Browse
Submit Data
Databases
API
Help

Dataset Information

36 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

De novo repeat classification and fragment assembly.

ABSTRACT: Repetitive sequences make up a significant fraction of almost any genome, and an important and still open question in bioinformatics is how to represent all repeats in DNA sequences. We propose a new approach to repeat classification that represents all repeats in a genome as a mosaic of sub-repeats. Our key algorithmic idea also leads to new approaches to multiple alignment and fragment assembly. In particular, we show that our FragmentGluer assembler improves on Phrap and ARACHNE in assembly of BACs and bacterial genomes.

SUBMITTER: Pevzner PA

PROVIDER: S-EPMC515325 | biostudies-literature | 2004 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

De novo repeat classification and fragment assembly.

Pevzner Pavel A PA Tang Haixu H Tesler Glenn G

Genome research 20040901 9

Repetitive sequences make up a significant fraction of almost any genome, and an important and still open question in bioinformatics is how to represent all repeats in DNA sequences. We propose a new approach to repeat classification that represents all repeats in a genome as a mosaic of sub-repeats. Our key algorithmic idea also leads to new approaches to multiple alignment and fragment assembly. In particular, we show that our FragmentGluer assembler improves on Phrap and ARACHNE in assembly o ...[more]

PMID: 15342561

Similar Datasets

De novo protein fold design through sequence-independent fragment assembly simulations.

Project description:De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

| S-EPMC9942881 | biostudies-literature

The dual role of fragments in fragment-assembly methods for de novo protein structure prediction.

Project description:In fragment-assembly techniques for protein structure prediction, models of protein structure are assembled from fragments of known protein structures. This process is typically guided by a knowledge-based energy function and uses a heuristic optimization method. The fragments play two important roles in this process: they define the set of structural parameters available, and they also assume the role of the main variation operators that are used by the optimiser. Previous analysis has typically focused on the first of these roles. In particular, the relationship between local amino acid sequence and local protein structure has been studied by a range of authors. The correlation between the two has been shown to vary with the window length considered, and the results of these analyses have informed directly the choice of fragment length in state-of-the-art prediction techniques. Here, we focus on the second role of fragments and aim to determine the effect of fragment length from an optimization perspective. We use theoretical analyses to reveal how the size and structure of the search space changes as a function of insertion length. Furthermore, empirical analyses are used to explore additional ways in which the size of the fragment insertion influences the search both in a simulation model and for the fragment-assembly technique, Rosetta.

| S-EPMC3849216 | biostudies-literature

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing.

Project description:Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.

| S-EPMC10762064 | biostudies-literature

Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis.

Project description:A new de novo protein structure prediction method for transmembrane proteins (FILM3) is described that is able to accurately predict the structures of large membrane proteins domains using an ensemble of two secondary structure prediction methods to guide fragment selection in combination with a scoring function based solely on correlated mutations detected in multiple sequence alignments. This approach has been validated by generating models for 28 membrane proteins with a diverse range of complex topologies and an average length of over 300 residues with results showing that TM-scores > 0.5 can be achieved in almost every case following refinement using MODELLER. In one of the most impressive results, a model of mitochondrial cytochrome c oxidase polypeptide I was obtained with a TM-score > 0.75 and an rmsd of only 5.7 Å over all 514 residues. These results suggest that FILM3 could be applicable to a wide range of transmembrane proteins of as-yet-unknown 3D structure given sufficient homologous sequences.

| S-EPMC3386101 | biostudies-literature

De novo assembly, transcriptome characterization, and simple sequence repeat marker development in duckweed <i>Lemna gibba</i>.

Project description:Lemna gibba is a species of duckweed showing great potential in bioenergy production and wastewater treatment. However, the relevant transcriptomic and genomic resources are very limited for this species, which dramatically hinders its genetic diversity and genome mapping researches. In this work, ~ 233.5 million clean reads were generated from L. gibba by Illumina paired-end sequencing, and subsequently they were de novo assembled into 131,870 unigenes, of which 61,622 were annotated and 43,319 were expressed with Fragments Per Kilobase of transcript per Million fragments mapped (FPKM) > 5. In total, 19,297 simple sequence repeats (SSRs) were identified from 15,261 SSR-containing unigenes. Dinucleotide (78.4%) were the most abundant SSRs, followed by tri- (14.9%), tetra- (4.1%), and penta-nucleotides (1.5%). The top three motifs were AG/CT (69.9%), AC/GT (6.5%), and ATC/ATG (4.9%). Further analysis revealed that the presence of SSR motif was independent of the expression level for a given gene. Based on the sequence of these SSR-containing unigenes, a total of 10,292 SSR markers were developed, of which only 2671 were further retained after removing those derived from unannotated or extra-low expressed (e.g., FPKM ≤ 5) unigenes. Finally, a subset of 70 SSR markers was randomly selected and examined in nine diverse L. gibba genotypes for the PCR amplification and polymorphism, as well as in other duckweed species for the inter-specifically amplifiability. This work is the first report on the transcriptome-based large-scale SSR markers development and analysis in L. gibba. The transcriptome generated and the SSR markers developed in this work will provide a valuable resource for genetic diversity assessment in L. gibba and also for species relationship investigation in Lemnaceae family.

| S-EPMC7036385 | biostudies-literature

De novo assembly of macaque gene models

Project description:We reported an atlas of de novo-defined, full-length macaque gene models on the basis of single molecule long-read transcriptome sequencing (Iso-seq).

2021-04-17 | GSE158668 | GEO

Complete De Novo Assembly of Monoclonal Antibody Sequences.

Project description:De novo protein sequencing is one of the key problems in mass spectrometry-based proteomics, especially for novel proteins such as monoclonal antibodies for which genome information is often limited or not available. However, due to limitations in peptides fragmentation and coverage, as well as ambiguities in spectra interpretation, complete de novo assembly of unknown protein sequences still remains challenging. To address this problem, we propose an integrated system, ALPS, which for the first time can automatically assemble full-length monoclonal antibody sequences. Our system integrates de novo sequencing peptides, their quality scores and error-correction information from databases into a weighted de Bruijn graph to assemble protein sequences. We evaluated ALPS performance on two antibody data sets, each including a heavy chain and a light chain. The results show that ALPS was able to assemble three complete monoclonal antibody sequences of length 216-441 AA, at 100% coverage, and 96.64-100% accuracy.

| S-EPMC4999880 | biostudies-other

The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences.

Project description:Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Leveraging highly compressed sequence transformations to accelerate sequence comparison, our approach yielded comparable accuracy to existing approaches, further demonstrating its suitability for sequences originating from diverse virus populations. We assessed the application of our methodology using both synthetic and real viral pathogen sequences. Our results show that the use of highly compressed sequence approximations can provide accurate results, with analytical performance retained and even enhanced through appropriate dimensionality reduction of sequence data.

| S-EPMC6563281 | biostudies-literature

Exploiting sparseness in de novo genome assembly.

Project description:BACKGROUND: The very large memory requirements for the construction of assembly graphs for de novo genome assembly limit current algorithms to super-computing environments. METHODS: In this paper, we demonstrate that constructing a sparse assembly graph which stores only a small fraction of the observed k-mers as nodes and the links between these nodes allows the de novo assembly of even moderately-sized genomes (~500 M) on a typical laptop computer. RESULTS: We implement this sparse graph concept in a proof-of-principle software package, SparseAssembler, utilizing a new sparse k-mer graph structure evolved from the de Bruijn graph. We test our SparseAssembler with both simulated and real data, achieving ~90% memory savings and retaining high assembly accuracy, without sacrificing speed in comparison to existing de novo assemblers.

| S-EPMC3369186 | biostudies-literature

Faucet: streaming de novo assembly graph construction.

Project description:Motivation:We present Faucet, a two-pass streaming algorithm for assembly graph construction. Faucet builds an assembly graph incrementally as each read is processed. Thus, reads need not be stored locally, as they can be processed while downloading data and then discarded. We demonstrate this functionality by performing streaming graph assembly of publicly available data, and observe that the ratio of disk use to raw data size decreases as coverage is increased. Results:Faucet pairs the de Bruijn graph obtained from the reads with additional meta-data derived from them. We show these metadata-coverage counts collected at junction k-mers and connections bridging between junction pairs-contain most salient information needed for assembly, and demonstrate they enable cleaning of metagenome assembly graphs, greatly improving contiguity while maintaining accuracy. We compared Fauceted resource use and assembly quality to state of the art metagenome assemblers, as well as leading resource-efficient genome assemblers. Faucet used orders of magnitude less time and disk space than the specialized metagenome assemblers MetaSPAdes and Megahit, while also improving on their memory use; this broadly matched performance of other assemblers optimizing resource efficiency-namely, Minia and LightAssembler. However, on metagenomes tested, Faucet,o outputs had 14-110% higher mean NGA50 lengths compared with Minia, and 2- to 11-fold higher mean NGA50 lengths compared with LightAssembler, the only other streaming assembler available. Availability and implementation:Faucet is available at https://github.com/Shamir-Lab/Faucet. Contact:rshamir@tau.ac.il or eranhalperin@gmail.com. Supplementary information:Supplementary data are available at Bioinformatics online.

| S-EPMC5870852 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data