabytes of recorded signals and annotations. PhysioNet is a public service of the Research Resource for Complex Physiologic Signals, a cooperative project initiated by researchers at Boston's Beth Israel Deaconess Medical Center/Harvard Medical School, Boston University, McGill University, and MIT.

Cardiovascular Physiological Phenomena↗

Online annotation tool for dental radiology.

Dental imaging plays a significant role in the process of diagnoses of oral cancer. In this project, an online annotation tool for dental radiology is developed to support systematic annotation, which includes image findings, descriptive information on the lesion, patient characteristics and pathology data. This tool will be available for the dental schools in US who are interested in the creation of dental image archives for teaching and research.

Humans↗

The computerization of the patient education process.

This article describes an on-line computer system developed to provide indexing, order entry, and documentation of patient education materials. The system offers an index of all materials, patient data, an ordering system, and a documentation system. Other features of the system include annotated briefs of each indexed material, on-line inventory records and the capability of generating summation reports. The system was developed with input from nursing staff members and revised as needed to improve its user-friendliness and the documentation capabilities.

Hospital Information Systems↗

Efficient filtering methods for clustering cDNAs with spliced sequence alignment.

MOTIVATION: Clustering sequences of a full-length cDNA library into alternative splice form candidates is a very important problem. RESULTS: We developed a new efficient algorithm to cluster sequences of a full-length cDNA library into alternative splice form candidates. Current clustering algorithms for cDNAs tend to produce too many clusters containing incorrect splice form candidates. Our algorithm is based on a spliced sequence alignment algorithm that considers splice sites. The spliced sequence alignment algorithm is a variant of an ordinary dynamic programming algorithm, which requires O(nm) time for checking a pair of sequences where n and m are the lengths of the two sequences. Since the time bound is too large to perform all-pair comparison for a large set of sequences, we developed new techniques to reduce the computation time without affecting the accuracy of the output clusters. Our algorithm was applied to 21 076 mouse cDNA sequences of the FANTOM 1.10 database to examine its performance and accuracy. In these experiments, we achieved about 2-12-fold speedup against a method using only a traditional hash-based technique. Moreover, without using any information of the mouse genome sequence data or any gene data in public databases, we succeeded in listing 87-89% of all the clusters that biologists have annotated manually. AVAILABILITY: We provide a web service for cDNA clustering located at https://access.obigrid.org/ibm/cluspa/, for which registration for the OBIGrid (http://www.obigrid.org) is required.

Algorithms↗

Operon prediction without a training set.

MOTIVATION: Annotation of operons in a bacterial genome is an important step in determining an organism's transcriptional regulatory program. While extensive studies of operon structure have been carried out in a few species such as Escherichia coli, fewer resources exist to inform operon prediction in newly sequenced genomes. In particular, many extant operon finders require a large body of training examples to learn the properties of operons in the target organism. For newly sequenced genomes, such examples are generally not available; moreover, a model of operons trained on one species may not reflect the properties of other, distantly related organisms. We encountered these issues in the course of predicting operons in the genome of Bacteroides thetaiotaomicron (B.theta), a common anaerobe that is a prominent component of the normal adult human intestinal microbial community. RESULTS: We describe an operon predictor designed to work without extensive training data. We rely on a small set of a priori assumptions about the properties of the genome being annotated that permit estimation of the probability that two adjacent genes lie in a common operon. Predictions integrate several sources of information, including intergenic distance, common functional annotation and a novel formulation of conserved gene order. We validate our predictor both on the known operons of E.coli and on the genome of B.theta, using expression data to evaluate our predictions in the latter.

Algorithms↗

MitoRes: a resource of nuclear-encoded mitochondrial genes and their products in Metazoa.

BACKGROUND: Mitochondria are sub-cellular organelles that have a central role in energy production and in other metabolic pathways of all eukaryotic respiring cells. In the last few years, with more and more genomes being sequenced, a huge amount of data has been generated providing an unprecedented opportunity to use the comparative analysis approach in studies of evolution and functional genomics with the aim of shedding light on molecular mechanisms regulating mitochondrial biogenesis and metabolism. In this context, the problem of the optimal extraction of representative datasets of genomic and proteomic data assumes a crucial importance. Specialised resources for nuclear-encoded mitochondria-related proteins already exist; however, no mitochondrial database is currently available with the same features of MitoRes, which is an update of the MitoNuc database extensively modified in its structure, data sources and graphical interface. It contains data on nuclear-encoded mitochondria-related products for any metazoan species for which this type of data is available and also provides comprehensive sequence datasets (gene, transcript and protein) as well as useful tools for their extraction and export. DESCRIPTION: MitoRes http://www2.ba.itb.cnr.it/MitoRes/ consolidates information from publicly external sources and automatically annotates them into a relational database. Additionally, it also clusters proteins on the basis of their sequence similarity and interconnects them with genomic data. The search engine and sequence management tools allow the query/retrieval of the database content and the extraction and export of sequences (gene, transcript, protein) and related sub-sequences (intron, exon, UTR, CDS, signal peptide and gene flanking regions) ready to be used for in silico analysis. CONCLUSION: The tool we describe here has been developed to support lab scientists and bioinformaticians alike in the characterization of molecular features and evolution of mitochondrial targeting sequences. The way it provides for the retrieval and extraction of sequences allows the user to overcome the obstacles encountered in the integrative use of different bioinformatic resources and the completeness of the sequence collection allows intra- and interspecies comparison at different biological levels (gene, transcript and protein).

Animals↗

Integrative multi-omics profiling of insomnia-related molecular features reveals microbiome, immune, and therapy-relevant heterogeneity in colorectal cancer.

Emerging evidence implicates insomnia as a potential risk factor in carcinogenesis, potentially involving systemic inflammation, circadian disruption, and microbiome alterations. However, the molecular associations linking insomnia-related features to colorectal cancer (CRC), particularly with respect to tumor biology, immune microenvironmental states, and therapy-relevant phenotypes, remain largely unexplored. Multi-omics integration of genomic, transcriptomic, and microbiome data from 3,026 CRC patients across seven independent cohorts, including a large, well-annotated Clinical Omics study of Colorectal Cancer in China (COCC) cohort, enabled insomnia-based molecular classification through unsupervised non-negative matrix factorization (NMF) clustering. The insomnia subtype (IS) was biologically characterized via pathway enrichment, immune deconvolution, microbial profiling, and single-cell transcriptomics. Furthermore, an insomnia score (ISscore) was developed and validated in multiple cohorts for risk stratification and assessment of treatment-response-related indicators in CRC. Unsupervised clustering revealed two distinct molecular subtypes (IS1/IS2), with IS2 demonstrating significantly poorer survival. IS2 exhibited marked activation of EMT/angiogenesis pathways versus cell cycle activation in IS1. The IS2 microenvironment showed increased immunosuppression-related infiltration and exhausted T cell signatures, together with intratumoral microbiome variation characterized by depletion of Ruminococcaceae UCG-002 and enrichment of Hungatella/Selenomonas. The ISscore system stratified survival risk and was associated with computational indicators of immunotherapy response. Single-cell analysis nominated PPIA-BSG as a potential cell-cell communication signal involving high-ISscore tumor cells, CXCL12+ endothelial cells, and CLEC9A+ dendritic cell subsets. This multi-omics characterization of insomnia-CRC interplay suggests that insomnia-related molecular features are associated with an immunologically distinct and microbiome-altered tumor ecosystem. The ISscore provides a reproducible framework for capturing insomnia-related molecular heterogeneity, supporting risk stratification and future evaluation of therapy-relevant phenotypes.IMPORTANCEChronic insomnia affects millions, but it is not typically considered a cancer risk factor. Our study, analyzing vast biological data from over 3,000 colorectal cancer patients, uncovers a potential link between a person's predisposition to insomnia and their risk of developing this disease. This suggests that the biological pathways related to sleep may play a role in cancer development. Understanding this connection opens up new avenues for identifying individuals at higher risk and developing novel prevention strategies for colorectal cancer.

colorectal cancer↗

EGassembler: online bioinformatics service for large-scale processing, clustering and assembling ESTs and genomic DNA fragments.

Expressed sequence tag (EST) sequencing has proven to be an economically feasible alternative for gene discovery in species lacking a draft genome sequence. Ongoing large-scale EST sequencing projects feel the need for bioinformatics tools to facilitate uniform EST handling. This brings about a renewed importance for a universal tool for processing and functional annotation of large sets of ESTs. EGassembler (http://egassembler.hgc.jp/) is a web server, which provides an automated as well as a user-customized analysis tool for cleaning, repeat masking, vector trimming, organelle masking, clustering and assembling of ESTs and genomic fragments. The web server is publicly available and provides the community a unique all-in-one online application web service for large-scale ESTs and genomic DNA clustering and assembling. Running on a Sun Fire 15K supercomputer, a significantly large volume of data can be processed in a short period of time. The results can be used to functionally annotate genes, to facilitate splice alignment analysis, to link the transcripts to genetic and physical maps, design microarray chips, to perform transcriptome analysis and to map to KEGG metabolic pathways. The service provides an excellent bioinformatics tool to research groups in wet-lab as well as an all-in-one-tool for sequence handling to bioinformatics researchers.

Computational Biology↗

Mitoproteome: human heart mitochondrial protein sequence database.

The human mitochondrial proteome database has been developed by deriving data from a combination of public repositories and experimental and computational prediction methods. The experimental data is derived from highly purified mitochondria from human heart tissue, whereas predictions have been performed by MITOPRED, a genome-scale method for the prediction of nucleus-encoded mitochondrial proteins. Mitochondrial protein sequences from different sources have been clustered to generate a nonredundant dataset. Annotations related to the protein function, structure, disease association, pathways, and so on are collected from a number of public databases using commonly used UNIX and Perl scripts. This chapter provides a detailed description of various data sources and methods used to download, curate, parse, and generate meaningful annotations from primary as well as derived databases.

Computational Biology↗

Genie--gene finding in Drosophila melanogaster.

A hidden Markov model-based gene-finding system called Genie was applied to the genomic Adh region in Drosophila melanogaster as a part of the Genome Annotation Assessment Project (GASP). Predictions from three versions of the Genie gene-finding system were submitted, one based on statistical properties of coding genes, a second included EST alignment information, and a third that integrated protein sequence homology information. All three programs were trained on the provided Drosophila training data. In addition, promoter assignments from an integrated neural network were submitted. The gene assignments overlapped >90% of the 222 annotated genes and 26 possibly novel genes were predicted, of which some might be overpredictions. The system correctly identified the exon boundaries of 70% of the exons in cDNA-confirmed genes and 77% of the exons with the addition of EST sequence alignments. The best of the three Genie submissions predicted 19 of the annotated 43 gene structures entirely correct (44%). In the promoter category, only 30% of the transcription start sites could be detected, but by integrating this program as a sensor into Genie the false-positive rate could be dropped to 1/16,786 (0.006%). The results of the experiment on the long contiguous genomic sequence revealed some problems concerning gene assembly in Genie. The results were used to improve the system. We show that Genie is a robust hidden Markov model system that allows for a generalized integration of information from different sources such as signal sensors (splice sites, start codon, etc.), content sensors (exons, introns, intergenic) and alignments of mRNA, EST, and peptide sequences. The assessment showed that Genie could effectively be used for the annotation of complete genomes from higher organisms.

Animals↗

MaxComp: Predicting single-cell chromatin compartments from 3D chromosome structures.

The genome is organized into distinct chromatin compartments with at least two main classes, a transcriptionally active A and an inactive B compartment, broadly corresponding to euchromatin and heterochromatin. Chromatin regions within the same compartment preferentially interact with each other over regions in the opposite compartment. A/B compartments are traditionally identified from ensemble Hi-C contact frequency matrices using principal component analysis of their covariance matrices. However, defining compartments at the single-cell level from sparse single-cell Hi-C data is challenging, especially since homologous copies are often not resolved. To address this, we present MaxComp, an unsupervised method, for inferring single-cell A/B compartments based on 3D geometric considerations in single-cell chromosome structures-derived either from multiplexed FISH-omics imaging or 3D structure models derived from Hi-C data. By representing each 3D chromosome structure as an undirected graph with edge-weights encoding structural information, MaxComp reformulates compartment prediction as a variant of the Max-cut problem, solved using semidefinite graph programming (SPD) to optimally partition the graph into two structural compartments. Our results show that the population average of MaxComp single-cell compartment annotations closely matches those derived from ensemble Hi-C principal component analysis, demonstrating that compartmentalization can be recovered from geometric principles alone, using only the 3D coordinates and nuclear microenvironment of chromatin regions. Our approach reveals widespread cell-to-cell variability in compartment organization, with substantial heterogeneity across genomic loci. When applied to multiplexed FISH imaging data, MaxComp also uncovers relationships between compartment annotations and transcriptional activity at the single-cell level. In summary, MaxComp offers a new framework for understanding chromatin compartmentalization in single cells, connecting 3D genome architecture, and transcriptional activity with the cell-to-cell variations of chromatin compartments.

Chromatin↗

The life sciences Global Image Database (GID).

Although a vast amount of life sciences data is generated in the form of images, most scientists still store images on extremely diverse and often incompatible storage media, without any type of metadata structure, and thus with no standard facility with which to conduct searches or analyses. Here we present a solution to unlock the value of scientific images. The Global Image Database (GID) is a web-based (http://www.gwer.ch/qv/gid/gid.ht m ) structured central repository for scientific annotated images. The GID was designed to manage images from a wide spectrum of imaging domains ranging from microscopy to automated screening. The annotations in the GID define the source experiment of the images by describing who the authors of the experiment are, when the images were created, the biological origin of the experimental sample and how the sample was processed for visualization. A collection of experimental imaging protocols provides details of the sample preparation, and labeling, or visualization procedures. In addition, the entries in the GID reference these imaging protocols with the probe sequences or antibody names used in labeling experiments. The GID annotations are searchable by field or globally. The query results are first shown as image thumbnail previews, enabling quick browsing prior to original-sized annotated image retrieval. The development of the GID continues, aiming at facilitating the management and exchange of image data in the scientific community, and at creating new query tools for mining image data.

Biological Science Disciplines↗

Pigs in sequence space: a 0.66X coverage pig genome survey based on shotgun sequencing.

BACKGROUND: Comparative whole genome analysis of Mammalia can benefit from the addition of more species. The pig is an obvious choice due to its economic and medical importance as well as its evolutionary position in the artiodactyls. RESULTS: We have generated approximately 3.84 million shotgun sequences (0.66X coverage) from the pig genome. The data are hereby released (NCBI Trace repository with center name "SDJVP", and project name "Sino-Danish Pig Genome Project") together with an initial evolutionary analysis. The non-repetitive fraction of the sequences was aligned to the UCSC human-mouse alignment and the resulting three-species alignments were annotated using the human genome annotation. Ultra-conserved elements and miRNAs were identified. The results show that for each of these types of orthologous data, pig is much closer to human than mouse is. Purifying selection has been more efficient in pig compared to human, but not as efficient as in mouse, and pig seems to have an isochore structure most similar to the structure in human. CONCLUSION: The addition of the pig to the set of species sequenced at low coverage adds to the understanding of selective pressures that have acted on the human genome by bisecting the evolutionary branch between human and mouse with the mouse branch being approximately 3 times as long as the human branch. Additionally, the joint alignment of the shot-gun sequences to the human-mouse alignment offers the investigator a rapid way to defining specific regions for analysis and resequencing.

Animals↗

Functional genomics approaches in arthritis.

The post-genomic era of functional genomics and target validation will allow us to narrow the bridge between clinically correlative data and causative data for complex diseases, such as arthritis, for which the etiological agent remains elusive. The availability of human and other annotated genome sequences, and parallel developments of new technologies that allow analysis of minute amounts of human and animal cells (peripheral blood cells and infiltrating cells) and tissues (synovium and cartilage) under different pathophysiological conditions, has facilitated high-throughput gene mining approaches that can generate vast amounts of clinically correlative data. Characterizing some of the correlative/causative genes will require reverting to the hypothesis-driven, low throughput method of complementary experimental biology using genomic approaches as a tool. This will include in silico gene expression arrays, genome-wide scans, comparative genomics using various animal models (such as rodents and zebrafish), bioinformatics and a team of well trained translational scientists and physicians. For the first time, the "genomic tools" will allow us to analyze small amounts of surgical samples (such as needle biopsies) and clinical samples in the context of the whole genome. Preliminary genomic analysis in osteoarthritis has already resurrected the debate on the semantic issues in the definition of inflammation. Further analyses will not only facilitate the development of unbiased hypotheses at the molecular level, but also assist us in the identification and characterization of novel targets and disease markers for pharmacological intervention, gene therapy, and diagnosis.

Animals↗