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Protein kinases with a conserved catalytic domain make up one of the largest 'superfamilies' of eukaryotic proteins and play many key roles in biology and disease. Efforts to identify and classify all the members of the eukaryotic protein kinase superfamily have recently culminated in the mining of essentially complete human genome data.
Comparative genomics is enhanced by data mining the rapidly expanding DNA sequence databases. Because of the immense amount of data, computational tools and methods are needed to augment traditional manual visualizations and manipulations of these data. GeneOrder2.0, a Java-based interactive software programme, organizes genome sequence data into tabular and graphical visualizations of the extent of colinearity of genes between any two chromosome genomes of < or =250 kilobases. Both GenBank and proprietary data can be analyzed with this tool.
Laboratories working with draft phase genomes have specific software needs, such as the unattended processing of hundreds of single scaffolds and subsequent sequence annotation. In addition, it is critical to follow the "movement" and the manual annotation of single open reading frames (ORFs) within the successive sequence updates. Even with finished genomes, regular database updates can lead to significant changes in the annotation of single ORFs. In functional genomics it is important to mine data and identify new genetic targets rapidly and easily. Often there is no need for sophisticated relational databases (RDB) that greatly reduce the system-independent access of the results. Another aspect is the internet dependency of most software packages. If users are working with confidential data, this dependency poses a security issue. GAMOLA was designed to handle the numerous scaffolds and changing contents of draft phase genomes in an automated process and stores the results for each predicted ORF in flatfile databases. In addition, annotation transfers, ORF designation tracking, Blast comparisons, and primer design for whole genome microarrays have been implemented. The software is available under the license of North Carolina State University. A website and a downloadable example are accessible under (http://fsweb2.schaub. ncsu.edu/TRKwebsite/index.htm).
Olfactory receptor (OR) genes are the largest gene superfamily in vertebrates. We have identified the mouse OR genes from the nearly complete Celera mouse genome by a comprehensive data mining strategy. We found 1,296 mouse OR genes (including 20% pseudogenes), which can be classified into 228 families. OR genes are distributed in 27 clusters on all mouse chromosomes except 12 and Y. One OR gene cluster matches a known locus mediating a specific anosmia, indicating the anosmia may be due directly to the loss of receptors. A large number of apparently functional 'fish-like' Class I OR genes in the mouse genome may have important roles in mammalian olfaction. Human ORs cover a similar 'receptor space' as the mouse ORs, suggesting that the human olfactory system has retained the ability to recognize a broad spectrum of chemicals even though humans have lost nearly two-thirds of the OR genes as compared to mice.
UNLABELLED: Anelloviruses are part of the normal human viral flora. Although their diversity in humans has been investigated in many countries, and despite their initial detection in Thailand in 1999, knowledge of Thai anelloviruses remains very limited. This study analyzed 1,175 whole-genome sequencing data sets from Thai individuals to mine for potential anellovirus sequences. Our analyses detected anellovirus sequences in 149 data sets (12.68%), uncovering 434 partial anellovirus sequences and 77 complete genome sequences, characterized by the presence of terminal redundancy, complete orf1, and the conserved untranslated region upstream of the orf1 gene. Sequence analyses indicated that these viruses belong to seven genera, including Alphatorquevirus, Betatorquevirus, Gammatorquevirus, Hetorquevirus, Lamedtorquevirus, Samektorquevirus, and Yodtorquevirus. Notably, Hetorquevirus, Lamedtorquevirus, Samektorquevirus, and Yodtorquevirus had not previously been reported in Thailand. Phylogenetic analysis of ORF1 protein sequences showed that Thai anelloviruses form multiple phylogenetic clusters with non-Thai anelloviruses, indicating frequent cross-country transmission and multiple origins of the virus in Thailand. Furthermore, sequence similarity network analysis identified 33 potentially novel anellovirus species in our data set. Our findings greatly expand the knowledge of anellovirus diversity in Thailand and demonstrate the potential of human whole-genome sequencing data as a valuable resource for viral discovery. Lastly, we highlight and discuss some challenges with the use of the current pairwise sequence similarity-based classification scheme, in particular, how gaps can influence similarity calculation and potentially lead to inconsistencies with a phylogenetic-based classification scheme. IMPORTANCE: Anelloviruses are widespread in humans, yet their diversity remains poorly characterized in many regions, including Thailand. Here, we demonstrate that human sequencing data sets, originally generated without the intention for virome research, can be effectively mined for anellovirus sequences, including complete genomes. Our findings reveal a substantial number of previously unreported anelloviruses in Thailand, significantly expanding the known diversity of the virus. We also highlight potential limitations of the current anellovirus species classification scheme, which is based on pairwise orf1 sequence similarity analysis with a hard threshold cutoff at 69%. Our results reveal that the current scheme can sometimes yield taxonomic groupings that are inconsistent with phylogenetic relationships, particularly when significant alignment gaps are present. Overall, our results show that existing human sequencing data can be effectively repurposed for virus discovery research and suggest the need for more robust and phylogenetically informed classification frameworks as viral sequence databases continue to expand.
The miRBase Sequence database is the primary repository for published microRNA (miRNA) sequence and annotation data. miRBase provides a user-friendly web interface for miRNA data, allowing the user to search using key words or sequences, trace links to the primary literature referencing the miRNA discoveries, analyze genomic coordinates and context, and mine relationships between miRNA sequences. miRBase also provides a confidential gene-naming service, assigning official miRNA names to novel genes before their publication. The methods outlined in this chapter describe these functions. miRBase is freely available to all at http://microrna.sanger.ac.uk/.
The wealth of genomic data available for many organisms has set the stage for the next phase of structure-function analysis. High-throughput structural genomics is currently the method of choice for rapid analysis of protein structure-function relationships on a proteome-wide basis. The Joint Center for Structural Genomics (JCSG), established in 2000 under the NIH/NIGMS Protein Structure Initiative, has developed and implemented an integrated high-throughput structure pipeline and applied it in a 2-tiered approach to mining the proteome of the thermophilic bacterium Thermotoga maritima. In the first tier, the successful application of this integrated pipeline has resulted in the cloning and expression of 73% of the T. maritima proteome (1376 out of 1877 predicted genes), and has identified 465 proteins which produced crystal hits. These 465 proteins were compared with existing structural information and a subset of 269 targets were selected to process towards structure determination in a second tier effort. To date, the JCSG pipeline applied to the Thermotoga maritima proteome has resulted in 55 new structures and has identified 6 novel folds and continues to identify structures with novel features.
MOTIVATION: The rapid accumulation of biological network data translates into an urgent need for computational methods for graph pattern mining. One important problem is to identify recurrent patterns across multiple networks to discover biological modules. However, existing algorithms for frequent pattern mining become very costly in time and space as the pattern sizes and network numbers increase. Currently, no efficient algorithm is available for mining recurrent patterns across large collections of genome-wide networks. RESULTS: We developed a novel algorithm, CODENSE, to efficiently mine frequent coherent dense subgraphs across large numbers of massive graphs. Compared with previous methods, our approach is scalable in the number and size of the input graphs and adjustable in terms of exact or approximate pattern mining. Applying CODENSE to 39 co-expression networks derived from microarray datasets, we discovered a large number of functionally homogeneous clusters and made functional predictions for 169 uncharacterized yeast genes. AVAILABILITY: http://zhoulab.usc.edu/CODENSE/
Over 500 researchers participated in a recent American Association for Cancer Research special conference, entitled "Apoptosis and Cancer: Basic Mechanisms and Therapeutic Opportunities in the Post-Genomic Era" (February 13-17, 2002) in sunny Hawaii (Hilton Waikoloa village, Kona, Hawaii). The meeting participants presented the most recent findings on the mechanisms regulating cell death in cancer. In the past decade, apoptosis research has undergone a quantum leap, metamorphosing from a descriptive, phenomenological discipline into a molecularly defined, highly complex signalling field. This transformation was highlighted in the conference's opening talk by meeting co-organizer, John Reed (The Burnham Institute, La Jolla, CA). Reed and colleagues used published protein functional information and bio-informatic mining of the available human genome databases to tabulate the number of human proteins predicted to be involved in regulating apoptosis. The list includes 11 catalytically active caspases, 26 CARD (caspase associated recruitment domain)-, 32 DD (death domain)-, 12 DED (death effector domain)-, 8 BIR (baculovirus inhibitor of apoptosis protein region)-, 24 BH (Bcl-2 homology)-, and 34 PAAD/PYD (pyrin/PAAD)-containing sequences.
Mammalian cells respond to a wide range of external stimuli including growth factors, peptide hormones, cytokines, osmotic stress, heat shock, pharmacological agents and toxicants via multiple signalling pathways. Genome-wide transcript profiling simultaneously monitors the gene expression programs downstream of all signal transduction pathways and can identify novel molecular targets for stress-inducing signals. Our laboratory has combined transcript profiling of cytotoxic compounds with experimental systems in which signalling components are disrupted (e.g. small molecule protein kinase inhibitors) to reveal the contribution of specific signalling pathways to the transcriptional response to toxicant-induced stress. A complementary approach for elucidating the molecular mechanisms that regulate transcriptional responses to toxicants involves DNA sequence analysis of gene regulatory regions obtained via data mining of recently completed mammalian genome sequences. Together, these approaches reveal the molecular mechanisms used to finely tune alterations in gene expression, enabling cells to react in an appropriate manner to external stress-inducing stimuli.
We describe the challenges faced when developing a Linux/PC-based cluster to apply bioinformatics algorithms to the rapidly increasing raw genomics data available. The calculations, which take around two months to complete, result in a powerful resource that can be used for data mining--most obviously for the human genome. Our current infrastructure consists of a 1314 node cluster with 1734 processors supporting both production and research. This paper highlights the problems in achieving high data throughput with such systems and shows that raw computer power is only one component of a complex problem.
Genes expressed specifically in malignant tissue may have potential as therapeutic targets but have been difficult to locate for most cancers. The information hidden within certain public databases can reveal RNA transcripts specifically expressed in transformed tissue. To be useful, database information must be verified and a more complete pattern of tissue expression must be demonstrated. We tested database mining plus rapid screening by fluorescent-PCR expression comparison (F-PEC) as an approach to locate candidate brain tumor antigens. Cancer Genome Anatomy Project (CGAP) data was mined for genes highly expressed in glioblastoma multiforme. From 13 mined genes, seven showed potential as possible tumor markers or antigens as determined by further expression profiling. Now that large-scale expression information is readily available for many of the commonly occurring cancers, other candidate tumor markers or antigens could be located and evaluated with this approach.
The recent determination of the complete genome sequence of Corynebacterium diphtheriae, the aetiological agent of diphtheria, has allowed a detailed comparison of its physiology with that of its closest sequenced pathogenic relative Mycobacterium tuberculosis. Of major importance to the pathogenicity and resilience of the latter is its particularly complex cell envelope. The corynebacteria share many of the features of this extraordinary structure although to a lesser level of complexity. The cell envelope of M. tuberculosis has provided the molecular targets for several of the major anti-tubercular drugs. Given a backdrop of emerging multi-drug resistant strains of the organism (MDR-TB) and its continuing global threat to human health, the search for novel anti-tubercular agents is of paramount importance. The unique structure of this cell wall and the importance of its integrity to the viability of the organism suggest that the search for novel drug targets within the array of enzymes responsible for its construction may prove fruitful. Although the application of modern bioinformatics techniques to the 'mining' of the M. tuberculosis genome has already increased our knowledge of the biosynthesis and assembly of the mycobacterial cell wall, several issues remain uncertain. Further analysis by comparison with its relatives may bring clarity and aid the early identification of novel cellular targets for new anti-tuberculosis drugs. In order to facilitate this aim, this review intends to illustrate the broad similarities and highlight the structural differences between the two bacterial envelopes and discuss the genetics of their biosynthesis.
Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology and human health. CRISPR-based gene editors derived from microorganisms, although powerful, often show notable functional tradeoffs when ported into non-native environments, such as human cells1. Artificial-intelligence-enabled design provides a powerful alternative with the potential to bypass evolutionary constraints and generate editors with optimal properties. Here, using large language models2 trained on biological diversity at scale, we demonstrate successful precision editing of the human genome with a programmable gene editor designed with artificial intelligence. To achieve this goal, we curated a dataset of more than 1 million CRISPR operons through systematic mining of 26 terabases of assembled genomes and metagenomes. We demonstrate the capacity of our models by generating 4.8× the number of protein clusters across CRISPR-Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. Several of the generated gene editors show comparable or improved activity and specificity relative to SpCas9, the prototypical gene editing effector, while being 400 mutations away in sequence. Finally, we demonstrate that an artificial-intelligence-generated gene editor, denoted as OpenCRISPR-1, exhibits compatibility with base editing. We release OpenCRISPR-1 to facilitate broad, ethical use across research and commercial applications.
The comparison of DNA and protein sequences of extant species might be informative for reconstructing the chronology of evolutionary events on Earth. A phylogenetic tree inferred from molecular data directly depicts the evolutionary affinities of species and indirectly allows estimating the age of their origin and diversification. Molecular dating is achieved by assuming the molecular clock hypothesis, i.e., that the rate of change of nucleotide and amino acid sequences is on average constant over geological time. If paleontological calibrations are available, then absolute divergence times of species can be estimated. However, three major difficulties potentially hamper molecular dating : (1) a limited sample of genes and organisms, (2) a limited number of fossil references, and (3) pervasive variations of molecular evolutionary rates among genomes and species. To circumvent these problems, different solutions have been recently proposed. Larger data sets are built with more genes and more species sampled through the mining of an increasing number of genomes. Moreover, independent key fossils are identified to calibrate molecular clocks, and the uncertainty on their age is integrated in subsequent analyses. Finally, models of molecular rate variations are constructed, and incorporated in the so-called relaxed molecular clock approaches. As an illustration of these improvements, we mention that the debated age of the animal (bilaterian metazoans) diversification may have occurred between 642-761 million years ago (Mya), roughly 100 Ma before the Cambrian explosion. Among mammals, the initial diversification of major placental groups may have taken place around 100 Mya, well before the Cretaceous/Tertiary boundary marking the extinction of dinosaurs.
VirGen is a comprehensive viral genome resource that organizes the 'sequence space' of viral genomes in a structured fashion. It has been developed with the objective of serving as an annotated and curated database comprising complete genome sequences of viruses, value-added derived data and data mining tools. The current release (v1.1) contains 559 complete genomes in addition to 287 putative genomes of viruses belonging to eight viral families for which the host range includes animals and plants. Viral genomes in VirGen are annotated using sequence-based Bioinformatics approaches. The genomic data is also curated to identify 'alternate names' of viral proteins, where available. VirGen archives the results of comparisons of genomes, proteomes and individual proteins within and between viral species. It is the first resource to provide phylogenetic trees of viral species computed using whole-genome sequence data. The module of predicted B-cell antigenic determinants in VirGen is an attempt to link the genome to its vaccinome. Comparative genome analysis data facilitate the study of genome organization and evolution of viruses, which would have implications in applied research to identify candidates for the design of vaccines and antiviral drugs. VirGen is a relational database and is available at http://bioinfo. ernet.in/virgen/virgen.html.
The members of the PUF family of RNA-binding proteins regulate the fate of mRNAs by binding to their 3'UTR sequence elements in eukaryotes. In trypanosomes, for which gene expression is polycistronic and controlled almost exclusively by post-transcriptional processes, PUF proteins could play a crucial role. We report here the complete analysis of the PUF protein family of Trypanosoma cruzi composed of 10 members. In silico analysis predicts the existence of at least three major groups within the T. cruzi family, based on their putative binding specificity. Using yeast three hybrid assays, we tested some of these predictions for TcPUF1, TcPUF3, TcPUF5, and TcPUF8 as representatives of these groups. Data mining of the T. cruzi genome led us to describe putative binding targets for the TcPUFs of the most conserved group, TcPUF1 and TcPUF2. The targets include genes for mitochondrial proteins and protein kinases. Finally, immunolocalization experiments showed that TcPUF1 is localized in multiple discrete foci in the cytoplasm supporting its proposed function.
The large subunit of the eukaryotic ribosome possesses a long and protruding stalk formed by the ribosomal P proteins. Four out of five ribosomal P proteins of Trypanosoma cruzi, TcP0, TcP1alpha, TcP2alpha, and TcP2beta had been previously characterized. Data mining of the T. cruzi genome data base allowed the identification of the fifth member of this protein group, a novel P1 protein, named P1beta. To gain insight into the assembly of the stalk, a yeast two-hybrid based protein interaction map was generated. A parasite specific profile of interactions amongst the ribosomal P proteins of T. cruzi was evident. The TcP0 protein was able to interact with all both P1 and both P2 proteins. Moreover, the interactions between P2beta with P1alpha as well as with P2alpha were detected, as well as the ability of TcP2beta to homodimerize. A quantitative evaluation of the interactions established that the strongest interacting pair was TcP0-TcP1beta.