Integrating overlapping structures and background information of words significantly improves biological sequence comparison.
Ontology highlight
ABSTRACT:
SUBMITTER: Dai Q
PROVIDER: S-EPMC3213098 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Analysis of genetic sequences is usually based on finding similar parts of sequences, e.g. DNA reads and/or genomes. For big data, this is typically done via "seeds": simple similarities (e.g. exact matches) that can be found quickly. For huge data, sparse seeding is useful, where we only consider seeds at a subset of positions in a sequence. Here we study a simple sparse-seeding method: using seeds at positions of certain "words" (e.g. ac, at, gc, or gt). Sensitivity is maximized by using words with minimal overlaps. That is because, in a random sequence, minimally-overlapping words are anti-clumped. We provide evidence that this is often superior to acclaimed "minimizer" sparse-seeding methods. Our approach can be unified with design of inexact (spaced and subset) seeds, further boosting sensitivity. Thus, we present a promising approach to sequence similarity search, with open questions on how to optimize it. Software to design and test minimally-overlapping words is freely available at https://gitlab.com/mcfrith/noverlap. Supplementary data are available at Bioinformatics online.
| S-EPMC8016470 | biostudies-literature
Project description:OBJECTIVE:Indices of body fat distribution are heritable, but few genetic signals have been reported from genome-wide association studies (GWAS) of computed tomography (CT) imaging measurements of body fat distribution. We aimed to identify genes associated with adiposity traits and the key drivers that are central to adipose regulatory networks. SUBJECTS:We analyzed gene transcript expression data in blood from participants in the Framingham Heart Study, a large community-based cohort (n up to 4303), as well as implemented an integrative analysis of these data and existing biological information. RESULTS:Our association analyses identified unique and common gene expression signatures across several adiposity traits, including body mass index, waist-hip ratio, waist circumference, and CT-measured indices, including volume and quality of visceral and subcutaneous adipose tissues. We identified six enriched KEGG pathways and two co-expression modules for further exploration of adipose regulatory networks. The integrative analysis revealed four gene sets (Apoptosis, p53 signaling pathway, Proteasome, Ubiquitin-mediated proteolysis) and two co-expression modules with significant genetic variants and 94 key drivers/genes whose local networks were enriched with adiposity-associated genes, suggesting that these enriched pathways or modules have genetic effects on adiposity. Most identified key driver genes are involved in essential biological processes such as controlling cell cycle, DNA repair, and degradation of regulatory proteins are cancer related. CONCLUSIONS:Our integrative analysis of genetic, transcriptional, and biological information provides a list of compelling candidates for further follow-up functional studies to uncover the biological mechanisms underlying obesity. These candidates highlight the value of examining CT-derived and central adiposity traits.
| S-EPMC6405310 | biostudies-literature
Project description:Owing to its increased tag length, LongSAGE tags are expected to be more reliable in direct assignment to genome sequences. Therefore, we evaluated the use of LongSAGE data in genome annotation by using our LongSAGE dataset of 202 015 tags (consisting of 41 718 unique tags), experimentally generated from mouse embryonic tail libraries. RESULTS: A fraction of LongSAGE tags could not be unambiguously assigned to its gene, due to the presence of widely conserved sequences downstream of particular CATG anchor sites. The presence of alternative forms of transcripts was confirmed in 45% of all detected genes. Surprisingly, a large fraction of LongSAGE tags with hits to the genome (66%) could not be assigned to any gene annotated in EnsEMBL. Among such cases, 2098 LongSAGE tags fell into a region containing a putative gene predicted by GenScan, providing experimental evidence for the presence of real genes, while 9112 genes were found out to be left out or wrongly annotated by the EnsEMBL pipeline. CONCLUSIONS: LongSAGE transcriptome data can significantly improve the genome annotation by identifying novel genes and alternative transcripts, even in the case of thus far best-characterized organisms like the mouse. Keywords: other Owing to its increased tag length, LongSAGE tags are expected to be more reliable in direct assignment to genome sequences. Therefore, we evaluated the use of LongSAGE data in genome annotation by using our LongSAGE dataset of 202 015 tags (consisting of 41 718 unique tags), experimentally generated from mouse embryonic tail libraries.
2005-07-20 | E-GEOD-2967 | biostudies-arrayexpress
Project description:Currently most terms and term-term relationships in Gene Ontology (GO) are defined manually, which creates cost, consistency and completeness issues. Recent studies have demonstrated the feasibility of inferring GO automatically from biological networks, which represents an important complementary approach to GO construction. These methods (NeXO and CliXO) are unsupervised, which means 1) they cannot use the information contained in existing GO, 2) the way they integrate biological networks may not optimize the accuracy, and 3) they are not customized to infer the three different sub-ontologies of GO. Here we present a semi-supervised method called Unicorn that extends these previous methods to tackle the three problems. Unicorn uses a sub-tree of an existing GO sub-ontology as training part to learn parameters in integrating multiple networks. Cross-validation results show that Unicorn reliably inferred the left-out parts of each specific GO sub-ontology. In addition, by training Unicorn with an old version of GO together with biological networks, it successfully re-discovered some terms and term-term relationships present only in a new version of GO. Unicorn also successfully inferred some novel terms that were not contained in GO but have biological meanings well-supported by the literature.AvailabilitySource code of Unicorn is available at http://yiplab.cse.cuhk.edu.hk/unicorn/.
| S-EPMC5157009 | biostudies-literature
Project description:The Protein Data Bank (PDB) is the global archive for structural information on macromolecules, and a popular resource for researchers, teachers, and students, amassing more than one million unique users each year. Crystallographic structure models in the PDB (more than 100,000 entries) are optimized against the crystal diffraction data and geometrical restraints. This process of crystallographic refinement typically ignored hydrogen bond (H-bond) distances as a source of information. However, H-bond restraints can improve structures at low resolution where diffraction data are limited. To improve low-resolution structure refinement, we present methods for deriving H-bond information either globally from well-refined high-resolution structures from the PDB-REDO databank, or specifically from on-the-fly constructed sets of homologous high-resolution structures. Refinement incorporating HOmology DErived Restraints (HODER), improves geometrical quality and the fit to the diffraction data for many low-resolution structures. To make these improvements readily available to the general public, we applied our new algorithms to all crystallographic structures in the PDB: using massively parallel computing, we constructed a new instance of the PDB-REDO databank (https://pdb-redo.eu). This resource is useful for researchers to gain insight on individual structures, on specific protein families (as we demonstrate with examples), and on general features of protein structure using data mining approaches on a uniformly treated dataset.
| S-EPMC5818736 | biostudies-literature
Project description:Owing to its increased tag length, LongSAGE tags are expected to be more reliable in direct assignment to genome sequences. Therefore, we evaluated the use of LongSAGE data in genome annotation by using our LongSAGE dataset of 202 015 tags (consisting of 41 718 unique tags), experimentally generated from mouse embryonic tail libraries. RESULTS: A fraction of LongSAGE tags could not be unambiguously assigned to its gene, due to the presence of widely conserved sequences downstream of particular CATG anchor sites. The presence of alternative forms of transcripts was confirmed in 45% of all detected genes. Surprisingly, a large fraction of LongSAGE tags with hits to the genome (66%) could not be assigned to any gene annotated in EnsEMBL. Among such cases, 2098 LongSAGE tags fell into a region containing a putative gene predicted by GenScan, providing experimental evidence for the presence of real genes, while 9112 genes were found out to be left out or wrongly annotated by the EnsEMBL pipeline. CONCLUSIONS: LongSAGE transcriptome data can significantly improve the genome annotation by identifying novel genes and alternative transcripts, even in the case of thus far best-characterized organisms like the mouse. Keywords: other
2005-07-20 | GSE2967 | GEO
Project description:BackgroundThe discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. It is important to quantify how comparative genomics can improve computational detection of such modules.ResultsWe run the Stubb software on the entire D. melanogaster genome, to obtain predictions of modules involved in segmentation of the embryo. Stubb uses a probabilistic model to score sequences for clustering of transcription factor binding sites, and can exploit multiple species data within the same probabilistic framework. The predictions are evaluated using publicly available gene expression data for thousands of genes, after careful manual annotation. We demonstrate that the use of a second genome (D. pseudoobscura) for cross-species comparison significantly improves the prediction accuracy of Stubb, and is a more sensitive approach than intersecting the results of separate runs over the two genomes. The entire list of predictions is made available online.ConclusionEvolutionary conservation of modules serves as a filter to improve their detection in silico. The future availability of additional fruitfly genomes therefore carries the prospect of highly specific genome-wide predictions using Stubb.
| S-EPMC521067 | biostudies-literature
Project description:Comparing and classifying the three-dimensional (3D) structures of proteins is of crucial importance to molecular biology, from helping to determine the function of a protein to determining its evolutionary relationships. Traditionally, 3D structures are classified into groups of families that closely resemble the grouping according to their primary sequence. However, significant structural similarities exist at multiple levels between proteins that belong to these different structural families. In this study, we propose a new algorithm, CLICK, to capture such similarities. The method optimally superimposes a pair of protein structures independent of topology. Amino acid residues are represented by the Cartesian coordinates of a representative point (usually the C(α) atom), side chain solvent accessibility, and secondary structure. Structural comparison is effected by matching cliques of points. CLICK was extensively benchmarked for alignment accuracy on four different sets: (i) 9537 pair-wise alignments between two structures with the same topology; (ii) 64 alignments from set (i) that were considered to constitute difficult alignment cases; (iii) 199 pair-wise alignments between proteins with similar structure but different topology; and (iv) 1275 pair-wise alignments of RNA structures. The accuracy of CLICK alignments was measured by the average structure overlap score and compared with other alignment methods, including HOMSTRAD, MUSTANG, Geometric Hashing, SALIGN, DALI, GANGSTA(+), FATCAT, ARTS and SARA. On average, CLICK produces pair-wise alignments that are either comparable or statistically significantly more accurate than all of these other methods. We have used CLICK to uncover relationships between (previously) unrelated proteins. These new biological insights include: (i) detecting hinge regions in proteins where domain or sub-domains show flexibility; (ii) discovering similar small molecule binding sites from proteins of different folds and (iii) discovering topological variants of known structural/sequence motifs. Our method can generally be applied to compare any pair of molecular structures represented in Cartesian coordinates as exemplified by the RNA structure superimposition benchmark.
| S-EPMC3152366 | biostudies-literature
Project description:High-throughput sequencing has opened numerous possibilities for the identification of regulatory RNA-binding events. Cross-linking and immunoprecipitation of Argonaute protein members can pinpoint microRNA target sites within tens of bases, but leaves the identity of the microRNA unresolved. A flexible computational framework that integrates sequence with cross-linking features reliably identifies the microRNA family involved in each binding event, considerably outperforms sequence-only approaches, and quantifies the prevalence of noncanonical binding modes. Ago2 (Argonaute 2) PAR-CLIP and RNA deep sequencing of Epstein-Barr virus B95.8-infected Lymphoblastoid Cell Lines (LCLs)
2013-05-25 | E-GEOD-46611 | biostudies-arrayexpress
Project description:The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.
| S-EPMC3314014 | biostudies-literature